U.S. patent application number 14/288302 was filed with the patent office on 2015-10-08 for methods of obtaining geometry from images.
This patent application is currently assigned to Bio-Tree Systems, Inc.. The applicant listed for this patent is Bio-Tree Systems, Inc.. Invention is credited to Raul A. Brauner, Joao Cruz, Kongbin Kang, Yanchun Wu.
Application Number | 20150287183 14/288302 |
Document ID | / |
Family ID | 40853693 |
Filed Date | 2015-10-08 |
United States Patent
Application |
20150287183 |
Kind Code |
A1 |
Kang; Kongbin ; et
al. |
October 8, 2015 |
METHODS OF OBTAINING GEOMETRY FROM IMAGES
Abstract
In one aspect, a method of detecting at least on feature
associated with a blood vessel in at least one image of at least
one blood vessel using a matched filter adapted to respond to the
at least one feature is provided. The method comprises applying a
scale detection filter to selected voxels in the at least one image
to determine a scale for the matched filter at each of the selected
voxels, determining an orientation for the matched filter at each
of the selected voxels, wherein determining the orientation is
assisted by using the scale determined at each of the selected
voxels, applying the matched filter at each of the selected voxels
at the scale and the orientation determined at each of the selected
voxels to obtain a filter response at each of the selected voxels,
and analyzing the filter response at each of the selected voxels to
determine if the respective voxel corresponds to the at least one
feature.
Inventors: |
Kang; Kongbin; (Providence,
RI) ; Brauner; Raul A.; (Framingham, MA) ; Wu;
Yanchun; (Sharon, MA) ; Cruz; Joao; (Rumford,
RI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Bio-Tree Systems, Inc. |
Framingham |
MA |
US |
|
|
Assignee: |
Bio-Tree Systems, Inc.
Framingham
MA
|
Family ID: |
40853693 |
Appl. No.: |
14/288302 |
Filed: |
May 27, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12811537 |
Jan 3, 2011 |
8761466 |
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PCT/US2009/000008 |
Jan 2, 2009 |
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14288302 |
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61010080 |
Jan 3, 2008 |
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61009872 |
Jan 2, 2008 |
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Current U.S.
Class: |
382/128 |
Current CPC
Class: |
G06K 9/4638 20130101;
G06K 9/6215 20130101; G06T 7/20 20130101; A61B 5/107 20130101; A61B
2576/02 20130101; G06T 2207/10116 20130101; G06T 2207/30101
20130101; G06T 2211/404 20130101; G06T 7/0012 20130101; G06T
2207/30172 20130101; A61B 5/489 20130101; G06T 2207/20004 20130101;
G06T 15/08 20130101; A61B 6/504 20130101; A61B 6/508 20130101; G06K
9/4609 20130101; G06T 7/62 20170101; G06T 7/74 20170101; A61B
5/02007 20130101; G06T 2207/10028 20130101; G06T 7/60 20130101;
G06T 2207/10072 20130101; A61B 5/055 20130101 |
International
Class: |
G06T 7/00 20060101
G06T007/00; G06T 7/20 20060101 G06T007/20; G06K 9/62 20060101
G06K009/62; G06T 15/08 20060101 G06T015/08 |
Claims
1. A method of linking geometry obtained from at least one image of
at least one blood vessel, the geometry including a plurality of
locations in the at least one image determined to be associated
with voxels representing the centerline of a vessel, each of the
plurality of locations having an associated orientation indicative
of a direction of a centerline of the vessel and an associated
filter response resulting from applying a centerline filter
centered at the respective location, the method comprising:
selecting a target location from the plurality of locations; and
comparing the target location with each other location in the
plurality of locations within a predetermined neighborhood, wherein
comparing includes: determining a distance between the target
location and each of the other locations; determining a difference
between the orientation at the target location and the orientation
at each of the other plurality of locations; and determining a
difference between the filter response at the target location and
the filter response at each of the other plurality of locations;
and linking the voxel associated with the target location with the
voxel associated with one of the other locations based, at least in
part, on the comparison.
2. The method of claim 1, wherein linking includes linking the
voxel associated with the target location with the voxel associated
with one of the other locations that minimizes the comparison.
3. The method of claim 2, wherein the distance comparison is
weighted to be more significant than the difference in orientation
and the difference in filter response.
4. At least one computer readable medium storing instructions that,
when executed by at least one processor, perform a method of
linking geometry obtained from at least one image of at least one
blood vessel, the geometry including a plurality of locations in
the at least one image determined to be associated with voxels
representing the centerline of a vessel, each of the plurality of
locations having an associated orientation indicative of a
direction of a centerline of the vessel and an associated filter
response resulting from applying a centerline filter centered at
the respective location, the method comprising: selecting a target
location from the plurality of locations; and comparing the target
location with each other location in the plurality of locations
within a predetermined neighborhood, wherein comparing includes:
determining a distance between the target location and each of the
other locations; determining a difference between the orientation
at the target location and the orientation at each of the other
plurality of locations; and determining a difference between the
filter response at the target location and the filter response at
each of the other plurality of locations; and linking the voxel
associated with the target location with the voxel associated with
one of the other locations based, at least in part, on the
comparison.
5. The at least one computer readable medium of claim 4, wherein
linking includes linking the voxel associated with the target
location with the voxel associated with one of the other locations
that minimizes the comparison.
6. The at least one computer readable medium of claim 4, wherein
the distance comparison is weighted to be more significant than the
difference in orientation and the difference in filter
response.
7. A system for linking geometry obtained from at least one image
of at least one blood vessel, the geometry including a plurality of
locations in the at least one image determined to be associated
with voxels representing the centerline of a vessel, each of the
plurality of locations having an associated orientation indicative
of a direction of a centerline of the vessel and an associated
filter response resulting from applying a centerline filter
centered at the respective location, the system comprising: at
least one computer readable medium for storing the at least one
image; and at least one processor capable of accessing the at least
one computer readable medium and configured to: select a target
location from the plurality of locations; and compare the target
location with each other location in the plurality of locations
within a predetermined neighborhood, wherein comparing includes:
determining a distance between the target location and each of the
other locations; determining a difference between the orientation
at the target location and the orientation at each of the other
plurality of locations; and determining a difference between the
filter response at the target location and the filter response at
each of the other plurality of locations; and linking the voxel
associated with the target location with the voxel associated with
one of the other locations based, at least in part, on the
comparison.
8. The system of claim 7, wherein linking includes linking the
voxel associated with the target location with the voxel associated
with one of the other locations that minimizes the comparison.
9. The system of claim 7, wherein the distance comparison is
weighted to be more significant than the difference in orientation
and the difference in filter response.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit under 35 U.S.C.
.sctn..sctn.120 and 121 and is a divisional of U.S. patent
application Ser. No. 12/811,537 entitled "METHODS OF OBTAINING
GEOMETRY FROM IMAGES," filed Jan. 3, 2011, which is a national
stage application under 35 U.S.C. .sctn.371 of International
Application No. PCT/US2009/000008 entitled "METHODS OF OBTAINING
GEOMETRY FROM IMAGES," filed on Jan. 2, 2009, which claims priority
under 35 U.S.C. .sctn.119(e) to U.S. Provisional Application Ser.
No. 61/009,872 entitled "METHODS OF ANALYZING VESSEL DISTRIBUTIONS
AND USES THEREOF," filed on Jan. 2, 2008, and U.S. Provisional
Application Ser. No. 61/010,080 entitled "METHODS OF ANALYZING
VESSEL DISTRIBUTIONS AND USES THEREOF," filed on Jan. 3, 2008, both
of which are herein incorporated by reference in their
entirety.
FIELD OF THE INVENTION
[0002] Aspects of the present invention relate to extracting
geometry from one or more images for use in analyzing biological
tubular structures for diagnostic and therapeutic applications in
animals. In particular, aspects of the invention relate to
extracting geometry from images of blood vessels to identify
structural features useful for detecting, monitoring, and/or
treating diseases, and/or for evaluating and validating new
therapies.
BACKGROUND OF THE INVENTION
[0003] A wide range of imaging methods and devices are commonly
used to evaluate different anatomical and physiological conditions
in a variety of medical and research environments. Tools have been
developed to image body structures based on different physical
properties. For example, X-rays, CT scans, MRIs, PET scans, IR
analyses and other technologies have been developed to obtain
images of various body structures. These tools are routinely used
for diagnostic, therapeutic, and research applications.
Combinations of two or more different imaging techniques are
sometimes used to provide complementary information about a
patient.
[0004] In conventional medical imaging, a human operator, such as a
physician or diagnostician, may visually inspect one or more images
to make an assessment, such as detection of a tumor or other
pathology or to otherwise characterize the internal structures of a
patient. However, this process may be difficult and time consuming.
For example, it may be difficult to assess 3D biological structure
by attempting to follow 2D structure through a series of stacked 2D
images. In particular, it may be perceptually difficult and time
consuming to understand how 2D structure is related to 3D structure
as it appears, changes in size and shape, and/or disappears in
successive 2D image slices. A physician may have to mentally
arrange hundreds or more 2D slices into a 3D picture of the
anatomy. To further frustrate this process, when anatomical
structure of interest is small, the structure may be difficult to
discern or it may be difficult to understand how numerous
structures relate to a biological whole.
[0005] Furthermore, in addition to the time consuming nature of
manual inspection, human visual interpretation of images has
further shortcomings. While the human visual cortex processes image
information to obtain qualitative information about structure in
the image, it does not compute quantitative geometry from the
image. However, the quantitative geometry of the structure
represented in one or more images may contain valuable information
about the structure that can be used to diagnose disease, assess
the efficacy of treatment and/or perform other analyses of the
structure. Such quantitative information about the structure is
beyond the capability of conventional human visual image
understanding alone.
[0006] Image processing techniques have been developed to automate
or partially automate the task of understanding and partitioning
the structure in an image and are employed in computer aided
diagnosis (CAD) to assist a physician in identifying and locating
structure of interest in a 2D or 3D image. CAD techniques often
involve segmenting the image into groups of related pixels and
identifying the various groups of pixels, for example, as those
comprising a tumor or a vessel or some other structure of interest.
However, conventional segmentation may produce unsatisfactory or
incomplete results, particularly when the structure being detected
appears in the image at arbitrary locations, sizes and
orientations. As a result, the limited geometry that may be
extracted from conventional image processing may be unsuitable for
use in further analysis based on the extracted geometry.
SUMMARY OF THE INVENTION
[0007] Applicant has developed methods and apparatus for extracting
geometry from images, scan data, and/or representations of tubular
body structures (e.g., blood vessels or other body vessels).
Aspects of the invention relate to obtaining vessel geometry,
determining one or more structural features from the vessel
geometry, and/or analyzing the one or more structural features for
medical diagnostic, prognostic, and/or research applications.
[0008] Applicant has developed methods and apparatus for extracting
geometry from images, scan data, and/or representations of tubular
body structures (e.g., blood vessels or other body vessels).
Aspects of the invention are useful for obtaining a geometrical
representation of a vascular tree that contains data relating to
three-dimensional location, orientation and/or size at any point in
the vascular tree of a subject. In some embodiments, a vascular
tree may be represented by a series of disks or poker chips (e.g.,
circular or eliptical disks) that are linked together to form a
three-dimensional structure containing information relating to the
local size, shape, branching, and other structural features at any
point in the vascular tree.
[0009] It should be appreciated that the entire vascular tree of a
subject may be represented by a network of linked poker chips
(e.g., circular or eliptical disks). However, in many embodiments,
only a subset or a portion of a vascular tree may be represented or
analyzed. In some embodiments, a portion of a vascular tree can be
represented by a single disc or poker chip that contains
information relating to the location of the center of the vessel,
vessel size (diameter), and/or orientation (e.g., the direction of
the centerline of the vessel). In some embodiments, a portion of a
vascular tree may be represented by a dataset that describes one or
more poker chips along with information relating to the linkage
between the poker chips within a region of interest of the vascular
tree.
[0010] Some embodiments includes a method of detecting at least one
feature associated with a blood vessel in at least one image of at
least one blood vessel using a matched filter adapted to respond to
the at least one feature, the method comprising applying a scale
detection filter to selected voxels in the at least one image to
determine a scale for the matched filter at each of the selected
voxels, determining an orientation for the matched filter at each
of the selected voxels, wherein determining the orientation is
assisted by using the scale determined at each of the selected
voxels, applying the matched filter at each of the selected voxels
at the scale and the orientation determined at each of the selected
voxels to obtain a filter response at each of the selected voxels,
and analyzing the filter response at each of the selected voxels to
determine if the respective voxel corresponds to the at least one
feature.
[0011] According to some embodiments, the at least one feature
includes the intensity at centerline voxels, which are detected
using a matched filter, wherein the detected centerline voxels are
further analyzed to link the centerline voxels together to provide
adjacency and vessel membership information.
[0012] Some embodiments include applying an orientation independent
scale filter that is invariant to direction to detect scale at
voxels in the image. Some embodiments include an orientation
independent scale filter that is independent of orientation
detection and/or feature detection. Some embodiments include a
first derivative orientation detection operation performed
separately from scale detection. Some embodiments include a matched
filter using a step function to detect vessels, the matched filter
being applied using the scale and orientation determined during the
separate scale detection and orientation detection.
[0013] Some embodiments include a method of determining a scale at
each of a plurality of selected voxels in at least one image of at
least one blood vessel, the scale at each of the plurality of
selected voxels being determined using an orientation independent
scale detection filter having a filter size defined by a radius,
wherein the scale is used to determine the size of a matched filter
adapted to respond to at least one feature associated with the at
least one blood vessel, the method comprising (A) selecting a
target voxel from the plurality of selected voxels at which to
determine the scale, (B) setting the radius to a predetermined
minimum value so that the filter size is at a predetermined
minimum, (C) applying the orientation independent scale detection
filter at the target voxel to obtain a filter response, (D)
comparing the filter response with a predetermined criteria, (E)
increasing the value of the radius of the orientation independent
scale detection filter to increase the filter size of the
orientation independent scale detection filter if the filter
response meets the predetermined criteria, (F) performing acts
(A)-(F) with increased filter size if the filter response meets the
predetermined criteria, and (G) setting the scale based on the
value of the radius of the orientation independent scale detection
filter if the filter response does not meet the predetermined
criteria.
[0014] Some embodiments include a method of linking geometry
obtained from at least one image of at least one blood vessel, the
geometry including a plurality of locations in the at least one
image determined to be associated with voxels representing the
centerline of a vessel, each of the plurality of locations having
an associated orientation indicative of a direction of the
centerline of the vessel and an associated filter response
resulting from applying a centerline filter centered at the
respective location, the method comprising linking centerline
voxels based on one or more of the following parameters: a distance
between centerline voxels; a change in the orientation of the
centerline between centerline voxels; a change in the filter
response between centerline voxels; and a change in vessel radius
between centerline voxels. The centerline voxels may be linked to
form a linked Poker Chip representation.
[0015] Some embodiments include a method of linking geometry
obtained from at least one image of at least one blood vessel, the
geometry including a plurality of locations in the at least one
image determined to be associated with voxels representing the
centerline of a vessel, each of the plurality of locations having
an associated orientation indicative of a direction of a centerline
of the vessel and an associated filter response resulting from
applying a centerline filter centered at the respective location.
The method comprises selecting a target location from the plurality
of locations, comparing the target location with each other
location in the plurality of locations within a predetermined
neighborhood, wherein comparing includes, determining a distance
between the target location and each of the other locations,
determining a difference between the orientation at the target
location and the orientation at each of the other plurality of
locations, and determining a difference between the filter response
at the target location and the filter response at each of the other
plurality of locations, and linking the voxel associated with the
target location with the voxel associated with one of the other
locations based, at least in part, on the comparison.
[0016] According to aspects of the invention, a poker chip
representation of a vasculature may be mined for physiological,
biological, and/or medical purposes. In some embodiments,
geometrical information associated with a single poker chip may be
mined. In some embodiments, geometrical information associated with
a plurality of poker chips, optionally including local linkage
information may be mined. Accordingly, aspects of the invention
relate to obtaining vessel geometry, determining one or more
structural features from the vessel geometry, and/or analyzing the
one or more structural features for medical diagnostic, prognostic,
and/or research applications.
[0017] Aspects of the invention provide methods for analyzing
structures such as blood vessels and evaluating their association
with disease, responsiveness to therapeutic treatments, and/or
other conditions. Aspects of the invention provide quantitative and
analytical methods for evaluating and/or comparing the vessels in
different regions of the same body (e.g., a human body) or within
ex vivo tissues or between different bodies (e.g., the same regions
in different bodies) or different ex vivo tissues. Aspects of the
invention can be useful in assisting and/or automating the analysis
of vascular patterns and their association with disease diagnosis,
prognosis, response to therapy, etc., or any combination thereof.
Aspects of the invention can be used in connection with vessel
structural information that is obtained from vessel images (e.g.,
blood vessel images), scan data, vessel representations (e.g., a
reconstructed vasculature, a representation that can be viewed as
being similar in some ways to a stack of poker chips with varying
diameters and is that is referred to herein as a Poker Chip
representation, or any other useful representation, or any
combination thereof).
[0018] Methods are provided for analyzing vessel structural
features, and blood vessel structural features in particular. In
some embodiments, a distribution of vessel parameters (e.g.,
structural features or morphological parameters) within a region of
interest may be generated and evaluated. In some embodiments, the
vessel parameters may relate to the size, shape, or number of
vessels with a region of interest. A distribution may be generated
based on quantitative measurements related to one or more
parameters. In some embodiments, a distribution of blood vessels
may be a population distribution of blood vessels as a function of
quantitative measures of one or more parameters. For example, a
distribution may represent the number of blood vessels (or the
percentage of the blood vessel population) as a function of their
diameter, branching frequency, distance between branches, degree of
tortuousity, curvature, or any other quantitative structural
feature or morphological parameter, e.g., as described herein, or
any combination of two or more thereof. In some embodiments, a
distribution may be divided into groups or bins representing
different value ranges of the quantitative measurements (e.g.,
ranges of vessel diameters such as 0-30 microns, 30-60 microns,
60-90 microns, 90-120 microns, 120-150 microns, 150-180 microns,
etc., or any combination thereof). It should be appreciated that a
distribution may be represented in any suitable form, for example
graphically (e.g., a graph or histogram), in the form of a table,
as a database, in a computer-readable or computer storage medium,
etc., or any combination thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] FIG. 1 illustrates a flow chart of extracting geometry from
an image, in accordance with some embodiments of the invention;
[0020] FIG. 2 illustrates a geometrical representation of vessel
structure, referred to as the Poker Chip representation, in
accordance with some embodiments of the present invention;
[0021] FIG. 3A illustrates a cylindrical segment used to model
vessel structure, in accordance with some embodiments of the
present invention;
[0022] FIG. 3B illustrates a grey scale representation of a
characteristic function of a model used to detect vessel
structures, in accordance with some embodiments of the present
invention;
[0023] FIG. 3C illustrates a plot of the intensity values along the
x-axis at the center of the grey scale Gaussian distribution in
FIG. 3B;
[0024] FIG. 3D illustrates a plot of the intensity values along the
x-axis of another model of vessel intensity profile;
[0025] FIG. 4 illustrates schematically a cylindrical vessel
segment intensity distribution illustrating a ridge or centerline
feature, in accordance with some embodiments of the present
invention;
[0026] FIG. 5 illustrates an embodiment of a mixture of truncated
Gaussian fit to 3D reconstruction intensity data, wherein the
vertical axis is in log scale and low part of the horizontal axis
is shown;
[0027] FIG. 6 illustrates an embodiment of a theoretical profile of
a centerline filter response using scale detection, in accordance
with some embodiments of the present invention;
[0028] FIG. 7 illustrates an embodiment of a detected scale versus
the choice of threshold .alpha.;
[0029] FIG. 8 illustrates pictorial an orientation independent
scale filter, in accordance with some embodiments of the present
invention;
[0030] FIG. 9 illustrates an embodiment of how R(X, r) behaviors on
real images--(a) a slice of 3D images is shown and blue point is
the point X where we apply rank-based scale filter--(b) the
rank-based scale filter's response with different radius is
shown--although the intensities have large variation inside vessel,
the rank-based scale filter behavior smoothly and have a rapidly
decay while cross the boundary of the vessel;
[0031] FIG. 10A illustrates a centerline filter, in accordance with
some embodiments of the present invention;
[0032] FIG. 10B illustrates a profile of the centerline filter
illustrated in FIG. 9A along the line x-x', in accordance with some
embodiments of the present invention;
[0033] FIG. 10C illustrates another profile of the centerline
filter illustrated in FIG. 9A along the line x-x', in accordance
with some embodiments of the present invention;
[0034] FIG. 11 illustrates centerline filtering on a 3D volume data
set, in accordance with some embodiments of the present
invention;
[0035] FIG. 12 illustrates net volume of the center line filter
versus different scales;
[0036] FIG. 13 illustrates a geometrical representation of
vasculature obtained from a 3D volumetric image, in accordance with
some embodiments of the present invention;
[0037] FIG. 14 illustrates blood vessel size distribution in an
example of casts of a xenograft tumor model after treatment with
Avastin.RTM. (an anti-angiogenic agent available from Genentech,
South San Francisco, Calif.), in accordance with some embodiments
of the present invention;
[0038] FIG. 15 illustrates the vessel population ratio between
small and middle size vessels in an example of casts of a xenograft
tumor model after treatment with Avastin.RTM., in accordance with
some embodiments of the present invention;
[0039] FIG. 16 illustrates the vessel population ratio between
large and middle size vessels in an example of casts of a xenograft
tumor model after treatment with Avastin.RTM., in accordance with
some embodiments of the present invention.
[0040] FIG. 17 illustrates the vessel population distribution in an
example of casts of a tumor model after treatment with
Avastin.RTM., in accordance with some embodiments of the present
invention;
[0041] FIG. 18 illustrates the vessel population ratio between
small and middle size vessels in an example of casts of a tumor
model after treatment with Avastin.RTM., in accordance with some
embodiments of the present invention; and
[0042] FIG. 19 illustrates the vessel population ratio between
large and middle size vessels in an example of casts of a tumor
model after treatment with Avastin.RTM., in accordance with some
embodiments of the present invention.
DETAILED DESCRIPTION
[0043] As discussed above, analyzing vessel structures (e.g., blood
vessel structures) and identifying structural profiles that are
characteristic of one or more physiological conditions or responses
(e.g., positive responses to pharmaceutical compounds) may be of
interest in many areas of diagnostics, therapeutics and/or
treatment. However, the amount of information that can be directly
obtained or ascertained from image data (e.g., x-ray, CT, MRI,
etc.) may be prohibitively limited in this respect. Accordingly,
Applicant has recognized the benefit of developing methods of
extracting geometry from images to facilitate the above described
analysis.
[0044] To extract geometrical properties of vessel structures in
one or more images, the vessels must first be detected in the image
and represented in a meaningful fashion. Various methods have been
proposed for detecting one or more features of a blood vessel using
a filter adapted to respond to the one or more features. For
example, filters have been designed to respond to the intensity
profile of a vessel to locate voxels that exhibit this intensity
profile. However, conventional filtering techniques may be
unsatisfactory at accurately and robustly detecting vessel
structures in one or more images. Filtering techniques typically
require some additional preprocessing to obtain information about
the image to improve the filtering process. For example, the scale
of the structure at a particular location in the image may be
obtained to determine what size filter should be used at that
location. That is, not only should the filter match the feature
being detected, in order to respond correctly, the filter should
also match the scale of the feature. Moreover, because the
orientation of the feature being detected is not known a priori,
filtering techniques often include some preprocessing to determine
the orientation of the feature at a particular location so that the
filter can be applied to the image in general alignment with the
feature.
[0045] Conventionally, scale detection and orientation detection
are performed simultaneously. Applicant has appreciated that
simultaneous scale and orientation detection may result in
sub-optimal detection of either scale, orientation or both. As a
result, subsequent filtering to detect one or more features applied
using sub-optimal scale and orientation parameters may be
substantially degraded. Applicant has developed a method for
detecting vessel features that includes a scale detection operation
and an orientation detection operation that are performed
separately. In some embodiments, scale detection is performed prior
to orientation detection, and orientation detection is performed
using the scale determined by the scale detection. The scale and
orientation values determined from the separate scale and
orientation detection operations may then be used to apply the
feature detection filter, for example, a centerline filter adapted
to respond to the centerline voxels of blood vessels.
[0046] According to some embodiments, scale detection employs an
orientation independent scale detector such that scale detection
may be performed independent of orientation detection. According to
some embodiments, an orientation independent scale filter is used
having a filter kernel that is symmetric with respect to
orientation such that the filter does not rely on orientation for
accurate scale detection. According to some embodiments, the
orientation independent scale filter includes a filter size defined
by a radius. At each of a plurality of selected voxels in an image,
the orientation independent scale filter is applied at increasing
radii until the filter response fails to meet a predetermined
criteria. The largest radius at which the filter response meets the
predetermined criteria is used to represent the scale. According to
some embodiments, the diameter of vessel structures in the images
is determined based on this largest radius. That is, according to
some embodiments, at least some geometry of vessel structures may
be determined by the scale detection operation.
[0047] Applicant has appreciated that performing scale detection,
orientation detection and centerline detection provides, at each
detected centerline voxel, the location, the direction of the
centerline and the radius of the vessel. This geometry can be used
to analyze vascular structure and these geometrical parameters have
been used to develop a mathematical representation of the detected
vessel structure. In some embodiments, each centerline location may
be represented as a circular or eliptical disk having a center at
the centerline location, a radius corresponding to the associated
scale, and a normal vector to the disk (e.g., circular disk)
corresponding to the direction of the centerline as determined
during orientation detection. This representation resembles a poker
chip and is referred to herein as the Poker Chip representation, as
described in further detail below.
[0048] While the Poker Chip representation provides much useful
information about the geometry of the vessel, without further
processing, there is no notion of adjacency or vessel membership,
which may be useful information in performing analysis on the
vasculature. Accordingly, in some embodiments, each of the detected
centerline voxels (e.g., center locations of a poker chip) are
linked together to capture adjacency information as well as vessel
membership. In some embodiments, the centerline voxels are linked
according to a criteria that includes one or any combination of
minimizing a distance, a direction change, a radius change and/or a
filter response change from a centerline voxel to an adjacent
centerline voxel. That is, when selecting between a number of
candidate centerline voxels to link to a target centerline voxel,
the centerline voxel candidate that creates the smallest change in
one or more of the above parameters may be preferred over candidate
centerline voxels having larger changes. The linked centerline
voxels can then be used to compute various structural
characteristics of the vasculature formed by the detected vessels
as represented by the stacked and linked poker chips.
[0049] Following below are more detailed descriptions of various
concepts related to, and embodiments of, methods and apparatus
according to the present invention. It should be appreciated that
various aspects of the invention described herein may be
implemented in any of numerous ways. Examples of specific
implementations are provided herein for illustrative purposes only.
In addition, the various aspects of the invention described in the
embodiments below may be used alone or in any combination, and are
not limited to the combinations explicitly described herein.
[0050] FIG. 1 illustrates a method of extracting vessel geometry
from one or more images of vasculature, in accordance with some
embodiments of the present invention. Act 110 includes obtaining
image information of at least a portion of a vasculature structure.
For example, the image information may be a two-dimensional (2D),
three-dimensional (3D) or other dimensional image obtained from
scanning an object using x-ray CT, MRI, PET, SPECT, etc. The
scanned object may be a live specimen such as a human or other
animal (i.e., an in-vivo scan), or obtained from a cast of a
specimen's vasculature.
[0051] The method of FIG. 1 may be performed on any image of any
dimension independent of how the image was obtained, as the aspects
of the invention are not limited in this respect. In 2D images,
each 2D location having an associated intensity is conventionally
referred to as a pixel. In 3D images, each volume location having
an associated intensity is conventionally referred to as a voxel.
The term voxel is used herein to refer to both 2D and 3D image
locations to eliminate the need to specify the dimensionality of
the images, as the methods described herein are generic to
dimensionality.
[0052] Many techniques for extracting information from images use
various filtering techniques. For example, filters are often
designed such that when applied to a portion of an image (e.g.,
convolved with a portion of the image) the filter response is
relatively large when the filter is applied to an image portion
having a feature or characteristic indicative of structure being
detected in the image, and relatively small otherwise. The filter
detection described below in connection with act 140 is one example
of matched filtering. However, other filtering techniques may be
used, as the aspects of the invention are not limited in this
respect.
[0053] When the feature or structure being detected appears in an
image at different sizes or scales, the size of the filter kernel
should be adjusted to the appropriate scale in order for the filter
response to accurately indicate the presence of the desired
feature. For example, in an image containing biological
vasculature, and in particular, tumor vasculature, the constituent
vessels will typically vary greatly in diameter. Accordingly, a
filter designed to detect relatively large vessels will not respond
accordingly to small vessels, even when applied on the correct
location. However, it is not known a priori where large and small
vessels are located. Accordingly, successful detection may require
determining the scale of the structure in the image prior to
applying the filter. This technique is herein referred to as "scale
detection." Scale detection may be performed on predetermined
portions of an image, or may be determined on a voxel by voxel
basis, as described in further detail below.
[0054] In addition to detecting the appropriate scale, it may be
beneficial to detect the orientation in which the filter should be
applied. In particular, the feature(s) being detected may appear in
the image at arbitrary orientations. For example, in the case of
vasculature, the vessel properties being detected may be oriented
in any arbitrary direction. Accordingly, even if a filter at the
appropriate scale is applied at an image region corresponding to
the feature being detected, the filter response may be relatively
low if it is not oriented in general alignment with the direction
of the feature for which the filter was designed to detect.
Accordingly, determining the orientation of the features or
properties being detected may benefit filter detection techniques.
This technique is herein referred to as "orientation
detection."
[0055] Conventional filtering techniques combine scale and
orientation detection in a single operation. That is, the
combination of possible scales and orientations are tested
simultaneously and the scale and orientation are selected when the
response is maximum. However, Applicant has appreciated that
maximum responses may not correspond to optimal scale and optimal
orientation simultaneously. Because the response is a combination
of scale and orientation, one or both may be sub-optimal while
together providing a strong response. Applicant has developed a
scale detection operation that is orientation independent. As a
result, the operations of scale detection and orientation detection
may be separated into two separate operations. In addition, the
detected scale may then be used to improve subsequent orientation
detection processes.
[0056] In act 120, scale detection is performed independently of
orientation detection. In some embodiments, scale detection 120 is
performed using a filter that is independent of orientation. Scale
detection 120 may provide the scale in the image at different
regions in the image. In some embodiments, scale detection 120
determines scale at each voxel in the image. Alternatively, a
preprocessing operation may be performed to roughly determine which
voxels in the image correspond to subject matter of interest (e.g.,
vessels) and which voxels correspond to background. Scale detection
may then be performed only on pixels determined to correspond to
subject matter of interest, thus reducing the amount of
computations. The result of scale detection is a scale associated
with each location at which the filter was applied (e.g., a scale
at each selected voxel in the image). An orientation independent
scale detection algorithm according to some embodiments is
described in further detail below.
[0057] In act 130, orientation detection may be performed. To
assist in more accurate orientation detection, the scale at the
selected regions of the image determined during scale detection 120
may be provided to the orientation detection operation. As
discussed above, determining the orientation of subject matter of
interest in one or more images may be important for accurate filter
detection of the subject matter of interest (e.g., structure,
feature, property or characteristic). For example, in embodiments
where the subject matter of interest is vasculature, it may be
important to detect the direction of the center or longitudinal
axis of the vessels before applying a filter that detects the
centerline of the vessel. In some embodiments, the scale determined
from scale detection 120 may be used to improve orientation
detection accuracy. The result of orientation detection is an
orientation or direction at each selected voxel indicating the
direction of the centerline at the respective location. An
orientation detection algorithm according to some embodiments is
described in further detail below.
[0058] In act 140, filter detection may be performed. In filter
detection 140, a filter designed to respond to the subject matter
of interest in the image may be applied. In some embodiments, the
filter is applied at the scale and/or orientation determined from
scale detection and/or orientation detection, respectively. The
magnitude of the filter response at selected locations in the image
indicates the likelihood that the location includes the subject
matter of interest. In some embodiments, the subject matter of
interest is vasculature and the filter is designed to respond to
the center of a vessel. That is, the filter may be designed to
respond to the intensity profile across a vessel and thus respond
most strongly when centered on a centerline voxel in the direction
of the intensity profile. Because the scale and direction of the
subject matter of interest has been determined at selected
locations in the image, filter detection may appropriately accurate
in detecting the subject matter of interest. Several methods of
centerline filtering are discussed in detail below, in accordance
with some embodiments of the present invention.
[0059] In act 150, non-maximal suppression may be performed on the
output of the filter detection operation performed in act 140. As
discussed above, the result of a filtering operation (e.g.,
centerline filtering) generally includes the filter response at
each voxel at which the filter was applied. The magnitude of the
response is typically proportional to the likelihood that the
feature being detected is present at the corresponding voxel
location. However, it should be appreciated that many voxel
locations will have associated non-zero filter responses. In
addition, some voxel locations will have associated local maximum
filter responses even though the true location of the feature is
elsewhere. However, accurate detection may require discriminating
between local maximum and the true maximum location, which
corresponds to the most likely location of the structure being
detected. Non-maximal suppression 150 attempts to eliminate or
suppress all but the true maximum filter responses to accurately
detect the subject matter of interest. A detailed description of
non-maximum suppression in the context of centerline filtering for
vessel detection is described below.
[0060] In act 160, linking may be performed. Linking may include
various operations that associate voxel locations with each other
to form related structures so that geometric properties may be
obtained from the linked voxels. For example, in the context of
vessel detection, the voxel locations that were determined as
centerline voxels after centerline detection and non-maximum
suppression may be linked together to form the associated
centerline of vessels. That is, analysis may be performed to link
together centerline voxels that are likely to have arisen from the
same vessel structure. In such a way, the geometry of the vessels
may be obtained (e.g., geometry 15). Methods for linking voxels in
the context of vessel detection are described in further detail
below.
[0061] As discussed above, some embodiments are directed to
detecting vasculature and extracting the geometry of the
vasculature to facilitate various analysis such as diagnosis,
therapeutics, drug efficacy, etc. Applicant has developed methods
for extracting geometrical information from 3D volumetric images
using a match filter based system to segment a vessel network and
extract a mathematical (geometry) vessel representation. Some
embodiments of a vessel representation are referred to herein as
the Poker Chip representation due to the similarity to a stack of
poker chips. The Poker Chip representation treats a vessel as an
aggregation of infinitesimal cylinder cross-sections with
continuously varying diameters. While in theory the "thickness" of
each poker chip is infinitesimal, in practice the thickness of each
poker chip may be related to the resolution of the image(s) from
which the geometry was extracted. Thus, each poker chip may have
associated geometry including, for example, center location, radius
and orientation, as discussed in further detail below.
[0062] FIG. 2 illustrates a schematic of the Poker Chip
representation. According to some embodiments, each poker chip 210
is defined by a center location, a radius and an orientation. The
center location c.sub.i represents the center of the vessel, for
example, determined by centerline filtering, as discussed in
further detail below. The radius r represents the radius of the
vessel at location c.sub.i and the orientation is the angle of the
normal of the poker chip at location c.sub.i, and represents the
tangent of the centerline of the vessel at location c.sub.i. It
should be appreciated that the Poker Chip representation may
include additional parameters, as the aspects of the invention are
not limited in this respect.
[0063] Applicant has appreciated that the above Poker Chip
representation may be used to determine characteristics of the
vasculature that may help in diagnosing disease, providing
information on appropriate treatment, and/or assessing the
effectiveness of treatment. For example, since the orientation is
known at each location, higher level information such as curvature
and tortuosity may be computed, as well as vessel density and
distribution measures, as discussed in further detail below.
Additionally, since vessel diameter may be determined, vessel size
and the change in vessel sizes may be computed as well. Various
analyses that can be performed using the Poker Chip representation
are discussed in further detail below.
[0064] To compute some of the higher order information, it may be
beneficial to also include in the Poker Chip representation
information about neighboring poker chips. For example, information
about how the poker chips link together may be valuable in
understanding the vessel structure as a whole. As discussed above,
Applicant has developed algorithms that facilitate linking poker
chips together to provide membership information with respect to
which poker chips belong to which vessel and information regarding
which poker chips are adjacent to one another. After linking has
been achieved, more sophisticated vessel analysis may be
performed.
[0065] Following below is a more detailed description of algorithms
capable of extracting geometry from 3D images to obtain a Poker
Chip representation of vasculature present in the images, in
accordance with some embodiments of the present invention. While
the various algorithms are discussed in connection with detecting
and extracting vessel information, the concepts disclosed herein
may be applied to detect and associate other structure, as the
aspects of the invention are not limited in this respect. In
addition, it should be appreciated that distribution analyses
according to various aspects of the invention may be applied to
information obtained from any vessel image, representation, or
combination thereof.
[0066] FIG. 3A illustrates one example of a cylindrical segment 300
that may be used to generally model a vessel segment. A
configuration of cylindrical segment 300 may be described by a
number of parameters in a particular coordinate frame. The position
of cylindrical segment 300 may be described by a location of the
cylindrical axis 305 at a point (x.sub.i, y.sub.i, z.sub.i) in
space, for example, the origin or termination of the cylindrical
segment. The orientation of cylindrical segment 300 may be
specified by the angle .phi..sub.i from the x-axis and the angle
.gamma..sub.i from the y-axis. Since cylindrical segment 300 is
axially symmetric, its rotation about the z-axis may not need to be
specified. The length of the cylindrical segment may be specified
by l.sub.i and the radius of the cylindrical segment 300 may be
specified by r.sub.i.
[0067] Applicant has appreciated that the cross-section of a vessel
may be characterized by a generally Gaussian shaped intensity
distribution. The cross-sectional density of a vessel may be
modeled by a Gaussian distribution, centered on the longitudinal
axis of the vessel, so that the modeled density is the highest at
the center of the vessel. For example, the cross-sectional density
distribution of a cylindrical vessel segment, when oriented such
that its longitudinal axis coincides with the z-axis, may be
modeled as,
.rho. ( - 1 r 2 ( ( x - x i ) 2 + ( y - y i ) 2 ) ) ( 1 )
##EQU00001##
[0068] where .rho. is the density coefficient at a center of the
cylindrical segment and r is the radius of the cylindrical segment,
so that the density is modeled as being greatest at the center
(i.e., equal to .rho.) and decays exponentially as a function of
radial distance from the center. FIG. 3B illustrates a grey scale
representation of the function given in Eq. (1), where darker grey
scale values indicate increased density values. FIG. 3C illustrates
a plot of the intensity values along the x-axis at the center of
the grey scale Gaussian distribution in FIG. 3B. FIG. 3D
illustrates a vessel intensity profile that may better model the
intensity profile of vessels in an image. Curve 1 and 2 illustrated
vessel profile intensity when vessel diameter is larger than the
resolution of the scan and when the vessel diameter is smaller,
respectively.
[0069] The density distribution along the longitudinal axis of the
cylinder (i.e., into and out of the page in FIG. 3B) is
substantially uniform and does not vary substantially and may be
modeled as a constant function of the cross-sectional distribution
along the longitudinal axis, that is, as a constant function of the
radial distance d from the center of the distribution. FIG. 4
illustrates schematically a cylindrical vessel segment intensity
distribution model. In particular, the model of the cylindrical
vessel segment has a maximum density at the center that decays
exponentially to the boundary of the vessel as a function of the
radial distance d, from the center. At each distance d, the density
is uniform along the z-axis. For example, the density at d=0 is the
density maximum along the length of the vessel. This density
maximum shown by line 405 is referred to as a ridge, and
corresponds to the centerline of a vessel.
[0070] If the herein described characteristic intensity
distribution or similar distribution can be identified in the
image, the associated pixels/voxels are likely to belong to a
vessel. The characteristic points may be used to facilitate
segmenting the image into vessel and non-vessel regions. Some
methods of detecting the characteristic shape illustrated in FIG. 4
include performing ridge detection on an image. A ridge point is
defined herein as a point in an image wherein the intensity assumes
a local extrema in the direction of principal curvature, i.e., the
direction having the steepest intensity gradient. For example, at
point 415 (and along ridge 405) in FIG. 4, the principal direction
of curvature is shown by u.sub.0 (i.e., the unit vector (1,0) in
the (d, z) coordinate frame). Each point along ridge 405 forms a
ridge point since each point is a local maximum along the z-axis.
Accordingly, a ridge may be characterized by local derivative
information in the image and may be detected by examining the
curvature of intensity about points of interest in the image.
[0071] Some conventional methods have proposed detecting the ridge
using the Hessian operator. However, the Hessian operator requires
performing second derivatives of the image information, which
reduces the signal-to-noise ratio (SNR) and may result in degraded
performance. Applicant has developed methods of detecting the
characteristic shape of blood vessels described above using
centerline filtering techniques that may avoid some of the
performance degradations commonly seen with conventional filters
such as the Hessian operator, as discussed in further detail
below.
[0072] As discussed above in connection with FIG. 1, a non-limiting
example of a method for extracting geometry from images may include
a number of processing blocks including: a scale detector, an
orientation detector, centerline filtering, non-maximum suppression
and linkage. Briefly speaking, the system works as follows:
firstly, the scale detection and orientation detection modules may
be applied on 3D images to obtain correct size and orientation
parameters for centerline detection (e.g., scale and orientation
parameters for the centerline filters); secondly, based on the
parameters obtained from scale detection and orientation detection
modules, the centerline filter may be applied on every voxel of a
3D image, or applied on a subsection of voxels for which centerline
detection is desired. The generated response field formed by
applying the centerline filter indicates the likelihood that the
associated voxel corresponds to the vessel centerline; finally,
non-maximum suppression and linkage is applied on the centerline
response field to extract the vessel centerline and obtain a vessel
mathematical representation (e.g., a linked Poker Chip
representation). Following below are more detailed descriptions of
embodiments of the five main blocks briefly discussed above, e.g.,
scale detection, orientation detection, centerline filtering,
non-maximum suppression and centerline linking.
[0073] Scale Detection
[0074] As discussed above, scale detection may be applied to
estimate the centerline filter size appropriate for each voxel at
which centerline detection is to be applied. Applying scale
detection on each voxel of a 3D image volume may be relatively
expensive computationally. That is, if each voxel in the 3D image
is deemed to be a potential centerline point, then scale detection
should be applied to each voxel in the image. However, Applicant
has appreciated that since vessels occupy only a portion of the
volume, it may not be necessary to detect scale on every voxel. In
particular, certain voxels may be eliminated based on the image
properties of the voxels, for example, the intensity level of the
voxel.
[0075] In general, intensities from vessels are higher than those
in the background. Using a conservative intensity threshold, voxels
may be classified as background voxels with a low false positive
rate that can be controlled based on how conservative the threshold
operator is set. That is, by setting the threshold conservatively,
a substantial percentage of the background voxels may be eliminated
from scale detection without the risk of eliminating any vessel
voxels. The term "background" refers herein to voxels that are not
part of the subject matter of interest that is being detected. By
eliminating background voxels, the computations needed to perform
scale detection can be reduced. That is, by removing at least some
voxels from consideration, scale detection need not be performed on
each voxel in the image.
[0076] It is reasonable to model both background intensity and
vessel intensities as a Gaussian distribution. In practice, the
assumption in FIG. 5 shows that a model using a mixture of
truncated Gaussians is a very good fit for the data in low
intensity regions. The truncated Gaussian distribution has the
Probability Density Function (PDF) as follows:
p ( I / .mu. , .sigma. ) = N ( I .mu. , .sigma. ) .intg. b 1 b 2 N
( x .mu. , .sigma. ) x ( 2 ) ##EQU00002##
[0077] where N(I|.mu., .sigma.) denotes a Gaussian distribution
with mean .mu. and variance .sigma., and b1 and b2 are the
truncation points. To capture both background and vessel
distributions, the mixture of two truncated Gaussians for the data
may be expressed as:
p ( I ) = c = 0 1 i { w c log [ N c ( I i .mu. c , .sigma. c )
.intg. b 1 b 2 N c ( x i .mu. c , .sigma. c ) x ] } ( 3 )
##EQU00003##
[0078] where w.sub.c is the weight percentage of each component.
Directly maximizing the likelihood may become challenging because
determining the marginal probability may require computations that
increase exponentially with the data. In some embodiments, the
problem is solved using an Expectation Maximization (EM) algorithm.
The EM process iteratively goes through two steps by soft
assignment of data (Expectation) and maximizing the whole
likelihood (Maximization). That is, an initial approximate
distribution may be used to classify voxels as either background or
foreground (e.g., vessels) in the Expectation step. Next, the
distribution is refined based on the classification (Maximization)
and classification (Expectation) is repeated on the refined
distribution. This process may be repeated until the process
converges on a final classification of background and foreground
voxels.
[0079] Applying an EM algorithm on a mixture of Gaussians is only
one method by which background voxels may be eliminated from
consideration, or by which voxels are classified as background and
foreground voxels. Other preprocessing or thresholding techniques
may be used to reduce the number of voxels on which further
processing is performed to reduce the computational expense, as the
aspects of the invention are not limited in this respect. In
addition, while voxel intensity may be one suitable parameter to
use to perform a conservative elimination of voxels belonging to
the background, any suitable parameter may be used, as the aspects
of the invention are not limited in this respect. For example,
higher order properties may be used.
[0080] As discussed above, separating scale detection and
orientation detection may have benefits over algorithms that
perform the two operations simultaneously. Applicant has designed a
scale detection filter which does not depend on the orientation of
the structure to be detected. According to some embodiments, an
orientation independent filter may be developed such that the
filter can be mathematically described in spherical coordinates as
f=f(r), which is a function that does not depend on orientation.
The symmetry of the filter allows the filter to be independent of
how the filter is oriented. To accurately detect centerline voxels
from 3D images, the response generated by the scale detection
filter should be maximum when it is located at a centerline voxel.
The scale .sigma..sub.r at a point (x, y, z) inside a cylinder may
be defined as the distance to the wall of the cylinder
boundary:
.sigma..sub.r(x,y,z)=dist(x,y,z; wall of the cyclinder) (4)
[0081] As shown in FIG. 6, this definition of scale guarantees a
unique maximum filter response inside the cylinder after scale
selection (in the absence of noise). Normally, the intensity of a
3D image outside of a vessel is significantly lower than the
intensity inside the vessel. This rapid intensity decay provides an
indication of scale. Applicant has developed a rank-based scale
filter that is orientation independent. Given a point X inside a
vessel, a rank based scale filter may be defined as:
( X , r ) = f - ( { I ( X ' ) : X ' - X = r + 1 } ) min r { f + ( {
I ( X ' ) : X ' - X = 1 , , r } ) } ( 5 ) ##EQU00004##
[0082] where R(X, r) is the filter response at image location X
with filter radius r, and f.sub.- and f.sub.+ are rank functions,
respectively. Note that the filter is parameterized by radius only,
resulting in filter symmetry that is orientation independent. Given
various noise models, there are many ways to choose the rank
functions. In order to cope with image reconstruction effects,
f.sub.- may be chosen as the median value of the last 10 lowest
intensities and f+ may be chosen as the median value of the last 10
highest intensities. That is, the rank function may be determined
from characteristics of the image. However, the rank functions may
be selected to be any value that facilitates detection of scale, as
the aspects of the invention are not limited in this respect. The
scale .sigma..sub.r(X) may then be obtained by finding the minimum
radius r so that R(X, r) reaches the threshold .alpha.:
.sigma. r ( X ) = min r { R ( X , r ) < 1 .alpha. } ( 6 )
##EQU00005##
[0083] Stated differently, the radius of the scale filter is
increased until the filter response no longer satisfies the
relationship in Eq. (6). As discussed above, the scale detection
filter may be designed to be independent of orientation. According
to some embodiments, the kernel or shell of the scale filter is a
circle in 2D and a sphere in 3D. As a result, the size of the
filter is defined by the radius r, where the center of the filter
is located at a target voxel at location X in the image. Since the
filter has the same radius in all directions, the application of
the scale filter is independent of orientation.
[0084] The criteria for the filter response may be chosen to be any
suitable criteria that can robustly determine when the filter
kernel has crossed a vessel boundary. The criteria in Eq. (6) is
merely exemplary. In some embodiments, the value of .alpha. is
chosen to be 5. However, other values may be used as well as the
aspects of the invention are not limited in this respect. In order
to examine the sensitivities of this rank-based scale filter to the
choice of the threshold parameter .alpha., a few points inside
different vessels may be randomly chosen to see how the selected
scale changes depending on the ratio threshold parameter .alpha..
FIG. 7 shows that the scale approaches the correct value when
.alpha. is chosen to be larger than 5.
[0085] FIG. 8 illustrates pictorial an orientation independent
scale filter, in accordance with some embodiments of the present
invention. It should be appreciated that while the scale detection
filter in FIG. 8 is shown (and is suitable) in the context of a 2D
image for convenience of illustration, the scale detection filter
is designed as a 3D filter to detect scale in 3D volumetric images.
In particular, the circular filter illustrated in FIG. 8 may be
made an expanded to a sphere to detect scale in 3D. In FIG. 8, a
portion of an image 805 is shown having a vessel structure 815
within the image portion. It should be appreciated that image
portion 805 is schematic and the vessel structure 815 and the
background 825 would be comprised of an intensity value at each
voxel location in the image portion. Moreover, it should be
appreciated that image portion 805 may be a small portion of a much
larger image. For the sake of clarity only a single vessel
structure is depicted in image portion 805, though the image
portion may in reality include any number of vessel structures.
[0086] FIG. 8 also illustrates three separate applications of an
orientation independent scale filter 850. It should be appreciated
that the scale filter 850 may be applied at all of the image voxels
or at a selected number of image voxels (e.g., voxels determined to
be vessel voxels using a preprocessing techniques such as the
intelligent thresholding method described above). The three
applications of the filter in FIG. 8 are merely exemplary and are
chosen at arbitrary locations to assist in describing the scale
detection filter. Each application of the filter begins by placing
the filter with a predetermined minimum radius r on a target pixel
at which scale is being detected. The scale filter is then applied
to the image, for example, by convolving the image pixels that fall
under the filter kernel or support with the values of the filter
kernel. If a certain criteria is met, the filter is assumed to
still be entirely within the vessel and the radius r is
increased.
[0087] In FIG. 8, the increasing of the filter radius is depicted
by the successively larger circles in dashed line. The circles in
solid line denote the last filter applied such that the criteria
was met. For example, the dotted line circle in filter application
850b shows a circle of r.sub.n that when applied to the underlying
image failed to meet the criteria, where n is the number of
successively larger radius filter kernels that have been applied to
the image. Thus, the scale at the corresponding image location is
determined to be r.sub.n-1. Not only does scale detection provide
the appropriate scale to be used in subsequent filtering processes
(e.g., centerline detection), it also may indicate the radius of
the vessel structure in the Poker Chip representation.
[0088] Applicant has used the fact that the intensity of voxels
within the vessel, in the absence of noise, is substantially higher
than the background voxels to establish the criteria such that the
criteria will not generally be met when the filter kernel is
extended outside the vessel structure. One embodiment of such a
criteria is described in Eq. 5 and Eq. 6. By employing the rank
functions illustrated in Eq. 5, and using the criteria in Eq. 6, a
robust filter may be designed that will fail to meet the criteria
when the filter kernel is increased in size such that it
encompasses voxels outside of the vessel. However, the above
described scale detection filter is exemplary and other scale
detection filters may be used, as the aspects of the invention are
not limited in this respect. In addition, any criteria that tends
not to be met as a filter is expanded across a vessel boundary may
be used, as the aspects of the invention are not limited in this
respect.
[0089] Because the centerline voxels are not known a priori, the
scale detection filter may be applied to non-centerline voxels. As
shown by filter application 850b, the scale detection is again
stopped when the filter kernel crosses the vessel boundary. Because
the target voxel is not a centerline voxel, the radius of the
filter will not correspond to the radius of the vessel. However,
this may be inconsequential because voxels that are not determined
to be centerline voxels are removed in subsequent processing, such
as during centerline filtering discussed below. Because only voxels
detected as centerline voxels will survive centerline filtering,
the radius of the scale detector may accurately reflect the radius
of the associated vessel.
[0090] FIG. 9 shows what R(X, r) looks like when it is applied on
real images. Although the intensities have large variation inside
the vessel, the rank-based scale filter behaves smoothly and decays
relatively rapidly across the boundary of the vessel. Thus,
rank-based scale filters may have the generally beneficial property
of relatively distinct response change as the filter crosses vessel
boundaries, and is relatively stable and insensitive to the choice
of ratio parameter. Accordingly, scale may be detected at each
selected voxel in the image. For example, scale may be detected at
each voxel in the image or the reduced number of voxels resulting
from performing thresholding on the image to eliminate at least
some of the background voxels. The selected voxels at which scale
detection is performed can be selected in other ways, as the
aspects of the invention are not limited in this respect.
[0091] Orientation Detection
[0092] As discussed above, centerline filtering may be improved by
first determining the orientation at which the centerline filter
should be applied. Since scale is detected independent of
orientation, orientation detection may be performed separately from
scale detection and, in some embodiments, orientation detection
uses the scale values detected during scale detection to improve
detection of the orientation of the subject matter of interest. In
some embodiments, a gradient based orientation detection algorithm
may be used, however, other algorithms may be used to detect vessel
orientation, as the aspects of the invention are not limited in
this respect. Because of the rotational symmetry along the axis of
a cylinder on which the vessel structure may be modeled, the
intensity along a line parallel to the vessel axis is constant in
the absence of noise. In other words, the directional derivative of
intensity along the direction v parallel to the vessel axis is zero
in the absence of noise:
v.gradient..rho.(X)=0 (7)
[0093] It should be appreciated that x-ray decay during image
acquisition depends on its penetrating length. Thus, the intensity
inside a vessel tends to vary along any direction other than the
axis direction. This fact indicates that Eq. (7) may be a necessary
and sufficient condition for finding the vessel direction since the
above argument holds for any point X inside the vessel. Therefore,
the direction of a small cylinder segment at each point X can be
estimated by finding a direction vector a along which the
intensities have the least change. However, direct estimation from
the derivative of one point X tends to be error prone. In some
embodiments, all the derivatives inside a small volume centering on
the point X may be used to increase the accuracy. To be more
precise, the axis direction a may be estimated by finding a
direction a that minimizes the sum of the directional intensity
gradient along this direction:
a ^ = arg min a { .intg. .intg. .intg. v a .gradient. .rho. ( x , y
, z ) x y z } ( 8 ) ##EQU00006##
[0094] where .sigma.(X) is the scale detected at point X and is
.parallel..parallel. the norm discussed herein. In the presence of
noise, a directional gradient of intensity convolved with an
adaptive Gaussian kernel may be used, as follows.
a ^ = arg min a { .intg. .intg. .intg. v a .gradient. ( G .sigma. (
x , y , z ) .smallcircle. .rho. ( x , y , z ) ) x y z } ( 9 )
##EQU00007##
[0095] In some embodiments, Eq. (9) can be solved by a least square
estimation by assuming the noise distribution is Gaussian i.i.d,
i.e., the norm in Eq. (9) is an L2-norm. However, it is well known
that an L2-norm may be sensitive to outliers present in the input
data, and outliers may frequently appear in reconstructed 3D
images. In some embodiments, a L1-norm in Eq. (9) may be used.
a ^ = arg min a { .intg. .intg. .intg. v a .gradient. ( G .sigma. (
x , y , z ) .smallcircle. .rho. ( x , y , z ) ) 1 x y z } ( 10 )
arg min a { .intg. .intg. .intg. v a 1 .gradient. ( G .sigma. ( x ,
y , z ) .smallcircle. .rho. ( x , y , z ) ) 1 x y z } ( 11 )
##EQU00008##
[0096] To avoid the trivial solution at a=0 in the above equation,
the constraint .SIGMA..sub.1.parallel.a.sub.1.parallel..sub.2=1 may
be used. Since a is independent of the point (x, y, z), a is moved
out of the triple integral so that:
a ^ = max a { a .intg. .intg. .intg. v .gradient. ( G .sigma. ( x ,
y , z ) .smallcircle. .rho. ( x , y , z ) ) x y M L2 } s . t . { i
a i 2 = 1 } ( 12 ) ##EQU00009##
[0097] It should be appreciated that in Eqs. (8)-(12), the
operation is being performed over a volume v. By performing
orientation detection over a neighborhood, rather than at a single
voxel, semi-global information may be captured in the orientation
assessment. The neighborhood information allows for robust
orientation detection in the presence of noise and outliers.
However, it should be appreciated that the neighborhood (e.g., the
volume v) may be different for detecting direction in relatively
large vessels versus relatively small vessels. Accordingly,
Applicant has developed an adaptive method that varies the size of
the neighborhood based on the scale at a target voxel. That is, the
scale determined during scale detection may be used to determine
the size of the volume v. In some embodiments, the size of (2.left
brkt-bot.s+2.right brkt-bot.+1) may be used as the size of volume.
However, any adaptive neighborhood based on scale may be used, as
the aspects of the invention are not limited in this respect. Thus,
the size of the neighborhood used for orientation detection may be
adapted according to the scale of the image at each location.
[0098] As discussed above, and L1-norm may be used to address
outliers. There are a number of ways to solve Eq. (12). In some
embodiments, the equation is solved by constraint optimization
using Lagrange multipliers. Applying Lagrange multipliers to the
above equation obtains:
.gradient..sub.a(a.sup.TM.sup.TMa+.lamda.a.sup.Ta)=0
(M.sup.TM)a+.lamda.a.sup.T)=0 (13)
[0099] Therefore the center line direction, a, may be obtained by
computing the eigenvector associated with the smallest eigenvalues
of matrix M. Referring back to FIG. 4, solving the above equations
to determine the direction a can be pictorial explained. In general
terms, the eigenvectors of matrix M indicate the characteristic
directions of curvature. The relationship between these
characteristic directions of curvature may be employed to identify
the direction of the centerline. The eigenvalues and associated
eigenvectors of a matrix may be determined in various ways, for
example, by any number of well known iterative methods of
diagonalizing a matrix or analytically by directly solving the
relationship:
Mu=.lamda.u (14)
[0100] where M is the matrix of Eq. 13, u is an eigenvector of
matrix M, and .lamda. is an eigenvalue associated with u. The
magnitude of each eigenvalue of the matrix M is related to the
"significance" of the associated eigenvector. Stated differently,
the eigenvalue indicates how much the curvature along the
associated eigenvector contributes to the local curvature
determined by the matrix M. Accordingly, a in Eq. 13 is the
eigenvector associated with the smallest eigenvalue and indicates
the direction in which the change in intensity is the smallest. The
largest eigenvalue of the matrix M is associated with the principal
direction of curvature.
[0101] In FIG. 4, the linearly independent eigenvectors u.sub.0 and
u.sub.1 (i.e., eigenvectors u.sub.0 and u.sub.1 are orthogonal) are
shown on the illustrated intensity curve. The eigenvalue
.lamda..sub.0 herein denotes the eigenvalue having the greatest
absolute value and is referred to as the principal eigenvalue.
Accordingly, the associated eigenvector u.sub.0 indicates the
principal direction of curvature at a target pixel and
.lamda..sub.0 is related to the magnitude of the curvature. The
eigenvalue .lamda..sub.1 (referred to as the secondary eigenvalue)
is related to the magnitude of curvature in the direction of
u.sub.1, i.e., in a direction orthogonal to the principal direction
of curvature indicated by u.sub.0. Along the ridge of the Gaussian
profile (i.e., in the direction u.sub.1), the intensity should be
substantially zero and the change in intensity relatively small and
in the noiseless case is zero (i.e., the intensity does not change
as a function of z in the direction of the centerline).
Accordingly, by determining the eigenvector associated with the
smallest eigenvalue, the direction a which corresponds to the
direction of the centerline may be determined. Thus, the
orientation of the centerline may be determined at each of the
selected voxels.
[0102] Centerline Detection
[0103] Having determined scale and orientation for the feature
detection filter, the feature of interest may be detected.
According to some embodiments, centerline detection is performed
using a Gaussian centerline filter. For example, assume the density
inside the vessel satisfies the Gaussian model:
I ( r ) = I 0 - r 2 2 .sigma. 2 ( 15 ) ##EQU00010##
[0104] Here, r is in the direction perpendicular to the vessel
axis; a is the radius of the vessel; and I.sub.0 is the intensity
at the center. In order to detect a Gaussian vessel, a filter with
radial variation corresponding to the 2nd derivative of the
Gaussian may be used:
h ( r ) = ( r 2 .sigma. 2 - 1 ) - r 2 .sigma. 2 ( 16 )
##EQU00011##
[0105] The application of this filter corresponds to a volume
integral over space. This volume integral should vanish if the
filter is embedded in material with constant density. However the
2nd derivative of the Gaussian does not, i.e.,
.intg. 0 .infin. ( r 2 .sigma. 2 - 1 ) - r 2 .sigma. 2 r r = 1 ( 17
) ##EQU00012##
[0106] This problem can be fixed by adding an offset,
.intg. 0 .infin. ( r 2 .sigma. 2 - 2 ) - r 2 .sigma. 2 r r = 0 ( 18
) ##EQU00013##
Therefore, the centerline filter has the form
f ( r ) = e 4 .PI..sigma. 2 [ 2 - [ r .sigma. ] 2 ] - r 2 2 .sigma.
2 ( 19 ) ##EQU00014##
[0107] This filter has a positive core when r< {square root over
(2)}.sigma.r< and negative shell when r> {square root over
(2)}.sigma..
[0108] Applicant has appreciated that in the presence of noise, a
centerline filter that closely mimics the shape of a Gaussian as
described above may at times be inaccurate, especially in
situations where vessel structures are relatively close together.
In particular, the continuous decay of the Gaussian may incorrectly
detect or fail to detect centerline voxels in certain situations,
such as when vessel structures are close together and/or in
circumstances where relatively small vessel structures appear
nearby relatively large vessel structures.
[0109] Applicant has appreciated that a modified centerline filter
may be more effective at accurately identifying centerline points,
particularly in the presence of noise. According to some
embodiments, centerline detection is performed using a filter that
better matches the profile of vessel structures in an image. FIG.
10A illustrates a matched filter in accordance with some
embodiments of the present invention. Filter 900 includes an inner
core and an outer core. Rather than a Gaussian kernel, filter 900
includes a step function between the inner and outer core. As a
result, the filter support is more compact and the filter is able
to more accurately detect vessel structures that are close
together. In addition, because the filter better matches vessel
profiles, centerline detection may be more accurate. An example of
values assigned to the matched filter 900 according to some
embodiments include:
f s ( r , z ) = { 1 r .ltoreq. s and z .ltoreq. 2 s 0 s < r
.ltoreq. 2 s and z .ltoreq. 2 s - 1 r > 2 s or z > 2 s ( 20 )
##EQU00015##
[0110] An illustration of the profile of the above filter along the
axis x-x' is shown pictorially in FIG. 10B. As shown, the size of
the matched filter is based on the scale s detected during scale
detection. Applying this filter, the centerline response may be
given as:
r(x,y,z)=.intg..intg..intg.T[f(r,z)G(0,.sigma.]I(x,y,z)dxdydz
(21)
where G(0, .sigma.) is a Gaussian smooth kernel. When the scale of
the filter is small (e.g., when scale detection determines that the
local scale is relatively small), the filter defined by Eq. (20)
may not have a zero net volume (volume of the positive core minus
the volume of the negative core). This may cause detection
difficulties because the filter may have non-zero response when
applied to a non-zero uniform background. As shown in the FIG. 12,
when the scale of the filter is small, the net volume percentage
may be quite large. For example, for a centerline filter with scale
of 1.5, the net volume is 35% of the total volume of the filter.
Thus, the filter may generate filter bias in the favor of small
scale.
[0111] Therefore, to address this bias the filter described above
may be modified as:
f s ( r , z ) = { 1 r .ltoreq. s and z .ltoreq. 2 s 0 s < r
.ltoreq. .sigma. ( s ) and z .ltoreq. 2 .sigma. ( s ) - w s r >
.sigma. ( s ) or z > 2 .sigma. ( s ) ( 22 ) ##EQU00016##
where,
.sigma. ( s ) = { 2 s + 0.5 if s < 10 2 s otherwise ( 23 )
##EQU00017##
and w.sub.s is a function of scale s so that,
.intg..intg..intg..sub.r>.sigma.(s) or {square root over
(z>2)}.sigma.(s)w.sub.sdxdydz=.intg..intg..intg..sub.r.ltoreq.s
and z.ltoreq. {square root over (2)}sdxdydz (24)
[0112] An illustration of the profile of the filter expressed in
Eq. (22) along the axis x-x' is shown pictorially in FIG. 10C. The
matched filters described above may be particularly effective at
accurately detecting centerline voxels in the presence of noise and
in circumstances when subject matter of interest is positioned in
close proximity to each other.
[0113] The matched filters described above may be applied to a
plurality of selected voxels in the image. Accordingly, for each
selected voxel at which the matched filter is applied, there will
be an associated filter response indicative of the likelihood that
the corresponding voxel is a centerline voxel. However, only the
maximum filter responses may be of interest. That is, the maximum
filter responses are those that are most likely to be centerline
voxels. Accordingly, filter responses that are not maximum may be
suppressed such that only those voxels having maximum filter
responses remain.
[0114] Non-Maximum Suppression
[0115] In some embodiments, non-maximum suppression may be
performed. For example, after centerline filtering, each voxel has
a response. The response on each voxel indicates how likely it is
that the voxel is a centerline voxel. Since the center line voxel
should have the maximum response in the plane perpendicular to the
axis, the purpose of non-maximum suppression is to suppress
non-maximum responses to eliminate non-centerline voxels. On each
voxel, a cutting plane perpendicular to the vessel axis may be used
to suppress the non-maximum responses. On the cutting plane, only
local maximums of centerline filter responses are kept and all
other responses are suppressed. Interpolating the centerline
location in order to achieve sub-voxel accuracy is described
below.
[0116] In some embodiments, location interpolation on the cutting
plane may be performed. After obtaining the direction of the
cylinder, a cutting plane perpendicular to this direction may be
used to apply the non-maximum suppression as an analog to the
traditional computer vision edge detection problem. Given an
arbitrary voxel x, the voxel x may be tested to determine whether
the voxel is a local maxima. According to some embodiments, the
cutting plane may be centered on x and the centerline response R(x)
may be compared with any other responses in its cutting plane
neighborhood N(x, v.sub.x). That is, the response field in the
neighborhood N (e.g., a 3.times.3.times.3 neighborhood) may be
projected onto this cutting plane. If the response at voxel x is
larger or equal to all of the responses of neighborhood voxel,
voxel x may be labeled as a local maxima. Otherwise, voxel x is
labeled as a non-maxima voxel and suppressed. This test may be
expressed as:
IsMaxima ( x ) = { true R ( x ) .gtoreq. R ( y ) , .A-inverted. y
.di-elect cons. N ( x , v x ) false otherwise ( 25 )
##EQU00018##
[0117] where N(x, vx) denotes the cutting plane neighborhood of the
point x. Once the neighborhood is determined, the parabolic
function as shown below may be used to interpolate the sub-voxel
maximum location.
r(x,y)=ax.sup.2+by.sup.2+cxy+dx+ey+f (26)
[0118] Given the above response model and the centerline filter
responses in a small region around the center, the following
equations may be used:
an 2 + bm 2 + cmn + dn + em + f = r ( n , m ) a ( n - 1 ) 2 + bm 2
+ cm ( n - 1 ) + d ( n - 1 ) + em + f = r ( n - 1 , m ) a ( n - 1 )
2 + bm 2 + cm ( n - 1 ) - d ( n - 1 ) + em + f = r ( 1 - n , - m )
an 2 + bm 2 + cmn - dn - em + f = r ( - n , - m ) ( 27 )
##EQU00019##
[0119] This linear form can be written as a matrix form
A [ a b c d e f ] = [ r ( n , m ) r ( n - 1 , m ) r ( 1 - n , - m )
r ( - n , - m ) ] ( 28 ) where A = [ n 2 m 2 mn n m 1 ( n - 1 ) 2 m
m ( n - 1 ) n - 1 m 1 n 2 m 2 m ( n - 1 ) 1 - n - m 1 n 2 m 2 mn -
n - m 1 ] ( 29 ) ##EQU00020##
[0120] The maximum location is determined by the stationary
condition
.differential. r .differential. x = .differential. r .differential.
y = 0. ##EQU00021##
That is,
[0121] 2ax+cy.sub.-d=0
cx+2by+e=0 (30)
Therefore,
[0122] [ x y ] = - [ 2 a c c 2 b ] - 1 [ d e ] = 1 4 ab - c 2 [ - 2
b c c - 2 a ] [ d e ] = [ cx - 2 bd 4 ab - c 2 cd - 2 ae 4 ab - c 2
] ( 31 ) ##EQU00022##
[0123] In some embodiments, the size of the neighborhood N(x, vx)
is determined based characteristics of the image in the
neighborhood. There is a natural question of how big the
neighborhood size should be chosen in the non-maximum suppression
algorithm. In some embodiments, the smallest size of
3.times.3.times.3 may be used, but this choice may cause outliers
to survive non-maximal suppression in noisy regions. An alternative
method of choosing the parameter is to use the results from radius
and/or scale detection. In some embodiments, to avoid suppressing
real vessels which are close to each other, a conservative approach
may be used when choosing the neighborhood:
n = 2 s 2 = 1 ( 32 ) ##EQU00023##
[0124] It should be appreciated that the neighborhood in Eq. (32)
is exemplary and an adaptive neighborhood, for example, based on
scale may be determined in other ways, as the aspects of the
invention are not limited in this respect.
[0125] Linking
[0126] As discussed above, the output from centerline filtering and
non-maximum suppression processes provides a 3D field in which each
point is marked as either belonging to or not belonging to a
centerline. In some embodiments, centerline points can be
associated with other information such as radius, strength and
orientation of the cylinder element (e.g., using the Poker Chip
representation). The task of cylinder element linking may include
connecting centerline points and identifying the junctions to
generate a vessel network. In some embodiments, practical
difficulties may arise associated with one or more of the
following: 1) small pieces of centerline may be missing; 2) due to
digitization, the centerline segments after non-maximum suppression
form "zig-zags." 3) small outlier centerline segments may appear to
be present due to noise where there is no real centerline; and 4)
junction region may confuse the linking algorithm and lead to wrong
linkages. Applicant has developed a linking method that addresses
one or more of these difficulties.
[0127] In some embodiments, a local cylinder element linking
algorithm may be used as follows: 1) start with a most prominent
cylinder segment; 2) search in front of the cylinder segment until
no more directly connected successors exist; 3) search behind the
cylinder segment until no more predecessors exist; 4) mark all the
connected cylinder elements; and 5) repeat the above steps until no
more cylinder segments are left unmarked. An example of a linking
method according to some embodiments, is described in further
detail below.
[0128] A single branch of a vessel may be modeled as a digitization
of a smooth, 3D curve which connects all the poker chips that
belong to this branch. Given a point y that has already been
selected as part of a branch (e.g., a centerline point with a large
response), point y is linked to a nearby point based on a given
criteria. For example, linking may be selected to prefer connecting
to a point which is close to point y (distance), that does not
require a large change in the expected direction v.sub.y
(direction), and that has a response that is as similar to the
response at point y as possible (response). Each candidate point x
may be subjected to this criteria to determine which candidate is
the most likely link.
[0129] According to some embodiments, the criteria is determined
using a probabilistic model. For example, the above tests may be
performed by finding the point x which maximizes the posterior
possibility,
Pr(L.sub.y=x|x,v.sub.x,r.sub.x) (33)
[0130] Without knowing the prior information, maximizing the
posterior probability is the same as maximizing the likelihood,
Pr(x,v.sub.x,r.sub.x|L=x) (34)
[0131] If the tests of the distance, direction and response are
conditional independent given L.sub.y=x, it may be sufficient to
provide marginal distribution for each tests.
Pr ( x , v x , R x | L y = x ) = Pr ( dist ( x , y ) , xy .fwdarw.
, R y | L y = x ) = Pr ( dist ( x , y ) | L y ( x , y ) , xy
.fwdarw. ) Pr ( xy .fwdarw. | L ( x , y ) ) Pr ( r y | L ( x , y )
) = Pr ( dist ( x , y ) | x ) Pr ( xy .fwdarw. | v x ) Pr ( R y , s
x | R x , s y ) ( 7 ) ( 35 ) ##EQU00024##
[0132] Among the three tests defined above, Applicant has
determined that distance tends to be the most reliable. Therefore,
it is possible to build a probability model for this distance test.
According to some embodiments, a Gaussian model is chosen for the
distance test to penalize the distance between point y and
candidate x exponentially:
Pr ( dist ( x , y ) | x ) = 1 2 .pi. exp ( - x - y 2 2 ) ( 36 )
##EQU00025##
[0133] As discussed above, another useful test is determining the
extent of direction change in the linked centerline points (e.g.,
as determined from orientation detection) that would be incurred by
linking point y with candidate point x. However, Applicant has
appreciated that the direction of the centerline from the
orientation detection may zig-zag locally due to digitization.
Therefore, relying entirely on the direction obtained from the
orientation detection may lead to linking errors. To address this
difficulty, some embodiments employ a super Gaussian model to test
the possibility of connecting point y with candidate x, given the
centerline direction of point x.
Pr ( xy .fwdarw. | v x ) = 1 Z exp ( - .theta. ( xy .fwdarw. | v x
) 4 .sigma. 4 ) ( 37 ) ##EQU00026##
[0134] The super Gaussian model has a flat top which allows the
test to tolerate relatively large angle variation. As discussed
above, the centerline response and scale may also be used to test
the viability of linking point y with candidate x. It is reasonable
to assume that the centerline responses and scale are smoothly
changing along a single branch. In the other words, linking to a
point which causes centerline to rapidly change may be assigned a
low probability. With this intuition, a response test model may be
constructed as follows:
Pr ( R y , s y | R x , s x ) = Pr ( s y | R x , s x ) Pr ( R y | R
x , s x , s y ) = Pr ( s y | s x ) Pr ( R y | s y , R x , s x ) = 1
Z exp ( - ( s - s x ) 2 2 .sigma. s 2 ( s ) ) exp ( - ( R y s y 2 _
R x s x 2 ) 2 .sigma. r 2 ) ( 38 ) ##EQU00027##
[0135] where Z is the normalization factor, .sigma..sub.s(s)=max
{0.5, 0.2s}. Thus, the above test may be employed in connection
with the algorithm described above to link the centerline points
(e.g., the centerline points that survived non-maximum
suppression). Due to errors in the direction finder and grid
discretization, some non-centerline points survive from non-maximum
suppression. However, the number of those points may be reduced by
applying an occupancy constraint. The occupancy constraints operate
on the notion that if a local space is occupied by a previously
linked branch, then it is not likely possible to be the center of
another branch. In the other words, a high confidence may be
assigned to long branches to suppress weak branches, if the weak
branch occupies the same space as the strong branch.
[0136] As a result of linking the centerline points together, each
of which represents a poker chip having a center location (the
centerline point), a radius and a direction of the centerline at
the center location, further geometry of the vessel may be
computed. Referring back to the schematic of the Poker Chip
representation in FIG. 2. Having computed each of the center
location c.sub.i, the radius r and the orientation a, and having
linked the adjacent poker chips, additional geometry of the blood
vessels may be determined. For example, the linked orientation
parameters capture information about the geometry of the
centerline. For example, by integrating the orientation vectors,
the centerline curve may be obtained. That is, because the
orientation vectors represent the tangents of the centerline curve
at each location c.sub.i, the centerline curve may be recovered
from linked tangents by integrating over some desired segment of
poker chips.
[0137] In addition, the linked poker chips may be used to determine
higher order and/or more sophisticated geometrical properties. For
example, derivatives of the linked orientation vectors may be used
to determine the curvature of the vessel. The centerline curve,
length of the curve and curvature parameters may be used to
determine various tortuosity parameters, which may be used to
characterize the vessels. Moreover, the Poker Chip representation
carries distribution information with respective to the density of
vessel material, the relative distribution of vessels at different
radii, etc. These geometrical, structural and distribution
parameters may be used in a number of ways to analyze vasculature,
as discussed in further detail below. FIG. 13 illustrates a
geometrical representation of vasculature using the linked Poker
Chip representation, wherein the geometry was extracted from a 3D
volumetric image using the methods described herein.
[0138] According to some embodiments, the linking algorithm may be
performed in parallel. Since linking is generally local and may not
need to rely on the information from far away voxels, the algorithm
can be parallelized by dividing the image into small blocks. Then
individual CPUs may operate on a single block without the need to
communicate with other blocks. Because of the computation requires
some neighborhood information, each block may include a fixed
margin overlapping with its neighbor's margin. The speed gained by
parallelization is the number of processors divided by one plus
overhead caused by margin. In one example, dividing a volume of
2000.times.2000.times.1400 into 500.times.500.times.500 blocks and
using 8 processors produced a gain of 4.49 times processing
speed.
[0139] The margin for parallelization may be chosen based on the
following: 1) the margin for the scale selection
m.sub.s=r.sub.max+1; 2) the margin for the smoothing
m.sub.sm=3.sigma.; 3) the margin for the gradient computation mg=1;
4) the margin for the direction detection
md=m.sub.g+r.sub.max+1+m.sub.sm; 5) the margin for centerline
filtering m.sub.c=max {2r.sub.max, m.sub.d}; and 6) the margin for
the non-maximum suppression m.sub.sprs=r.sub.max+m.sub.c.
[0140] Because the block algorithm for parallelization needs to
divide the volume into blocks at beginning and assembling the
blocks into a volume at the end, away to transform between global
coordinates and block coordinates may be needed. The block id
(b.sub.x, b.sub.y, b.sub.z) for a point (i, j, k) in the global
coordinate is given as:
b x = i s b y = j s b z = k s ( 39 ) ##EQU00028##
The local coordinates in its block is (i', j', k')
i'=i-b.sub.xs
j'=j-b.sub.ys
k'=k-b.sub.zs (40)
[0141] The dimension (s.sub.x, s.sub.y, s.sub.z) of the block
(b.sub.x, b.sub.y, b.sub.z) is:
s x ( b x ) = { mod ( N x , s ) if b x = N x s - 1 N x s .noteq. 0
0 if b x < 0 s otherwise s y ( b y ) = { mod ( N y , s ) if b y
= N y s - 1 N x s .noteq. 0 0 if b y < 0 s otherwise s z ( b z )
= { mod ( N y , s ) if b z = N z s - 1 N x s .noteq. 0 0 if b z
< 0 s otherwise ( 41 ) ##EQU00029##
[0142] Given a point (i', j', k') at block (b.sub.x, b.sub.y,
b.sub.z), the global offset in the file is:
pos = i ' s y s z + j ' s z + k ' + ( b z N x N y s z ( b z - 1 ) +
b y N x s y ( b y - 1 ) s z ( b z ) + b x s x ( b x - 1 ) s y ( b y
) s z ( b z ) block offset ) ( 42 ) ##EQU00030##
[0143] The number of blocks in the x dimension is
n bx = N s s , ##EQU00031##
the number of block in the y dimension is
n by = N y s ##EQU00032##
and the number of blocks in the z dimension is
n bz = N z s . ##EQU00033##
A one dimensional block ID 1 (1, . . . , n.sub.bxn.sub.byn.sub.bz)
to 3D index
b x = l n by n bz b y = l - b x n by n bz n bz b z = l - b y n bz -
b x n by n bz ( 43 ) ##EQU00034##
Three dimensional block ID (b.sub.x, b.sub.y, b.sub.z) to one
dimensional block ID.
[0144] As discussed above, the linked Poker Chip representation may
be used to determine a number of geometrical and structural
parameters of the vasculature, and also may be used to determine
distribution information of the vasculature. Provided herein is a
description of methods that utilize the extracted geometry to
analyze the vasculature for diagnostic, treatment efficacy
assessment, therapeutic, and other applications, or any combination
thereof.
[0145] Information relating to the geometry of a subject's
vasculature, or a portion thereof, can be used to determine one or
more qualitative and/or quantitative measures of geometrical,
structural, and/or distribution parameters of the subject's
vasculature that are informative for diagnostic, predictive,
prognostic, therapeutic, interventional, research and/or
development purposes, as well as for grading and/or staging a
disease. It should be appreciated that vasculature geometry may be
obtained for any suitable blood vessel volume, as the invention is
not limited in this respect. In some embodiments, all the
geometrical information captured by the linked Poker Chips within a
target volume of interest may be evaluated. However, in some
embodiments, useful information may be obtained from analyzing only
a subset of Poker Chips within a target volume (e.g., about 10%,
about 20%, about 30%, about 40%, about 50%, about 60%, about 70%,
about 80%, or about 90%) as the invention is not limited in this
respect.
[0146] According to aspects of the invention, the types of
geometrical or structural information that may be extracted from
images (e.g., extracted from a linked Poker Chip representation)
includes a measure of vessel curvature, tortuosity, branching,
diameter, etc., or any combination thereof. Optionally, or
additionally, a measure of vessel density (and/or the density of
vessels having one or more predetermined structural
characteristics) may be determined and/or analyzed. It should be
appreciated that a Poker Chip may consist of or include information
relating to the size (radius), angle, etc. of the vessels being
represented. In some embodiments, the Poker Chip representation may
include linking information (e.g., relating to the linkage angle
etc. between a first Poker Chip and one or more adjacent Poker
Chips).
[0147] Tubular structures (e.g., blood vessels in a cast or in
vivo) of different size ranges may be analyzed separately and
compared to different threshold or reference values as described
herein. In some embodiments, one or more structural parameters are
obtained (e.g., calculated or modeled, etc.) for only a subset of
size ranges (e.g., only for those size ranges for which changes are
known to be associated with a diagnostic, prognostic, clinical, or
research application of interest). However, in certain embodiments,
all of the size ranges are analyzed. In some embodiments, one or
more different parameters are analyzed for different size ranges.
However, in certain embodiments, the same parameter(s) is/are
analyzed for all of the size ranges that are being assayed.
Analyses may be provided in the form of histograms or curves
representing a distribution of numerical values or scores obtained
for the different ranges.
[0148] It should be appreciated that analytical techniques used to
categorize blood vessels based on size may be used to categorize
other tubular body structures based on size. In some embodiments,
once the tubular structures (e.g., blood vessels) are categorized
based on size, the associated values or scores obtained for
different parameters of interest can also be categorized and
analyzed. Aspects of the invention may be automated, for example,
as described herein.
[0149] Aspects of the invention relate to analyzing data obtained
for body structures in animals (e.g., in test animals). In one
embodiment, the invention relates to obtaining pattern information
relating to one or more aspects or regions of the vasculature of an
animal. Pattern information obtained according to aspects of the
invention may be used to analyze a disease model (e.g., to assess
whether an animal disease model is representative of an actual
disease based on structural vascular features, or to assess the
progression of one or more vascular changes in a test animal that
provides a validated disease model, etc.), to evaluate the
effectiveness of a treatment regimen, to identify candidate
compounds or treatment regimens that are therapeutically effective,
or for other applications where data relating to vascular
structures (e.g., the progression of vascular structures, changes
in vascular structure over time or in response to different drugs
or drug dosages or administration frequencies, etc., or any
combination thereof) is informative. For example, aspects of the
invention may be used to identify one or more pattern elements that
can be used to help diagnose or evaluate diseases, provide
prognostic information, monitor treatments, screen therapeutic
agents, select one or more therapeutic agents (e.g., help determine
or predict a subject's responsiveness to a particular drug), etc.,
or any combination thereof.
[0150] Aspects of the invention may be used to study, identify, and
or analyze geometrical, structural, and/or distributional features
of blood vessels that are associated with one or more diseases or
conditions represented by an animal of interest. In some
embodiments, an animal may be a disease model as described herein.
In some embodiments, an animal may be undergoing a therapeutic
regimen of interest. In some embodiments, an animal may be treated
with a candidate therapeutic compound. Accordingly, aspects of the
invention may be used to identify, analyze, and/or evaluate one or
more vascular patterns or changes in vascular patterns associated
with a disease. Aspects of the invention also may be used to
evaluate the effects of one or more therapeutic regimens or
candidate compounds. In some embodiments, therapeutic effectiveness
may be evaluated using one or more vascular patterns or changes
therein as a marker of a response (or lack thereof) to treatment.
Accordingly, aspects of the invention may be used to identify
particular vascular patterns that are indicative of certain
diseases or disease stages. These patterns can subsequently be used
in sensitive assays to detect diseases in vivo (e.g., in human
subjects). Other aspects of the invention may be used to select
therapeutic regimens or candidate compounds for administration to a
patient (e.g., a human patient) in a therapeutically effective
amount and in a physiologically acceptable form.
[0151] It should be appreciated that in some embodiments, an animal
(e.g., an animal that is perfused with a casting agent composition)
may be sacrificed prior to analysis regardless of whether the
analysis is performed in situ or not. Accordingly, in some
embodiments, changes over time may be studied using a plurality of
animals and using one or more animals for each time point of
interest. In some embodiments, different dosages, different
therapeutic regimens, different drugs or drug combinations, or any
combination of two or more thereof may be studied using different
animals (with at least one animal for each condition of interest).
It should be appreciated that combinations of time courses and
drugs, drugs dosages, or other therapeutic regimens similarly may
be studied using a plurality of different animals, each
representing a unique condition. It should be appreciated that the
different animals are preferably genetically identical or similar
(e.g., identical for at least one trait that is associated with a
disease or condition of interest). In some embodiments, the animals
may be mice, rats, sheep, cats, dogs, primates, or any suitable
non-human experimental animal.
[0152] In some embodiments, a combination of different drugs,
different doses, etc., may be evaluated at a series of time points
according to aspects of the invention. Again, it should be
appreciated that a different animal may represent a different drug,
dosage, time point, or combination thereof, because each animal may
be sacrificed for analysis. However, in some embodiments, a single
animal may be tested at different sites (representing, e.g.,
different drugs, dosages, time points, etc.) depending on the
impact of the casting agent that is used and the site of
administration of the casting agent.
[0153] In some embodiments, samples from one or more animals may be
prepared and analyzed periodically during the time course of a
treatment (e.g., using a group of animals exposed to the same
experimental conditions). In some embodiments, different conditions
may be compared. For example, separate groups of animals (e.g.,
groups of mice) may be exposed to a candidate drug and a placebo
(or other control). In some embodiments, subsets of animals (e.g.,
one or more animals) may be perfused with a casting agent
composition at different time points and vascular structures may be
imaged (e.g., directly or through reconstruction) for each time
point. For example, tumors may be induced in genetically-altered
mice using appropriate controls and different dose levels or
regimens (e.g., 1, 2, 3, 4, 5, or more different dose levels or
regimens) of one or more therapeutic compounds or compositions.
Vascular structures then may be analyzed at different time points
using methods of the invention to evaluate the effectiveness of a
drug composition and/or to identify biological markers that can be
used to monitor a patient response to the drug composition. It
should be appreciated that vascular structures of different sizes
may be studied to identify structural features and/or distribution
patterns of interest. In some embodiments, blood vessels having a
diameter of about 50 microns are studied. However, it should be
appreciated that smaller or larger vessels, or a combination
thereof, may be studied.
[0154] In some embodiments, a vasculature characteristic may be
evaluated over time by comparing results at different time points.
However, it should be appreciated that the end-point of a study may
be used as a single time point and characteristics associated with
different diseases or treatments may be compared to identify or
infer changes associated with a disease, treatment, or other
condition of interest. Aspects of the invention can be used to
analyze data obtained from any suitable image source to identify
one or more patterns associated with tubular structures of
different sizes (e.g., structural patterns of blood micro-vessels).
One or more parameters of a structural pattern can be used as
biomarkers for different biological conditions and processes
(including pathogenic conditions). Accordingly, aspects of the
invention relate to disease detection, diagnosis, grading, staging,
disease monitoring, monitoring the effectiveness of therapy and
interventional applications based on an analysis of structures
(e.g., in situ structures) to identify patterns that may be
associated or correlated with a disease or other physiological
condition. According to the invention, a pattern may comprise one
or more different parameters. Parameters may be one or more
structural features of individual tubular structures and/or one or
more distribution properties (e.g., spatial distribution, spatial
orientation, frequency, number, etc., or any combination thereof)
of one or more tubular structures and/or one or more distribution
properties (e.g., spatial distribution, spatial orientation,
frequency, number, etc., or any combination thereof) of one or more
individual tubular structural features within a subject or a within
a region of interest in the subject, or any combination thereof.
Accordingly, a vasculature pattern may include one or more
structural features of an individual blood vessel (e.g.,
micro-vessels), a distribution of one or more blood vessels (e.g.,
micro-vessels) within a subject, a distribution of one or more
individual blood vessel structural features (e.g., individual
micro-vessel structural features), or any combination thereof. An
individual blood vessel structural feature may include, but is not
limited to, vessel tortuosity, curvature, branching (e.g.,
frequency, angle, hierarchy, etc.), diameter, direction, etc., or
any change (e.g., variation or frequency) of any of these features
over a predetermined length of the blood vessel being analyzed, or
any combination thereof. A distribution of blood vessels or
individual blood vessel structural features may include, but is not
limited to, a blood vessel density, a distribution of blood vessel
directions, a distribution of blood vessel diameters, a
distribution of distances between blood vessels, a distribution of
blood vessel spatial orientations (e.g., relative to each other), a
distribution of blood vessel curvatures, a distribution of any
other individual blood vessel structural features described herein,
other distributions of blood vessel parameters or any combination
of two or more thereof. It should be appreciated that the
distribution of blood vessels or blood vessel structural features
may be determined and/or analyzed for a predetermined region within
a subject (e.g., a target volume of tissue within a subject) or
within predetermined tissues or organs within a subject or
throughout the subject (e.g., within a vascular cast). It also
should be appreciated that either the absence or presence of blood
vessels or of individual blood vessel structural features within a
predetermined volume being analyzed may be a pattern parameter that
can be used in analytical methods of the invention. It also should
be appreciated that one or more pattern parameters may be monitored
and/or analyzed as a function of time. Accordingly, blood vessel
patterns can be used as biomarkers for different biological
conditions and processes (including pathogenic conditions).
Accordingly, aspects of the invention relate to identifying and
evaluating biological markers that may be used for in vivo disease
detection, diagnosis, grading, staging, for disease monitoring, for
monitoring the effectiveness of therapy and interventional
applications in live animals, including humans, based on an
analysis of vasculature patterns including vasculature morphology
and/or architecture in experimental subjects, for example
experimental animals (e.g., animals perfused with one or more
casting agent compositions). In one embodiment, the in vivo
density, and/or diameter distribution, and/or geometric orientation
of blood vessels (e.g., micro-vessels) may be analyzed, quantified,
and/or evaluated for disease detection, monitoring, and/or
interventional applications. In one embodiment, the sensitivity and
specificity of disease diagnosis may be enhanced by analyzing and
evaluating in vivo vasculature morphology and/or architecture
associated with a tissue lesion. Accordingly, aspects of the
invention include detecting in vivo indicia of diseases associated
with abnormal vascular structures or patterns. Other aspects
include disease diagnosis, staging, grading, monitoring and
prognosis, patient treatment, drug development and validation, and
research applications. It should be appreciated that one or more
biological markers identified in vascular casts in association with
a response to a known drug or treatment may be used as a reference
markers to evaluate the effectiveness of additional drugs or
treatments in comparison to the known drug or treatment.
[0155] Certain embodiments according to the present invention
includes a method of analyzing geometric features of blood vessels
and correlating one or more features with a biological process,
condition, or disease. Accordingly, certain geometric features of
blood vessels may be used as biomarkers indicative of particular
biological processes, conditions, and/or diseases.
[0156] In some embodiments, data for tubular structures (e.g.,
blood vessels) may been sorted into bins based on their size (e.g.,
their diameter). Aspects of the invention may increase the
analytical resolution when evaluating structural information that
is obtained for one or more experimental models and/or subjects
being evaluated. According to aspects of the invention, a binned
structural analysis refers to any analysis of tubular structures
that have been sorted or categorized according to size (e.g.,
according to the diameter or radius of the tubular structure in an
area of interest). For example, in some embodiments a binned
micro-vessel density (BMVD) analysis refers to an analysis of blood
vessel density based on blood vessels that have been categorized
according to vessel diameter in an area of interest.
[0157] Binned analytical techniques can be applied to the analysis
of many different parameters that may be characteristic of tubular
structures. Binned analytical techniques may be performed on
tubular structures observed in casts or in vivo (e.g., in situ).
For example, bins of tubular structures having different diameters
can be evaluated to determine one or more of the following
parameters: tortuosity, curvature, density, branching frequency,
branching hierarchy (e.g., presence or absence of a branching
hierarchy), relative distribution and/or direction of tubular
structures (e.g., blood vessels), etc., or any combination thereof.
By performing the analysis on binned data, small changes that
primarily affect structures in one size range are more likely to be
detected, because they are not masked by a relative absence of
change in structures in other size ranges. Accordingly, methods of
the invention can be used to refine an analysis of tubular
structures (e.g., blood vessels) over time or in response to
disease or treatment, etc., where the analysis may be performed on
casts and/or in vivo. Aspects of the invention can also be used to
detect or delineate diseased tissue (e.g., cancerous or
pre-cancerous tissue, necrotic regions, etc.) in casts and/or in
vivo.
[0158] It should be appreciated that, regardless of the source of
information relating to vessel geometry, structure, and/or
distribution (e.g., from analysis of BMVD, casts, in vivo, images,
representations, etc., or any combination thereof), analytical
methods described herein may be used. Accordingly, any analytical
descriptions of vessel distributions that are provided in the
context of one source of information may be applied to that
analysis of vessel distributions obtained from one or more other
sources as appropriate.
[0159] In some embodiments, spatiotemporal information about the
vessel distribution provides numerous indicators about the health
of a tumor, the effectiveness of a treatment such as the efficacy
of a particular anti-angiogenic drug, and how a tumor is changing
over time with respect to differently sized vessels. Numerous
exemplary applications using one or more distribution analyses
(e.g., based on BMVD measurements), in accordance with various
aspects of the present invention are described herein. Applicant
has identified and disclosed various applications that are
facilitated by the acquisition of information about vessel
characteristics, distribution, size, shape, etc., in PCT
application US2005/047081 filed on Dec. 22, 2005, which is hereby
incorporated by reference in its entirety. Applicant has
appreciated that certain of these applications are facilitated by
obtaining one or more BMVD measurements or by using one or more
alternative binned analyses. It should be appreciated that any
application may involve an analysis limited to one or more (e.g.,
2, 3, 4, 5, 6, 7, 8, 9, 10, or more) bins of microvasculature of
different sizes. For example, binned analyses may be useful for
diagnostic applications. In one embodiment, aspects of the
invention can be used to detect and diagnose diseases associated
with patterns (e.g., individual structural features or
distributions) of in situ tubular networks. In some cases, a
diagnosis can be rendered from an examination of the patterns
(e.g., individual structural features or distributions) of interest
at a single time. Alternatively, disease progression in a subject
can be tracked by performing a structural analysis at two or more
(e.g., 3, 4, 5, 6, 7, 8, 9, 10, or more) time points. Disease
tracking can be used to provide diagnostic and prognostic
information for a patient. For example, disease progression
information can be used to assess the aggressiveness and/or
invasiveness of a tumor.
[0160] The invention can be used to screen an individual or a
population for the presence of indicia relating to one or more
diseases. As mentioned herein, the screen may be a whole body
screen, or may be focused on one or more target regions (e.g.,
specific organs or tissues).
[0161] In one embodiment, the techniques described herein can be
used automatically to identify individuals with one or more
disease-associated structural patterns or features. These
individuals can be subsequently tested for additional indicia of
disease. The subsequent testing can take any suitable form, as the
aspects of the present invention described herein are not limited
in this respect. For example, follow on testing can employ
conventional techniques. As a non-limiting example, the use of
aspects of the present invention may enable cost-effective
screening techniques that may identify a relatively small pool of
candidates as at risk of a disease, and may justify the use of
relatively more expensive testing procedures to reach a final
diagnosis or prognosis, wherein the follow on techniques may be too
expensive to administer to a wider sample that has not been
narrowed using the techniques of the present invention described
herein. As a further example, aspects of the present invention
described herein, either alone or in combination with other
techniques, can be used to perform subsequent tests. In this
respect, the sensitivity of the initial screening can be set
relatively high, such that it may indicate some false positives,
and subsequent application of techniques in accordance with aspects
of the present invention described herein can be employed with a
higher degree of sensitivity that may provide more detailed
information.
[0162] In one embodiment, aspects of the present invention can be
used to screen a population of at risk individuals (e.g.,
individuals with genetic or other risk factors for a disease such
as cancer, a circulatory disorder, or other disease) to identify
the presence of disease indicia in one or more individuals.
[0163] In one embodiment, diagnostic methods of the invention are
computer-implemented to increase efficiency and throughput, and
reduce variability associated with individual physicians. However,
as discussed herein, in some embodiments, the final diagnosis may
be made by a physician based on information generated by an
automated analysis or a structural representation using aspects of
the invention described herein.
[0164] As shall be appreciated from the foregoing, aspects of the
invention can be used on patients known to have a disease, or can
be used to screen healthy subjects on a regular basis. A subject
can be screened for one or more diseases. Screening can be done on
a regular basis (e.g., weekly, monthly, annually, or other time
interval); or as a one time event. Different conditions can be
screened for at different time intervals and in function of
different risk factors (e.g., age, weight, gender, history of
smoking, family history, genetic risks, exposure to toxins and/or
carcinogens etc., or a combination thereof).
[0165] In one embodiment, aspects of the invention can be employed
to diagnose, evaluate or stage diseases associated with changes in
vasculature structure. The detection of small changes in
vasculature structure may be informative for early stage disease
detection and disease monitoring. A morphological determination of
binned blood vessels may be analyzed and one or more patterns
(e.g., individual structural features or distributions) may be
evaluated for the presence of abnormal properties. In one
embodiment, a vasculature structure may be obtained including a
series of interconnected branched blood vessels and may include
arteries, arterioles, veins, venules, capillaries, and other sized
blood vessels. However, according to aspects of the invention, an
interconnected vasculature structure is not required and different
sizes of blood vessels can be analyzed separately and represented
on a histogram or other form of distribution representation. In
some aspects of the invention, blood vessels of the entire body can
be analyzed, and in other aspects the blood vessels of a target
organ, tissue, or part thereof can be analyzed. In some aspects of
the invention, only a subset of blood vessel sizes is binned and
analyzed (e.g., blood vessels with a diameter below about 500
microns, preferably below about 200 microns, more preferably below
100 microns, even more preferably below 50 microns, and even more
preferably below 25 microns). In one embodiment, only capillary
blood vessels are analyzed. In another embodiment, capillaries and
small arteries and veins (e.g., arterioles and venules) are
analyzed. For example, an arborescent vasculature can be analyzed
in any tissue where it is found (e.g., an arborescent mucosal
vasculature such as the oesophageal arborescent mucosal
vasculature).
[0166] The branches of a vascular tree may be analyzed to glean
information about the status of the patient. In one embodiment, the
branches of a vascular tree may be followed to identify specific
regions where certain characteristics of angiogenesis may be
evaluated (e.g., start with a large branch and follow the tree to
second, third, or fourth, or subsequent levels of branching to
identify small blood vessels that may have abnormal structures if
they are providing a blood supply associated with a disease).
Alternatively, several different blood vessel sizes in the vascular
tree may be evaluated for signs of angiogenesis. In another
embodiment, the overall branching pattern of a vascular tree can be
analyzed. For example, a healthy vascular tree may be approximately
hierarchical in that the size of the blood vessels generally
decreases as the vessels branch. In contrast, a diseased (e.g.,
angiogenic) vascular tree may be less hierarchical with areas of
significant blood vessel branching with little or no decrease in
blood vessel size. It should be appreciated that the nature and
extent of the analysis may depend on the goal of the diagnostic
evaluation. For example, a full body scan can be evaluated
selecting all vascular structures and analyzing the entire vascular
network for signs of different diseases. Alternatively, a region of
a body suspected of being diseased may be selected and the data may
be processed to focus on the vasculature in that region (e.g., to
obtain a segmented representation of structures in the region of
interest). A region of interest may be an organ (e.g., pancreas,
liver, breast, colon etc.) or a tissue (e.g., skin epidermal
tissue). The presence of an abnormal vasculature structure can be
an early indication of a range of diseases for which early
detection is critical for effective treatment.
[0167] Diseases associated with changes in vascular structure
(e.g., that can be detected by the presence of abnormal vascular
patterns at a given time or abnormal structural changes observed as
a function of time) include, but are not limited to, cancer, heart
diseases and related circulatory disorders, eye diseases, skin
disorders, and surgical conditions. For example, diseases and
conditions associated with changes in vascular structure include,
but are not limited to, tumor angiogenesis, recurrent and
progressive cancers, coronary artery disease, cardiomyopathy,
myocardial ischemia, arteriosclerosis, atherosclerosis,
atherosclerotic plaque neovascularization, arterial occlusive
disease, ischemia, ischemic or post-myocardial ischemia
revascularization, peripheral vascular disease (including diabetic
retinopathy), thromboembolic diseases (e.g., stroke, pulmonary
embolism, brain aneurisms, and deep venous thrombosis),
claudication, rheumatologic disorders (e.g., arthritis), immune
disorders (e.g., rheumatoid arthritis, vasculitis, Wegner's
granulomatosis, and systemic lupus erythematosis (SLE)), pulmonary
disorders (including, emphysema, COPD, idiopathic pulmonary
fibrosis, pulmonary arterial hypertension, and other respiratory
disorders), myeloma, vascular proliferative disorders,
gastrointestinal disorders (e.g., Crohn's disease, ulcerative
colitis, and inflammatory bowel disease (IBD)), gynecologic
disorders (endometrial polyp, vaginal bleeding, endometriosis,
dysfunctional uterine bleeding, ovarian hyperstimulation syndrome,
preeclempsia, polycystic ovarian syndrome (PCO), cervical cancer,
and cervical dysplasia), skin disorders (infantile hemangioma,
verruca vulgaris, psoriasis, neurofibromatosis, epidermolysis
bullosa, Stevens-Johnson syndrome, and toxic epidermal necrolysis
(TEN)), eye disorders (macular degeneration, maculopathies,
diabetic retinopathy, and retinopathy of prematurity (retrolental
fibroplasia)) wound healing, inflammation associated with immune
responses, ischemia including limb ischemia and cardiac ischemia,
Alzheimer's disease and other disorders such as wound dehiscence,
Buerger Disease (thromboangitis obliterans, arteriosclerosis
obliterans (ASO), ischemic ulcers) multiple sclerosis, idiopathic
pulmonary fibrosis, HIV infections, plantar fasciosis, plantar
fasciitis, Von Hippel-Lindau Disease, CNS hemangioblastoma, retinal
hemangioblastoma, thyroiditis, benign prostatic hypertrophy,
glomerulonephritis, ectopic bone formation, and keloids.
[0168] These different diseases are characterized by different
changes in vasculature structure. Accordingly, in one aspect of the
invention, parameters and scoring methodologies are used to detect,
diagnose, and monitor particular diseases and their related
therapies based upon particular characteristics of vasculature
structure indicative of the disease. Even within each disease
category, different diseases can be characterized by different
changes in vasculature structure. Accordingly, structure mining and
scoring can be fine-tuned to increase the sensitivity for
particular types of disease within a category (e.g., lung cancer
score, breast cancer score, etc., can be developed).
Patient-specific scoring parameters can also be developed to follow
the progression of a specific disease or disorder in a patient.
[0169] Structural vasculature changes include changes in vascular
architecture and vascular morphology affecting blood vessels and/or
lymph vessels. Structural changes can involve neovascularization
(including the growth of large blood vessels (e.g., arteriogenesis)
and the growth of microvasculature (angiogenesis)), large blood
vessel expansion, and vascular necrosis. Angiogenesis involves the
formation of new blood vessels that sprout from preexisting blood
vessels. Angiogenesis is different from vasculogenesis, which is
the de novo formation of vessels that occurs primarily during
development. Vasculogenesis is rarely associated with a disease or
disorder. However, aspects of the invention can be used to study
the natural process of vasculogenesis to help identify and
understand defects in de novo blood vessel formation.
[0170] Angiogenesis is often associated with tumor growth and is a
useful biomarker for cancer. Angiogenesis also can be associated
with conditions where new blood vessel growth occurs in response to
a reduced oxygen supply or blood flow (whether due to thrombosis,
embolism, atherosclerosis, or other chronic occlusion or narrowing
of the vasculature). Certain respiratory, cardiovascular, and
inflammatory disorders also are associated with angiogenesis.
[0171] Angiogenic blood vessels have structural characteristics
that are different from those of established blood vessels. For
example, the branching patterns and tortuosity of angiogenic blood
vessels are very different from those of normal blood vessels.
These and other structural features are found predominantly in
microvasculature and can be used for mining and scoring vasculature
structural images. However, changes in larger blood vessels such as
arteries and veins also may be associated with certain diseases or
disease stages (e.g., growth and development of large tumors or
late-stage tumors).
[0172] The vasculature that supports a tumor is typically
associated with the connective tissue of the tumor (the stroma)
that supports the malignant cells (in the parenchyma). As discussed
herein, tumor blood vessels are irregularly spaced and
characterized by heterogeneous structural patterns or features.
However, the formation of tumor blood vessels and other forms of
angiogenesis may involve a series of characteristic stages (see,
for example, Dvorak, 2003, American Journal of Pathology, Vol.
162:6, pp. 1747-1757, the disclosure of which is incorporated
herein by reference in its entirety). Early stage angiogenesis may
be characterized by vascular hyper-permeability, fibrin deposition
and gel formation, and edema. This may result in the enlargement of
micro-vessels such as venules. The cross-sectional area of an
enlarged micro-vessel may be about 4 fold that of a normal
micro-vessel. The perimeter of an enlarged micro-vessel may be
about 2 fold that of a normal micro-vessel. Enlarged micro-vessels
may occupy about 4-7 fold the volume of normal micro-vessels in a
region of active angiogenesis. The appearance of enlarged
micro-vessels may be followed by the appearance of "mother" vessels
that are enlarged, thin-walled, serpentine, and hyper-permeable.
Mother vessels may undergo a process of bridging whereby
trans-luminal bridges are formed dividing the blood flow within the
vessel into smaller channels. A developing mother vessel also may
contain one or more glomerular bodies that may expand to divide the
lumen of the mother vessel into several smaller channels that are
typically tortuous. Bridging and glomerular body formation in
mother vessels may lead to the appearance of small capillaries
characteristic of angiogenesis. However, certain mother vessels
persist as abnormally enlarged vessels with thin walls. These
vascular malformations are often characterized by the presence of
an asymmetric muscular coat and perivascular fibrosis. Small
arteries and arterioles also may increase in size in diseased
tissue. Aspects of the invention include detecting and/or
monitoring any one or more of the blood vessel structural changes
described herein. In one embodiment, the presence of one or more
patterns (e.g., individual structural features or distributions)
characteristic of new blood vessel formation may be used to detect
or monitor a disease. In another embodiment, the presence of one or
more specific patterns (e.g., individual structural features or
distributions) may be used to determine the stage of angiogenesis
(e.g., early-stage, mid-stage, late-stage, etc.) in a body
region.
[0173] Accordingly, abnormal changes in blood vessel size (diameter
and/or length) can be early signs of diseases such as cancer or
other disease associated with an increased blood supply. Changes in
blood vessel size may occur before any structural signs of
angiogenesis appear. In one embodiment, aspects of the invention
are useful to detect blood vessels (e.g., capillaries) that are
swollen and/or longer than normal. For example, aspects of the
invention are useful to detect abnormally long intrapapillary
capillary loops in situ (e.g., associated with early stages of
cancer in oesophageal mucosa).
[0174] In some embodiments, blood vessel changes indicative of
necrosis in tumor tissues may be indicative of the aggressiveness
of the tumor tissue and/or the likelihood of metastasis, and/or the
responsiveness to therapy, and/or the efficacy of a therapeutic
treatment (e.g., a candidate drug), and/or an therapeutic treatment
selection and/or modification (e.g., a change in drug or dose for
an individual patient). Accordingly, in situ patterns (e.g.,
individual structural features or distributions) indicative of
necrosis may be useful biomarkers for patient prognosis. In certain
embodiments, necrosis within a region of a tumor may be indicated
by one or more of the following patterns (e.g., individual
structural features or distributions) within that region: a
collapse in blood vessel structure, poor vascularization (e.g., a
low blood vessel density relative to other regions of the tumor or
relative to the perimeter of the tumor), a change in blood vessel
size or shape over time, a lower than threshold number of blood
vessels, blood vessels (e.g., in the microvasculature or the
capillaries) that are separated by a greater than threshold
distance (e.g., by more than 100 microns, more than 150 microns, or
more than 200 microns) within a volume of the tumor, micro-vessel
diameter and/or density indicative of undervascularization, etc.,
or any combination thereof. In some embodiments, a volume of
avascularization or undervascularization may be evaluated or
quantified and used as an indicator of necrosis. It should be
appreciated that other indicia of necrosis may be used, alone or in
combination with blood vessel features. Other indicia may include
indicia of tissue collapse or cavitation that may be visualized
(e.g., using CT etc.) and/or indicia of tissue viability using one
or more markers of metabolic activity (e.g., ones that may be
analyzed using a PET scan, etc.). One or more reference indicia
(e.g., a reference volume of avascularization or
undervascularization may be identified by analyzing vascular casts
of necrotic tumor tissue (e.g., in a xenograft tumor model, for
example in an orthotopic or an ectopic tumor xenograft).
[0175] Aspects of the invention may be used for the detection
(e.g., the automatic detection)
[0176] Aspects of the invention may be used for the detection
(e.g., the automatic detection) of necrotic areas in a subject
(e.g., in a tumor in a subject). A necrotic region is an avascular
region within the boundary of a diseased tissue. Methods of the
invention may be used to detect (e.g., automatically) the
transition between the vascularized diseased tissue and avascular
region that defines the boundary of the necrotic region.
[0177] Aspects of the invention also may be used to detect or
evaluate (e.g., automatically) a response to therapy. For example,
a response to therapy (e.g., to a specific drug and/or a specific
dosage of a drug, and/or to a combination of drugs and specific
dosages of these drugs, etc.) can be detected and assessed as
follows. Changes in the vascular patterns (e.g. vessel
normalization/straightening, disappearance of smaller diameter
vessels leading to lower micro-vessel density and to skewing of the
vessel diameter distribution towards the larger vessels) may be
detected and/or evaluated within the volume defined by the boundary
of the diseased tissue and the boundary of the necrotic area. An
increase in the absolute volume size of the necrotic area and/or
the rate of such change while the total volume of the disease (e.g.
tumor) volume stays constant may be detected and/or evaluated as an
indicator that the therapy is effective. An increase in the ratio
between the absolute volume size of the necrotic area and the total
disease (e.g., tumor) volume and/or the rate of change in this
ratio may be detected and/or evaluated and used as an indicator
that the therapy is effective. A ratio of the diseased tissue
volume and the necrotic region volume may be detected and/or
evaluated and when it approaches 1 and the overall diseased tissue
volume starts shrinking it provides an indication that a therapy is
effective. In some embodiments, reference indicia may be obtained
from analyzing casts (e.g., appropriate vascular casts). However,
reference indicia may be obtained from any suitable data relating
to blood vessel structures (e.g., view data, scan data, in vivo
data, etc., or any combination thereof).
[0178] Structural representations of blood vessels can be mined to
identify and evaluate certain patterns (e.g., individual structural
features or distributions) that can be used to provide a score that
is related to the probability that the blood vessels are normal or
abnormal (e.g., disease associated). Accordingly, in some
embodiments a binned analysis may be predictive of a response to
therapy.
[0179] In certain embodiments, a binned analysis may be sensitive
to vasculature changes resulting from unwanted side-effects
associated with one or more therapeutic drugs. Accordingly, binned
analysis may be used to detect or quantify toxic side-effects of
certain drugs.
[0180] The morphology of blood vessels (e.g., binned blood vessels)
can be mined to identify and evaluate certain patterns (e.g.,
individual structural features or distributions) that can be used
to provide a score that is related to the probability that the
blood vessels are normal or abnormal (e.g., disease associated).
Patterns (e.g., individual structural features or distributions)
for scoring blood vessels include, but are not limited to, the
following: diameter, curvature, tortuosity (including, for example,
the degree of tortuosity, the length of the blood vessel along
which abnormal tortuosity is observed, etc.), variability or
heterogeneity (including spatial variability or heterogeneity over
distance or in a volume), branching shape or pattern, branching
density, branching hierarchy, blood vessel density, distribution of
vessel size (ratio of microvasculature to macrovasculature) a field
effect (the presence of blood vessels bending towards a specific
region), blood vessel diameter distribution, variability of the
geometric orientation of blood vessels or fragments thereof, and
the distribution of the orientation(s) within a field. The score
may have more significance if two or more (e.g., 3, 4, 5, 6, 7, 8,
9, 10, or more, or all) of these parameters are evaluated. In some
embodiments, a score is generated using one or more of these
structural parameters combined with additional information such as
patient-specific medical information (e.g., age, weight, height,
gender, etc.) and the presence of one or more additional indicators
of disease such as a visible lesion on an X-ray or other image. In
some embodiments, a score can be provided for a tumor. An example
of a useful score is one that reflects the vascularity of a tumor.
An abnormally high vascularity (measured as a higher than normal
blood vessel number, density, length, or combination of the above)
is generally indicative of a more aggressive or invasive tumor. In
one embodiment, vascularity is evaluated by measuring the volume of
the lumen of angiogenic vasculature (the volume within the blood
vessel tree associated with a tumor). In another embodiment, a
measure of vascularity is provided by dividing the volume of the
angiogenic lumen by the volume of the solid tumor. Additional
information can be gleaned from obtaining a score (or other
structural evaluation) at two or more times. A changing score (or
other structural evaluation) is indicative of an evolving
vasculature that could be associated with a disease or disorder. It
should be appreciated that the patterns (e.g., individual
structural features or distributions) described herein can be
identified and analyzed for a field of analysis without imposing a
connectivity on the vessels being studied. In some embodiments, it
may be sufficient to analyze only fragments of blood vessels in
order to detect one or more structural features of individual
vessels or geometrical features of a field of vessels that are
different from normal features. For example, blood vessel fragments
having an average length of 0.5 mm, 1 mm, 5 mm, 10 mm, 50 mm, 1 cm,
5 cm, 10 cm, 50 cm, etc. may be used. However, it should be
appreciated that shorter or longer or intermediate lengths may be
used. The scoring and mining aspects of the invention described
herein can be automated. Accordingly, diseased (e.g., angiogenic)
vasculature can be automatically detected amidst normal
vasculature. Various vasculature parameters can be automatically
detected and scored, either separately or in any combination,
including vessel tortuosity, vessel branching, vessel density, and
total intra-vascular volume, but the invention is not limited to
any particular parameter or combination.
[0181] In one embodiment, aspects of the invention can be used to
detect blocked blood vessels, and thromboembolic events, including
stroke, lung emboli, blocked micro-coronaries, deep-vein
thrombosis, etc. Blocked blood vessels can be detected (1) directly
by detecting structural changes in the blocked blood vessel (e.g.,
detecting a clot, wall thickening, or other signs of reduced flow)
and/or (2) indirectly by detecting new vasculature that was
generated in response to the blockage. In general, the formation of
collateral blood vessels is more ordered than angiogenesis
associated with cancer. One aspect of the invention described
herein also allows clots to be detected in small blood vessels.
[0182] As discussed herein, aspects of the invention can be used to
screen the entire vasculature structure of a human or other animal
to screen for any form of abnormality in any tissue. Alternatively,
a subset of the body may be screened. Accordingly, the structures
of binned vessels can be analyzed for one or more organs or tissue
types. In addition, only a portion of the vessels in any
predetermined bin may be analyzed within any target volume as
opposed to the entire vascular tree in that volume. This may be
done by analyzing structure data focused on the area of interest,
or large amounts of structure data may be obtained, but an analysis
may be restricted to a subset of the available data. In some
embodiments, only a portion of a vascular tree may be binned and/or
analyzed, for example only a portion of those vessels that are of a
particular size range. In some embodiments, only fragments of a
vascular tree are represented and/or analyzed if the fragments are
sufficiently informative to provide patterns (e.g., individual
structural features or distributions) of interest. Fragments may
include branches or may be unbranched. The portion of the
vasculature being analyzed may be statistically significant, such
that any observation (normal or abnormal) is physiologically
significant. For example, branched structures may not be required
for the analysis if a sufficient number of vessel substructures are
analyzed to confidently detect any other patterns (e.g., individual
structural features or distributions) that may be associated with
vasculature changes (e.g., angiogenesis) such as high vessel
density. In aspects of the invention, vascular patterns may be
detected and/or evaluated in situ in a volume of 1 mm.sup.3, 2
mm.sup.3, 5 mm.sup.3, 1 cm.sup.3, 2 cm.sup.3, 5 cm.sup.3, 10
cm.sup.3, etc. However, smaller or larger or intermediate volumes
also may be analyzed. In some embodiments, vascular patterns or
structures are evaluated over an entire model tissue or organ
(e.g., for an entire orthotopic or ectopic tumor model).
[0183] Different tissues and organs have different and
characteristic blood vessel patterns (e.g., the lung which is
highly vascularized). Accordingly, in one embodiment, structural
analyses and associated structural parameters may be optimized for
evaluating different tissues.
[0184] In some embodiments, scan data is obtained and/or analyzed
for one or more organs (e.g., lung, heart, colon, brain, liver,
pancreas, kidney, breast, prostate, etc.) or tissue (e.g., skin,
bone, etc.) or portion of any of the above.
[0185] Brains may be evaluated for signs of brain tumors and/or
other neurological disorders that can be associated with changes in
vascular patterns. For example, Alzheimer's may be associated with
certain vascular abnormalities. In one embodiment, one or more
changes in blood vessel pattern (e.g., shape and/or size) may be
detected as an indicator of high blood pressure in the brain.
[0186] In some embodiments, certain specific regions of organs or
tissues are focused on. For example, atherosclerosis is typically
found in certain parts of the arterial tree (e.g., bifurcations,
side branches, regions opposite flow dividers, and other areas
where angiogenesis often occurs in association with
atherosclerosis) and certain cancers tend to occur more frequently
in certain organ or tissue regions (e.g., colon cancers are not
distributed evenly along the length of the colon).
[0187] In other embodiments, aspects of the present invention may
be used to follow up with individuals who have been identified as
having one or more other indicia of disease (e.g., fecal occult
blood, a colon polyp, a lung nodule, one or more cysts or other
indicia of disease). Aspects of the invention may be used to
confirm the presence of a disease, determine a location for the
disease-associated lesion, or provide an evaluation or prognosis of
a disease. For example, aspects of the invention may be used to
determine whether abnormal vasculature is present at the site of a
lesion (e.g. a colon polyp, a lung nodule, a bladder cyst, a
prostate cyst, a breast cyst, a spot on a mammography, or any other
cyst, lump, or spot that may be detected physically, visually, or
using any other diagnostic technique) and help evaluate the
likelihood of a malignancy (or other carcinogenic disease stage)
associated with the lesion. Accordingly, aspects of the invention
may be used for virtual malignancy detection (e.g., virtual
colonoscopy, virtual colon malignancy detection, virtual
bronchoscopy, virtual lung malignancy detection, virtual
mammography, virtual cystoscopy, etc.).
[0188] In other embodiments, aspects of the invention may be used
for screening a cancer patient to evaluate the extent of a
cancerous lesion and/or to screen for the presence of one or more
metastatic lesions (e.g., one or more loci associated with
angiogenesis). A cancer patient may be screened upon initial
diagnosis of a primary cancer. In addition or alternatively, a
cancer patient may be screened at least once after an initial
cancer treatment (e.g., surgery, radiation, and/or chemotherapy).
This screening may include the original cancer locus to detect any
cancer recurrence. This screening may include similar body tissue
to screen for the presence of other lesions in the same tissue or
organ (e.g., the entire colon may be screened when a cancerous
lesion is detected in one region of the colon, the second breast
may be screened when a cancerous lesion is detected in one breast,
etc.). This screening also may be extended to the whole body or to
one or more other loci suspected of containing a metastatic lesion.
In one embodiment, a cancer patient may be screened several times
after an initial cancer treatment (e.g., at time intervals of about
6 months, about 1 year, about 2 years, about 5 years, or at other
time intervals).
[0189] In one embodiment, a follow up procedure may involve
screening one or more organs or tissues for the presence of a
metastatic lesion. Different cancers may have different
characteristic patterns of metastasis. Accordingly, different
target loci may be screened for different cancers. For example,
metastatic breast cancer typically spreads to the lungs, the liver,
bone, and/or the CNS. Therefore, one or more of these tissue types
or organs may be screened after a patient is diagnosed with breast
cancer. Similarly, other target loci may be screened after a
patient is diagnosed with another cancer type. In some embodiments,
the entire body of a cancer patient may be screened for indicia of
metastasis.
[0190] In one aspect, an initial screen may be performed on an
entire body, or an entire organ, using a low resolution
representation and/or, for example, analyzing only one or two or a
small number (e.g., less than five) pattern parameters in order to
detect indicia of a disease. Subsequently, the presence and or
nature of the disease may be diagnosed using a higher resolution
representation and/or, for example, analyzing one or more
additional pattern parameters or alternative pattern parameters
than those that were analyzed for the initial detection.
[0191] In some embodiments, small changes in blood vessel
distributions may be observed (for example as measured by a ratio
between the number of blood vessels of two or more different sizes
in a region of interest, for example, a tumor in an animal model)
and used as a biomarker. Such biomarkers may represent early
changes (e.g., early changes in tumor growth or response to
therapy) that occur before later changes in tumor size and/or tumor
morphology. It should be appreciated that some or all of the
diagnostic aspects of the invention can be automated as described
herein.
[0192] It should be appreciated that some or all of the diagnostic
aspects of the invention can be automated as described herein.
[0193] Aspects of the invention also can be used to identify the
location of a disease by locating one or more structural
abnormalities associated with the disease. This information can be
used to target a biopsy procedure or a treatment (e.g., a treatment
with one or more toxic chemicals, radiation, heat, cold, small
molecules, gene therapy, surgery, any other treatment, or a
combination of two or more of the above) to the precise location of
a disease lesion, or for any other purpose.
[0194] In one embodiment, an imaging device is connected to a
computer that provides a real-time visual display of the disease
lesion. In one embodiment, a real-time visual display may be an
accurate model of a body region and lesion along with associated
vasculature (as opposed to an actual image). This visual
information can be used to guide a surgical instrument for a
biopsy. Alternatively, the information can be used to guide an
invasive (e.g., surgical removal or bypass) or non-invasive (e.g.,
radiation) treatment procedure to the site of the disease lesion
(e.g., tumor or blood clot).
[0195] In some embodiments, aspects of the invention may be used to
define the boundary between diseased and non-diseased tissues, or
between necrotic and non-necrotic tissue, etc., or any combination
thereof. For example, a boundary may be identified or defined by
analyzing binned data for several areas of interest and identifying
adjacent areas having very different blood vessel densities (or
differences in other morphological parameters that are associated
with disease, necrosis, etc., or any combination thereof.
[0196] In one embodiment, aspects of the invention may be used to
identify an area of tissue for treatment before the treatment is
applied. For example, a treatment target region may be identified
by detecting a boundary of chaotic blood vessel structures. The
area may be assessed after treatment to confirm that the treatment
was appropriately targeted. In one embodiment, a structure may be
analyzed pre-operatively to identify the extent of tissue to be
removed from a body region. In one embodiment, a body region may be
analyzed post-operatively to determine whether any abnormal
structures were missed. This may be used to confirm the success of
a radiation treatment or a surgical removal of diseased tissue.
Alternatively, this may be used to decide on further surgery and/or
another form of treatment. In another embodiment, a disease
boundary may be defined or depicted by the boundary of abnormal
vasculature. A treatment (e.g., radiation therapy, surgery, etc.)
may be guided by and/or restricted to a volume encompassed by the
disease boundary.
[0197] In one embodiment, aspects of the invention can be used to
evaluate the success of a surgical implant or transplant. For
example, aspects of the invention can be used to evaluate the
formation of new blood vessels after an organ or tissue
transplant.
[0198] In another embodiment, the development of new blood vessels
may be monitored after removal of tumor tissue or after a tumor
biopsy, both of which may trigger angiogenesis and/or convert a
dormant tumor into a malignant tumor.
[0199] It should be appreciated that some or all of the
interventional aspects of the invention can be automated as
described herein.
[0200] Aspects of the invention also can be used to optimize a
therapeutic treatment for a patient. The extent of disease
progression or regression can be monitored in response to different
treatment types or dosages, and an optimal treatment can be
identified. The optimal treatment may change as the disease
progresses. The effectiveness of the treatment over time can be
monitored by analyzing changes in disease-associated patterns
(e.g., individual structural features or distributions) using the
aspects of the present invention described herein.
[0201] In one embodiment, a first therapy can be administered and
its effectiveness on slowing, stopping, or reversing abnormal blood
vessel growth can be monitored either irregularly or at certain
time intervals (e.g., daily, weekly, monthly, or other time
intervals). In some embodiments, if a first therapeutic regimen
does not have a desired effect on disease progression, a second
therapeutic regimen can be evaluated. Similarly, additional
therapeutic regimens can be evaluated on a patient-by-patient
basis. Additionally, the invention can be used to optimize a chosen
therapeutic regimen (e.g., optimize dosage, timing, delivery, or
other characteristic of a drug or other treatment) by monitoring
the effect of minor therapeutic changes and using the conditions
that appear to be most effective for the condition and the
patient.
[0202] When looking at the therapeutic effectiveness of a
treatment, disease-specific parameters may be monitored. Of course,
all parameters can be obtained and only a subset reviewed. However,
it may be more efficient to simply obtain binned data only for
those parameters that characterize the disease.
[0203] According to aspects of the invention, patterns (e.g.,
individual structural features or distributions) that are used to
detect angiogenic vasculature and other abnormal blood vessels also
can be used to monitor a disease response to treatment. For
example, the total vascularity or any other volumetric analysis of
angiogenic or other diseased vasculature, and the distribution of
vessel size (e.g., a ratio of small to large blood vessels) can be
used independently or together as indicators of disease progression
or regression. In general, microvasculature disappears before
macrovasculature if an anti-angiogenic treatment (or other disease
treatment) is effective. Therefore, an effective treatment results
in a shift in the distribution of blood vessel sizes towards larger
vessels. An index of anti-angiogenic activity can be scored as
either a loss of small blood vessels or a shift of observed blood
vessels towards a single size (or both).
[0204] In another aspect, the parameters can be (or include)
changes over time. For example, a structure present at a second
time can be compared to a structure present at a first time. In one
embodiment, a disease may be tracked pre-therapy and/or
post-therapy. Naturally, additional time points can be used. The
time points may depend on the condition being observed (e.g., is it
the progression of a disease that is already identified, is it the
screening of patient(s) over time). Time periods can be daily,
weekly, monthly, annual, or shorter, intermediate or longer time
periods. Time intervals may be a series of regular time periods.
However, other time intervals may also be useful. In one
embodiment, a patient-specific baseline is established and
monitored over time. For example, vasculature changes in the colon,
breast, or other tissue or organ can be monitored periodically.
[0205] In one aspect of the invention, a type of treatment may be
determined by the degree or extent of abnormal vascular structures
(e.g., angiogenesis) that is detected at one or more suspected
disease loci (e.g., cancerous loci). For example, if a suspected
cancerous locus or metastasis is pre-angiogenic or associated with
early stage angiogenesis, it may be appropriate to monitor the
locus without any form of treatment. However, an appropriate
therapy may involve the administration of one or more angiogenesis
inhibitors to prevent the formation of any new vasculature. If a
suspected cancerous locus or metastasis is associated with
mid-stage angiogenesis, an appropriate therapy may be the
administration of one or more angiogenesis inhibitors. A patient
with mid-stage angiogenesis at a suspected locus also should be
monitored so that any further blood vessel development can be
treated more aggressively. If a suspected cancerous locus or
metastasis is associated with late stage angiogenesis, an
appropriate treatment may involve at least one or more of
chemotherapy (e.g., cytotoxic chemotherapy and/or hormone-based
chemotherapy), radiation, surgery, and/or treatment with one or
more angiogenesis inhibitors. However, it should be appreciated
that any of the above treatment options may be used to treat a
patient with any one or more lesions associated with any degree of
angiogenesis.
[0206] Examples of angiogenesis inhibitors include but are not
limited to 2-methoxyestradiol (2-ME), AG3340, Angiostatin,
Angiozyme, Antithrombin III, VEGF inhibitors (e.g., Anti-VEGF
antibody), Batimastat, bevacizumab (avastatin), BMS-275291, CAI,
2C3, HuMV833 Canstatin, Captopril, Cartilage Derived Inhibitor
(CDI), CC-5013, Celecoxib (CELEBREX.RTM.), COL-3, Combretastatin,
Combretastatin A4 Phosphate, Dalteparin (FRAGIN.RTM.), EMD 121974
(Cilengitide), Endostatin, Erlotinib (TARCEVA.RTM.), gefitinib
(Iressa), Genistein, Halofuginone Hydrobromide (TEMPOSTATIN.TM.),
Id1, Id3, IM862, imatinib mesylate, IMC-IC11 Inducible protein 10,
Interferon-alpha, Interleukin 12, Lavendustin A, LY317615 or AE-941
(NEOVASTAT.TM.), Marimastat, Maspin, Medroxpregesterone Acetate,
Meth-1, Meth-2, Neovastat, Osteopontin cleaved product, PEX,
Pigment epithelium growth factor (PEGF), Platelet factor 4,
Prolactin fragment, Proliferin-related protein (PRP), PTK787/ZK
222584, ZD6474, Recombinant human platelet factor 4 (rPF4), Restin,
Squalamine, SU5416, SU6668, SU11248 Suramin, Taxol, Tecogalan,
Thalidomide, Thrombospondin, TNP-470, TroponinI, Vasostatin, VEG1,
VEGF-Trap, and ZD6474.
[0207] Some embodiments may include a method of selecting a subject
for treatment and/or selecting a treatment or a course of therapy
based on the analysis of certain in situ vascular structures. A
method may involve analyzing in situ vascular structure(s) in a
human subject to obtain, for example, a score. The score may be
compared to a control score (e.g., in an apparently healthy
population) or to a previous score from a previous analysis on the
same subject. The treatment or the course of therapy may be based
on such a comparison. In some embodiments, obtaining an analysis of
vascular structures is repeated so as to monitor the human
subject's response to therapy over time. In some embodiments of
this aspect of the invention, the method further comprises
measuring a second index of disease in the human subject wherein
deciding on the treatment or course of therapy is also based upon
the measurement of said second index.
[0208] In certain embodiments, patients having a tumor that is
under-vascularized (e.g., one that shows signs of necrosis) may be
selected for treatment with one or more anti-angiogenic compounds.
Under-vascularized tumors may be identified as those that have a
low density of blood vessels, or for which the blood vessel
diameters are low (e.g., below a threshold number typical of
vascularized tumors).
[0209] Aspects of the invention also may include monitoring the
effectiveness of a therapy by monitoring the presence of blood
vessel patterns or features over time. For example, the progressive
loss of blood vessels in a tumor in response to treatment may be a
sign that a therapy is effective. In contrast, the absence of any
impact on vascularization may be an indicator that a treatment is
not being effective in a patient and that an alternative therapy
should be considered or used.
[0210] It should be appreciated that some or all of the therapeutic
aspects of the invention can be automated as described herein.
[0211] In one embodiment, aspects of the invention can be used to
understand structural changes associated with biological processes
of interest (e.g., disease development and progression). For
example, an animal's vasculature can be analyzed to identify
additional patterns (e.g., individual structural features or
distributions or changes associated only with certain binned size
ranges) that may be associated with wound healing or different
diseases or different disease stages. These additional patterns
(e.g., individual structural features or distributions) may be used
in one of more of the diagnostic, intervention, therapeutic, and
development aspects of the invention.
[0212] In one embodiment, aspects of the invention can be used to
understand structural changes associated with medical procedures.
For example, an animal's vasculature can be analyzed to identify
changes associated with post-surgical wound healing or
implant/transplant (including xenografts) growth or rejection.
[0213] It should be appreciated that some or all of the research
aspects of the invention can be automated as described herein.
[0214] In another embodiment, aspects of the invention can be used
in screens of compound libraries or to validate candidate compounds
for treating diseases associated with abnormal internal structures
(e.g., abnormal tubular networks). Aspects of the invention allow
efficient high throughput analyses of internal structural changes
using binned data (e.g., BMVD). These changes can act as surrogate
markers (biomarkers) for certain diseases. As a result, the
screening process can be automated to a large extent, and the time
for obtaining results significantly shortened when compared to
current validations that often involve waiting for disease symptoms
to change and also may require tissue biopsies.
[0215] Aspects of the invention may be used for identifying and
quantifying vascular patterns (e.g., structural features) that can
be used as surrogate markers for diagnostic, therapeutic, and
research and development purposes. Surrogate markers are useful for
reducing the time of diagnosis, therapy evaluation, and drug
development. A surrogate marker can be used as an early indicator
for disease diagnosis, disease prognosis, or drug effectiveness,
without waiting for a clinical outcome (e.g., increased survival
time in response to a drug). So, a vasculature analysis can be used
as a surrogate marker for drug development (in both pre-clinical
and clinical trials), for clinical screening (e.g., breast, lung,
or colon screening), and for clinical therapy monitoring. For
example, binned vasculature structure may be a useful surrogate
marker for angiogenesis related diseases such as cancer.
[0216] In one embodiment, aspects of the invention provide methods
for screening and/or validating candidate compounds or therapies
for their effectiveness in treating neo-vasculature formation
and/or vasculature pattern changes associated with disease.
[0217] Aspects of the invention may be used to evaluate individual
or small numbers of compounds or to screen libraries to evaluate
and/or identify a plurality of candidate compounds (e.g., by
administering these compounds, individually or in groups, to an
experimental animal such as a mouse and evaluating their effect on
angiogenic vasculature). Libraries may contain any number of
compounds (e.g., from approximately 100 to approximately 1,000,000)
Different types of compounds can be screened, including antibodies,
small molecules, etc., or any combination thereof. However, the
invention is not limited by the number and/or type of compounds
that can be evaluated.
[0218] In one embodiment, the effectiveness of a candidate compound
can be compared to a reference compound. A reference compound can
be any compound with a known effect on a structure. For example,
Avastin (Genentech) is a known monoclonal antibody against vascular
endothelial growth factor (VEGF) that can be used as a reference to
test the effect of a candidate compound on neovasculature growth.
Other examples of compounds include, but are not limited to, Sutent
and Nexavar.
[0219] It should be appreciated that some or all of the development
aspects of the invention can be automated as described herein.
[0220] It also should be appreciated that any one or more
geometrical, structural, and/or distributional parameters described
herein may be evaluated by comparison to a reference parameter. In
some embodiments, a reference parameter may be an amount or score
for that parameter in a normal or healthy subject. In other
embodiments, a reference may represent a diseased condition. In
some embodiments, a change or amount of any structural parameter
that is correlated or associated with a disease or condition as
described herein may be a statistically significant change or
difference in that parameter in a diseased or test subject relative
to a reference subject. In some embodiments, a difference or change
in a structural parameter may be an increase or a decrease in a
particular parameter (or a combination of parameters). An increase
in a parameter may be at least a 5%, 10%, 20%, 30%, 40%, 50%, 60%,
70%, 80%, 90%, 100%, or greater increase in that parameter in a
test subject relative to a reference subject. Similarly, a decrease
in that parameter may be at least a 1%, 5%, 10%, 20%, 30%, 40%,
50%, 60%, 70%, 80%, 90%, 100%, or greater decrease of a measure of
that parameter in a test subject relative to a reference subject.
Once an amount of change or difference in a parameter has been
correlated or associated with a disease or condition, that level
may be used in subsequent methods according to the invention.
Accordingly, in some embodiments, a difference of at least at least
1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, or more
of any given structural parameter (e.g., tortuosity, density,
volume, or any other individual structural feature or distribution
of structures or structural features as described herein) within a
data bin relative to a reference value may be used as a threshold
for methods of the invention. It should be appreciated that higher
or lower or intermediate values may be used. It also should be
appreciated that different parameters may have different threshold
or reference levels. Also, different parameters (and/or different
levels for each parameter) may be associated with different
conditions or diseases. Accordingly, specific disease or condition
values or thresholds may be identified for different parameters or
combinations thereof. These threshold values may be used for
disease detection, diagnosis, monitoring, or for any other
therapeutic, clinical, or research application described herein
(e.g., in automated methods described herein).
[0221] Accordingly, aspects of the invention provide methods and
devices for obtaining and/or analyzing data relating to internal
tubular structures in casts and/or in human and/or other animal
bodies. In some embodiments, methods of the invention involve
analyzing one or more parameters (or parameter changes over time)
for binned blood vessels that have been categorized based on their
size. For example, blood vessels may be binned according to the
following non-limiting diameter ranges: about 0-10 microns, about
10-25 microns, about 25-50 microns, about 50-75 microns, about
75-100 microns, about 100-150 microns, about 150-200 microns, about
200-300 microns, about 300-400 microns, about 400-500 microns,
about 500-1,000 microns, or any combination thereof. However, any
other suitable bin size ranges (including larger, smaller, or
intermediate size ranges) may be used. In some embodiments, the
number of different bins may be between about 2 and about 10.
However, higher numbers of bins also may be used. In some
embodiments, only 2 to 5 bins are used (e.g., 2, 3, 4, or 5). In
certain embodiments, three blood vessel bin sizes are used: small,
medium, and large. In some embodiments, a single bin is chosen
having a predetermined size range and no other size ranges are
analyzed.
[0222] Profiles may be extracted from the distribution of
quantitative values for one or more structural features as
described herein (including for example, features observed in
vascular casts). In some embodiments, volume independent or density
independent profiles may be extracted from distributions by
comparing ranges within each distribution being analyzed (e.g., a
subpopulation within a single range as a percentage of the total
population across all ranges, or a ratio of subpopulations within a
first and a second range that each represent different subsets the
entire range of values).
[0223] Aspects of the invention may include the analysis of one or
more regions of interest in animal disease models (e.g., in situ
and/or in casts of one or more regions of interest). Animal disease
models may be, but are not limited to, engineered (e.g.,
recombinant) animals, transgenic animals, metastatic cancer models,
xenograft models, orthotopic transplant models, etc., or any
combination thereof. In some embodiments, different animal models
may have different known genetic markers (e.g., particular
mutations) associated with a disease of interest (e.g., a cancer).
Any suitable animal may be used as an animal model, including, but
not limited to, a mouse, rat, hamster, guinea pig, pig, dog, cat,
rabbit, zebrafish, or other suitable animal. It should be
appreciated that whole experimental animals may be analyzed.
However, in some embodiments, tissues and/or organs may be
analyzed. In some embodiments, models may be based on xenografts
(e.g., xenografts of cancer or tumor cells that will form cancer or
tumor tissues in a host animal). For example, human cells may be
introduced into a non-human host animal. Other uses of xenografts
include analyzing responses to certain tissue and/or organ
transplantation (e.g., a non-human tissue or organ into a human
host). In some embodiments, vascular casts of regions of interest
in an animal model may be obtained to thoroughly analyze the
vascular structures, and/or changes therein, associated with the
condition being modeled. In some embodiments, observations made on
casts may be compared (e.g., using appropriate statistical
techniques) to in vivo (e.g., in situ) observations to identify one
or more common structural characteristics and/or changes that are
statistically significant in vivo in association with a disease,
condition, or response of interest. These can then be used in
subsequent applications as described herein.
[0224] According to aspects of the invention, compounds and
therapies can be evaluated in the context of an in-vivo model such
as an animal disease model. For example, a mouse with cancer or
atherosclerosis can be used to evaluate, optimize, and identify
useful therapies. Other animal models also can be used. Aspects of
the invention may be useful for high-throughput analyses because
they can detect small changes in vasculature and can be used to
evaluate a therapy in a short time period with minimal manipulation
since little or no invasive procedures are required.
[0225] Vascular analysis aspects of the invention can be used on an
orthotopic model to test, for example, the effectiveness of a drug
in a short period of time. For example, the effect of a candidate
drug on angiogenesis in an orthotopic mouse tumor model may be
quantifiable after about 5 days (e.g., between 1 and 10 days,
depending on the model and the drug). In contrast, a subcutaneous
cancer animal model requires approximately one month for tumor
growth to be analyzed and compared to controls.
[0226] An orthotopic model can be used to model different diseases
or clinical conditions. Examples include, cancer, tissue
regeneration, wound healing (including healing after traumatic
injury, healing after surgical intervention, healing of burnt
tissue such as skin), tissue or organ transplant therapy, medical
device implant therapy, other conditions associated with
neovascularization or changes in normal vascular structure, or any
combination of two or more of the above. However, the invention is
not limited by the type of orthotopic model or the type of disease
or clinical condition that is being analyzed.
[0227] A single orthotopic disease model animal may be useful for
testing more than one candidate drug molecule since the analysis
does not involve sacrificing the model animal. Accordingly, once a
test with a first candidate is complete, a subsequent candidate can
be evaluated in the same model animal. A series of candidates can
be tested in a single model animal, with appropriate controls,
provided the model retains features of neovascularization that are
necessary for the assay.
[0228] It should be appreciated that any of the geometrical,
structural, and/or distributional parameters described herein may
be used as biomarkers. Biomarkers of the invention can be qualified
and/or quantified and compared using standard statistical methods.
These biomarkers can be compared on individual basis, but also in
combination as a signature of vascular morphology and function.
Whole signatures can be compared between treated and untreated
samples, or samples with physiological and pathological vascular
pattern.
[0229] It should be appreciated that in some embodiments, one or
more of the biomarkers described herein may be used to aid in the
diagnosis, prognosis, prediction, or other medical application
along with other types of physiological and or biological markers
(e.g., physiological measurements, genetic markers, etc., or any
combinations thereof).
[0230] It should be appreciated that aspects of the invention may
be applied to features of vascular geometry (e.g., curvature,
tortuosity, distributions of vascular structural features, etc., or
any combination thereof) that are obtained from an analysis of
vascular casts (e.g., using any suitable image analysis technique
described herein or known in the art). In some aspects, vascular
casts are analyzed to identify distributions of one or more blood
vessel structural features (including, for example, abnormal excess
or absence of blood vessels or blood vessel structures) that are
associated with a disease or other condition of interest.
Structural features identified in casts may be used as biomarkers
or references to evaluate in situ vasculature, for example, to
detect indicia of a disease or other condition of interest in a
subject. Structural characteristics of vascular casts also may be
used to evaluate therapeutic treatments, screen candidate
compounds, and for other applications as described in more detail
herein. In some embodiments, one or more structural parameters are
analyzed over time (e.g., using a series of vascular casts obtained
at different time points) to monitor and/or identify structural
changes that occur during development, disease progression or
regression, or in response to therapy. In some embodiments,
structural analysis is performed on vascular casts obtained from
experimental models (e.g., whole animal models, or organ or tissue
models). However, in some embodiments, vascular casts are obtained
and analyzed for one or more regions of interest (e.g., diseased
regions) in dead animals, including for example dead humans (e.g.,
human cadavers).
[0231] As used herein, a vascular cast refers to a physical
structure that is generated to represent blood vessels of an entire
vasculature or portion thereof. A cast may be obtained by perfusing
a vasculature or a vascular region (e.g., the blood vessels of an
organ, for example, of a kidney or liver) with a casting material
that solidifies (e.g., polymerizes) to form a stable structure. The
surrounding tissue and cells (e.g., including the blood vessel
walls) may be removed to reveal the cast. The cast retains the
structural features of the original blood vessels. Cast may include
structures of blood vessels of different sizes as described herein.
Certain casts are more flexible than others, certain casts are more
brittle than others. Vascular casts can be used to identify
vascular structural features with high resolution and/or to
identify correlations between structural features and conditions of
interest with high degrees of confidence since the structures of
the blood vessels are retained in the casts and other biological
structures that could interfere with an analysis are removed.
Vascular casts may be obtained using any suitable casting material.
In some embodiments, the casting agent may be a polymer. In some
embodiments, the casting agent may react with the blood vessel
walls. Non-limiting examples of casting agents include, but are not
limited to Microfil.RTM., methyl methacrylate, prepolymerized
methyl methacrylate (Mercox.TM.), Mercox.TM. CL-2B, other acrylic
resins, silicon, gold nanoparticles, Batson No. 17,
polyurethane-based casting agents (e.g., PU4ii), etc., or
combinations of two or more thereof.
[0232] It should be appreciated that casting agents may be
supplemented with contrast agents and/or other detectable agents.
Examples of contrast agents include, but are not limited to,
BaSo.sub.4 and UAc (e.g., mixed into the casting material). In some
embodiments, already polymerized casts can be soaked in OSO.sub.4
to achieve better contrast using CT imaging. In certain
embodiments, any suitable heavy metal can be mixed into the resin
to make it more radioopaque.
[0233] In some embodiments, a large volume of an animal body (e.g.,
the entire body) may be perfused with a casting agent composition.
In certain embodiments, a small volume of an animal (e.g., a
tissue, an organ or a region of either one thereof) may be perfused
with a casting agent composition. In some embodiments, a casting
agent may be perfused into a tissue or an organ or a region of
either one thereof after removal from an animal (e.g., after biopsy
or other surgical excision). In some embodiments, a casting agent
composition may be perfused into a live animal. It should be
appreciated that an animal may be sacrificed after perfusion with a
casting agent depending, in part, on the amount and type of casting
agent composition that is used and the tissue or organ to which the
casting agent composition is targeted. According to aspects of the
invention, casting agent(s) may be used to preserve in vivo
structures for detailed analysis. In some embodiments, this
analysis identifies particular structural or distribution
properties that can be subsequently used as markers for in vivo
diagnostic, therapeutic, research, and/or other applications in
live animals (including humans).
[0234] In some aspects, vascular structures may be analyzed in situ
in an animal after perfusion with a casting agent composition. In
some aspects, a tissue or an organ or a region of either one
thereof may be removed from an animal for analysis (e.g., before or
after perfusion with a casting agent composition).
[0235] Accordingly, aspects of the invention can be used to
represent and/or visualize blood vessels with a casting agent or
medium.
[0236] Data relating to one or more selected structures (e.g.,
structural patterns obtained from an analysis of a vascular cast)
may be obtained and/or analyzed to glean information about a
physiological condition of an animal based on the structure (or
changes in the structure). For example, patterns identified in
casts may be used as biomarkers to screen in situ vasculatures for
the presence of one or more similar patterns or to quantify the
extent of the pattern in situ. This information may be used for
diagnostic, predictive, prognostic, therapeutic, interventional,
research and/or development purposes, as well as for grading and/or
staging a disease. In some embodiments, methods of the invention
may involve analyzing one or more structural parameters (or one or
more structural parameter changes over time) based on binned
structure data or information obtained for casts (e.g., vascular
casts) or in situ structures (e.g., in vivo blood vessels).
[0237] In some embodiments, one or more structures and/or
structural changes that are identified using casts may be detected
or monitored in vivo to determine whether a predetermined disease,
condition, or response is present in vivo.
[0238] In some embodiments, structural parameters and/or structural
changes observed for vascular casts from experimental animals (or
organs or tissues) can be used as references when analyzing
vasculature in vivo. For example, structural vasculature parameters
and/or changes that are identified in casts using experimental
animal models subsequently can be detected or monitored in vivo
(e.g., in a human subject) and used to evaluate the development of
a disease, a drug response or other biological or disease property
associated with the vasculature parameters and/or changes in a
subject. In some embodiments, structural characteristics identified
in vascular casts may be used to identify one or more patient
subpopulations that are (or are predicted to be) more responsive to
a particular treatment. For example, responsive subjects may be
identified as those having one or more blood vessel characteristics
that were associated with responsiveness in animal models and
identified by analyzing vascular casts from the responsive
animals.
[0239] One or more of the characteristics described herein, or
combinations of characteristics, or related structural changes over
time, may be identified as structural patterns that can be
associated with one or more conditions of interest. Once
identified, these patterns can be used as biomarkers to identify or
monitor the conditions of interest in vivo in a subject, for
example, by analyzing the in situ vasculature of the subject (or a
portion thereof) and detecting the presence of and/or quantifying
the extent of a specific vascular structural pattern.
[0240] Accordingly, one or more of the following non-limiting
structural characteristics (e.g., combinations of 2, 3, 4, 5, 6, 7,
8, 9, 10 or all of the following structural characteristics) may be
evaluated (e.g., quantified) in vascular casts and/or in situ
(e.g., in vivo): diameter binned vessel distribution, mean vessel
diameter distribution, branching point density, vessel branching
distribution, angle of vessel branching distribution,
interbranching distances, vessel density, vessel tortuosity,
intervessel distances, luminal vessel surface, vessel dilation
(changes in vessel diameter over a segment), sinosoidalation
(dilation in sinosoids), or permeability (vessel leakiness).
Distributions of the quantified characteristics may be prepared and
analyzed (e.g., compared). However, it should be appreciated that
other structural characteristics, for example, other
characteristics described herein also may be analyzed by analyzing
and comparing distributions of those characteristics or
features.
[0241] For example, the quantification of any of the following
non-limiting features may be performed and related distributions
may be analyzed as described herein: Total Intra-Vascular Volume
(TIVV)--e.g., over the entire Tumor Vascular Tree and Region of
Interest (ROI), over only the Small Vessels Volume within the Total
Volume (or the ROI), over only the Medium Vessels Volume within the
Total Volume (or the ROI), or over only Large Vessels Volume within
Total Volume (or the ROI); Intra-Vascular Volume Distribution
(IVVD)--e.g., broken by Total Volume, Small, Mid & Large
Vessels Volumes, color encoded into small, mid, large vessels on a
segmented vascular tree (e.g., based on a Poker Chip
representation), linked vascular volume values through color
encoding of regions within a segmented vascular tree (e.g., on a
Poker Chip representation), or detected locations/regions of Max
Volume, Mid Volume, Min Volume and link to regions within a
segmented vascular tree (e.g., based on a Poker Chip
representation); Inter-Vessel Distance (IVD)--e.g., in the form of
average/Min/Max values, histograms, values in select locations (for
example single locations), color encoded Vessel Tree/ROI(s) with
IVD values & IVD Value Clusters; Inter-Branching Distance
(IBD)--e.g., in the form of average/Min/Max values, histograms,
values in select locations (for example single locations), color
encoded Vessel Tree/ROI(s) with IBD values and IBD Value Clusters;
Vascular Diameter Variability (VDV) along the length of the
vessel--e.g., in the form of histograms for the entire vascular
tree or w/in a ROI, with the ability to view such variability for a
single vessel or a group of vessels on the whole tree of within
select (ROI)s, or color encoded segments within a tree/ROI (e.g.,
based on a Poker Chip representation) based on VDV values; Vessel
Branch Curvature (VBC) and Tortuosity (VBT)--e.g., in the form of
histograms of each BC and BT for the entire vascular tree or within
select ROI(s), with the ability to view such variability for a
single vessel or a group of vessels on the whole tree or within
select ROI(s), or color encoded regions within a vascular tree/ROI
(e.g., color encoded chips a Poker Chip representation) based on BC
or BT values; or any combination of two or more thereof.
Distributions of one or more of these characteristics, or
combinations of characteristics, or related structural changes over
time, may be identified as structural patterns that can be
associated with one or more conditions of interest.
[0242] Blood vessels may be binned according to about any of the
following non-limiting diameter ranges (in microns): 0-10, 10-25,
25-50, 50-75, 75-100, 100-150, 150-200, 200-300, 300-400, 400-500,
500-1,000, or any combination thereof. However, any other suitable
bin size ranges (including larger, smaller, or intermediate) may be
used. In some embodiments, the number of different bins may be
between about 2 and about 10. However, higher numbers of bins also
may be used. In some embodiments, only 2 to 5 bins are used (e.g.,
2, 3, 4, or 5). For example, three blood vessel bin sizes may be
used: small, medium, and large diameters (e.g., small at less than
about 35 microns or about 20-35 microns, medium about 35-70 or
about 35-100 microns, and large above about 100 microns or about
100-200 microns). However, other vessel size ranges may be used to
calculate population percentages or ratios as described herein. In
some embodiments, a single bin is chosen with a predetermined size
range and no other sizes are analyzed.
[0243] In some embodiments, a parameter may be evaluated as a
percentage of the total population of vessels. For example, the
percentage of blood vessels having a particular diameter (e.g.,
20-40 microns) as a percentage of the total population of blood
vessels may be used. In some embodiments, a parameter may be
evaluated as a ratio of two subpopulations within a population of
vessels. It should be appreciated that the percentage populations
of vessels having different properties may be evaluated by
determining the relative lengths of blood vessels having different
properties within a region being analyzed. However, other
techniques may be used.
[0244] Aspects of the invention relate to business methods that may
involve the marketing and/or licensing of biomarkers associated
with particular biological processes, conditions, and/or diseases.
In some embodiments, patterns (e.g., geometric features) of blood
vessels (e.g., observed in vivo or in casts) are analyzed to
identify or evaluate associations or correlations with certain
biological processes, conditions, and/or diseases of interest.
Pattern parameters may be identified that can be used as structural
biomarkers (e.g., for clinical, diagnostic, therapeutic, and/or
research applications as described herein). These biomarkers may be
used to reduce the cost and increase the efficiency and sensitivity
of medical and research techniques. In one embodiment, one or more
biomarkers or methods of using the biomarkers may be marketed to
medical or research customers or potential customers. In one
embodiment, a fee-based service may be provided to medical or
research organizations wherein information relating to a medical
image is obtained and analyzed for the presence of one or more
biomarkers and the resulting information is returned in exchange
for a fee. The amount of the fee may be determined, at least in
part, by the type of image information that is provided, the type
and degree of analysis that is requested, and the format and timing
of the analysis. It should be understood that aspects of the
invention may be applicable to image information obtained from one
or more of many different scanning modalities (including, but not
limited to, micro CT, MDCT, rotational angiography, MRI, PACS).
This information may be received from many different sources,
including, but not limited to one or more of the following: medical
centers, large pharmaceutical companies (e.g., in association with
pre-clinical evaluations or during clinical trials), CROs (for both
pre-clinical and clinical analyses), medical laboratories and
practices (e.g., scanning centers), hospitals, clinics, medical
centers, small biotechnology companies (e.g., in association with
pre-clinical evaluations or during clinical trials), and
bio-medical research organizations. The results of the analysis
then may be returned to any one of these organizations. In some
embodiments, the analysis results may be returned to the same
entity that sent the image information. In other embodiments, the
results may be returned to a different entity (e.g., the image
information may be received from a scanning laboratory and the
analysis may be returned to a physician). One or more steps
involved with receiving the information, analyzing the structural
features, processing the results and forwarding the results to a
recipient may be automated. It also should be appreciated that one
or more of these steps may be performed outside the United States
of America. Business procedures (e.g., marketing, selling,
licensing) may be performed individually or collaboratively.
[0245] Aspects of the invention may be described herein in the
context of individual analytical steps, particular structural
features, etc. However, it should be appreciated that any of the
methods and devices described herein also may be incorporated into
a business method associated with the use of a biomarker based on
one or more blood vessel structural features or patterns (e.g.,
structural features or changes observed in vascular casts obtained
from therapeutic and/or disease models or conditions).
[0246] Aspects of the invention may be automated (e.g., using one
or more computer-implemented acts described herein). It should be
appreciated that one or more pattern parameters (e.g., individual
blood vessel structural feature(s), distributions of blood vessels
or blood vessel structural features, or combinations thereof) may
be analyzed using one or more quantitative and/or qualitative
methods (e.g., based on binned data). In some embodiments, one or
more parameters may be measured and quantified and the measurements
may be analyzed using standard quantitative and/or statistical
techniques for evaluation and/or comparison with threshold or
reference values as described herein. In certain embodiments, one
or more parameters may be evaluated using a predetermined scoring
method, for example based on predetermined factors (e.g., for
binned data). Geometrical parameters may be represented using
vectors. For example, a distribution of blood vessels, blood vessel
curvatures, blood vessel tortuosity, or blood vessel directions
within a volume of interest may be represented using a plurality of
vectors. Separate vectors may be used to represent separate vessels
(e.g., vessels for which a connectivity has not been determined
during the analysis). However, separate vectors also may be used to
represent individual segments or fragments of a single blood vessel
or portion of a vascular tree (e.g., for which connectivity has
been or may be determined during the analysis). Vasculature pattern
parameters may be analyzed using any appropriate technique for
separating and/or categorizing numerical values or scores.
[0247] In some embodiments, a score may be obtained to relate a
pattern parameter to the probability of a physiological condition
such as a disease or disease stage. Aspects of the invention can be
used for in situ diagnostic, interventional and therapeutic
analysis of one or more disease loci associated with aberrant
internal structures. As used herein "in situ" means in an animal
(e.g., a human) body as opposed to in a biopsy or other tissue
sample. Aspects of the invention can be used to research structural
changes associated with a disease, for developing and evaluating
disease treatments including therapeutic drugs, and for other
purposes. Aspects of the invention include automatically analyzing
a structural feature or pattern and automatically generating a
score based on the analysis.
[0248] In some embodiments, aspects of the invention include
detecting and/or analyzing selected internal tubular networks in
situ in animals and/or in vascular casts. As used herein, an
internal tubular network means a network of connected cylindrical
internal body structures. Tubular networks include, but are not
limited to, cardiovascular, respiratory, gastro-intestinal, and
genito-urinary systems and portions thereof within animal bodies.
Accordingly, the cylindrical structures may include branched,
straight, curved, and/or twisted cylindrical elements. The
cylindrical structures and elements may include not only cylinders,
but also may include flattened or otherwise distorted regions. The
cross-section of a cylindrical structure or element may be
circular, oval, approximately circular, approximately oval, or more
irregular in nature. The internal diameter of the cylindrical
elements may vary or may be approximately the same over the region
of interest. A tubular network such as a circulatory network may be
closed off from the environment outside the animal. In contrast,
tubular networks such as respiratory and gastro-intestinal networks
may be open to the outside environment. In some embodiments,
appropriate casting and/or contrast agents (e.g., inhaled agents)
may be used to analyze respiratory and/or gastro-intestinal
networks.
[0249] In one embodiment, aspects of the invention include
analyzing a representation of a tubular network (e.g., a
mathematical representation of a vascular network). In one
embodiment, a representation of a network, or a portion thereof,
may be obtained (e.g., from an existing database or a remote site)
and analyzed. In another embodiment, a representation of a network,
or a portion thereof, may be generated from structural data and
then analyzed. According to aspects of the invention, an analysis
may include detecting the presence or absence of one or more
structural features or patterns, measuring or evaluating the extent
of one or more structural features or patterns, or a combination
thereof.
[0250] In one embodiment, aspects of the invention are useful for
selectively detecting and/or analyzing patterns (e.g., structures)
of an animal's vasculature to detect or monitor one or more blood
vessel patterns (e.g., structures) that may be indicative of a
physiological condition of the animal. A structural pattern or
feature may be detected and/or analyzed for blood vessels of any
size including, but not limited to, arteries, arterioles, veins,
venules, and capillaries.
[0251] In one embodiment, aspects of the invention are useful for
selectively detecting and/or analyzing structural features or
patterns of an animal's vasculature to detect or monitor one or
more blood vessel structures that are characteristic of disease
(e.g., a disease associated with angiogenesis). A blood vessel
structure or pattern characteristic of a disease (e.g., a disease
associated with angiogenesis) may provide an early diagnostic
indication of the presence of the, which can allow for early
treatment that can improve a patient's prognosis. In other
embodiments, a blood vessel structure or pattern characteristic of
a disease (e.g., a disease associated with angiogenesis) can be
used as a marker (e.g., a biomarker) for staging and/or grading, to
monitor disease progression, evaluate a prescribed therapy, and/or
identify and/or validate a drug or treatment regimen for the
disease. Diseases associated with abnormal vasculature structures
or patterns include, but are not limited to, cancer,
cardiovascular, dermatologic (skin), arthritic, musculoskeletal,
central nervous system, neurologic, pulmonary, renal,
gastrointestinal, gynecologic, genitourinary, inflammatory,
infectious, and immunologic diseases.
[0252] A cancer may be a solid tumor or a leukemia. When the cancer
is a leukemia, methods of the invention may be directed to
detecting and/or analyzing vasculature pattern(s) in the bone
marrow of an animal (e.g., human).
[0253] It also should be appreciated that aspects of the invention
may include performing any combination of two or more acts
described herein and that certain acts may be omitted in some
embodiments. In one embodiment, the presence of one or more
structural abnormalities may be identified or detected in a body
region without generating and/or analyzing a structural
representation of that body region. For example, the presence of a
blood vessel abnormality may be detected directly from structure
data for a body region without generating a structural
representation of the vasculature for that entire body region. In
another embodiment, an analysis may involve selectively
representing one or more abnormal structures if they are present in
a body region without representing normal structures in that body
region (e.g., abnormal blood vessel structures may be represented
without representing any normal blood vessels, or without
representing all the normal blood vessels, without representing
most of the normal blood vessels, etc.). In another embodiment, an
abnormal vascular structure may be identified or detected without
obtaining a detailed representation of the all the blood vessels in
a body region. It may be sufficient to detect the presence of or
outline of a vascular tree in a body region and perform an analysis
that identifies or detects abnormal structures on specific blood
vessels or the presence of excessive vascularization (e.g., a clump
of neovasculature representing malignancy) without representing all
the normal details of the vascular tree or even detecting
individual blood vessels in the vascular tree. Accordingly, in some
aspects a low resolution data set for a body region may be
sufficient to detect or identify certain structural indicia of a
disease such as cancer.
[0254] Aspects of the invention may include automating one or more
acts. For example, an analysis may be automated in order to
generate an output automatically. Acts of the invention may be
automate using, for example, a computer system.
[0255] As should be appreciated from the foregoing, in one
embodiment, raw or processed structure data may be obtained at a
medical or research center and sent to a computer at a remote site
where one or more of the analytical steps described above may be
performed (e.g., for a fee). The output from the analysis may be
then returned to the medical or research center either in computer
readable form to a computer at the medical or research center, in a
hard copy, in another tangible form, or in any other suitable form
including those described herein.
[0256] In another embodiment, one or more software programs that
implement one or more functionalities described herein may be
provided and installed at a medical or research center (e.g., for a
fee). The programs can be provided on disk, downloaded from an
internal or remote (e.g., external) site, or loaded in any suitable
manner. Reference information that is used in any functionality
described herein may be provided along with the software or
separately. In one embodiment, reference information (e.g.,
information relating to normal or abnormal blood vessel structures)
may be available on disk, downloaded from an internal or remote
(e.g., external) site, or loaded in any suitable manner.
[0257] As used herein, "remote" means at a site that is different
from the immediate location of the imaging device (e.g., the
medical scanner). The remote site can be a central computer or
computing facility at a hospital, medical, or research center
(e.g., within the network or intranet of the center), or can be
outside the hospital, medical, or research center (e.g., outside
the network or intranet of the center). The remote site can be in
the same state, in a different state, or in a different country
from the site of data acquisition by the imaging device.
[0258] In some embodiments, multimodal analyses (e.g., using
structure data from two or more different types of imaging devices)
may be used together. Accordingly, aspects of the present invention
may include the ability to process and analyze different types of
structure data and either combine the results to generate a
combined output, or to generate a separate output is generated for
each imaging modality. In some embodiments, an organ, tissue, or
animal perfused with a casting agent and/or an imaging agent may be
sent to an imaging center for analysis.
[0259] In some embodiments, in vivo and/or ex vivo casting methods
of the invention can be used to identify one or more vascular
patterns (e.g., including one or more structural parameters,
structure distributions, combinations thereof) and/or
time-dependent changes thereof that can be used as biomarker(s) for
a disease or a response to a therapy, or for monitoring patients
for indicia of disease or response to therapy, or for other
applications where vascular information may be informative.
Accordingly, such vascular patterns or changes thereof identified
according to methods of the invention can be used for diagnostic,
interventional, therapeutic, research, and treatment development
and evaluation. Non-limiting examples of some of these embodiments
are described below.
EXAMPLES
Example 1
Xenotopic Tumor Models
[0260] A tumor model can be generated by inoculating human
non-small cell lung tumor cell line (A549 from ATCC, Inc.)
subcutaneously in immunodeficient mice (SCID). SCID male mice (6-8
weeks old from Charles River Inc.) are inoculated subcutaneously in
the lower back with a suspension of 1.times.10.sup.6 human lung
tumor cells (A549) in 0.2 ml of PBS. All mice are fed normal chow
diet throughout the duration of the experiment. All mice weights
are measured throughout the experiment. Tumor size is measured with
calipers twice-a-week and tumor volume is calculated using the
formula Length.sup.2.times.Width.times.0.52. All mice are
randomized into two treatment groups (approximately 10 mice per
group) when the median tumor volume reaches approximately 500
mm.sup.3. The treatment groups can be treated according to the
following schedule using intraperitoneal (i.p.) administration of
either a control composition or an anti-angiogenic compound. For
example, different levels of an anti-angiogenic compound can be
used and the results compared to a control group that is not
treated with an anti-angiogenic compound (e.g., Avastin.RTM.
available from Genentech, South San Francisco, Calif.). For
example:
[0261] Group 1: Control group--treated with saline/PBS twice a
week.
[0262] Group 2: High Avastin.RTM.--treated with Avastin.RTM. at 5
mg/kg/i.p. twice a week.
[0263] Group 3: Low Avastin.RTM.--treated with Avastin.RTM. at 0.5
mg/kg/i.p. twice a week.
[0264] Experiments are terminated 1.5 weeks after initial
treatment.
[0265] At the end-point, all mice are anesthetized and systemically
perfused with a casting agent.
Example 2
Perfusion with Casting Agent
[0266] Perfusion with a casting agent, Mercox (available from Ladd
Research, Williston, Vt.) can be performed as follows. An initial
anticoagulation step for each animal is performed using an i.v.
injection of heparin (10,000 U/ml, 0.3 cc/mouse). After 30 minutes,
the animals are anesthetized. Each animal's heart is cannulated and
the animal perfused with warm physiological saline at physiological
pressure (with an open vein draining the organ or with an open vena
cava). Perfusion is continued until the organ or animal is clear of
blood. Mercox monomer is filtered through a 0.5 .mu.m filter and a
casting resin is prepared by mixing 8 ml Mercox, 2 ml
methylmethacrylate, and 0.3 ml catalyst. The resin is infused
through the same cannula until the onset of polymerization (the
resin changes color to brown and emits heat, .about.10 min). The
organ or animal is carefully immersed in a 60.degree. C. water bath
for 2 hours (or overnight in a sealed container). The tissue is
removed by incubating in alternating rinses of 5% KOH and distilled
water (for example in a 60.degree. C. water bath sealed) followed
by thorough rinsing in distilled water. The cast is cleaned in 5%
formic acid for 15 minutes and rinsed thoroughly in distilled water
and frozen in distilled water. The resulting block of ice is
lyophilized (care should be taken not to melt the ice, the ice
should melt as it lyophilizes). The resulting cast can be analyzed
to identify one or more structural characteristics of interest.
Example 3
Xenotopic Tumor Models Response to Anti-Angiogenic Therapy
[0267] Xenotopic mouse models obtained as described in Example 1
were treated with either a control solution of saline/PBS or an
anti-angiogenic preparation of Avastin.RTM. at 0.5 mg/kg/i.p. as
described above. At the end-point, vascular casts were prepared as
described in Example 2 above and analyzed for two treated mice
(both treated with Avastin.RTM. at 0.5 mg/kg/i.p.) and one control
mouse. The resulting vascular casts were scanned using a micro
CT-scanner and the results of the structural analysis are shown in
FIGS. 14-17. The analysis was performed by determining the number
of blood vessels within bins of different diameter ranges for the
xenotopic tumor in the treated and control animals. The bins were
each 13.8 .mu.m wide and the smallest bin included blood vessels
having a diameter of between 20.7 .mu.m and 34.5 .mu.m. Mean tumor
volumes did not differ significantly between the groups at the end
of the experiment. However differences in blood vessel diameter
distributions were detected as shown in FIGS. 14-17. FIG. 14 shows
the resulting vessel population distribution. Treated tumors had
20% less small diameter sized vessels than untreated tumors, and
treated tumors had a higher percentage of middle diameter sized
vessels than untreated tumors. The blood vessel population
distributions were consistent for both treated animals. FIG. 15
shows the vessel population ratio between small (approximately
21-35 .mu.m) and middle (approximately 35-49 .mu.m) size vessels in
the tumors of the control and treated animals. The ratio decreased
after inhibitor treatment with Avastin.RTM., and this ratio was
consistent within the treated group. FIG. 16 shows the vessel
population ratio between large (approximately 147-161 .mu.m) and
middle (approximately 33-77 .mu.m) size vessels. The ratio
decreased after treatment with Avastin.RTM., and this ratio was
consistent within the treated group. Additional experimental
results are shown in FIGS. 17-19.
[0268] The following considerations apply to the specific examples
and the entire written specification herein (including the summary,
detailed description, and claims). It should be appreciated that
casts, like in situ blood vessels, are three-dimensional
structures. Accordingly, imaging and analytical techniques
described herein provide information about three-dimensional
structural characteristics. In some embodiments, techniques are
used to generate three-dimensional representations of vascular
casts and/or in situ blood vessels. In some embodiments, techniques
are used to generate three-dimensional images of vascular casts
and/or in situ blood vessels. The three-dimensional representations
and/or images can be analyzed as described herein.
[0269] However, it should be appreciated that aspects of the
invention are not limited to three-dimensional structural
characteristics. In some embodiments, aspects of vascular casts
and/or in situ blood vessels may be represented and/or imaged in
one or two dimensions and an analysis of one or two-dimensional
features may be performed and used as described herein. It also
should be appreciated that the structural features described herein
may be measured or quantified using any appropriate units,
including numbers, lengths or distances, angles, percentages, etc.,
or any combination thereof, further including any of these units as
a function of volume or area. Similarly, it should be appreciated
that vascular changes over time or in response to treatment may
involve an increase or a decrease of one or more of these
structural features. For example, an increase in structures
associated with angiogenesis may be associated with certain disease
progressions. In contrast, a decrease in structures associated with
angiogenesis may be associated with disease regression (e.g., in
response to treatment).
[0270] It also should be appreciated that descriptions herein
related to obtaining distributions of quantitative values for
vessel parameters within a region of interest are preferably based
on methodologies that detect and quantify all or substantially all
of the detectable vessels within the region of interest based on
the detection technique that is used for that analysis. Different
techniques may have different efficiencies. However, profiles and
comparisons are preferably based on data from the same or
equivalent detection and/or reconstruction techniques. It also
should be appreciated that comparisons and/or analyses described
herein may involve a statistical analysis using one or more
standard statistical techniques to determine whether a change in a
structure or pattern or other characteristic described herein
(e.g., an increase or decrease over time, or in response to a
therapeutic drug), or a difference or similarity between two
structures or patterns or other characteristics described herein
are statistically significant.
[0271] Having thus described several aspects of at least one
embodiment of this invention, it is to be appreciated various
alterations, modifications, and improvements will readily occur to
those skilled in the art. Such alterations, modifications, and
improvements are intended to be within the spirit and scope of the
invention. Any suitable analytical techniques may be used for
perfused tissue and organs according to the methods described
herein, including for example, the analytical techniques that are
described in PCT US2005/047081 and PCT US2007/026048 the
disclosures of which are incorporated herein by reference in their
entirety. Accordingly, the foregoing description and embodiments
are by way of example only. In the event of conflict between
different disclosures, the disclosure of the present application
shall control.
[0272] It should be appreciated from the foregoing, there are
numerous aspects of the present invention described herein that can
be used independently of one another or in any combination. In
particular, any of the herein described operations may be employed
in any of numerous combinations and procedures. In addition,
aspects of the invention can be used in connection with a variety
of types of images or any dimensionality. Moreover, one or more
automatic operations can be used in combination with one or more
manual operations, as the aspects of the invention are not limited
in this respect. Distribution analyses, however obtained, may be
used to facilitate the characterization of any of various
morphological changes to tissue and/or to assist in assessing the
efficacy of treatment using any of the herein described techniques,
alone or in combination.
[0273] The herein-described embodiments of the present invention
can be implemented in any of numerous ways. For example, the
embodiments of automatic distribution analysis may be implemented
using hardware, software or a combination thereof. When implemented
in software, the software code can be executed on any suitable
processor or collection of processors, whether provided in a single
computer or distributed among multiple computers. It should be
appreciated that any component or collection of components that
perform the functions described herein can be generically
considered as one or more controllers that control the
herein-discussed functions. The one or more controllers can be
implemented in numerous ways, such as with dedicated hardware, or
with general purpose hardware (e.g., one or more processors) that
is programmed using microcode or software to perform the functions
recited herein.
[0274] It should be appreciated that the various methods outlined
herein may be coded as software that is executable on one or more
processors that employ any one of a variety of operating systems or
platforms. Additionally, such software may be written using any of
a number of suitable programming languages and/or conventional
programming or scripting tools, and also may be compiled as
executable machine language code. It should be appreciated that one
embodiment of the invention is directed to a computer-readable
medium or multiple computer-readable media (e.g., a computer
memory, one or more floppy disks, compact disks, optical disks,
magnetic tapes, etc.) encoded with one or more programs that, when
executed, on one or more computers or other processors, perform
methods that implement the various embodiments of the invention
discussed herein. The computer-readable medium or media can be
transportable, such that the program or programs stored thereon can
be loaded onto one or more different computers or other processors
to implement various aspects of the present invention as discussed
herein. It should be understood that the term "program" is used
herein in a generic sense to refer to any type of computer code or
set of instructions that can be employed to program a computer or
other processor to implement various aspects of the present
invention as discussed herein. Additionally, it should be
appreciated that according to one aspect of this embodiment, one or
more computer programs that, when executed, perform methods of the
present invention need not reside on a single computer or
processor, but may be distributed in a modular fashion amongst a
number of different computers or processors to implement various
aspects of the present invention.
[0275] Use of ordinal terms such as "first", "second", "third",
etc., in the claims to modify a claim element does not by itself
connote any priority, precedence, or order of one claim element
over another or the temporal order in which acts of a method are
performed, but are used merely as labels to distinguish one claim
element having a certain name from another element having a same
name (but for use of the ordinal term) to distinguish the claim
elements. Also, the phraseology and terminology used herein is for
the purpose of description and should not be regarded as limiting.
The use of "including," "comprising," or "having," "containing",
"involving", and variations thereof herein, is meant to encompass
the items listed thereafter and equivalents thereof as well as
additional items.
* * * * *