U.S. patent application number 14/160496 was filed with the patent office on 2014-07-17 for computer-aided detection of regions of interest in tomographic breast imagery.
This patent application is currently assigned to iCAD, Inc.. The applicant listed for this patent is iCAD, Inc.. Invention is credited to Michael J. Collins, Senthil Periaswamy, Robert Van Uitert, Kevin Woods.
Application Number | 20140198965 14/160496 |
Document ID | / |
Family ID | 42196312 |
Filed Date | 2014-07-17 |
United States Patent
Application |
20140198965 |
Kind Code |
A1 |
Woods; Kevin ; et
al. |
July 17, 2014 |
COMPUTER-AIDED DETECTION OF REGIONS OF INTEREST IN TOMOGRAPHIC
BREAST IMAGERY
Abstract
Disclosed are methods, and associated systems comprising
processors, input devices and output devices, of detecting regions
of interest in a tomographic breast image. The methods may
comprise: acquiring tomographic breast image data; deriving a
plurality of synthetic sub-volumes from the tomographic breast
image data; wherein each subvolume is defined by parallel planar
top and bottom surfaces; wherein planar top and bottom surfaces of
successive subvolumes are parallel to each other; and wherein a top
planar surface of a sub-volume is offset from a top planar surface
of a prior sub-volume, such that successive sub-volumes overlap;
for each sub-volume, deriving a two-dimensional image; for each
two-dimensional image, identifying regions of interest therein;
deriving at least one region of interest of potential clinical
interest from a plurality of identified regions of interest; and
outputting information associated with at least one derived region
of interest of potential clinical interest.
Inventors: |
Woods; Kevin; (Beavercreek,
OH) ; Collins; Michael J.; (Beavercreek, OH) ;
Periaswamy; Senthil; (Hollis, NH) ; Van Uitert;
Robert; (Hollis, NH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
iCAD, Inc. |
Nashua |
NH |
US |
|
|
Assignee: |
iCAD, Inc.
Nashua
NH
|
Family ID: |
42196312 |
Appl. No.: |
14/160496 |
Filed: |
January 21, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12579898 |
Oct 15, 2009 |
8634622 |
|
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14160496 |
|
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61105895 |
Oct 16, 2008 |
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Current U.S.
Class: |
382/131 |
Current CPC
Class: |
G06T 2207/30068
20130101; G06K 9/3233 20130101; A61B 6/5217 20130101; G06T
2207/10124 20130101; A61B 6/03 20130101; G06T 7/0012 20130101; G06K
2209/053 20130101; A61B 6/502 20130101; G06T 2207/20104 20130101;
G06T 2207/20108 20130101; G06T 2207/10072 20130101 |
Class at
Publication: |
382/131 |
International
Class: |
G06T 7/00 20060101
G06T007/00; A61B 6/00 20060101 A61B006/00 |
Claims
1. In a system comprising a processor, an input device and an
output device, a method of detecting regions of interest in a
tomographic breast image, comprising: a. by means of an input
device, acquiring tomographic breast image data; b. by means of a
processor, deriving a plurality of synthetic sub-volumes from the
tomographic breast image data; wherein each subvolume is defined by
parallel planar top and bottom surfaces; wherein planar top and
bottom surfaces of successive subvolumes are parallel to each
other; and wherein a top planar surface of a sub-volume is offset
from a top planar surface of a prior sub-volume, such that
successive sub-volumes overlap; c. by means of a processor, for
each sub-volume, deriving a two-dimensional image; d. by means of a
processor, for each two-dimensional image, identifying regions of
interest therein: e. by means of a processor, deriving at least one
region of interest of potential clinical interest from a plurality
of identified regions of interest; and f. by means of an output
device, outputting information associated with at least one derived
region of interest of potential clinical interest.
2. The method of claim 1, wherein the tomographic breast image data
is acquired by means of an image acquisition unit.
3. The method of claim 1, wherein the tomographic breast image data
comprises a tomographic breast volume.
4. The method of claim 3, wherein the tomographic breast volume is
acquired by means of an image acquisition unit obtaining a
plurality of two-dimensional breast images of an anatomical breast
at differing angles, and a processor computing the tomographic
breast volume from the plurality of two-dimensional breast
images.
5. The method of claim I , wherein the tomographic breast image
data is acquired from at least one of a computer network and a
storage device.
6. The method of claim 5, wherein the tomographic breast image data
comprises a tomographic breast volume.
7. The method of claim 6, wherein the tomographic breast volume is
acquired by obtaining a plurality of two-dimensional breast images
of an anatomical breast at differing angles by means of at least
one of a computer network and a storage device, computing the
tomographic breast volume from the plurality of two-dimensional
breast images by means of a processor.
8. The method of claim I, wherein deriving at least one region of
interest of potential clinical interest from a plurality of
identified regions of interest comprises: e1. for each region of
interest identified in a plurality of two-dimensional images,
determining a location of the region of interest; e2. for each
located region of interest, deriving a further sub-volume enclosing
said located region of interest; and e3. for each located region of
interest, further evaluating said located region of interest to
determine if it is of potential clinical interest.
9. The method of claim 1, wherein each subvolume has a same
thickness as all other subvolumes.
10. The method of claim 9, wherein the same thickness is a
predetermined thickness.
11. The method of claim wherein each subvolume is offset from the
prior subvolume by a same offset amount.
12. The method of claim 11, wherein the same offset amount is a
predetermined offset amount.
13. The method of claim 1, wherein outputting information
associated with at least one derived region of interest of
potential clinical interest comprises displaying said at least one
derived region of interest in conjunction with at least a portion
of a breast image.
14. The method of claim 13, wherein information associated with at
least one derived region of interest of potential clinical interest
comprises at least one CAD mark.
15. The method of claim 13, wherein information associated with at
least one derived region of interest of potential clinical interest
comprises a location of the said at least one region of
interest.
16. The method of claim 1, wherein each synthetic sub-volume is
derived from a plurality of consecutive thin slices of the
tomographic breast volume.
17. The method of claim 16, wherein each thin slice is in the range
of about 1 mm, about 3 mm. thick.
18. The method of claim 1, wherein a thickness of each subvolume is
in the range of about 3 mm. to about 30 mm.
19. The method of claim 18, wherein an offset between top planar
surfaces of successive sub-volumes is in the range of about 1 mm.
to about 3 mm.
20. The method of claim 18, wherein an offset between top planar
surfaces of successive sub-volumes is equal to a thickness of a
thin slice of a tomographic breast volume.
21. The method of claim 1, wherein each two-dimensional image is
derived performing an intensity projection algorithm on a
subvolume.
22. The method of claim 1, wherein each region of interest is
identified by executing a suspicious lesion detection algorithm on
a two-dimensional image.
23. The method of claim 22, wherein the suspicious lesion detection
algorithm identifies regions of interest comprising
microcalcifications, density masses, and/or spiculated masses.
24. A system for detecting regions of interest in a tomographic
breast image, comprising: at least one input device, configured to
acquire tomographic breast image data; at least one processor,
configured to: a. derive a plurality of synthetic sub-volumes from
the tomographic breast image data; wherein each subvolume is
defined by parallel planar top and bottom surfaces; wherein planar
top and bottom surfaces of successive subvolumes are parallel to
each other; and wherein a top planar surface of a sub-volume is
offset from a top planar surface of a prior sub-volume, such that
successive sub-volumes overlap; b. for each sub-volume, derive a
two-dimensional image; c. for each two-dimensional image, identify
regions of interest therein; d. derive at least one region of
interest of potential clinical interest from a plurality of
identified regions of interest; and at least one output device,
configured to output information associated with at least one
derived region of interest of potential clinical interest.
25. The system of claim 24, wherein the tomographic breast image
data is acquired by means of an image acquisition unit.
26. The system of claim 24, wherein the tomographic breast image
data comprises a tomographic breast volume.
27. The system of claim 26, wherein the tomographic breast volume
is acquired by means of an image acquisition unit obtaining a
plurality of two-dimensional breast images of an anatomical breast
at differing angles, and a processor computing the tomographic
breast volume from the plurality of two-dimensional breast
images.
28. The system of claim 24, wherein the tomographic breast image
data is acquired from at least one of a computer network and a
storage device.
29. The system of claim 28, wherein the tomographic breast image
data comprises a tomographic breast volume.
30. The system of claim 29, wherein the tomographic breast volume
is acquired by obtaining a plurality of two-dimensional breast
images of an anatomical breast at differing angles by means of at
least one of a computer network and a storage device, computing the
tomographic breast volume from the plurality of two-dimensional
breast images by means of a processor.
31. The system of claim 24, wherein deriving at least one region of
interest of potential clinical interest from a plurality of
identified regions of interest comprises: d1. for each region of
interest identified in a plurality of two-dimensional images,
determining a location of the region of interest; d2. for each
located region of interest, deriving a further sub-volume enclosing
said located region of interest; and d3. for each located region of
interest, further evaluating said located region of interest to
determine if it is of potential clinical interest.
32. The system of claim 24, wherein each subvolume has a same
thickness as all other subvolumes.
33. The system of claim 32, wherein the same thickness is a
predetermined thickness.
34. The system of claim 24, wherein each subvolume is offset from
the prior subvolume by a same offset amount.
35. The system of claim 34, wherein the same offset amount is a
predetermined offset amount.
36. The system of claim 24, wherein outputting information
associated with at least one derived region of interest of
potential clinical interest comprises displaying said at least one
derived region of interest in conjunction with at least a portion
of a breast image,
37. The system of claim 36, wherein information associated with at
least one derived region of interest of potential clinical interest
comprises at least one CAD mark.
38. The system of claim 36, wherein information associated with at
least one derived region of interest of potential clinical interest
comprises a location of the said at least one region of
interest.
39. The system of claim 24, wherein each synthetic sub-volume is
derived from a plurality of consecutive thin slices of the
tomographic breast volume.
40. The system of claim 39, wherein each thin slice is in the range
of about 1 mm. to about 3 mm. thick.
41. The system of claim 24, wherein a thickness of each subvolume
is in the range of about 3 mm. to about 30 mm.
42. The system of claim 41, wherein an offset between top planar
surfaces of successive sub-volumes is in the range of about 1 mm.
to about 3 mm.
43. The system of claim 41, wherein an offset between top planar
surfaces of successive sub-volumes is equal to a thickness of a
thin slice of a tomographic breast volume.
44. The system of claim 24, wherein each two-dimensional image is
derived by performing an intensity projection algorithm on a
subvolume.
45. The system of claim 24, wherein each region of interest is
identified by executing a suspicious lesion detection algorithm on
a two-dimensional image.
46. The system of claim 45, wherein the suspicious lesion detection
algorithm identifies regions of interest comprising
microcalcifications, density masses, and/or spiculated masses.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This Application is a continuation of co-pending U.S. patent
application Ser. No. 121579,898, filed Oct. 15, 2009, entitled
COMPUTER-AIDED DETECTION OF REGIONS OF INTEREST IN TOMOGRAPHIC
BREAST IMAGERY, the entire disclosure of which is herein
incorporated by reference, which claims the benefit of U.S.
Provisional Application 61/105,895 filed on Oct. 16, 2008, the
entire disclosure of which is also incorporated herein by
reference.
BACKGROUND
[0002] In the fields of medical imaging and radiology, various
techniques may be employed for creating images of an anatomical
region of the human body. For example, in mammography, the breast
is often imaged at two fixed angles using x-rays. Physicians may
review two-dimensional (2-D) or planar x-ray images of the breast
(i.e., mammograms) to uncover and diagnose disease-like conditions,
such as breast cancer.
[0003] Numerous medical imaging procedures now employ systems and
techniques that create three-dimensional (3-D) or volumetric
imagery of the human body. For example, significant attention has
been given to tomographic imaging techniques. One such example is
digital breast tomosynthesis (DBT), a relatively new imaging
procedure in which systems image a breast by moving a source and
exposing the breast to radiation from a plurality of angles, thus
acquiring high resolution, planar images (i.e., "direct
projections") at different angles. For example, a DBT system may
acquire 10 direct projection images in which the source moves in
such a way as to change the imaging angle by a total angle of 40
degrees. From these direct projections, computer software can be
used to construct a 3-D volume of the breast (i.e., a
"reconstructed volume"). A reconstructed volume may be used to
derive a series of 40-60 individual images called slices that are
oriented parallel to a single plane of the imaged object (i.e., the
"reconstruction plane"). The computer software may reconstruct each
slice at a different depth and may use different thicknesses,
allowing physicians to visualize the breast and information of
interest at various depths of field that were previously
unavailable with traditional 2-D imaging systems and
procedures.
[0004] In mammography, a physician typically may review four images
to diagnose a patient: a cranial-caudal (CC) image and a
medial-lateral oblique (MLO) image of each of the right and left
breasts. In contrast, in tomographic imaging such as DBT, a
physician may review any of the direct projections and/or
reconstructed volume slices to diagnose a patient. For example, the
physician may use an input device in combination with a computer
system and graphical user interface (GUI) to "scroll" through slice
images displayed on the GUI, so as to simulate moving through the
breast perpendicular to the reconstruction plane. Any of these
images or combination of images may depict information of interest
in a way that allows a physician to detect and diagnose a potential
disease-like condition. Thus, while tomographic imaging may allow a
physician to improve the overall quality of care to a patient over
traditional mammographic imaging, the substantial amount of image
data available may have a negative impact on the physician's
workload and interpretation time.
[0005] Computer-aided detection (CAD) is one solution to help a
physician to overcome problems such as workload and interpretation
time. Using sophisticated computer algorithms based on image
processing and pattern recognition disciplines, CAD systems may
detect and present information (e.g., lesions) in medical imagery
that may be of interest to a reviewing physician. CAD has enjoyed
widespread success in its application to mammographic medical
imaging, as it has been shown to improve patient care, reduce human
workload, and reduce human error associated with fatigue or
variability between observers. More recently, CAD has been proposed
and developed to assist physicians moving to tomographic imaging
procedures.
[0006] Given the increase in the amount of image data acquired by
tomographic imaging techniques, several different approaches are
feasible in which a computer system can perform CAD and present
information of interest to a physician.
[0007] CAD may be performed on direct projection images acquired by
the system. For example, each individual direct projection may be
analyzed. However, direct projections may be noisy and, like
mammography, may have a very limited depth of field. Thus, if CAD
is performed on the direct projections, important regions may be
obscured by other uninteresting tissue in the direct projections
and therefore, not detected by CAD.
[0008] Alternatively, CAD may be performed on the voxels of the
entire reconstructed volume. However, the spatial distortion and
noise characteristics of the reconstructed volumes may be
complicated, requiring more sophisticated and/or customized
algorithms, computational power, computational storage, and
computational time. Thus, if CAD is performed on the entire
reconstructed volume, the workflow of a physician may be negatively
impacted by the speed of such a system. Physicians may require CAT)
to assist them in reviewing and diagnosing the imagery of numerous
patients each day.
[0009] Alternatively, CAD may be performed on the individual slices
derived from a reconstructed tomographic volume, An overview of
such a technique, as well as an overview of the aforementioned
techniques, may be found in U.S. Pat. No. 6,748,044, "Computer
assisted analysis of tomographic mammography data," assigned to GE
Medical Systems Global Technology Company, LLC. While each
individual slice may have information about lesions or other
structures of interest at a range of depths throughout the object,
a lesion or other structure of interest may be spread across a
plurality of slices. This may be particularly true if the slices of
the tomographic volume are reconstructed with thicknesses that are
less than the expected size of the lesion or other structure of
interest. Resolving this information may be problematic for a
computer system, leading to false detections, missed detections,
and/or poorly-represented detections.
[0010] It is therefore an object of this disclosure to present
methods and systems to automatically detect and present information
about lesions and other structures of interest in tomographic
imagery of the breast in a manner that is advantageous for use in a
clinical setting, both in terms of computational speed and
detection accuracy.
SUMMARY
[0011] Disclosed are methods, and associated systems comprising
processors, input devices and output devices, of detecting regions
of interest in a tomographic breast image. The methods may
comprise: by means of an input device, acquiring tomographic breast
image data; by means of a processor, deriving a plurality of
synthetic sub-volumes from the tomographic breast image data;
wherein each subvolume is defined by parallel planar top and bottom
surfaces; wherein planar top and bottom surfaces of successive
subvolumes are parallel to each other; and wherein a top planar
surface of a sub-volume is offset from a top planar surface of a
prior sub-volume, such that successive sub-volumes overlap; by
means of a processor, for each sub-volume, deriving a
two-dimensional image; by means of a processor, for each
two-dimensional image, identifying regions of interest therein; by
means of a processor, deriving at least one region of interest of
potential clinical interest from a plurality of identified regions
of interest; and by means of an output device, outputting
information associated with at least one derived region of interest
of potential clinical interest.
[0012] In the methods, the tomographic breast image data may be
acquired by means of an image acquisition unit. The tomographic
breast image data may comprise a tomographic breast volume. The
tomographic breast volume may be acquired by means of an image
acquisition unit obtaining a plurality of two-dimensional breast
images of an anatomical breast at differing angles, and a processor
computing the tomographic breast volume from the plurality of
two-dimensional breast images. The tomographic breast image data
may be acquired from at least one of a computer network and a
storage device. The tomographic breast volume may be acquired by
obtaining a plurality of two-dimensional breast images of an
anatomical breast at differing angles by means of at least one of a
computer network and a storage device, computing the tomographic
breast volume from the plurality of two-dimensional breast images
by means of a processor.
[0013] Deriving at least one region of interest of potential
clinical interest from a plurality of identified regions of
interest may comprise: each region of interest identified in a
plurality of two-dimensional images, determining a location of the
region of interest; for each located region of interest, deriving a
further sub-volume enclosing said located region of interest; and
for each located region of interest, further evaluating said
located region of interest to determine if it is of potential
clinical interest.
[0014] Each subvolume may have a same thickness as all other
subvolumes. The same thickness may be a predetermined thickness.
Each subvolume may be offset from the prior subvolume by a same
offset amount. The same offset amount may be a predetermined offset
amount.
[0015] Outputting information associated with at least one derived
region of interest of potential clinical interest may comprise
displaying said at least one derived region of interest in
conjunction with at least a portion of a breast image. Information
associated with at least one derived region of interest of
potential clinical interest may comprise at least one CAD mark.
Information associated with at least one derived region of interest
of potential clinical interest may comprise a location of the said
at least one region of interest.
[0016] Each synthetic sub-volume may be derived from a plurality of
consecutive thin slices of the tomographic breast volume. Each thin
slice may be in the range of about 1 mm. to about 3 mm. thick. A
thickness of each subvolume may be in the range of about 3 mm. to
about 30 mm. An offset between top planar surfaces of successive
sub-volumes may be in the range of about 1 mm. to about 3 mm. An
offset between top planar surfaces of successive sub-volumes may be
equal to a thickness of a thin slice of a tomographic breast
volume.
[0017] Each two-dimensional image may be derived by performing an
intensity projection algorithm on a subvolume. Each region of
interest may be identified by executing a suspicious lesion
detection algorithm on a two-dimensional image. The suspicious
lesion detection algorithm may identify regions of interest
comprising microcalcifications, density masses, and/or spiculated
masses.
FIGURES
[0018] FIG. 1 is a block diagram of an illustrative tomographic
imaging system that may be used to perform the methods disclosed
herein.
[0019] FIG. 2 is a flowchart that illustrates steps that may be
performed by a computer system to automatically identify and
present suspicious regions of interest (ROis) in tomographic
medical imagery of the breast in accordance with certain
embodiments disclosed herein.
[0020] FIG. 3 is a flowchart that illustrates steps that may be
performed by a computer system to automatically detect candidate
ROis in tomographic medical imagery of the breast in accordance
with certain embodiments disclosed herein.
[0021] FIG. 4 is an illustration visually depicting how a candidate
ROI may appear in projection imagery computed from a tomographic
breast volume.
DETAILED DESCRIPTION
[0022] In the following detailed description of embodiments,
reference is made to the accompanying drawings that form a part
hereof, and in which are shown, by way of illustration and not by
way of limitation, specific embodiments in which the methods and
systems disclosed herein may be practiced. R is to be understood
that other embodiments may be utilized and that logical,
mechanical, and electrical changes may be made without departing
from the scope of the methods and systems disclosed herein.
[0023] FIG. 1 is a view of an illustrative tomographic imaging
system 100 that may he used to perform the methods disclosed
herein. Preferably, tomographic imaging system 100 is used in
clinical practice to acquire tomographic medical imagery of the
breast, automatically detect regions of interest (ROIs) in the
imagery, and present the results to a physician. The system
described is for reference purposes only; other types and
variations of tomographic imaging systems may be employed to
acquire tomographic medical imagery for processing in accordance
with the methods disclosed herein.
[0024] Tomographic imaging system 100 includes an image acquisition
unit 110 for performing a tomographic imaging procedure on a
patient and an image viewing station 120 for processing and
displaying the imagery to a user a physician). Image acquisition
unit 110 may connect to and communicate with image viewing station
120 via any type of communication interface which may include, but
is not limited to, physical interfaces, network interfaces,
software interfaces, and the like. Alternatively, it will be
understood by a person of skill in the art that image acquisition
unit 110 and image viewing station 120 may be deployed in different
configurations. For example, and not by way of limitation, image
acquisition unit 110 and image viewing station 120 may be parts of
a single computer, computer processor or computer system. As
another example, image viewing station 120 may be deployed
independently of an image acquisition unit, and may retrieve
tomographic images from storage devices or over a network or by
other communication means as will be known to a person of skill in
the art. As still another example, the functions of image viewing
station 120 may be divided between two or more processors, so that
for example, and not by way of limitation, one processor may
perform certain processing according to the methods disclosed
herein, while another processor accessible to a user is responsible
for other processing steps, and/or communicates with display and
input means.
[0025] Image acquisition unit 110 is representative of systems that
can acquire a series of images of a patient's breast using
tomographic imaging procedures. For example, and not by way of
limitation, image acquisition unit 110 may be a digital breast
tomosynthesis (DBT) imaging system such as offered by the General
Electric Company of Fairfield, Connecticut (GE); Hologic, Inc, of
Bedford, Massachusetts (Hologic); or Siemens AG of Munich, Germany
(Siemens). DBT imaging systems image a breast by moving a source,
acquiring a number of projection images (e.g., 10-25 direct
projections) at different angles (e.g., at 4 degree
increments).
[0026] Image viewing station 120 is representative of any system
that can perform the automated identification methods disclosed
herein, henceforth interchangeably referred to as CAD, on medical
imagery acquired by image acquisition unit 110. (As set forth
above, in alternative embodiments a plurality of processors may be
used, and these processors may be disposed in more than one
location, in lieu of using a single integrated image viewing
station.) Image viewing station 120 may further output both the
medical imagery and results of CAD. Image viewing station 120 may
further comprise one or more processor units 122, memory units 124,
input interfaces 126, output interfaces 128, and program code 130
containing instructions that can be read and executed by image
viewing station 120. One or more input interfaces 126 may connect
processor units 122 to input devices such as keyboards 136, mouse
devices 138, and/or other suitable devices. Thus, one or more input
interfaces 126 allows a user to communicate commands to the
processor(s), one such exemplary command being the initiation of
automated, computer-aided detection (CAD) methods disclosed herein.
Output interface(s) 128 may further be connected to processor
unit(s) 122 and one or more output devices such as a graphical user
interface (GUI) 140. Thus, an output interface 128 allows image
viewing station 120 to transmit data from one or more processor
units 122 to an output device, one such exemplary transmission
including medical imagery and ROis identified by CAD for display to
a user on the GUI.
[0027] Memory unit(s) 124 may be comprised of conventional
semiconductor random access memory (RAM) 142 or other forms of
memory known in the art and one or more computer readable-storage
mediums 144, such as a hard drive, floppy drive, read/write CD-ROM,
tape drive, etc. Stored in program code 130 may be an image
reconstruction unit 146 for constructing additional imagery from
the images acquired directly by image acquisition unit 110; and a
CAD processing unit 148 for automatically identifying ROis in
medical imagery in accordance with the methods disclosed
herein.
[0028] It is further noted that while image reconstruction unit 146
and CAD processing unit 148 are depicted as being components within
image viewing station 120, as discussed above it will be understood
by a person of skill in the art that such components may be
deployed as parts of separate computers, computer processors, or
computer systems. For example, image reconstruction unit 146 may be
deployed as part of a tomographic review workstation system (e.g.,
DexTop Breast Imaging Workstation, Dexela Limited, London, United
Kingdom).
[0029] General operation of tomographic imaging system 100 for
performing the methods disclosed herein is as follows. As one
potential prelude to the novel methods disclosed herein, a patient
enters image acquisition unit 110 and a series of projection x-ray
images ,e., direct projections) of one or both breasts are
acquired. Each direct projection may have, for example, a spatial
resolution of 85 microns, and dimensions of 3500 rows of pixels by
2800 columns of pixels. Image acquisition unit 110 transfers the
direct projections to image reconstruction unit 146, which
constructs an image volume of the breast (i.e., a reconstructed
tomographic breast volume). Image reconstruction unit 110
optionally may construct a series of individual thin slices using
the direct projection image data acquired by image acquisition unit
110. The tomographic breast volume optionally may be constructed in
40-60 image thin slices, each thin slice having a spatial
resolution of 100 microns per pixel, a thickness of 1 millimeter
(mm), and dimensions of 2500 rows of pixels by 1500 columns of
pixels. Of course, other numbers of image thin slices, other
spatial resolutions, other thicknesses, and other dimensions, are
possible within the scope of the disclosed methods. Optionally,
individual thin slices need not be constructed in association with
the reconstructed breast volume.
[0030] As is known in the art, the tomographic breast volume may be
created by "reconstruction algorithms," which may be implemented as
software program modules in a processing unit such as image
reconstruction unit 146. A specific example of one reconstruction
algorithm can be seen in Claus BEH et al: "A new method for 3D
Reconstruction in Digital Tomosynthesis," Medical Imaging 2002,
Proceedings of the SPIE, vol. 4684, no. Part 1-3, Feb. 24-28, 2002,
pp. 814-824. Other examples of reconstruction algorithms include
"shift and add" algorithms and iterative maximum-likelihood
expectation maximization (ML-EM) algorithms. It is to be
appreciated that the methods s disclosed herein are not limited to
any one type of reconstruction algorithm used to reconstruct the
tomographic breast volume. Either automatically or upon the
execution of a command by a user of tomographic imaging system 100,
image reconstruction unit 146 may transmit the reconstructed
tomographic breast volume to CAD processing unit 148, which may
automatically process the imagery to detect regions of interest.
This process will be more fully described in reference to FIGS. 24,
The imagery and CAD processing results may then be outputted via an
output device such as GUI 140 to a user for review, such as a
physician who may require a computerized. assessment of the
imagery, Such an assessment may be made in addition to the
physician's manual assessment of the imagery using image viewing
station 120.
[0031] FIG. 2 illustrates the steps of a method 200 to
automatically identify and present suspicious regions of interest
(ROis) in tomographic medical imagery in accordance with certain
embodiments disclosed herein. Such a method may be performed by CAD
processing unit 148 in accordance with the general operation of
tomographic imaging system 100, for example. Method 200 may
overcome the limitations of prior approaches to detecting
suspicious ROis in tomographic image data by computing a plurality
of projection images from a reconstructed breast tomographic volume
in ways that may enhance the signature or "signal-to-noise" of
suspicious ROis. In certain embodiments to be described, each ROI
candidate detected may be further automatically evaluated on an
individual basis by the computer system to eliminate false positive
detections.
[0032] Suspicious ROis may include any plurality of pixels or
voxels that exhibit characteristics that may be of interest to a
physician or other user. Exemplary ROIs in the breast include
lesions such as micro-calcification clusters or masses, all of
which may be indicators of disease states such as breast cancer.
False positives such as vascular calcifications, lymph nodes, skin
folds, or parenchyma, all of which ultimately may be ruled out as
possible indicators of disease states, may also be present. Image
reference points such as the nipple, which may be used by a
reviewer during interpretation of the data, may also be present.
Other abnormalities that may be of interest to a physician or other
user of tomographic imaging system 100, or that may be confused
with abnormalities of interest, may also be present.
[0033] At step 210, a reconstructed volume of tomographic breast
data, such as that constructed in accordance with the general
operation of tomographic imaging system 100 described above, is
input to a processing unit suitable for executing the methods
disclosed herein, one such example being CAD processing unit
148.
[0034] At step 220, CAD processing unit 148 computes a plurality of
computed (i.e., synthetic) projection images from the tomographic
breast volume and performs a detection process on each computed
projection image. As will be fully described in reference to FIG.
3, each computed projection image is created from a synthetic
sub-volume. If thin image slices are available, the synthetic
sub-volumes may be created, each using a plurality of the thin
image slices available in the tomographic breast volume at
different depths of field. Thus, each computed projection image may
depict details of the breast at a particular location with greater
"thickness" or in-depth (i.e. "out-of-plane") information than that
which can be depicted in a single thin slice of the tomographic
breast volume or a single direct projection image. In addition,
multiple synthetic sub-volumes may be created such that sub-volumes
overlap each other. Accordingly, the likelihood that the system
will detect each ROI in the imagery is improved because the
characteristics of the ROI will be presented with sufficient detail
both "in-plane" and "in-depth" in a single sub-volume, rather than
being divided between multiple thin slices or even multiple
non-overlapping thicker subvolumes.
[0035] The detection process involves searching each computed
projection image for clusters of pixels or voxels that have the
general characteristics of a suspicious ROT. For example, the
detection process may search for clusters that are bright, dense,
and of a certain size. In further embodiments to be filly described
in reference to FIG. 3, a sliding window in effect may be used by
the system to move through the entire volume, create the computed
projections from sequences of overlapping sub-volumes, and detect
candidate ROis in each sub-volume.
[0036] By performing such a process on a set of projection images
rather than the entire tomographic breast volume, it should be
understood that the computational time in which to detect ROIs may
be significantly reduced. This has the advantage of presenting
results to a physician or other user of the system with faster
speeds than approaches that perform detection on entire
reconstructed volumes.
[0037] As is known in the art, from a detection process, numerous
ROis may be identified that have suspicious characteristics but are
actually non-suspicious (i.e., false positives). The results of
detection at step 220 may be used as input to an evaluation process
at step 230, where each individual ROI candidate is further
evaluated to eliminate false positives. For example, various
feature or characteristic measurements (e.g., contrast, brightness,
shape, size, density, texture, and/or converging lines) may be
computed on a ROI, each of which may or may not indicate
suspiciousness. The specific features computed at step 230 will
depend on the detection process executed at step 220 and the type
of ROI to detect. The values of these features may then be compared
against various parameters and rules by a classification process or
"classifier," the results of which may be used to determine which
an ROI should be presented to a physician or whether an ROI should
be disregarded. Examples of such classification algorithms include
a linear classifier, a quadratic classifier, a neural network, a
decision-tree, a fuzzy logic classifier, a support vector machine
(SVM), a Bayesian classifier, or a k-nearest neighbor classifier,
and other classification approaches as will be known by a person
skilled in the art. The parameters and rules of the classification
process may be established by presenting the classifier with sample
feature values of true positives and false positives, as is known
in the art.
[0038] The evaluation process at step 230 may be performed directly
using the pixels of each ROI detected in the individual computed
projection images. However, it should be recognized that while the
projection imagery may computed at step 220 in ways that are
satisfactory for detecting the general characteristics of
suspicious ROis, such imagery may not be satisfactory for
evaluating the specific characteristics of a suspicious ROI to
determine if the candidate warrants outputting to the
physician.
[0039] Thus, in accordance with certain embodiments, the results of
the candidate detection process performed at step 220 may be used
to compute additional imagery that is optimized to extract features
of single candidate ROis. For example, a new projection image may
be computed at the location of each candidate ROI detected. In such
embodiments, each computed image more finely encapsulates a
candidate and thus, further maximizes the signature or
"signal-to-noise" ratio of a candidate's features. In turn, by
computing feature measurements on such imagery, the evaluation
results for each candidate will be more accurate. It is also noted
that the sample feature values of true positives and false
positives in which to establish the parameters and rules of the
classification process described at step 230 may also be computed
from this improved imagery. This is in contrast to computing the
feature values on true positives and false positives in the
individual computed projection images, which are better suited for
identifying the general characteristics of candidate ROIs.
[0040] At step 240, ROis detected and evaluated as suspicious are
output and displayed to a physician or other user of tomographic
imaging system 100. For example, suspicious ROis are typically
output and displayed along with imagery of the breast on an output
device such as GUI 140, such that a reference is presented as to
the location of each suspicious ROI in the patient's breast. For
example, the series of individual thin slices of a reconstructed
tomographic breast volume may be displayed to a user so as to
appear in a "stacked" manner. Each slice may further contain a
visual mark corresponding to the pixels or voxels of a suspicious
ROI which was identified using the methods disclosed herein.
[0041] In clinical practice, tomographic image data may be analyzed
in accordance with the methods disclosed herein, but no suspicious
ROis may be identified. This may suggest to a physician that the
patient's breast may be normal, healthy, or without indicators of
cancer. Thus, in certain embodiments, zero suspicious ROis may be
outputted to a physician. In this case, the absence of suspicious
ROis is as substantially important as the presence of suspicious
ROIs. In certain other embodiments, one or more suspicious ROis may
be outputted. This suggests to a physician that the patient's
breast may be abnormal, unhealthy, or with cancer indicators. In
this case, the physician's attention is drawn to each individual
ROI for further evaluation as potential indicators of disease
state.
[0042] FIG. 3 illustrates the exemplary steps of a method 300 to
automatically detect candidate ROis in tomographic medical imagery
of the breast, such exemplary steps corresponding to one embodiment
of step 220 of FIG. 2.
[0043] At step 310, the tomographic breast volume is segmented into
a plurality of synthetic sub-volumes, Each subvolume may be
constructed so as to have planar top and bottom surfaces parallel
to each other. The planar surfaces of successive sub-volumes may in
turn be parallel with each other. in other embodiments the surfaces
of the synthetic sub-volumes need not be planar or parallel. In
some embodiments, the surfaces of successive sub-volumes may be
parallel to a fixed reconstruction plane. Each subvolume may have a
thickness (which may be perpendicular to the reconstruction plane
in embodiments with reconstruction planes) such that all ROis
within an expected target abnormality size range will be
encapsulated in a single sub-volume. As previously discussed,
tomographic breast volumes are typically divided into thin slices
such that the "thickness" of each slice in the volume is less than
the expected size of a ROI. For example, slice images may be
reconstructed with thicknesses of 1-3 mm, while lesions such as
masses or micro-calcification clusters may have an expected size
ranging anywhere from 3-30 mm. Thus, a single ROI (e.g., ROI 410
illustrated in FIG. 4) may appear in multiple thin slices (e.g.,
slices 420) of a tomographic breast volume (e.g., tomographic
breast volume 400). While a human viewer may prefer such an
arrangement while reviewing multiple thin slice images in sequence,
for a computer system resolving such artifacts may be troublesome,
prone to error, and therefore, suboptimal for an automated
detection process. Worse still, a computer system may fail to
detect an ROI at all if the overall signature of the ROI is weakly
distributed across multiple thin slices. Clusters of
micro-calcifications may be particularly problematic, since they
may appear as only individual points on each slice.
[0044] To overcome such issues, each sub-volume according to the
methods disclosed herein may be constructed by selecting two or
more adjacent thin slice images from the reconstructed volume to
form a "thicker" synthetic sub-volume, The specific number of
slices used to form a synthetic sub-volume may be determined
according to a ratio of maximum target abnormality size to
reconstructed volume thin slice thickness. For example, as shown in
FIG. 4, presented with a tomographic breast volume 400 of thin
slices 420 each having thicknesses of 2 mm, to capture ROIs with a
maximum target abnormality size of 30 mm, a synthetic sub-volume of
15 slices (illustrated as sub-volume 430) may be formed.
Alternatively, if presented with a tomographic breast volume having
thin slices of various thicknesses, a computation such as the
average slice thickness across a set of thin slices may be used to
set a slice thickness parameter for use in such a ratio.
[0045] In sonic embodiments, the maximum target abnormality size(s)
may be stored in image viewing station 120 as one or more
predefined parameters. For example, to detect a single mass cancer
with the methods disclosed herein, a target abnormality size
parameter may be preset within a range of 3-30 mm to detect such
lesions. Alternatively, in some other embodiments, the maximum
target abnormality size parameter may be defined in CAD processing
unit 148 at "run-time" (i.e., during execution of the methods
disclosed herein). For example, prior to the execution of step 310,
the parameter may be determined by computing an initial, rough
estimate as to the maximum dimensions of the ROI appearing in the
tomographic breast volume. Exemplary steps in which to compute such
an estimate may include projecting the entire tomographic breast
volume into a single projection image, executing a detection
algorithm on the single projection image, computing an estimate as
to the size of each ROI detected, and setting the parameter
according to the maximum computed size of an ROI detected. he
establishing of such a parameter at "run-time" may further reduce
the noise in each projection image around candidate ROis, leading
more accurate detection results.
[0046] CAD processing unit 148 need not form sub-volumes from all
pixels of a given thin slice or all voxels across thin slices.
Sub-volumes may be formed from segments of each thin slice (i.e.,
specific columns and rows of pixels in each thin slice, or voxels
created from multiple thin slices) so as to create sub-volumes of
varying dimensions that might best encapsulate a given ROT.
Examples include, but are not limited to, cubes, curved slabs,
thick slabs, thin slabs, or any other manifold formed from the
pixels or voxels of two or more adjacent thin slice images of a
tomographic breast volume. For example, if the target ROI to detect
is parenchyma, curved slab sub-volumes may be particularly useful,
as parenchyma will typically be long, curved strand-like
objects.
[0047] At step 320, CAD processing unit 148 projects each synthetic
sub-volume to a planar, 2-D image (i.e., a computed or synthetic
projection image). An example of a computed projection image 440
constructed from sub-volume 430 is illustrated in FIG. 4. Numerous
projection algorithms exist that map the voxels of a volume to a
2-D image plane. Any number of these algorithms may be used to
perform step 320. One example is a maximum intensity projection
(MIP) algorithm. Other projection algorithms may compute the mean
or median intensity to form computed projections. The projection
may be performed along any axis. In certain embodiments, the
projection may be performed perpendicular to and onto the
reconstruction plane.
[0048] At step 330, the anatomical breast may be identified within
each computed projection image (e.g., breast region 450, as shown
in FIG. 4) and any pixels outside of this breast region may be
excluded from further processing. A number of algorithms are known
that identify the breast region area from a cranial-caudal (CC) or
medial-lateral oblique (MLO) mammogram. Any of these algorithms may
be suitable for performing step 330, particularly if the computed
projection images are formed at a similar plane to that used to
create conventional mammographic imagery. For example, the
algorithm disclosed in U.S. Pat. No. 6,091,841 "Method and system
for segmenting desired regions in digital mammograms" by Qualia
Computing, Inc., may be performed at step 330. In other
embodiments, the breast may be identified using available imagery
other than the computed projection images. For example, the entire
tomographic breast volume may be projected into a single projection
image and the breast region then identified in this image.
[0049] In an optional pre-processing step, as illustrated by step
340, CAD processing unit 148 may improve each computed projection
image and thus, the efficacy of the methods disclosed herein by
performing one or more pre-processing operations known in the art.
For example, cropping may be performed to reduce the amount of data
to be processed in subsequent steps, or filtering may be performed
to enhance the ROis to be detected. Other pre-processing operations
might further include, but are not limited to, smoothing, noise
reduction, sharpening, etc.
[0050] At step 350, ROI candidates are identified in each computed
or synthetic projection image. As is known in the art, numerous
mass detection algorithms exist that identify lesions based on
measurements of the core or density in the imagery, numerous
spiculation detection algorithms exist that identify lesions based
on measurements of spiculated lines extending from mass-like
regions in the imagery, and numerous micro-calcification detection
algorithms exist that identify lesions based on measurements
related to the cluster of individual calcification points around a
neighboring area. Examples of such detection algorithms can be seen
in U.S. Pat. No. 5,999,639, "Method and system for automated
detection of clustered microcalcifications from Digital
Mammograms"; U.S. Pat. No. 6,137,898, "Gabor filtering for improved
microcalcification detection in digital mammograms"; U.S. Pat. No.
6,167,146, "Method and system for segmentation and detection of
microcalcifications from digital mammograms"; U.S. Pat. No.
6,205,236, "Method and system for automated detection of clustered
microcalcifications from digital mammograms"; U.S. Pat. No.
6,389,157, "Joint optimization of parameters for the detection of
clustered microcalcifications in digital mammograms," all of which
are assigned to Qualia Computing, Inc. and are incorporated herein
by reference; and U.S. Pat. No. 6,801,645, "Computer aided
detection of masses and clustered microcalcifications with single
and multiple input image context classification strategies," and
U.S. Pat. No. 7,298,877, "Information fusion with Bayes networks in
computer-aided detection systems," all of which are assigned to
iCAD, Inc, and are incorporated herein by reference. Any of the
detection. algorithms disclosed in these references or other prior
art references may be performed in accordance with the type of ROI
to detect.
[0051] In accordance with further embodiments, sub-volumes and
associated computed projection images may be constructed from thin
slice images in an overlapping manner. More specifically, each
sub-volume and associated computed projection image may be
constructed from: at least one thin slice image of the tomographic
breast volume, a subset of the pixels of at least one thin slice
image of the tomographic breast volume, or a subset of the voxels
of a plurality of thin slice images of the tomographic breast
volume that are also used to construct at least one other computed
projection image. In certain embodiments, the overlap may be set
equal to the thickness of individual thin slices in the volume so
that potential candidates in a given slice are captured in at least
two sub-volumes and associated computed projection images.
[0052] CAD processing unit 148 may perform the detection methods
disclosed herein on the image volume in an overlapping manner using
a sliding window technique or mode. For example, given a 10 cm
thick reconstructed tomosynthesis volume with an individual thin
slice thickness of 2 mm and a maximum expected target size
parameter of 25 mm, a CAD system may move through the volume using
a sliding window of 2 mm increments at a sub-volume thickness of 25
mm. CAD processing unit 148 will obtain a first 25 mm thick
sub-volume of imagery from 1-26 mm in depth in the volume, compute
a first projection image from this sub-volume, and identify
candidate ROI detections within the first computed projection. CAD
processing unit 148 will then slide to the next 25 mm thick
sub-volume of imagery from 3-28 mm in depth in the volume, compute
a second projection image from this sub-volume, identify candidate
ROI detections within the second computed projection, etc. An
advantage of using a sliding window technique is the computational
speed improvements gained in which the various processing
components of tomographic imaging system 100 (e.g., processor unit
122, memory unit 124, etc.) are provided access to the tomographic
imagery (e.g., blocks of pixels or voxels) to perform the methods
disclosed herein.
[0053] Without the use of overlap as disclosed herein. CAD
processing unit 148 may detect a single ROI separately across
multiple sub-volumes and associated computed projection images.
Such a situation may occur even if the sub-volume thickness is set
to be larger than the maximum size of an ROI appearing in the
volume, as it will not he known at which image slices the ROI will
appear, begin, or end. Using an overlapping technique as disclosed
herein, a given ROI may appear in multiple computed projection
images, but the "signal-to-noise" ratio or signal of that ROI will
be strongest in a single computed projection. The overlapping
technique thus allows a computation as to the image thin slice in
which a given ROI begins and the image thin slice in which a given
ROI ends, which may be particularly useful information for a
candidate evaluation process such as the evaluation process
introduced at step 230 and more fully described below.
[0054] It should be recognized that the candidate detection process
described in reference to FIG. 3 is a "coarse" approach in which
many potential ROIs (i.e., candidates) are identified. This
"coarse" approach detects candidates with high sensitivity and at
acceptable processing speeds. While feature measurements may be
computed on candidates detected in imagery created using such
approach, doing so may lead to unacceptable sensitivity and false
positive rates. For example, candidate ROis may be detected that
have a size substantially less than the maximum target abnormality
size parameters used to create each computed projection image and
thus, a significant amount of noise may exist in the surrounding
area of the ROI. Such noise may not be sufficiently minimized to
the point where specific features or characteristics of each
candidate may be measured with sufficient accuracy. For example, it
may be suboptimal to compute features on a 5 mm candidate ROI in a
projection image created by projecting 30 mm of slice imagery.
[0055] To overcome these problems, the results of the candidate
detection process performed at step 220 may be used as input to the
candidate evaluation process performed at step 230. For example,
features of each candidate ROI detected at step 220, such as the
location in which the candidate appears in the computed projection,
may be used to determine the location of the candidate in the
tomographic breast volume. CAD processing unit 148 may perform any
number of forward projection algorithms that are known in the art
to make such a determination.
[0056] In accordance with certain embodiments, volumetric features
(i.e., "3-D" features) may be computed directly on a volumetric
representation of each candidate ROI. Exemplary volumetric features
that may be computed include curvature, aspect ratio, volume,
crossing line, contrast features, etc. Volumetric features may be
computed to measure characteristics of suspiciousness (e.g.
malignancy, non-malignancy) that cannot be accurately measured in
planar projection images.
[0057] Alternatively, in accordance with certain embodiments, from
the location of an ROI in the tomographic breast volume, a new
projection image may be computed that more finely encapsulates the
individual ROI, For example, referring back to FIG. 4, a computed
projection may be formed using seven thin slice images, determined
based on the location of an ROI detected in a projection image
computed from 15 thin slices. Planar features may then be computed
on the planar or 2-D representation of the ROI. Exemplary planar
features include contrast, brightness, shape, size, density,
texture, converging lines, etc.
[0058] It is further noted that a combination of volumetric
features and planar features may be computed on any given ROI and
input to a classification process such as that described in
reference to step 230. More specifically, a single vector of both
volumetric and planar features may be formed. The use of this
combination vector by a classification process to measure
suspiciousness may lead to more reliable classification results
than if only volumetric features or if only planar features are
measured and used to classify a ROI.
[0059] Any of the projection images computed at either the
candidate detection or candidate evaluation processes may be output
to the physician at step 240 as a means for the physician to
visualize a detected ROl. The projection images computed at the
candidate evaluation process may be particularly useful for the
physician, as each of these computed projections may present a
planar representation of the ROI with sufficient detail both
"in-plane" and "in-depth" for the physician to review the ROI.
[0060] For example, now referring back to step 240, the series of
individual thin slices of a reconstructed tomographic breast volume
may be displayed to a user on GUI 140 so as to appear in a
"stacked" manner, Each slice may further contain a visual mark
corresponding to the pixels or voxels of a suspicious ROI which was
identified using the methods disclosed herein. Upon command from
the physician who uses keyboard 136 and/or mouse 138 to select or
"query" a suspicious ROI for more detail, the projection image
computed to perform the evaluation process at step 230 may be
retrieved from memory unit 124 and displayed on GUI 140, This
projection image may be displayed so as to appear superimposed over
the individual slice image also depicting the ROI.
[0061] The methods disclosed herein automatically detect regions of
interest in tomographic imagery of the breast in a manner that is
acceptable for use in a clinical setting, both in terms of
computational speed and accuracy. The speed of such a method is
optimized by first performing a "coarse" detection process on a
plurality of computed projection images, each of which may contain
a candidate region of interest (ROI). The accuracy of the detection
process described herein may be optimized by creating the computed
projection images from a plurality of thin slices at a specific
"thickness" that may be predefined in the system or, alternatively,
computed at run-time. The use of a sliding window to perform the
detection process in an overlapping manner may further improve the
accuracy of the method. Each candidate ROI may then be individually
evaluated using a more computationally intensive or "fine"
evaluation process, which includes the computation of feature
characteristics and classification using the feature
characteristics.
[0062] It is noted that terms like "preferably," "commonly," and
"typically" are not utilized herein to limit the scope of the
claimed invention or to imply that certain features are critical,
essential, or even important to the structure or function of the
claimed. invention. Rather, these terms are merely intended to
highlight alternative or additional features that may or may not be
utilized in a particular embodiment of the present invention.
[0063] Having described the invention in detail and by reference to
specific embodiments thereof, it will be apparent that
modifications and variations are possible without departing from
the scope of the disclosures herein or the methods and systems
defined in the appended claims. More specifically, although sonic
aspects of the present disclosure may be identified herein as
preferred or particularly advantageous, it is contemplated that the
present disclosure is not limited to these preferred aspects.
* * * * *