U.S. patent application number 12/620670 was filed with the patent office on 2010-03-18 for methods of chromogen separation-based image analysis.
This patent application is currently assigned to TriPath Imaging, Inc.. Invention is credited to Raphael Marcelpoil, Cedrick Orny, Ryan Williams.
Application Number | 20100067775 12/620670 |
Document ID | / |
Family ID | 37027048 |
Filed Date | 2010-03-18 |
United States Patent
Application |
20100067775 |
Kind Code |
A1 |
Marcelpoil; Raphael ; et
al. |
March 18, 2010 |
METHODS OF CHROMOGEN SEPARATION-BASED IMAGE ANALYSIS
Abstract
Methods for chromogen separation-based image analysis are
provided, with such methods being directed to quantitative
video-microscopy techniques in cellular biology and pathology
applications.
Inventors: |
Marcelpoil; Raphael;
(Grenoble, FR) ; Williams; Ryan; (Carrboro,
NC) ; Orny; Cedrick; (Grenoble, FR) |
Correspondence
Address: |
ALSTON & BIRD LLP
BANK OF AMERICA PLAZA, 101 SOUTH TRYON STREET, SUITE 4000
CHARLOTTE
NC
28280-4000
US
|
Assignee: |
TriPath Imaging, Inc.
|
Family ID: |
37027048 |
Appl. No.: |
12/620670 |
Filed: |
November 18, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
11433114 |
May 12, 2006 |
|
|
|
12620670 |
|
|
|
|
60680991 |
May 13, 2005 |
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Current U.S.
Class: |
382/133 ;
382/164 |
Current CPC
Class: |
G06T 2207/20152
20130101; G06T 7/155 20170101; G06T 7/0012 20130101; G01N 1/30
20130101; G06K 9/0014 20130101; G06T 2207/10056 20130101; G06T
7/136 20170101; G06T 2207/30024 20130101; G01N 1/31 20130101; G06T
7/11 20170101; G06K 9/34 20130101; G06T 2207/10016 20130101 |
Class at
Publication: |
382/133 ;
382/164 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A method of segmenting a sample from an image thereof,
comprising: determining a background component of an RGB image of
the sample via a thresholding process; segmenting the image by
creating a component image of at least one of a membrane, a
cytoplasm, and a nucleus; refining the segmented image; and
filtering any unwanted objects from the image.
2. A method according to claim 1 wherein determining a background
component of the image via a thresholding process further
comprises: converting the RGB image to a corresponding luminance
image; and determining a background threshold luminance level from
the luminance image, with above-threshold pixels in the luminance
image correspond to the background component and below-threshold
pixels in the luminance image correspond to a non-background
component.
3. A method according to claim 2 wherein determining a background
threshold luminance level from the luminance image further
comprises: smoothing the luminance image; determining a histogram
of pixels for the smoothed luminance image, the histogram having a
higher portion and a lower portion; and scanning the histogram,
beginning from about the higher end thereof, to determine a local
minima corresponding to the threshold luminance level.
4. A method according to claim 3 further comprising limiting the
scan of the histogram to pixels having less than about 90%
transmittance.
5. A method according to claim 1 wherein creating a component image
further comprises applying a chromogen separation procedure to the
RGB image of the sample for at least one dye indicating one of the
membrane, the cytoplasm, and the nucleus.
6. A method according to claim 1 wherein segmenting the image
further comprises: determining an average intensity of pixels of a
non-background component of a membrane component image; replacing
any pixel having an intensity greater than the average intensity
with the average intensity so as to form a membrane mask;
generating a difference image between large and small smoothing
convolution kernels of the membrane component image to form a
contrast image; binarizing the contrast image using a local
contrast threshold; forming a skeleton of membrane masks from the
binarized contrast image; deleting any portion of the skeleton
smaller than a minimal size; expanding the skeleton of membrane
masks by at least one pixel in each direction; and deleting any
membrane masks not corresponding to the skeleton.
7. A method according to claim 1 wherein segmenting the image
further comprises: determining an average intensity and a median
intensity of pixels of a non-background component of a nucleus
component image; replacing any pixel having an intensity greater
than a threshold intensity comprising the greater of the average
intensity and the median intensity with the threshold intensity so
as to form an initial nucleus mask; low-pass filtering the initial
nucleus mask with a kernel of 1.5 times an expected nucleus size;
watershed transformation segmenting the initial nucleus mask so as
to produce an output image; and combining the watershed
transformation segmentation output image with the initial nucleus
mask to form a resulting nucleus mask, and such that mask pixels
are designated where the watershed transformation segmentation
output image has a catchment basin and where the initial nucleus
mask has a pixel intensity below the threshold intensity.
8. A method according to claim 7 further comprising, if a membrane
mask is available, deleting any pixel of the resulting nucleus mask
within a membrane mask.
9. A method according to claim 7 further comprising cleaning the
resulting nucleus mask by filling indeterminate portions thereof
having an area less than about one-fifth of the expected nucleus
size, and deleting objects smaller than about one-fourth of the
expected nucleus size.
10. A method according to claim 7 wherein segmenting the image
further comprises: inverting and distance-transforming the
resulting nucleus mask; binarizing the inverted and
distance-transformed resulting nucleus mask such that pixels within
an expected cell size are included in a first potential cytoplasm
mask; combining the first potential cytoplasm mask with a
background component of the nucleus component image so as to form a
mask of a non-background component of the first potential cytoplasm
mask.
11. A method according to claim 10 further comprising: inverting
and distance-transforming the resulting nucleus mask to form a
second potential cytoplasm mask; and combining the first and second
potential cytoplasm masks to form a resulting cytoplasm mask.
12. A method according to claim 11 wherein refining the segmented
image further comprises: identifying each segmented object in the
resulting cytoplasm mask; associating each identified segmented
object with a labeled object in a labeled image, each identified
segmented object being identified by a unique pixel value, by
dilating the labeled image; binarizing each labeled object using an
individual threshold associated therewith so as to refine the
resulting nucleus mask.
13. A method according to claim 12 wherein binarizing each labeled
object to refine the resulting nucleus mask further comprises:
determining a histogram for pixels of the labeled object, the
histogram having an upper limit and a lower limit, and determining
a mean intensity of the pixels; determining an upper bound and a
lower bound for a threshold, the upper bound being determined by
integrating the histogram starting from the upper limit thereof for
about 20% of an area of the labeled object, the lower bound being
determined by integrating the histogram starting from the lower
limit thereof for about 20% of the expected nucleus size; if the
lower bound is less than the upper bound, applying Fisher
discriminate analysis to the histogram between the upper and lower
bounds to determine the threshold; otherwise, designating a mean of
the upper and lower bounds as the threshold; and re-inserting the
labeled object into the resulting nucleus mask by binarizing the
nucleus component image using the threshold.
14. A method according to claim 13 further comprising: distance
transforming the resulting nucleus mask; and watershed transforming
the resulting nucleus mask following distance transformation
thereof so as to separate any merged nuclei and minimize
under-segmentation.
15. A method according to claim 14 further comprising filling
indeterminate portions of the resulting nucleus mask having an area
less than about one-fifth of the expected nucleus size.
16. A method according to claim 15 further comprising removing any
objects less than about one-third of the expected nucleus size from
the resulting nucleus mask so as to produce a refined nucleus
mask.
17. A method according to claim 16 further comprising: inverting
and distance-transforming the refined nucleus mask; binarizing the
inverted and distance-transformed refined nucleus mask such that
pixels within an expected cell size are included in a first
potential refined cytoplasm mask; combining the first potential
refined cytoplasm mask with a background component of the nucleus
component image so as to form a mask of a non-background component
of the first potential refined cytoplasm mask.
18. A method according to claim 17 further comprising: inverting
and distance-transforming the refined nucleus mask to form a second
potential refined cytoplasm mask; and combining the first and
second potential refined cytoplasm masks to form a refined
cytoplasm mask.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. patent application
Ser. No. 11/433,114, filed May 12, 2006, which claims priority to
U.S. Provisional Patent Application No. 60/680,991, filed May 13,
2005, both of which are hereby incorporated by reference in their
entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to image analysis and, more
particularly, to methods for chromogen separation-based image
analysis related to quantitative video-microscopy techniques in
cellular biology and pathology applications.
[0004] 2. Description of Related Art
[0005] The assessment and analysis of tissues is the domain of
pathology. During the recent past, methodological and technological
developments have turned digital image analysis into one of the
most efficient tools to assist pathologists in interpreting images
with increased accuracy. Though such image analysis techniques
contribute substantially to provide cytologists with accurate,
reproducible and objective cellular analysis, histological
interpretation techniques still tend to depend on the subjective
analysis of specimens. Such histological interpretation techniques
may also be subject to varying intra- as well as inter-observer
agreement, which further tend to provide less accurate, less
reproducible, and less objective results. For such reasons, image
analysis of tissues was initially restricted to technologies
developed for the analysis of cytological specimens.
[0006] With the evolution and availability of high performance
computers, local and wide area communication, cost-effective
database solutions, improved storage technology, and cost-effective
high-resolution digital cameras and/or scanners, the situation has
now changed. More sophisticated algorithms, formerly ineffective
due to lack of CPU power, could not before be applied to tissue
sections in a routine environment. However, such algorithms can now
be used to assess and quantify tissue-specific features related to
marker quantification and sub cellular localization. At the same
time, more comprehensive support for a reproducible and more
standardized visual assessment of tissue sections has become
available based on the initial step in image analysis, namely the
creation and management of digital images. This is especially true
in the fields of quality control, quality assurance and
standardization. Digital images of difficult cases can be exchanged
with reference pathologists via telepathology to get a second
opinion. Such images can also be effectively used for proficiency
testing. Digital images are also the basis of powerful image
reference databases, which can be accessed via network, and play an
increasingly important role in the documentation of cases and
evaluation results, particularly in comprehensive electronic or
printed reports.
[0007] Once a tissue slide is prepared, a pathologist visually
examines the tissue specimen under a microscope. If image analysis
should be applied with respect to the slide, the microscope must be
at least equipped with a camera or other image capturing device,
which is connected to a computer system via an interface. The
camera samples the optical microscopic image of the tissue sample
via the microscope. As a result, a digital image is collected in
the memory of the computer and can be displayed on the monitor
thereof. However, the acquisition of these digital images must be
performed such that the important details of the optical images are
still correctly represented by the stored data.
[0008] Generally, the next step for a quantitative assessment of
the digitized images is segmentation, which sometimes includes an
additional intermediate step of preprocessing. During segmentation,
the cells are separated from each other and from the image
background. In some instances, algorithmic advances have made it
possible to segment cells down to the sub-cellular component level
(i.e., nucleus, cytoplasm, and membranes). Although it may appear
an easy task, segmentation is often a difficult and error-prone
step in image analysis. For slides where the cells are nicely
separated and stained in a way that good contrasts occur in the
digitized image, segmentation can be done very reliably in many
cases. As soon as one of the above conditions is not fulfilled,
however, highly sophisticated and time consuming segmentation
algorithms, using additional a priori knowledge about the cells and
their relationship to each other, or about marker and counter stain
sub-cellular localization, have to be applied. This is the case,
for example, in instances of tissue sections of infiltrating
tumors, where most of the cells are no longer nicely separated on
the slide, but tend to be touching and overlapping each other.
[0009] Using a marker-based algorithm, it is possible to
circumscribe the region of interest automatically, and let the
pathologist decide, using his own subjective expertise, if the
region presented is adequate or needs to be manually refined. Once
the meaningful areas of an image are determined, the feature
extraction takes place. For each cell (and its sub-cellular
components), a set of densitometric, morphometric, texture, and
contextual features can be measured, with a goal of characterizing
the individual cells and their interactions as comprehensively as
possible.
[0010] The last step is the presentation of the raw data and
compilation thereof into meaningful results and/or scores. The
resulting output of an image analysis system should desirably match
the form of visual and/or semi-quantitative grading systems already
in use by the pathologist so as to promote consistency, to be
easily applicable, or to be capable of being interpreted in routine
use.
[0011] The platform for the evaluation of tissue samples via image
analysis is shifting more and more from the general-purpose image
analyzer to specialized and dedicated "pathology workstations"
configured for routine work. Such workstations combine tools needed
to provide the pathologist with the necessary information to derive
the best results possible. Central to such a workstation is the
microscope, possibly equipped with robotic parts including a
motorized stage, an automatic focus device, an objective changer,
and a light intensity adjustment device. Different input devices,
such as cameras capable of fast automatic focusing and acquisition
of high resolution images, are linked to the workstation. The
workstation can be part of a Local Area Network (LAN). The
workstation can also support different communication protocols, so
that available communication channels can be used to connect the
workstation with other places in the world (Wide Area Network or
WAN).
[0012] When integrated within a LAN and/or WAN, the workstation can
be granted access to existing reference databases and Hospital
Information Systems (HIS) such that any new cases to be examined
can be compared with the pictures and accompanying information of
reference cases which have been accumulated over time. In addition,
images acquired from the slides under review can be complemented
with the patient and case history.
[0013] The pathology workstation is preferably suited for a
comprehensive tissue evaluation. Starting with information and
digital pictures of the initial tissue sample, images of the slides
prepared from the tissue can be taken. The patient and case
information, the images themselves, and any quantitative
information about the cell components of the tissue sample can all
be stored in the same database.
[0014] All of the information accumulated by the workstation for
one case, such as images, measurement results, patient data,
preparation data, can be selected to be part of a report which can
either be printed or signed out electronically via the network. The
report provides a comprehensive picture of the case under
evaluation and facilitates quality assurance and
standardization.
[0015] During preprocessing/segmentation of the captured images,
many different techniques/algorithms can be implemented for image
analysis, particularly for quantitative video-microscopy in the
field of cellular biology and pathology applications, by using
multi-spectral imaging adapted to color cameras (i.e., RGB 3CCD
cameras).
[0016] Effective analysis of microscopic images is essential in
cellular biology and pathology, particularly for detection and
quantification in genetic material (genes, messenger RNA) or the
expression of this genetic information in the form of proteins, for
example, gene amplification, gene deletion, gene mutation, number
of messenger RNA molecules or protein expression analyses. Gene
amplification is the presence of too many copies of the same gene
in one cell, wherein a cell usually contains two copies, otherwise
known as alleles, of the same gene. Gene deletion indicates that
less than two copies of a gene can be found in a cell. Gene
mutation indicates the presence of incomplete or non-functional
genes. Messenger RNAs (mRNA) are molecules of genetic information,
synthesized from gene reading, that serve as templates for protein
synthesis. Protein expression is the production of a given protein
by a cell. If the gene coding for this protein is up regulated or
too many copies of the gene or mRNA are present, the protein may be
over-expressed. If the gene is down regulated or deleted, the
protein expression level may be low or absent.
[0017] Normal cellular behaviors are precisely controlled by
molecular mechanisms involving a large number of proteins, mRNAs
and genes. Gene amplification, gene deletion, and gene mutation are
known to have a prominent role in abnormal cellular behaviors
through abnormal protein expression. The range of cellular
behaviors of concern includes behaviors as diverse as, for example,
proliferation or differentiation regulation. Therefore, effective
detection and quantification in gene amplification, deletion and
mutation, mRNAs levels or protein expression analyses, is necessary
in order to facilitate useful research, diagnostic and prognostic
tools.
[0018] There are numerous laboratory techniques dedicated to
detection and quantification in gene amplification, deletion and
mutation, mRNA levels or protein expression analyses. For example,
such techniques include Western, Northern and Southern blots,
polymerase chain reaction ("PCR"), enzyme-linked immunoseparation
assay ("ELISA"), and comparative genomic hybridization ("CGH")
techniques. However, microscopy is routinely utilized because it is
an informative technique, allowing rapid investigations at the
cellular and sub-cellular levels, which may be implemented at a
relatively low cost.
[0019] When microscopy is the chosen laboratory technique, the
biological samples usually first undergo specific detection and
revelation preparations. Once the samples are prepared, a human
expert analyzes the samples with a microscope alone or with a
microscope coupled to a camera and a computer, allowing both a more
standardized and quantitative study. The microscope may be
configured for fully automatic analysis, wherein the microscope is
automated with a motorized stage and focus, motorized objective
changers, automatic light intensity controls and the like.
[0020] The preparation of the samples for detection may involve
different types of preparation techniques that are suited to
microscopic imaging analysis, such as, for example,
hybridization-based and immunolabeling-based preparation
techniques. Such detection techniques may be coupled with
appropriate revelation techniques, such as, for example,
fluorescence-based and visible color reaction-based techniques.
[0021] In Situ Hybridization ("ISH") and Fluorescent In Situ
Hybridization ("FISH") are detection and revelation techniques
used, for example, for detection and quantification of genetic
information amplification and mutation analyses. Both ISH and FISH
can be applied to histological or cytological samples. These
techniques use specific complementary probes for recognizing
corresponding precise sequences. Depending on the technique used,
the specific probe may include a chemical (ISH) marker or a
fluorescent (FISH) marker, wherein the samples are then analyzed
using a transmission microscope or a fluorescence microscope,
respectively. The use of a chemical marker or a fluorescent marker
depends on the goal of the user, each type of marker having
corresponding advantages over the other in particular
instances.
[0022] In case of protein expression analyses, further
immunohistochemistry ("IHC") and immunocytochemistry ("ICC")
techniques, for example, may be used. IHC is the application of
immunochemistry to tissue sections, whereas ICC is the application
of immunochemistry to cultured cells or tissue imprints after they
have undergone specific cytological preparations, e.g. liquid based
preparations. Immunochemistry is a family of techniques based on
the use of specific antibody, wherein antibodies are used to
specifically target molecules inside or on the surface of cells.
The antibody typically, contains a marker that will undergo a
biochemical reaction, and thereby experience a color change, upon
encountering the targeted molecules. In some instances, signal
amplification may be integrated into the particular protocol,
wherein a secondary antibody that includes the marker stain follows
the application of a primary specific monoclonal antibody.
[0023] In both hybridization and immunolabeling studies, chromogens
of different colors are used to distinguish the different markers.
As these markers may be cell compartment specific, this a priori
knowledge can be used to automatically segment the cells (i.e.
separates the nucleus masks from the cytoplasmic and or membrane
masks). Overall, "colorimetric" algorithms are aimed to provide
sample information to ease diagnosis and/or prognosis of the
particular case. For illustration, the detection and quantification
of the breast ER, PR and HER2 protein expression levels may be
provided using a quantitative microscopy algorithm applied to
immunohistochemistry (IHC) techniques.
[0024] In light of such image analysis techniques, however, there
exists a need for improvements that facilitate flexibility in such
analysis while providing a pathologist with accurate and useful
information for allowing the pathologist to form an appropriate
diagnosis and/or prognosis.
SUMMARY OF THE INVENTION
[0025] The above and other needs are met by the present
invention(s) which, in one embodiment, provides a method of
staining a sample for microscopy imaging whereby the image of the
stained sample is configured to exhibit an optimum contrast between
sub-cellular components for diagnosis by a pathologist. Such a
method comprises staining a sample with a dye; determining a
transmittance value of the dye from a microscopy image of the
sample; forming an artificial image of the sample from the
determined transmittance value of the dye; varying the
transmittance value of the dye so as to form a series of artificial
images; selecting one image, from the series of images, exhibiting
the optimum contrast between sub-cellular components for the dye
and determining the corresponding transmittance value of the dye in
the one image; and varying staining of the sample with the dye so
as to provide a stained sample having the transmittance value of
the dye corresponding to the optimum contrast between sub-cellular
components.
[0026] Another aspect of the present invention comprises a method
of artificially staining a sample. Such a method includes staining
a sample with a first dye; determining a transmittance value and an
extinction coefficient of the first dye from a microscopy image of
the sample; forming an artificial image of the sample from the
determined transmittance value of the first dye; and substituting
an extinction coefficient of a second dye for the extinction
coefficient of the first dye so as to artificially stain the sample
with the second dye.
[0027] Still another aspect of the present invention comprises a
method of obtaining measurements of a sample from an image thereof.
Such a method includes selecting a region of interest in the sample
from an RGB image thereof segmenting the region of interest in the
RGB image to identify any objects of interest therein; implementing
feature extraction to determine measurements for the identified
objects of interest; and determining cell scores with respect to at
least one of marker localization and signal to noise ratio.
[0028] A further aspect of the present invention comprises a method
of selecting a region of interest on a slide, wherein the region is
positively contrasted from a surrounding thereof in a marker-only
image corresponding to an RGB image of the sample, and the
positively contrasted region includes at least one of a relatively
larger nuclei and a relatively higher cell density than the
surrounding. Such a method includes applying a low pass filter to a
marker-only image of a sample, wherein the marker-only image is
obtained through chromogen separation of the RGB image of the
sample; determining a marker-only histogram of pixels in the
marker-only image; and binarizing the marker-only image according
to a threshold in the marker-only histogram so as to form a mask
for discriminating between negative and positive regions of the
sample.
[0029] Another aspect of the present invention comprises a method
of segmenting a sample from an image thereof. Such a method
includes determining a background component of an RGB image of the
sample via a thresholding process; segmenting the image by creating
a component image of at least one of a membrane, a cytoplasm, and a
nucleus; refining the segmented image; and filtering any unwanted
objects from the image.
[0030] Yet another aspect of the present invention comprises a
method of determining optical density data for at least one dye
staining a sample, for a high dye concentration, from an image
obtained with a low bit resolution imaging device. Such a method
includes capturing a series of images of the sample at different
integration times; selecting a highest non-saturated intensity in
each of a red, green, and blue channel of the imaging device; and
reconstructing an optimized image of the sample using the highest
non-saturated intensity levels in the red, green, and blue channels
such that the optimized image is suitable for chromogen
separation.
[0031] Another aspect of the present invention comprises a
chromogen separation method for an image of a biological sample
stained with four dyes obtained with a three channel imaging
device. Such a method includes defining a priori known significant
three dye combinations of the four dyes spatially collocated in the
biological sample; obtaining an image of a sample stained with four
dyes with an imaging device having a red, green, and blue channel,
such that the image thereby includes a plurality of pixels each
having a corresponding RGB triplet; projecting each RGB triplet
onto an extinction coefficient plane where Ecr+Ecg+Ecb=1;
determining the three dye combination of the four dyes in the
extinction coefficient plane corresponding to each RGB triplet; and
separating the image of the sample by tabulating an amount of
pixels in the image corresponding to each three dye combination in
the extinction coefficient plane.
[0032] Embodiments of the present invention thus meet the needs
identified herein and provide significant advantages as further
detailed herein.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0033] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0034] Having thus described the invention in general terms,
reference will now be made to the accompanying drawings, which are
not necessarily drawn to scale, and wherein:
[0035] FIG. 1 schematically illustrates a series of
electronically-stained images of a sample, wherein the
transmittance value of one of the dyes staining the sample is
varied so as to determine the optimal marker intensity, as shown in
the nucleus, allowing both a morphological read by the pathologist
and a positive decision of the cell based upon the marker
expression;
[0036] FIGS. 2A and 2B show some examples of automatically-selected
regions of interest in accordance with one aspect of the present
invention;
[0037] FIGS. 3A1-3A2 and 3B1-3B2 show examples of
automatically-selected regions of interest and subsequent
sub-cellular segmentation according to one aspect of the present
invention;
[0038] FIG. 4 schematically illustrates a method of cell scoring
according to one aspect of the present invention;
[0039] FIGS. 5A and 5B illustrate a method of analyzing samples
including high dye concentrations using a time integration approach
according to one aspect of the present invention;
[0040] FIGS. 6A-6D illustrate data regarding each of 4 dyes for
staining a sample for a 4 dye chromogen separation procedure
according to one aspect of the present invention;
[0041] FIGS. 7A, 7B1, and 7B2 schematically illustrate the 4 dyes
of FIGS. 6A-6D represented in the Maxwell equivalent extinction
coefficient plane, and the 2 accepted 3 dye combinations thereof,
respectively, in accordance with one aspect of the present
invention;
[0042] FIG. 8A illustrates a modified PAP field of view stained
with the 4 dyes of FIG. 6;
[0043] FIGS. 8B-8E illustrate the modified PAP field of view of
FIG. 8A for each of the 4 dyes separately from the other dyes using
extended chromogen separation; and
[0044] FIG. 9A illustrates a source (RGB) field of view of a
sample, while FIG. 9B illustrates a simulated e-stained sample
thereof for two of the four dye components, and FIG. 9C illustrates
a simulated PAP-only e-stained image of the sample reconstructed
with all dye components except DAB.
DETAILED DESCRIPTION OF THE INVENTION
[0045] The present inventions now will be described more fully
hereinafter with reference to the accompanying drawings, in which
some, but not all embodiments of the inventions are shown. Indeed,
these inventions may be embodied in many different forms and should
not be construed as limited to the embodiments set forth herein;
rather, these embodiments are provided so that this disclosure will
satisfy applicable legal requirements. Like numbers refer to like
elements throughout.
The Microscope Imaging Platform
[0046] In a typical microscopy device for image acquisition and
processing, the magnified image of the sample must first be
captured and digitized with a camera. Generally, charge coupled
device (CCD) digital cameras are used in either light or
fluorescence quantitative microscopy. Excluding spectrophotometers,
two different techniques are generally used to perform such
colorimetric microscopic studies. In one technique, a black and
white (BW) CCD camera may be used. In such an instance, a gray
level image of the sample is obtained, corresponding to a
monochromatic light having a wavelength specific to the staining of
the sample to be analyzed. The specific wavelength of light is
obtained either by filtering a white source light via a specific
narrow bandwidth filter, or by directly controlling the wavelength
of the light source, using either manual or electronic controls.
Accordingly, using this technique, the analysis time increases as
the number of colors increases because a light source or a filter
must be selected for every different sample staining or every
different wavelength. Therefore, many different images of the
sample, showing the spectral response of the sample at different
wavelengths, must be individually captured in a sequential order to
facilitate the analysis. When multiple scenes or fields of view
must be analyzed, the typical protocol is to automate the sequence
in a batch mode to conserve processing time.
[0047] According to a second technique, a color CCD digital camera
is used, wherein three gray level images of the sample are
simultaneously captured and obtained. Each gray level image
corresponds to a gray level image in each of the respective Red,
Green and Blue channel (RGB) of the color CCD camera. When a color
CCD digital camera is used, wherein three gray level images of the
sample are simultaneously captured and obtained (each gray level
image corresponds to a gray level image in each of the respective
Red, Green and Blue channel (RGB)), chromogen separation techniques
can be applied, which may allow the optical density of each
molecular species (revealed by their associated chromogen or dye)
to be evaluated in any location of the image (pixel). On the
biological sample, markers and counter stains generally indicate
the dyes to detect and quantify.
[0048] According to an arising third technique (e.g., using a
JUMBOSCAN multispectral camera by Lumiere Technology), up to 13
gray level images of the sample can be simultaneously captured and
obtained. This type of camera/scanner could increase the potential
of chromogen separation techniques in the future by increasing the
number of dyes that can be simultaneously solved for a given
sample.
[0049] Regardless, the concentration of the molecular specie can be
determined from a color image of the sample, where the color image
includes 3 or more channels. In a video-microscopy system equipped
with a 3CCD camera, the image should desirably be balanced and
normalized according to an empty field white reference and a black
field image, and then corrected for shading. Furthermore, the image
should desirably be spatially corrected for chromatic aberrations,
channel by channel. An optical density of the sample can then be
computed in each of the red, green, and blue channels of the RGB
image, at a particular pixel in the image, from the measured
transmitted light. A corresponding optical density vector is
thereafter formed for that pixel. The optical density vector is
then multiplied by the inverse of a relative absorption coefficient
matrix of the dyes present in the sample so as to form a resultant
vector for the pixel, representing the optical density
contributions from each dye. The relative absorption coefficient
matrix comprises a relative absorption coefficient for each of the
dye (marker(s) and counter stain(s)) used in the sample preparation
protocol, in each of the red, green, and blue channels. The
resultant vector thus comprises the concentration of the molecular
species, as indicated by the marker(s), and by the counter
stain(s), for that pixel.
[0050] Such imaging techniques, also known as multi-spectral
imaging techniques, when adapted to color imaging (RGB camera),
allow a real time (video rate) processing of the sample (typically
40 millisecond per frame), which provides a considerable advantage.
In effect, for speed issues and real time processing, or displaying
purposes in case of the use of an RGB camera, the acquisition
through the different channels is performed in parallel and look-up
tables (LUT) can be generated which map the RGB color input values
to pre-computed concentrations and/or transmittance of each of the
participating dyes.
[0051] Such techniques are discussed in more detail, for example,
in U.S. Patent Application Publication Nos. US 2003/0091221A1
(Method for quantitative video-microscopy and associated system and
computer software program product) and US 2003/0138140A1 (Method
for quantitative video-microscopy and associated system and
computer software program product), both to Marcelpoil et al. and
assigned to Tripath Imaging, Inc, also the assignee of the present
invention, the contents of which are incorporated herein in their
entirety by reference.
The Lambert-Beer Law
[0052] The microscopic imaging platform is configured to analyze
the sample in accordance with the Lambert-Beer law. The
Lambert-Beer law generally describes a proportionality that can be
observed between the concentration of molecules in a solution (the
concentration of the "molecular specie" or the "sample") and the
light intensity measured through the solution. The Lambert-Beer law
is typically expressed as:
OD=.epsilon.lC (1)
OD is the optical density of the solution, .epsilon. is the
proportionality constant called molar extinction or absorption
coefficient, l is the thickness of the sample, and C is the
concentration of the molecular specie. The absorption coefficients
is specific to the molecular specie and is typically expressed in
units of Lmol.sup.-1cm.sup.-1.
[0053] This proportionality relationship defined by the
Lambert-Beer law has been verified under the several conditions
including, for example, monochromatic light illuminating the
sample, low molecular concentration within the sample, generally no
fluorescence or light response heterogeneity (negligible
fluorescence and diffusion) of the sample, and lack of chemical
photosensitivity of the sample. The Lambert-Beer law may have
additional requirements, however, such as, for instance, correct
Koehler illumination of the sample under the microscope.
[0054] Koehler illumination is offered on almost all
state-of-the-art microscopes, and provides even illumination in the
image plane, while allowing for effective contrast control. Koehler
illumination is typically critical for densitometry analysis.
Correct Koehler illumination is provided, for example, by a
two-stage illuminating system for the microscope in which the
source is imaged in the aperture of the sub-stage condenser by an
auxiliary condenser. The sub-stage condenser, in turn, forms an
image of the auxiliary condenser on the object. An iris diaphragm
may also be placed at each condenser, wherein the first iris
controls the area of the object to be illuminated, and the second
iris varies the numerical aperture of the illuminating beam.
[0055] The Lambert-Beer law has an additive property such that, if
the sample comprises several light-absorbing molecular species, for
example, s.sub.1 and s.sub.2, having respective concentration
C.sub.1 and C.sub.2, the OD of a sample of thickness l (in
solution, l.sub.1=l.sub.2=l) can be expressed as:
OD=.epsilon.1l1C1+.epsilon.2l2C2 (2)
This situation may occur, for example, in a biological analysis
where a "scene" or field of view or portion of the sample has been
stained with two dyes consisting of a marker dye for targeting the
molecular specie of interest and a counter stain for staining the
remainder of the sample.
Correction of Chromatic Aberration
[0056] To accurately measure the concentration of given species
imaged under a microscope, the measurements of the optical
densities performed at different wavelengths should correspond to
the same portion of the sample. That is, the system can be
physically corrected for chromatic aberration or, otherwise, the
correction can be made through another methodology such as
software.
[0057] The natural dispersion power of glass causes a simple lens
to focus blue light at a shorter distance than red light. That is,
a simple lens has different focal lengths for light of different
wavelength (different colors). Two phenomena occur as a direct
consequence:
1) The difference in position along the vertical axis of the focal
points for light of different wavelength is called longitudinal
chromatic aberration. That is, when focusing the image for a given
color (green, for example), the images corresponding to the other
colors tend to be slightly out of focus (blue and red, in this
example, will appear out of focus). 2) The difference in
magnification (focal length) for light of different wavelengths is
called lateral chromatic aberration. That is, the image of a blue
(short) wavelength will appear larger than the image of a red
(large) wavelength.
[0058] In systems with high quality objectives (apochromatic
objectives), chromatic aberration is corrected. If chromatic
aberration is otherwise structurally not well corrected, a
software-based method for correcting lateral chromatic aberration
can be implemented as follows:
1) Determine the coordinate of the objective center as compared to
the camera chip center; 2) Evaluate the observed magnification
factor for each wavelength as compared to an arbitrary chosen
wavelength (usually the central wavelength, i.e., green if using an
RGB camera); and 3) Resample each image according to its relative
magnification and the coordinate of the objective center.
Performing Chromogen Separation
[0059] Once the microscope has been set in Koehler illumination
mode for image acquisition, and any chromatic aberrations have been
addressed or apochromatic objectives used, the additive property of
the Lambert-Beer law can be used to perform chromogen separation
using linear algebraic equations.
[0060] More particularly, the additive property of the Lambert-Beer
law can also be expanded to a situation in which the scene is
analyzed in a color image environment, such as, for example,
generated by a RGB camera having separate red, green, and blue
channels. In such an example, the marker dye (or "dye 1") would
exhibit absorption coefficients, .epsilon..sub.1r,
.epsilon..sub.1g, and .epsilon..sub.1b, in the red, green and blue
channels, respectively. Note that the analysis of the image in each
of the red, green, and blue channels essentially comprises
analyzing a red representation of the image across the red
spectrum, a green representation of the image across the green
spectrum, and a blue representation of the image across the blue
spectrum. Accordingly, the counter stain (or "dye 2") would exhibit
absorption coefficients, .epsilon..sub.2r, .epsilon..sub.2g, and
.epsilon..sub.2b, in the red, green and blue channels,
respectively. Therefore, according to the additive property of the
Lambert-Beer law, analysis of the sample in the RGB environment
would lead to the system of three equations for the optical density
thereof:
OD.sub.r=.epsilon..sub.1rl.sub.1C.sub.1+.epsilon..sub.2rl.sub.2C.sub.2
(3)
OD.sub.g=.epsilon..sub.1gl1C.sub.1+.epsilon..sub.2gl.sub.2C.sub.2
(4)
OD.sub.b=.epsilon..sub.1bl.sub.1C.sub.1+.epsilon..sub.2bl.sub.2C.sub.2
(5)
where OD.sub.r, Od.sub.g, and OD.sub.b represent the optical
densities of the sample measured in the red, green and blue
channels, respectively. Still further, in the case of increased
sample preparation complexity such as, for example, the treatment
of the sample with three different dyes, equations (3), (4), and
(5) become:
OD.sub.r=.epsilon..sub.1rl.sub.1C.sub.1+.epsilon..sub.2rl.sub.2C.sub.2+.-
epsilon..sub.3rl.sub.3C.sub.3 (6)
OD.sub.g=.epsilon..sub.1gl.sub.1C.sub.1+.epsilon..sub.2gl.sub.2C.sub.2+.-
epsilon..sub.3gl.sub.3C.sub.3 (7)
OD.sub.b=.epsilon..sub.1bl.sub.1C.sub.1+.epsilon..sub.2bl.sub.2C.sub.2+.-
epsilon..sub.3bl.sub.3C.sub.3 (8)
[0061] In such a situation, the three dyes may comprise, for
instance, one marker dye and two counter stains, or two marker dyes
and one counter stain, or even three separate marker dyes. This
property of the Lambert-Beer law might be expanded to include an
even greater plurality of dye combinations. However the chromogen
separation procedure described herein focuses on making use of a
fast color-image capture device with 3 channels, such as for
example a 3CCD RGB camera, for multi-spectral imaging of biological
markers. Therefore, due to the 3 distinct information channels (R,
G, B) only three equations can be used in any location.
[0062] In applying the Lambert-Beer law to a digital microscopy
system, it is difficult and complex, inaccurate, or sometimes not
possible to measure the thickness l of the sample. Consequently,
the concentration C of the molecular specie can be extended and
examined as the product of l and C (lC), and the results treated
accordingly. For example, where the concentration of one dye is
being compared to the concentration of another dye in a particular
sample, the sample thickness term will be common to both
concentrations and thus it becomes less important to determine the
sample thickness as an absolute and accurate value. Therefore, it
will be understood that an accurate determination of the thickness
is usually not required, but assumed constant and therefore
generally negligible in the analysis disclosed herein.
[0063] The application of the Lambert-Beer law to the digital
microscopy system also recognizes that the Lambert-Beer law can be
expressed as:
OD.sub.(x,y)=log I.sub.0(x,y)=log I.sub.(x,y) (9)
for a digital image of the sample, where (x,y) signifies a
particular pixel in the image, OD.sub.(x,y) is the optical density
of the sample at that pixel, I.sub.(x,y) is the measured light
intensity or transmittance of the sample at that pixel, and
I.sub.0(x,y) is the light intensity of the light source as measured
without the light-absorbing sample. Accordingly:
IOD = N ( log I 0 ( x , y ) - log I ( x , y ) ) ( 10 )
##EQU00001##
where IOD is the integrated optical density of the digital image of
the sample, and N is the number of pixels in the surface image of
the sample. A proportionality constant may be appropriately
considered where relative comparisons are drawn in light
intensities. Further, in quantitative microscopy according to the
Lambert-Beer law, the proportionality relationship between the
optical density OD of the sample and the dye concentrations is
conserved.
[0064] Therefore, for a prepared sample examined by the digital
microscopy system, the appropriate relation is expressed as:
ln I.sub.0=ln I=ln I.sub.0/I=OD=.epsilon.lC (11)
Where, for example, an 8 bit RGB camera is used in the system, the
light intensity transmitted through the sample will be expressed as
2.sup.8 (=256) values between 0 and 255. For example, the initial
intensity I.sub.o of the light source, which corresponds to 100%
transmittance, will be expressed as values close to 255
(representing the brightest possible value) in each of the red,
green, and blue channels. Indeed, the operator adjusts the camera
frame grabber/light source so that a pure "white" light in absence
of the sample, corresponding to 100% transmittance, would have an
intensity value close to 255 in each of the red, green, and blue
channels, whereas in the absence of light, corresponding to 0%
transmittance, the "black image" will have an intensity value close
to 0 in each of the red, green, and blue channels. At any pixel,
100% transmittance, I.sub.o, is therefore expressed as the
difference between the value measured by the camera in presence of
the light source, minus the value measured by the camera in absence
of the light source, for each of the red, green, and blue channels.
Because the intensity of the light source may vary spatially over
the measured field of view, and because the optics may
heterogeneously absorb light, 100% transmittance may correspond to
different dynamic ranges over the measured field of view. The OD of
the sample is expressed (11) as the logarithm of the ratio of the
transmittance in absence of the sample (I.sub.o), and transmittance
in presence of the sample (I), and is therefore largely spatially
independent of the small variations in the real dynamic range
measured at 100% transmittance.
[0065] Since the light source intensity remains substantially
constant over time, or can be easily re-evaluated, the reading of
the light intensity in any pixel can therefore be translated into a
measure of the relative transmittance at the pixel location for
each of the red, green, and blue channels. Once I.sub.o and I are
known, the corresponding OD can be computed.
[0066] Any location on the field of view where a unique dye is
present (the only absorbing material) allows the relative
extinction coefficients of the dye to be measured for the different
RGB channels. Because in equation (1), lC is equal for each of the
RGB channels at a given location, if both l and C are known at this
particular location the exact extinction coefficient can be
computed as being OD/(lC). The absorption coefficient .epsilon. in
each of the red, green, and blue channels can thus be consequently
extracted as being:
.epsilon..sub.r=OD.sub.r/(lC)=(ln(I.sub.or/I.sub.r))/(lC) (12)
.epsilon..sub.g=OD.sub.g/(lC)=(ln(I.sub.og/I.sub.g))/(lC) (13)
.epsilon..sub.b=OD.sub.b/(lC)=(ln(I.sub.ob/I.sub.b))/(lC) (14)
Unfortunately, (lC) is usually unknown and therefore, the
extinction coefficients .epsilon. are computed arbitrarily, as
being the ratio of the OD measured at the given pixel in the
considered channel and the maximum OD measured at this location for
any of the RGB channels (the determination of the absorption
coefficient .epsilon. in each of the red, green, and blue channels
in absence of a priori knowledge concerning (lC) is a matter of
linear equation manipulation in order to achieve a relative
solution where l and C are arbitrarily set to 1), wherein:
.epsilon..sub.r=OD.sub.r/1=OD.sub.r=ln(I.sub.or/I.sub.r) (13)
.epsilon..sub.g=OD.sub.g/1=OD.sub.g=ln(I.sub.og/I.sub.g) (14)
.epsilon..sub.b=OD.sub.b/1=OD.sub.b=ln(I.sub.ob/I.sub.b) (15)
[0067] Consequently if the absolute concentration of the dye
remains unknown, it is still possible to compute arbitrary (or
relative) dye concentrations in any pixel, with a known absolute
error factor equal to (lC).
[0068] Because l is unique at a given pixel location and can
arbitrarily be set to 1, equations 6, 7, and 8 may be rewritten as
follow where C.sub.1, C.sub.2 and C.sub.3 are related to 1.
OD.sub.r-.epsilon..sub.1rC.sub.1+.epsilon..sub.2rC.sub.2+.epsilon..sub.3-
rC.sub.3 (16)
OD.sub.g-.epsilon..sub.1gC.sub.1+.epsilon..sub.2gC.sub.2+.epsilon..sub.3-
gC.sub.3 (17)
OD.sub.b-.epsilon..sub.1bC.sub.1+.epsilon..sub.2bC.sub.2+.epsilon..sub.3-
bC.sub.3 (18)
[0069] When all the extinction coefficients have been evaluated for
different dyes, and optical densities are known from the reading of
the image data, solving these equations to extract C.sub.1, C.sub.2
and C.sub.3 just involves solving a set of linear equations.
Solution of Linear Algebraic Equations/Matrices
[0070] A set of linear algebraic equations appear, for example,
as:
a 11 x 1 + a 12 x 2 + a 13 x 3 + + a 1 N x N = b 1 a 21 x 1 + a 22
x 2 + a 23 x 3 + + a 2 N x N = b 2 a 31 x 1 + a 32 x 2 + a 33 x 3 +
+ a 3 N x N = b 3 a M 1 x 1 + a M 2 x 2 + a M 3 x 3 + + a M N x N =
b M ( 19 ) ##EQU00002##
Here the N unknowns x.sub.j,j=1, 2, . . . , N are related by M
equations. The coefficients a.sub.ij with i=1, 2, . . . , M and
j=1, 2, . . . , N are known numbers, as are the right-hand side
quantities b.sub.i, i=1, 2, . . . , M.
[0071] If M<N, there is effectively fewer equations than
unknowns. In this case there can be either no solution, or else
more than one solution vector x.
[0072] If N=M then there are as many equations as unknowns, and
there is a good chance of solving for a unique solution set of
x.sub.j's.
[0073] If M>N that there are more equations than unknowns, and
there is, in general, no solution vector x to equation (1), the set
of equations is said to be over determined. In such a case, the
most appropriate solution will be considered in general as the one
fitting the best all the equations (i.e., the solution minimizing
the sum of reconstruction errors).
[0074] Equation (19) can thus be written in matrix form as
Ax=b (20)
Here () denotes matrix multiplication, A is the matrix of
coefficients, and b is the right-hand side written as a column
vector. By convention, the first index on an element a.sub.ij
denotes its row; the second index its column. a.sub.i or a[i]
denotes a whole row a[i][j], j=1, . . . , N.
[0075] The solution of the matrix equation Ax=b for an unknown
vector x, where A is a square matrix of coefficients, and b is a
known right-hand side vector, usually requires the determination of
A.sup.-1 which is the matrix inverse of the matrix A.
x=A.sup.-1b (21)
A.sup.-1 which is the matrix inverse of matrix A, i.e.,
AA.sup.-1=A.sup.-1A=1, where 1 is the identity matrix. In one
particular case, experimental conditions are set up so that there
are more (or equal number) equations than unknowns, M.gtoreq.N.
When M>N occurs, there is, in general, no solution vector x to
equation (19), and the set of equations is said to be over
determined. Frequently, however, the best "compromise" solution is
one that comes closest to satisfying all equations simultaneously.
If closeness is defined in the least-squares sense (i.e., that the
sum of the squares of the differences between the left- and
right-hand sides of equation (19) are minimized), then the over
determined linear problem reduces to a (usually) solvable linear
problem, also referred to as the linear least-squares problem, that
can be solved using singular value decomposition (SVD). SVD
involves the parametric modeling of data, and is one method for
solving most linear least-squares problems. (NUMERICAL RECIPES IN
C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright
(C) 1988-1992 by Cambridge University Press. Programs Copyright (C)
1988-1992 by Numerical Recipes Software.).
[0076] In applying these concepts to the present case, the
determination of the absorption coefficient .epsilon. matrix for
different dyes may be performed independently of sample evaluation
and stored for further application to samples treated with at least
one of the respective dyes. Computing solutions for all possible
pixel values allows substantially real time processing. Since, in
the chosen example of an 8 bit 3CCD color image acquisition device,
the measured light intensity I of a sample ranges between limits of
0 and 255 in each of the red, green, and blue channels, all
possible gray values (with respect to the original light intensity
I.sub.o) may be pre-computed (256.sup.3 in case of an 8 bit RGB
system) and stored, for example, within the computer. Thus, for a
sample stained with a particular dye, the transmitted light
intensity I (or the optical density OD) can be measured at a pixel
in each of the red, green, and blue channels and then compared to
the previously stored gray values and the absorption coefficient
.epsilon. matrix for that particular dye to thereby determine the
dye concentration C (or an estimate thereof as the product lC) at
that pixel. In this regard, there are
[256(red).times.256(green).times.256(blue)]=256.sup.3 solutions to
compute, giving rise to a 16 megabyte (raw data) look-up table
(LUT) for each of the dyes. Gray value resolutions exceeding 8 bits
per channel will lead to larger LUTs (i.e., >1 gigabyte if 10
bits per channel).
Electronic Staining
[0077] According to one aspect of the present invention, gray
levels or RGB transmittance values of an artificial image resulting
from any combination of the previously-examined dyes can be
generated since there are not anymore unknown variables. As such,
for a particular pixel and its solved dye concentrations, the
single dye images would correspond to the following Black and White
(BW) or RGB pixel intensities:
OD.sub.BW=C and I.sub.BW=Exp(ln(I.sub.o)-OD.sub.BW) (22)
OD.sub.r=.epsilon..sub.rC and I.sub.r=Exp(ln(I.sub.o)-OD.sub.r)
(23)
OD.sub.g=.epsilon..sub.gC and I.sub.g=Exp(ln(I.sub.o)-OD.sub.g)
(24)
OD.sub.b=.epsilon..sub.bC and I.sub.b=Exp(ln(I.sub.o)-OD.sub.b)
(25)
[0078] When this process is applied to each pixel of a captured
digital image, an artificial picture of the same field of view can
be generated using only the respective contribution of any of the
constituent dyes. As such, if the extinction coefficients of one
dye are exchanged with the extinction coefficients of another dye,
it is then possible to simulate how the same artificial image
corresponding to a given marker only would be seen through a
microscope, if the dye used to reveal this marker is changed to a
secondary dye.
[0079] Furthermore, using the additive property of the Lambert-Beer
law, it is also possible, as shown in FIG. 1, to generate an
artificial image where the relative contributions of each dye are
changed, for example, using absolute weighting coefficients or
relative weighting coefficients (see equation 26-28 for a 2 dye
electronically stained ("e-stained") image where the RGB image is
reconstructed after changing the Dye 1 and Dye 2 proportions by
weighting factors w.sub.1 and w.sub.2.
OD.sub.r=w.sub.1.epsilon..sub.1rC.sub.1+w.sub.2.epsilon..sub.2rC.sub.2
and I.sub.r=Exp(ln(I.sub.o)-OD.sub.r) (26)
OD.sub.g=w.sub.1.epsilon..sub.1gC.sub.1+w.sub.2.epsilon..sub.2gC.sub.2
and I.sub.g=Exp(ln(I.sub.o)-OD.sub.g) (27)
OD.sub.b=w.sub.1.epsilon..sub.1bC.sub.1+w.sub.2.epsilon..sub.2bC.sub.2
and I.sub.b=Exp(ln(I.sub.o)-OD.sub.b) (28)
[0080] More particularly, FIG. 1 illustrates an estrogen receptor
(ER) example in which a series of images of the same cell (original
image at a determined transmittance of about 32% is shown
surrounded in red) in which the amount of a marker (Brown DAB) is
changed electronically (artificially), after chromogen separation,
from about a 22% transmittance to about a 40% transmittance,
without changing the hematoxylin content. In this manner, an
optimum contrast between sub-cellular components can be determined
from the artificial images, as well as the amount of the dye
necessary to provide the transmittance value corresponding to the
optimum contrast between marker-specific targeted and non-targeted
sub-cellular components.
Measurement Strategy
[0081] According to another aspect of the present invention, a
measurement strategy can be based upon and can make use of the
chromogen separation technique(s) described above in many aspects,
from allowing only the marker of interest to be specifically
measured, to the e-staining capabilities which allow
segmentation-optimized contrasted images to be generated.
[0082] Obtaining measurement results from the acquired image
includes several steps: 1) selecting the region of interest (tumor
region); 2) segmentation to identify the objects of interest in the
image; and 3) feature extraction to calculate the various
measurement features for the identified objects and affect cell
scores based upon, for example, their marker localization and
signal to noise ratio.
1) Region of Interest Pre-Selection
[0083] In order to reduce the workload for the pathologist, a
pre-selection methodology was developed for automatically
delineating the potential region of interest within the field of
view that will be the region used for analysis, wherein any
excluded part is thus excluded from the analysis. Such a
pre-selection methodology generally requires two a priori
factors:
[0084] The region of interest is positively contrasted from the
surrounding when looking at the marker-only image.
[0085] Cancer targets epithelial cells which differ from the stroma
cells by, for example, a larger nucleus and higher cell
density.
[0086] Consequently, a large low pass filter may be applied to the
marker-only image resulting from the chromogen separation
technique(s) applied to the RGB field of view. The marker-only
histogram is measured (avoiding background regions based upon the
luminance image), and then the image is binarized according to the
best threshold in the histogram that could discriminate two classes
(negative and positive regions). Any small holes are filled to
smooth the final mask. The mask is outlined on the top of the
original RGB field of view image to allow acceptance/rejection by
the pathologist, as shown in FIGS. 2A and 2B. More particularly,
FIG. 2A illustrates a PSMB9 example and FIG. 2B illustrates a HER2
example of automatic definition of the region of interest according
to one embodiment of the pre-selection methodology disclosed
herein. The region of interest is automatically computed or
otherwise determined, and can be presented to the pathologist for
final refinement and/or approval. If the pathologist rejects the
proposed mask, drawing tools allow the pathologist to manually
select the appropriate region of interest.
2) Segmentation Strategy
[0087] The segmentation strategy includes the following steps:
[0088] Background determination
[0089] Cell component image creation
[0090] Membrane segmentation*
[0091] Nucleus segmentation
[0092] Cytoplasm segmentation
[0093] Segmentation refinement
[0094] Filtering of unwanted objects
*In the case of membrane markers, such as Her2, an additional
specific step of membrane segmentation is performed.
[0095] Various examples of such segmentation are shown, for
example, in FIGS. 3A1-3A2 and 3B1-3B2, respectively. More
particularly, FIG. 3A1 shows a PSMB9 (cytoplasmic marker) example
of automatic definition of the region of interest followed by
sub-cellular segmentation in FIG. 3A2. Within the region of
interest, automatically defined cells have been segmented, such
that the nucleus masks appear in blue and the cytoplasm boundaries
appear in red, while background pixels are shown in black. FIG. 3B1
illustrates a HER2 (membrane marker) example of automatic
definition of the region of interest followed by sub-cellular
segmentation in FIG. 3B2. Within the region automatically defined,
cells have been segmented, such that nucleus masks appear in blue
and the membrane appears in green, while background pixels are
shown in black. One skilled in the art will appreciate, however,
that additional image processing steps or refinements may, in some
instances, be needed to adapt such generic algorithms to tissue or
marker specificities.
2a) Background Determination
[0096] The first segmentation step is to divide the image content
into fore- and background. Since the imaging platform is designed
to support bright field microscopy, objects will appear darker than
the bright background. To create a background mask for an image,
the image is converted into a luminance image and a background
threshold level is calculated. Every pixel having a luminance value
above the background threshold level is considered to belong to the
background. Conversely, any pixel with luminance less than the
threshold belongs to the foreground which has to be processed
further in the following steps.
[0097] Determining this background threshold value involves
smoothing the luminance image and calculating the histogram of the
smoothed image. The histogram is then scanned, beginning at the
higher end, for a local minima to be used for the threshold value.
The search is limited when an arbitrary 90% transmission is
reached, which translates, for the case of 8-bit images, into the
value of 230.
2b) Cell Component Image Creation
[0098] In the next segmentation step, cell component images for the
nucleus and cytoplasm are created using chromogen separation
techniques previously described. The separation is initiated
according to the specification of the optical density contribution
of each dye to the specific cell component. Those component images
are then used as input for subsequent nucleus and cytoplasm
segmentation steps. The component images are based upon e-staining
capabilities and generate images which best contrast the targeted
cell compartment from neighboring regions.
2c) Membrane Segmentation
[0099] Membrane segmentation is performed using the following
steps:
[0100] Find the average value over the entire image that is not
background.
[0101] Fill any location in the image with this mean value, if the
local value is brighter.
[0102] Find the membrane by generating the image difference between
large and small smoothing convolution kernels.
[0103] Binarize the resulting contrast image based upon the
measured local contrast.
[0104] Extract the skeleton of the candidate membrane masks.
[0105] Delete any skeleton piece smaller than a requested minimal
length.
[0106] Expand the skeleton of the membrane masks by one pixel in
any direction and keep only membrane masks that fall underneath the
skeleton.
[0107] Membrane segmentation is performed first to facilitate
further nucleus segmentation, since membranes are generally
expected to separate nuclei from one another.
2d) Nucleus Segmentation
[0108] In the beginning of the nucleus segmentation process, both
the mean and median pixel values of the nucleus component image are
calculated under consideration of the background mask. The greater
of those values is used to create an initial nucleus mask through
thresholding the nucleus component image with this value. Any pixel
having a value higher than this threshold is set to the threshold
value so that only pixels having a lower value remain with their
original value in this initial nucleus mask. If membrane masks are
available, any potential nucleus mask pixel falling within a
membrane mask is deleted.
[0109] This preliminary or initial nucleus mask is then low-passed
with a kernel of 1.5 times the expected nucleus size to prepare the
initial nucleus mask for a watershed transformation or segmentation
procedure. The output of the watershed segmentation procedure is
combined with the initial nucleus mask so that only mask pixels are
set where the watershed image has catchment basins and the initial
nucleus mask has a pixel value below the threshold value. The
resulting nucleus mask is then finalized by a clean-up step
including filling holes having an area less than about one-fifth of
the expected nucleus size, and removing objects that are smaller
than about one-fourth of the expected nucleus size.
2e) Cytoplasm Segmentation
[0110] The cytoplasm segmentation process uses a two-way approach
to create the cytoplasm mask. Both ways use the nucleus mask
created in the previous step as the starting point. First, the
nucleus mask is inverted and distance-transformed. The first
potential cytoplasm mask is created by binarizing the output of the
distance transform such that all pixels within the expected cell
size are included in the resulting mask. In order to mask only the
foreground, the resulting first potential cytoplasm mask is then
combined with the background mask. For the second potential
cytoplasm mask, the nucleus mask is again inverted and then
watershed-transformed. Both the first and second potential
cytoplasm masks are then combined to create the final cytoplasm
mask.
2f) Segmentation Refinement
[0111] Once both the nucleus and cytoplasm segmentation masks have
been established, those masks are further refined using the
knowledge of the combined masks. Starting with the cytoplasm mask,
each segmented object in the cytoplasm mask is identified and is
associated with a labeled image, wherein each object is identified
by a unique pixel value. Due to the watershed transformation in the
cytoplasm segmentation, the labeled objects are separated from each
other. As such, the labeled image is dilated once in order to
reconnect the labeled objects.
[0112] The labeled image is then used to refine the nucleus mask.
That is, each labeled object is binarized using an individual
threshold. For each labeled object, the process is as follows:
[0113] Calculate the histogram for each pixel belonging to the
labeled object and determine the mean pixel value.
[0114] Determine an upper and lower bound for the threshold search.
The upper bound is determined by integrating the histogram starting
from the upper limit until 20% of the object area is accumulated.
The lower bound is determined in a similar way by integrating the
histogram from the lower limit until also 20% of the expected
nucleus size is accumulated.
[0115] If the lower bound is less than the upper bound, the
threshold is calculated by applying Fisher discriminate analysis to
the range of values in the histogram between the boundaries;
otherwise, the threshold is the mean value of the upper and lower
bounds.
[0116] Redraw the object into the nucleus mask by binarizing the
nucleus component image using the just-determined threshold
value.
[0117] Next, holes in the nucleus mask having an area smaller than
about one-fifth of the expected nucleus size are filled. To prevent
under-segmentation, the mask is first distance transformed and then
watershed transformed to split up potentially merged nuclei.
[0118] Finally, the nucleus mask is cleared of artifacts by
removing all objects smaller than about one-third of the expected
nucleus size. Once the refined nucleus mask is determined, the
cytoplasm segmentation procedure is repeated and results in a
refined cytoplasm mask.
[0119] For Her2neu segmentation, an additional step of membrane
removal is performed, which deletes any membrane mask located
within about 3 pixels of a nucleus mask, so as to facilitate
discrimination of a cell membrane from a nucleus membrane.
2g) Filtering of Unwanted Cells
[0120] The last processing step in the segmentation procedure
involves filtering of unwanted cells. For this procedure, each
object in the refined cytoplasm mask is labeled.
[0121] Also, the acquired FOV image is chromogen separated into the
dye images for the marker and the counter stain. For each
identified object, a bounding rectangle is determined and, if the
object is positioned closer than a certain distance to any image
border, the object is no longer taken into account and discarded so
as to prevent processing of cells extending beyond the image
border. If the cell passes this criterion, its key measurement
features, such as densitometry, texture, shape, contextual
information, are calculated. Further examples (non-inclusive)
include:
[0122] Area
[0123] Perimeter
[0124] Center of Gravity (CoG)
[0125] Minimum OD
[0126] Mean OD
[0127] Maximum OD
[0128] Each feature is computed for the nucleus, the cytoplasm,
and/or the entire cell, as well as for each of the luminance,
marker dye(s) and counter stain dye(s).
[0129] Using the mean transmittance determined from the Mean OD,
another pass/fail criterion is applied to the cell. That is, if the
cell's mean transmittance is higher than a threshold value
specified in the segmentation setup, the cell is not considered any
further and discarded.
3a) Cell Scoring
[0130] Based upon the features evaluated for each cell, a score can
be attributed to that cell depending on the marker intensity and
signal to noise ratio thereof in the targeted compartment. A cell
is considered positive when the marker content of that cell in the
marker-specific targeted-compartment optical density (intensity) is
significantly higher than in neighboring compartments. For
instance, if the marker is a nucleus marker, the contrast, or
signal to noise ratio, is computed from the marker-specific optical
density measure in the nucleus versus the residual optical density
measured over the cytoplasm. Because the background noise is not
specific by definition, the overall background mean optical density
is measured over all of the cytoplasm compartment of the cells
within the selected region of interest.
Nucleus Marker: Cell SNR=NucleusMOD/CytoplasmMOD (28)
[0131] To facilitate optimum correlation with the pathologist's
know-how, the contrast required to designate a cell as being
positive can be adapted from strong to weak, since some
pathologists consider only very intense nuclei as being positive,
while other pathologists consider any faint positive staining as
being positive. Such a subjective positive determination based on
contrast level may also be affected by the particular pathology
being considered.
A cell is positive for a nucleus marker if
NucleusMOD>CytoplasmMOD+max[.epsilon.,k(1-CytoplasmMOD)]
(29)
[0132] For ER (estrogen receptors) it was found that .epsilon.=0.02
and k=0.11
[0133] For PR (progesterone receptors) it was found that
.epsilon.=0.02 and k=0.20
[0134] Accordingly, as shown in FIG. 4, any cell below the curve is
negative, and positive otherwise. That is, FIG. 4 illustrates SNR
and Nucleus OD curves defining, for ER and PR, the negative and
positive status of a cell. For such nucleus markers, the Signal to
Noise Ratio (SNR) is evaluated as a ratio of the Nucleus OD to the
Cytoplasmic marker OD. If a cell falls above the curve (upper right
corner) the cell is considered positive, and negative otherwise.
Generally, the stronger the nucleus intensity, the less the SNR
must be in order to call the cell positive (and vice-versa).
3b) Overall Score
[0135] An overall score can be attributed to a case that reflects,
for that case, the information requested by the pathologist to
establish his diagnosis/prognosis.
Overall score=100*# positive cells/# cells in ROI (30)
[0136] In case of the ER and/or PR tests, the overall score
requested by the pathologist is the percentage of positive cells
within the tumor region. Therefore, once the pathologist is
confident in his diagnosis/prognosis of the proposed region of
interest (automatically proposed or manually drawn), the percentage
of positively-scored cells is reported.
Integration Concept
[0137] To further investigate the OD contribution of the different
dyes when concentrations are very high and bit-wise limitations of
the camera are reached, a strategy based upon time integration
(shutter speed) of the camera can be implemented. That is, the same
field of view is imaged with the same camera, but with different
integration times. As shown in FIGS. 5A and 5B, the measured OD is
normalized with the integration time and measured non-saturated
values corresponding to the maximum integration time in each
channel are retained. More particularly, FIG. 5A shows a particular
cell with high marker intensity that is image-captured using
different integration times (4000 s.sup.-1 to 250 s.sup.-1) to
improve bit resolution in the darkest regions. According to such a
methodology, pixelation of the chromogen-separated image in the
nucleus (hematoxylin only) substantially disappears when the
appropriate bit resolution is used. FIG. 5B shows RGB transmitted
light intensities, as well as time-normalized OD values for one
representative pixel captured using different integration times
(4000 s.sup.-1 to 250 s.sup.-1) to improve bit resolution in the
darkest regions of the image shown in FIG. 5A. The bit resolution
improvement is derived from RGB transmitted light intensity values
that are selected in each of the RGB channels for the integration
time prior to saturation.
Breaking the 3D Limit Using RGB Input: 4D Chromogen Separation
[0138] One example of such a procedure for 4D chromogen separation
is provided by a combination of 4 dyes for a modified PAP
procedure, as shown in FIGS. 6A-6B, namely Hematoxylin (FIG. 6A),
Eosin (FIG. 6B), Green (FIG. 6C), and DAB (FIG. 6D). In this
instance, 3 channels (R, G, and B) comprise the input channels,
with 4 unknowns (dyes). In such an instance, a priori knowledge can
be used. The dyes are represented in a Maxwell equivalent plane
which includes the extinction coefficient plane where
EcR+EcG+EcB=1. In this plane, a dye is represented by a unique XY
location. In each XY location of the plane, different RGB triplets
showing different transmittances (different intensities of a given
dye) can be presented, wherein, in the present example, an RGB
triplet having the closest to 50% transmittance is shown in FIG.
7A. More particularly, FIG. 7A shows different RGB triplets, such
as the RGB triplet closest to 50% transmittance. Each dye is
projected on the Ec plane based upon its extinction coefficients in
the red, green and blue channels of the image capturing device
(camera), with each dye being represented by its initial
letter.
[0139] With respect to the nature of the respective dyes, there are
two accepted 3 dye configurations among the 4 possible
configurations of the 3 dyes, as shown in FIGS. 7B1 and 7B2,
respectively, wherein these two 3 dye configurations are each
highlighted by a surrounding triangle. From a priori knowledge, it
is known to be unlikely that all 4 dyes will be significantly
present at the same geographical location with respect the sample.
Therefore, chromogen separation in this instance considers only 3
dyes configurations where the 3 dyes could be co-located with
respect to the sample. More particularly, Eosin and Green are
mainly cytoplasmic dyes which stain cells with different
cytoplasmic attributes. Consequently, these dyes are not likely to
be present at the same location with respect to the sample even
though, due to the location of the Hematoxylin between the Eosin
and Green dyes in this extinction coefficient plane, a mixture of
Eosin and Green could be mistaken with Hematoxylin (but is very
unlikely to be mistaken for DAB).
[0140] Thus, in order to solve the 4D problem, the chromogen
separation procedure is applied by looking for each RGB triplet of
this FOV where, at the XY location thereof, the corresponding stain
would be located, the XY location being the location in the
extinction coefficient plane where EcR+EcG+EcB=1. In this plane,
the surrounding 3D configuration, or by default the closest 3D
configuration, is determined and used to solve the equations for
optical density for the 3 corresponding dyes, while the remaining
dye's optical density is set to 0. One skilled in the art will note
that most of the XY locations of the investigated RGB triplets
should lay within one of the 2 accepted 3 dye configurations. FIG.
8A illustrates a field of view having all 4 dyes represented (i.e.,
a typical modified PAP field of view where all 4 dyes are
represented, wherein the dark central cell is DAB positive, as
shown in FIG. 8C). FIGS. 8B-8E illustrate the same field for each
of the 4 dyes.
Ultra-Fast Adaptation of the Scanner to Search for Positive (DAB)
Cells in a Modified PAP Environment
[0141] The discussed aspects of 4D chromogen separation and
e-staining may, in some instances, combine to form another aspect
of the present invention. More particularly, in continuation of the
above example directed to the use of DAB and Hematoxylin, a scanner
can be implemented that is capable of reading modified PAP slides
(a DAB positive rare event solution), as shown in FIG. 9A (an RGB
image of an original field of view). Then, based on the 4D
chromogen separation and e-staining procedures, the 4 dye situation
can be solved. Once solved, a simulated image can be reconstructed
using an "e-staining" process to includes only the DAB and
Hematoxylin contributions, as shown in FIG. 9B. In addition, the
Hematoxylin and DAB only channels could be used as an input to the
scanner, such that the scanner would be configured to capture a
"Hematoxylin and DAB only" image, which would produce an image
substantially the same as shown in FIG. 9B. Further, a simulated
PAP-only image could be reconstructed using only the Hematoxylin,
Eosin and Green contributions, as shown in FIG. 9C.
Taking RGB Distortion into Consideration
[0142] To accommodate and/or compensate for RGB distortion due to
the image path, electronics, and/or staining variations, a
modification of the chromogen separation can be considered. That
is, imaging biological material stained with only one dye
demonstrates that the extinction coefficient model, which can be
calculated from each RGB triplet within the source FOV, varies
slightly around the averaged accepted measure. Consequently when a
dye mixture is present, multiple solutions of dye mixtures could be
de facto accepted or acceptable. Different sources of noise could
be responsible for such RGB distortion. For example, acquiring the
image with a CMOS camera instead of a 3CCD camera could be one
factor.
[0143] To compensate for these distortions, the dye-respective
contribution solution for a given RGB triplet and a given multiple
dye model is computed in a slightly different manner. More
particularly, the RGB triplet under investigation is considered as
the center of a ball in the RGB space having a given radius r. All
triplets within this ball are investigated for their dye
contribution solutions, and the solutions are averaged for each dye
for all of the RGB triplets that satisfy the dye combination model.
If no RGB triplet belongs to the dye combination model, the nearest
RGB triplet within the ball to the dye combination model is
retained as best potential candidate solution.
Dynamic Procedures
[0144] Traditionally, all algorithms or computational procedures
used in quantitative microscopy applications are implemented or
built into the system by software engineers. As such, each software
release generally includes a limited set of algorithms, which
cannot be changed without modification of the software ("software
upgrades").
[0145] For example, an application may calculate the percent of
positive cells on a slide by calculating the ratio of the number of
cells having a mean optical density (MOD) of a marker stain in the
cell nucleus greater than a threshold value to the total number of
cells on the slide. In a traditional application, the threshold
value may be configurable, but the formula used to calculate the
ratio remains fixed; it will always compare the number of cells
over a certain threshold to the total number of cells. Even if a
procedure or algorithm allows the threshold value to vary based on
other extracted features, the formulas used to determine the
threshold are still fixed.
[0146] Accordingly, another aspect of the present invention
comprises a methodology whereby the algorithms or procedures are
configured to be dynamic (i.e., producing results based on formulas
entered by a user). That is, instead of the algorithms or
procedures being coded directly into the software, the software can
evaluate the formulas to be used at actual analysis runtime. More
particularly, a quantitative microscopy application implementing
such dynamic algorithms first calculates or otherwise determines a
general set of features at several levels, including a slide level,
a TMA core level, a field level, and a cellular level. Such general
features can then be aliased, thus defining different "variables"
that may be combined in various forms with each other using, for
example, standard mathematical operations, to form higher level
features, or to define functions. As such, at analysis runtime, the
application would load the list of aliased features and applicable
formulas. When a formula is needed in the analysis, that formula is
dynamically evaluated and the aliased features used to alter the
formula as necessary. If a formula is frequently recalculated, or
is sufficiently complex, such a formula or portion thereof may be
precompiled to speed execution.
[0147] Such a method thus allows the set of algorithms or
procedures implemented by the application to be updated, added to,
or otherwise modified, in the field, without requiring any external
modification to the software. As such, the application provides
flexibility to the users, since new functionality can be created,
as necessary and/or desired, without requiring any complex external
software development. Such functions can, for example, generate
numeric scores for the slides, cores, fields, or cells. In addition
or in the alternative, such functions may provide a filtering
capacity. As an example of the application of such functions, a
user may define a function that calculates a percent positive, as
described above, wherein the dynamic formulas may also be used to
define a function that allows a display to highlight `positive`
cells, fields, or cores. Such dynamic formulas can also be used,
for example, to define ranges for expected normal values, or named
bins such as `0`, `1+`, `2+`, etc.
[0148] Many modifications and other embodiments of the inventions
set forth herein will come to mind to one skilled in the art to
which these inventions pertain having the benefit of the teachings
presented in the foregoing descriptions and the associated
drawings. Therefore, it is to be understood that the inventions are
not to be limited to the specific embodiments disclosed and that
modifications and other embodiments are intended to be included
within the scope of the appended claims. Although specific terms
are employed herein, they are used in a generic and descriptive
sense only and not for purposes of limitation.
* * * * *