U.S. patent application number 15/834920 was filed with the patent office on 2018-04-19 for method for analyzing biological specimens by spectral imaging.
The applicant listed for this patent is CIRECA THERANOSTICS, LLC. Invention is credited to Stanley H. REMISZEWSKI, Clay M. THOMPSON.
Application Number | 20180108163 15/834920 |
Document ID | / |
Family ID | 47361900 |
Filed Date | 2018-04-19 |
United States Patent
Application |
20180108163 |
Kind Code |
A1 |
REMISZEWSKI; Stanley H. ; et
al. |
April 19, 2018 |
METHOD FOR ANALYZING BIOLOGICAL SPECIMENS BY SPECTRAL IMAGING
Abstract
A method for registering a visual image and a spectral image of
a biological sample includes aligning a first set of coordinate
positions of a plurality of reticles on a slide holder and a second
set of coordinate positions of the plurality of reticles on the
slide holder. The method further includes generating a registered
image of a visual image of a biological sample and a spectral image
of the biological sample based upon the alignment of the first and
second set of coordinate positions.
Inventors: |
REMISZEWSKI; Stanley H.;
(Spencer, MA) ; THOMPSON; Clay M.; (Camano Island,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CIRECA THERANOSTICS, LLC |
Parsippany |
NJ |
US |
|
|
Family ID: |
47361900 |
Appl. No.: |
15/834920 |
Filed: |
December 7, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15351010 |
Nov 14, 2016 |
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15834920 |
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14818149 |
Aug 4, 2015 |
9495745 |
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15351010 |
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13507386 |
Jun 25, 2012 |
9129371 |
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14818149 |
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13067777 |
Jun 24, 2011 |
9025850 |
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13507386 |
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61358606 |
Jun 25, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2207/10048
20130101; G01N 21/314 20130101; G01N 2021/3595 20130101; A61B
5/0071 20130101; G06T 2207/30204 20130101; G06T 2207/30024
20130101; G01N 21/35 20130101; G06T 11/60 20130101; G06K 9/00127
20130101; G06T 7/337 20170101; G01N 21/3581 20130101; G06T
2207/10056 20130101; G06T 7/33 20170101; G06T 2207/20221 20130101;
A61B 5/7257 20130101; A61B 5/0075 20130101 |
International
Class: |
G06T 11/60 20060101
G06T011/60; G06K 9/00 20060101 G06K009/00; G06T 7/33 20060101
G06T007/33 |
Claims
1-17. (canceled)
18. A method for analyzing biological specimens by spectral
imaging, comprising: acquiring spectral data from a spectral image
of a biological specimen; pre-processing the spectral data by
selecting a spectral range, computing a second derivative,
performing reverse Fourier transformation, performing zero-filling
and reverse Fourier transformation, and performing a phase
correction to generate a pre-processed spectral image; performing
multivariate analysis on the pre-processed spectral image data to
detect spectral differences in the pre-processed spectral data;
creating at least one group of data with similar spectral data
based on the multivariate analysis; and generating at least one or
more of a grayscale or pseudo-color spectral image, supervised
spectral images, and unsupervised pseudo-color cluster images based
on the at least one group of data.
19. The method of claim 18, further comprising: using one or more
of the pre-processed spectral data, the pre-processed spectral
image, the grayscale or pseudo-color spectral image, the supervised
spectral images, and the unsupervised pseudo-color cluster images
for one or more of diagnostic analysis, prognostic analysis, and
predictive analysis.
20. The method of claim 18, further comprising: performing
unsupervised analysis on one or more of the pre-processed spectral
data, the pre-processed spectral image, the grayscale or
pseudo-color spectral image, the supervised spectral images, and
the unsupervised pseudo-color cluster images to identify disease
conditions.
21. The method of claim 18, further comprising: acquiring a visual
image of the biological specimen; and registering the grayscale or
pseudo-color spectral image with the visual image by spatially
matching the grayscale or pseudo-color spectral image to align with
the visual image into a common coordinate system to generate a
registered image.
22. The method of claim 21, wherein in the registered image, the
pixels in the grayscale or pseudo-color spectral image and the
visual image coincide to same points in the common coordinate
system.
23. The method of claim 22, wherein when a pixel region in the
grayscale or pseudo-color spectral image is selected, the
corresponding pixel region in the visual image is accessed, and
wherein when a pixel region in the visual image is selected, the
corresponding pixel region in the grayscale or pseudo-color
spectral image is accessed.
24. The method of claim 21, wherein the registered image is used
for one or more of diagnostic analysis, prognostic analysis, and
predictive analysis.
25. The method of claim 21, further comprising: identifying a
region of a visual image containing a disease or condition; using
the registered image to correlate the region of the visual image to
spectral data in the grayscale or pseudo-color image corresponding
to the region of the visual image; and developing a training set of
data for a supervised algorithm for use with one or more of
diagnostic analysis, prognostic analysis, and predictive analysis
based on the correlation.
26. The method of claim 18, wherein the phase correction is
performed on one or more of second derivative data and
non-derivative data.
27. A system for analyzing biological specimens by spectral
imaging, comprising: a memory in communication with a processor,
wherein the memory and the processor are cooperatively configured
to: acquire spectral data from a spectral image of a biological
specimen; pre-process the spectral data by selecting a spectral
range, computing a second derivative, performing reverse Fourier
transformation, performing zero-filling and reverse Fourier
transformation, and performing a phase correction to generate a
pre-processed spectral image; perform multivariate analysis on the
pre-processed spectral image data to detect spectral differences in
the pre-processed spectral data; create at least one group of data
with similar spectral data based on the multivariate analysis; and
generate at least one or more of a pre-processed spectral image, a
grayscale or pseudo-color spectral image, supervised spectral
images, and unsupervised pseudo-color cluster images based on the
at least one group of data.
28. The system of claim 27, wherein the memory and the processor
are further operable to use one or more of the pre-processed
spectral data, the pre-processed spectral image, the grayscale or
pseudo-color spectral image, the supervised spectral images, and
the unsupervised pseudo-color cluster images for one or more of
diagnostic analysis, prognostic analysis, and predictive
analysis.
29. The system of claim 27, wherein the memory and the processor
are further operable to perform unsupervised analysis on one or
more of the pre-processed spectral data, the pre-processed spectral
image, the grayscale or pseudo-color spectral image, the supervised
spectral images, and the unsupervised pseudo-color cluster images
to identify disease conditions.
30. The system of claim 27, wherein the memory and the processor
are further operable to: acquire a visual image of the biological
specimen; and register the grayscale or pseudo-color spectral image
with the visual image by spatially matching the grayscale or
pseudo-color spectral image to align with the visual image into a
common coordinate system to generate a registered image.
31. The system of claim 30, wherein in the registered image, the
pixels in the grayscale or pseudo-color spectral image and the
visual image coincide to same points in the common coordinate
system.
32. The system of claim 31, wherein when a pixel region in the
grayscale or pseudo-color spectral image is selected, the
corresponding pixel region in the visual image is accessed, and
wherein when a pixel region in the visual image is selected, the
corresponding pixel region in the grayscale or pseudo-color
spectral image is accessed.
33. The system of claim 30, wherein the registered image is used
for one or more of diagnostic analysis, prognostic analysis, and
predictive analysis.
34. The system of claim 30, wherein the memory and the processor
are further operable to: identify a region of a visual image
containing a disease or condition; use the registered image to
correlate the region of the visual image to spectral data in the
grayscale or pseudo-color image corresponding to the region of the
visual image; and develop a training set of data for a supervised
algorithm for use with one or more of diagnostic analysis,
prognostic analysis, and predictive analysis based on the
correlation.
35. The system of claim 27, wherein the phase correction is
performed on one or more of second derivative data and
non-derivative data.
36. A computer-readable medium storing instructions executable by a
computer device, comprising: at least one instruction for causing
the computer device to acquire spectral data from a spectral image
of a biological specimen; at least one instruction for causing the
computer device to pre-process the spectral image data by selecting
a spectral range, computing a second derivative, performing reverse
Fourier transformation, performing zero-filling and reverse Fourier
transformation, and performing a phase correction to generate a
pre-processed spectral image; at least one instruction for causing
the computer device to perform multivariate analysis on the
pre-processed spectral data to detect spectral differences in the
pre-processed spectral data; at least one instruction for causing
the computer device to create at least one group of data with
similar spectral data based on the multivariate analysis; and at
least one instruction for causing the computer device to generate
at least one or more of a pre-processed spectral image, a grayscale
or pseudo-color spectral image, supervised spectral images, and
unsupervised pseudo-color cluster images based on the at least one
group of data.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation of U.S. patent
application Ser. No. 13/507,386, filed Jun. 25, 2012, which is a
Continuation-In-Part of U.S. patent application Ser. No.
13/067,777, filed on Jun. 24, 2011, which claims the benefit of
U.S. Provisional Patent Application No. 61/358,606, filed on Jun.
25, 2010. The entirety of each of the foregoing applications is
hereby incorporated by reference herein.
FIELD OF THE INVENTION
[0002] Aspects of the invention relate to a method for analyzing
biological specimens by spectral imaging to provide a medical
diagnosis, prognostic and/or predictive classification. The
biological specimens may include medical specimens obtained by
surgical methods, biopsies, and cultured samples.
BACKGROUND
[0003] Various pathological methods are used to analyze biological
specimens for the detection of abnormal or cancerous cells. For
example, standard histopathology involves visual analysis of
stained tissue sections by a pathologist using a microscope.
Typically, tissue sections are removed from a patient by biopsy,
and the samples are either snap frozen and sectioned using a
cryo-microtome, or they are formalin-fixed, paraffin embedded, and
sectioned via a microtome. The tissue sections are then mounted
onto a suitable substrate. Paraffin-embedded tissue sections are
subsequently deparaffinized. The tissue sections are stained using,
for example, an hemotoxylin-eosin (H&E) stain and are
coverslipped.
[0004] The tissue samples are then visually inspected at a high
resolution visual inspection, for example, 10.times. to 40.times.
magnification. The magnified cells are compared with visual
databases in the pathologist's memory. Visual analysis of a stained
tissue section by a pathologist involves scrutinizing features such
as nuclear and cellular morphology, tissue architecture, staining
patterns, and the infiltration of immune response cells to detect
the presence of abnormal or cancerous cells.
[0005] If early metastases or small clusters of cancerous cells
measuring from less than 0.2 to 2 mm in size, known as
micrometastases, are suspected, adjacent tissue sections may be
stained with an immuno-histochemical (IHC) agent/counter stain such
as cytokeratin-specific stains. Such methods increase the
sensitivity of histopathology since normal tissue, such as lymph
node tissue, does not respond to these stains. Thus, the contrast
between unaffected and diseased tissue can be enhanced.
[0006] The primary method for detecting micrometastases has been
standard histopathology. The detection of micrometastases in lymph
nodes, for example, by standard histopathology is a formidable task
owing to the small size and lack of distinguishing features of the
abnormality within the tissue of a lymph node. Yet, the detection
of these micrometastases is of prime importance to stage the spread
of disease because if a lymph node is found to be free of
metastatic cells, the spread of cancer may be contained. On the
other hand, a false negative diagnosis resulting from a missed
micrometastasis in a lymph node presents too optimistic a
diagnosis, and a more aggressive treatment should have been
recommended.
[0007] Although standard histopathology is well-established for
diagnosing advanced diseases, it has numerous disadvantages. In
particular, variations in the independent diagnoses of the same
tissue section by different pathologists are common because the
diagnosis and grading of disease by this method is based on a
comparison of the specimen of interest with a database in the
pathologist's memory, which is inherently subjective. Differences
in diagnoses particularly arise when diagnosing rare cancers or in
the very early stages of disease. In addition, standard
histopathology is time consuming, costly and relies on the human
eye for detection, which makes the results hard to reproduce.
Further, operator fatigue and varied levels of expertise of the
pathologist may impact a diagnosis.
[0008] In addition, if a tumor is poorly differentiated, many
immunohistochemical stains may be required to help differentiate
the cancer type. Such staining may be performed on multiple
parallel cell blocks. This staining process may be prohibitively
expensive and cellular samples may only provide a few diagnostic
cells in a single cell block.
[0009] To overcome the variability in diagnoses by standard
histopathology, which relies primarily on cell morphology and
tissue architectural features, spectroscopic methods have been used
to capture a snapshot of the biochemical composition of cells and
tissue. This makes it possible to detect variations in the
biochemical composition of a biological specimen caused by a
variety of conditions and diseases. By subjecting a tissue or
cellular sample to spectroscopy, variations in the chemical
composition in portions of the sample may be detected, which may
indicate the presence of abnormal or cancerous cells. The
application of spectroscopy to infrared cytopathology (the study of
diseases of cells) is referred to as "spectral cytopathology"
(SCP), and the application of infrared spectroscopy to
histopathology (the study of diseases of tissue) as "spectral
histopathology" (SHP).
[0010] SCP on individual urinary tract and cultured cells is
discussed in B. Bird et al., Vibr. Spectrosc., 48, 10 (2008) and M.
Romeo et al., Biochim Biophys Acta, 1758, 915 (2006). SCP based on
imaging data sets and applied to oral mucosa and cervical cells is
discussed in WO 2009/146425. Demonstration of disease progression
via SCP in oral mucosal cells is discussed in K. Papamarkakis et
al., Laboratory Investigations, 90, 589 (2010). Demonstration of
sensitivity of SCP to detect cancer field effects and sensitivity
to viral infection in cervical cells is discussed in K.
Papamarkakis et al., Laboratory Investigations, 90, 589,
(2010).
[0011] Demonstration of first unsupervised imaging of tissue using
SHP of liver tissue via hierarchical cluster analysis (HCA) is
discussed in M. Diem et al., Biopolymers, 57, 282 (2000). Detection
of metastatic cancer in lymph nodes is discussed in M. J. Romeo et
al., Vibrational Spectrosc., 38, 115 (2005) and M. Romeo et al.,
Vibrational Microspectroscopy of Cells and Tissues,
Wiley-Interscience, Hoboken, N.J. (2008). Use of neural networks,
trained on HCA-derived data, to diagnose cancer in colon tissue is
discussed in P. Lasch et al., J. Chemometrics, 20, 209 (2007).
Detection of micro-metastases and individual metastatic cancer
cells in lymph nodes is discussed in B. Bird et al., The Analyst,
134, 1067 (2009), B. Bird et al., BMC J. Clin. Pathology, 8, 1
(2008), and B. Bird et al., Tech. Cancer Res. Treatment, 10, 135
(2011).
[0012] Spectroscopic methods are advantageous in that they alert a
pathologist to slight changes in chemical composition in a
biological sample, which may indicate an early stage of disease. In
contrast, morphological changes in tissue evident from standard
histopathology take longer to manifest, making early detection of
disease more difficult. Additionally, spectroscopy allows a
pathologist to review a larger sample of tissue or cellular
material in a shorter amount of time than it would take the
pathologist to visually inspect the same sample. Further,
spectroscopy relies on instrument-based measurements that are
objective, digitally recorded and stored, reproducible, and
amenable to mathematical/statistical analysis. Thus, results
derived from spectroscopic methods are more accurate and precise
then those derived from standard histopathological methods.
[0013] Various techniques may be used to obtain spectral data. For
example, Raman spectroscopy, which assesses the molecular
vibrations of a system using a scattering effect, may be used to
analyze a cellular or tissue sample. This method is described in N.
Stone et al., Vibrational Spectroscopy for Medical Diagnosis, J.
Wiley & Sons (2008), and C. Krafft, et al., Vibrational
Spectrosc. (2011).
[0014] Raman's scattering effect is considered to be weak in that
only about 1 in 10.sup.10 incident photons undergoes Raman
scattering. Accordingly, Raman spectroscopy works best using a
tightly focused visible or near-IR laser beam for excitation. This,
in turn, dictates the spot from which spectral information is being
collected. This spot size may range from about 0.3 .mu.m to 2 .mu.m
in size, depending on the numerical aperture of the microscope
objective, and the wavelength of the laser utilized. This small
spot size precludes data collection of large tissue sections, since
a data set could contain millions of spectra and would require long
data acquisition times. Thus, SHP using Raman spectroscopy requires
the operator to select small areas of interest. This approach
negates the advantages of spectral imaging, such as the unbiased
analysis of large areas of tissue.
[0015] SHP using infrared spectroscopy has also been used to detect
abnormalities in tissue, including, but not limited to brain, lung,
oral mucosa, cervical mucosa, thyroid, colon, skin, breast,
esophageal, prostate, and lymph nodes. Infrared spectroscopy, like
Raman spectroscopy, is based on molecular vibrations, but is an
absorption effect, and between 1% and 50% of incident infrared
photons are likely to be absorbed if certain criteria are
fulfilled. As a result, data can be acquired by infrared
spectroscopy more rapidly with excellent spectral quality compared
to Raman spectroscopy. In addition, infrared spectroscopy is
extremely sensitive in detecting small compositional changes in
tissue. Thus, SHP using infrared spectroscopy is particularly
advantageous in the diagnosis, treatment and prognosis of cancers
such as breast cancer, which frequently remains undetected until
metastases have formed, because it can easily detect
micro-metastases. It can also detect small clusters of metastatic
cancer cells as small as a few individual cells. Further, the
spatial resolution achievable using infrared spectroscopy is
comparable to the size of a human cell, and commercial instruments
incorporating large infrared array detectors may collect tens of
thousands of pixel spectra in a few minutes.
[0016] A method of SHP using infrared spectroscopy is described in
Bird et al., "Spectral detection of micro-metastates in lymph node
histo-pathology", J. Biophoton. 2, No. 1-2, 37-46 (2009),
(hereinafter "Bird"). This method utilizes infrared
micro-spectroscopy (IRMSP) and multivariate analysis to pinpoint
micro-metastases and individual metastatic cells in lymph
nodes.
[0017] Bird studied raw hyperspectral imaging data sets including
25,600 spectra, each containing 1650 spectral intensity points
between 700 and 4000 cm.sup.-1. These data sets, occupying about
400 MByte each, were imported and pre-processed. Data preprocessing
included restriction of the wavenumber range to 900-1800 cm.sup.-1
and other processes. The "fingerprint" infrared spectral region was
further divided into a "protein region" between 1700 and 1450
cm.sup.-1, which is dominated by the amide I and amide II
vibrational bands of the peptide linkages of proteins. This region
is highly sensitive to different protein secondary and tertiary
structure and can be used to stage certain events in cell biology
that depend on the abundance of different proteins. The lower
wavenumber range, from 900 to 1350 cm.sup.-1, the "phosphate
region", contains several vibrations of the phosphodiester linkage
found in phospholipids, as well as DNA and RNA.
[0018] In Bird, a minimum intensity criterion for the integrated
amide I band was imposed to eliminate pixels with no tissue
coverage. Then, vector normalization and conversion of the spectral
vectors to second derivatives was performed. Subsequently, data
sets were subjected individually to hierarchical cluster analysis
(HCA) using the Euclidean distance to define spectral similarity
and Ward's algorithm for clustering. Pixel cluster membership was
converted to pseudo-color spectral images.
[0019] According to Bird's method, marks are placed on slides with
a stained tissue section to highlight areas that correspond to
areas on the unstained adjacent tissue section that are to be
subjected to spectral analysis. The resulting spectral and visual
images are matched by a user who aligns specific features on the
spectral image and the visual image to physically overlay the
spectral and visual images.
[0020] By Bird's method, corresponding sections of the spectral
image and the visual image are examined to determine any
correlation between the visual observations and the spectral data.
In particular, abnormal or cancerous cells observed by a
pathologist in the stained visual image may also be observed when
examining a corresponding portion of the spectral image that
overlays the stained visual image. Thus, the outlines of the
patterns in the pseudo-color spectral image may correspond to known
abnormal or cancerous cells in the stained visual image.
Potentially abnormal or cancerous cells that were observed by a
pathologist in a stained visual image may be used to verify the
accuracy of the pseudo-color spectral image.
[0021] Bird's method, however, is inexact because it relies on the
skill of the user to visually match specific marks on the spectral
and visual images. This method is often imprecise. In addition,
Bird's method allows the visual and spectral images to be matched
by physically overlaying them, but does not join the data from the
two images to each other. Since the images are merely physically
overlaid, the superimposed images are not stored together for
future analysis.
[0022] Further, since different adjacent sections of tissue are
subjected to spectral and visual imaging, Bird's overlaid images do
not display the same tissue section. This makes it difficult to
match the spectral and visual images, since there may be
differences in the morphology of the visual image and the color
patterns in the spectral image.
[0023] Another problem with Bird's overlaying method is that the
visual image is not in the same spatial domain as the infrared
spectral image. Thus, the spatial resolution of Bird's visual image
and spectral image are different. Typically, spatial resolution in
the infrared image is less than the resolution of the visual image.
To account for this difference in resolution, the data used in the
infrared domain may be expanded by selecting a region around the
visual point of interest and diagnosing the region, and not a
single point. For every point in the visual image, there is a
region in the infrared image that is greater than the point that
must be input to achieve diagnostic output. This process of
accounting for the resolution differences is not performed by Bird.
Instead, Bird assumes that when selecting a point in the visual
image, it is the same point of information in the spectral image
through the overlay, and accordingly a diagnostic match is
reported. While the images may visually be the same, they are not
the same diagnostically.
[0024] To claim a diagnostic match, the spectral image used must be
output from a supervised diagnostic algorithm that is trained to
recognize the diagnostic signature of interest. Thus, the spectral
image cluster will be limited by the algorithm classification
scheme to driven by a biochemical classification to create a
diagnostic match, and not a user-selectable match. By contrast,
Bird merely used an "unsupervised" HCA image to compare to a
"supervised" stained visual image to make a diagnosis. The HCA
image identifies regions of common spectral features that have not
yet been determined to be diagnostic, based on rules and limits
assigned for clustering, including manually cutting the dendrogram
until a boundary (geometric) match is visually accepted by the
pathologist to outline a cancer region. This method merely provides
a visual comparison.
[0025] Other methods based on the analysis of fluorescence data
exist that are generally based on the distribution of an external
tag, such as a stain or label, or utilize changes in the inherent
fluorescence, also known as auto-fluorescence. These methods are
generally less diagnostic, in terms of recognizing biochemical
composition and changes in composition. In addition, these methods
lack the fingerprint sensitivity of techniques of vibrational
spectroscopy, such as Raman and infrared.
[0026] A general problem with spectral acquisition techniques is
that an enormous amount of spectral data is collected when testing
a biological sample. As a result, the process of analyzing the data
becomes computationally complicated and time consuming. Spectral
data often contains confounding spectral features that are
frequently observed in microscopically acquired infrared spectra of
cells and tissue, such as scattering and baseline artifacts. Thus,
it is helpful to subject the spectral data to pre-processing to
isolate the cellular material of interest, and to remove
confounding spectral features.
[0027] One type of confounding spectral feature is Mie scattering,
which is a sample morphology-dependent effect. This effect
interferes with infrared absorption or reflection measurements if
the sample is non-uniform and includes particles the size of
approximately the wavelength of the light interrogating the sample.
Mie scattering is manifested by broad, undulating scattering
features, onto which the infrared absorption features are
superimposed.
[0028] Mie scattering may also mediate the mixing of absorptive and
reflective line shapes. In principle, pure absorptive line shapes
are those corresponding to the frequency-dependence of the
absorptivity, and are usually Gaussian, Lorentzian or mixtures of
both. The absorption curves correspond to the imaginary part of the
complex refractive index. Reflective contributions correspond to
the real part of the complex refractive index, and are dispersive
in line shapes. The dispersive contributions may be obtained from
absorptive line shapes by numeric KK-transform, or as the real part
of the complex Fourier transform (FT).
[0029] Resonance Mie (RMie) features result from the mixing of
absorptive and reflective band shapes, which occurs because the
refractive index undergoes anomalous dispersion when the
absorptivity goes through a maximum (i.e., over the profile of an
absorption band). Mie scattering, or any other optical effect that
depends on the refractive index, will mix the reflective and
absorptive line shapes, causing a distortion of the band profile,
and an apparent frequency shift.
[0030] FIG. 1 illustrates the contamination of absorption patterns
by dispersive band shapes observed in both SCP and SHP. The bottom
trace in FIG. 1 depicts a regular absorption spectrum of biological
tissue, whereas the top trace shows a spectrum strongly
contaminated by a dispersive component via the RMie effect. The
spectral distortions appear independent of the chemical
composition, but rather depend on the morphology of the sample. The
resulting band intensity and frequency shifts aggravate spectral
analysis to the point that uncontaminated and contaminated spectra
are classified into different groups due to the presence of the
band shifts. Broad, undulating background features are shown in
FIG. 2. When superimposed on the infrared micro-spectroscopy
(IR-MSP) patterns of cells, these features are attributed to Mie
scattering by spherical particles, such as cellular nuclei, or
spherical cells.
[0031] The appearance of dispersive line shapes in FIG. 1
superimposed on IR-MSP spectra was reported along with a
theoretical analysis in M. Romeo, et al., Vibrational Spectroscopy,
38, 129 (2005) (hereinafter "Romeo 2005"). Romeo 2005 identifies
the distorted band shapes as arising from the superposition of
dispersive (reflective) components onto the absorption features of
an infrared spectrum. These effects were attributed to incorrect
phase correction of the instrument control software. In particular,
the acquired raw interferogram in FTIR spectroscopy frequently is
"chirped" or asymmetric, and needs to be symmetrized before FT.
This is accomplished by collecting a double sided interferogram
over a shorter interferometer stroke, and calculating a phase
correction to yield a symmetric interferogram.
[0032] In Romeo 2005, it was assumed that this procedure was not
functioning properly, which causes it to yield distorted spectral
features. An attempt was made to correct the distorted spectral
features by calculating the phase between the real and imaginary
parts of the distorted spectra, and reconstructing a power spectrum
from the phase corrected real and imaginary parts. Romeo 2005 also
reported the fact that in each absorption band of an observed
infrared spectrum, the refractive index undergoes anomalous
dispersion. Under certain circumstances, various amounts of the
dispersive line shapes can be superimposed, or mixed in, with the
absorptive spectra.
[0033] The mathematical relationship between absorptive and
reflective band shapes is given by the Kramers-Kronig (KK)
transformation, which relates the two physical phenomena. The
mixing of dispersive (reflective) and absorptive effects in the
observed spectra was identified, and a method to correct the effect
via a procedure called "Phase Correction" (PC) is discussed in
Romeo 2005. Although the cause of the mixing of dispersive and
absorptive contributions was erroneously attributed to instrument
software malfunction, the principle of the confounding effect was
properly identified. Due to the incomplete understanding of the
underlying physics, however, the proposed correction method did not
work properly.
[0034] P. Bassan et al., Analyst, 134, 1586 (2009) and P. Bassan et
al., Analyst, 134, 1171 (2009) demonstrated that dispersive and
absorptive effects may mix via the "Resonance Mie Scattering"
(RMieS) effect. An algorithm and method to correct spectral
distortion is described in P. Bassan et al., "Resonant Mie
Scattering (RMieS) correction of infrared spectra from highly
scattering biological samples", Analyst, 135, 268-277 (2010). This
method is an extension of the "Extended Multiplicative Signal
Correction" (EMSC) method reported in A. Kohler et al., Appl.
Spectrosc., 59, 707 (2005) and A. Kohler et al., Appl. Spectrosc.,
62, 259 (2008).
[0035] This method removes the non-resonant Mie scattering from
infrared spectral datasets by including reflective components
obtained via KK-transform of pure absorption spectra into a
multiple linear regression model. The method utilizes the raw
dataset and a "reference" spectrum as inputs, where the reference
spectrum is used both to calculate the reflective contribution, and
as a normalization feature in the EMSC scaling. Since the reference
spectrum is not known a priori, Bassan et al. use the mean spectrum
of the entire dataset, or an "artificial" spectrum, such as the
spectrum of a pure protein matrix, as a "seed" reference spectrum.
After the first pass through the algorithm, each corrected spectrum
may be used in an iterative approach to correct all spectra in the
subsequent pass. Thus, a dataset of 1000 spectra will produce 1000
RMieS-EMSC corrected spectra, each of which will be used as an
independent new reference spectrum for the next pass, requiring
1,000,000 correction runs. To carry out this algorithm, referred to
as the "RMieS-EMSC" algorithm, to a stable level of corrected
output spectra required a number of passes (.about.10), and
computation times that are measured in days.
[0036] Since the RMieS-EMSC algorithm requires hours or days of
computation time, a fast, two-step method to perform the
elimination of scattering and dispersive line shapes from spectra
was developed, as discussed in B. Bird, M. Miljkovi and M. Diem,
"Two step resonant Mie scattering correction of infrared
micro-spectral data: human lymph node tissue", J. Biophotonics, 3
(8-9) 597-608 (2010). This approach includes fitting multiple
dispersive components, obtained from KK-transform of pure
absorption spectra, as well as Mie scattering curves computed via
the van Hulst equation (see H. C. Van De Hulst, Light Scattering by
Small Particles, Dover, Mineola, N.Y., (1981)), to all the spectra
in a dataset via a procedure known as Extended Multiplicative
Signal Correction (EMSC) (see A. Kohler et al., Appl. Spectrosc.,
62, 259 (2008)) and reconstructing all spectra without these
confounding components.
[0037] This algorithm avoids the iterative approach used in the
RMieS-EMSC algorithm by using uncontaminated reference spectra from
the dataset. These uncontaminated reference spectra were found by
carrying out a preliminary cluster analysis of the dataset and
selecting the spectra with the highest amide I frequencies in each
cluster as the "uncontaminated" spectra. The spectra were converted
to pure reflective spectra via numeric KK transform and used as
interference spectra, along with compressed Mie curves for RMieS
correction as described above. This approach is fast, but only
works well for datasets containing a few spectral classes.
[0038] In the case of spectral datasets containing many tissue
types, however, the extraction of uncontaminated spectra can become
tedious. Furthermore, under these conditions, it is unclear whether
fitting all spectra in the dataset to the most appropriate
interference spectrum is guaranteed. In addition, this algorithm
requires reference spectra for correction, and works best with
large datasets.
[0039] In light of the above, there remains a need for improved
methods of analyzing biological specimens by spectral imaging to
provide a medical diagnosis. Further, there is a need for an
improved pre-processing method that is based on a revised phase
correction approach, does not require input data, is
computationally fast, and takes into account many types of
confounding spectral contributions that are frequently observed in
microscopically acquired infrared spectra of cells and tissue.
SUMMARY
[0040] One aspect of the invention relates to a method for
analyzing biological specimens by spectral imaging to provide a
medical diagnosis. The method includes obtaining spectral and
visual images of biological specimens and registering the images to
detect abnormalities in the biological specimen, such as, but not
limited to, cell abnormalities, pre-cancerous cells, and cancerous
cells. This method overcomes the obstacles discussed above, among
others, in that it eliminates the bias and unreliability of
diagnoses and prognosis that are inherent in standard
histopathological, cytological, and other spectral methods.
[0041] Another aspect of the invention relates to a method for
correcting confounding spectral contributions that are frequently
observed in microscopically acquired infrared spectra of cells and
tissue by performing a phase correction on the spectral data. This
phase correction method may be used to correct various kinds of
absorption spectra that are contaminated by reflective
components.
[0042] According to aspects of the invention, a method for
analyzing biological specimens by spectral imaging includes
acquiring a spectral image of the biological specimen, acquiring a
visual image of the biological specimen, and registering the visual
image and spectral image.
[0043] A method of developing a data repository according to
aspects of the invention includes identifying a region of a visual
image displaying a disease or condition, associating the region of
the visual image to spectral data corresponding to the region, and
storing the association between the spectral data and the
corresponding disease or condition.
[0044] A method of providing a medical diagnosis according to
aspects of the invention includes obtaining spectroscopic data for
a biological specimen, comparing the spectroscopic data for the
biological specimen to data in a repository that is associated with
a disease or condition, determining any correlation between the
repository data and the spectroscopic data for the biological
specimen, and outputting a diagnosis associated with the
determination.
[0045] A system for providing a medical diagnosis and prognosis,
according to aspects of the invention includes a processor, a user
interface functioning via the processor, and a repository
accessible by the processor, where spectroscopic data of a
biological specimen is obtained, the spectroscopic data for the
biological specimen is compared to repository data that is
associated with a disease or condition, any correlation between the
repository data and the spectroscopic data for the biological
specimen is determined; and a diagnosis associated with the
determination is output.
[0046] A computer program product according to aspects of the
invention includes a computer usable medium having control logic
stored therein for causing a computer to provide a medical
diagnosis. The control logic includes a first computer readable
program code means for obtaining spectroscopic data for a
biological specimen, second computer readable program code means
for comparing the spectroscopic data for the biological specimen to
repository data that is associated with a disease or condition,
third computer readable program code means for determining any
correlation between the repository data and the spectroscopic data
for the biological specimen, and fourth computer readable program
code means for outputting a diagnosis and/or or a prognostic
determination associated with the determination.
DESCRIPTION OF THE DRAWINGS
[0047] The file of this patent contains at least one drawing
executed in color. Copies of this patent with color drawing(s) will
be provided by the Patent and Trademark Office upon request and
payment of the necessary fee.
[0048] FIG. 1 illustrates the contamination of absorption patterns
by dispersive band shapes typically observed in both SCP and
SHP;
[0049] FIG. 2 shows broad, undulating background features typically
observed on IR-MSP spectral of cells attributed to Mie scattering
by spherical particles;
[0050] FIG. 3 is a flowchart illustrating a method of analyzing a
biological sample by spectral imaging according to aspects of the
invention;
[0051] FIG. 3A is a flowchart illustrating steps in a method of
acquiring a spectral image according to aspects of the
invention;
[0052] FIG. 3B is a flowchart illustrating steps in a method of
pre-processing spectral data according to aspects of the
invention;
[0053] FIGS. 4A and 4B are a flowchart illustrating an example
method of performing image registration on a spectral image and a
visual image in accordance with aspects of the present
invention;
[0054] FIG. 4C illustrates an example slide holder in accordance
with aspects of the present invention;
[0055] FIG. 5A is a flowchart illustrating an example method of
refining image registration in accordance with aspects of the
present invention;
[0056] FIG. 5B is an example a graphical user interface (GUI)
interface for setting a threshold value in accordance with aspects
of the present invention;
[0057] FIG. 5C is an example GUI interface illustrating an example
optimization window in accordance with aspects of the present
invention;
[0058] FIG. 6A shows a typical spectrum, superimposed on a linear
background according to aspects of the invention;
[0059] FIG. 6B shows an example of a second derivative spectrum
according to aspects of the invention;
[0060] FIG. 7 shows a portion of the real part of an interferogram
according to aspects of the invention;
[0061] FIG. 8 shows that the phase angle that produces the largest
intensity after phase correction is assumed to be the uncorrupted
spectrum according to aspects of the invention;
[0062] FIG. 9A shows that absorption spectra that are contaminated
by scattering effects that mimic a baseline slope according to
aspects of the invention;
[0063] FIG. 9B shows that the imaginary part of the forward FT
exhibits strongly curved effects at the spectral boundaries, which
will contaminate the resulting corrected spectra according to
aspects of the invention;
[0064] FIG. 10A is H&E-based histopathology showing a lymph
node that has confirmed breast cancer micro-metastases under the
capsule according to aspects of the invention;
[0065] FIG. 10B shows data segmentation by Hierarchical Cluster
Analysis (HCA) carried out on the lymph node section of FIG. 10A
according to aspects of the invention;
[0066] FIG. 10C is a plot showing the peak frequencies of the amide
I vibrational band in each spectrum according to aspects of the
invention;
[0067] FIG. 10D shows an image of the same lymph node section of
FIG. 10A after phase-correction using RMieS correction according to
aspects of the invention;
[0068] FIG. 11A shows the results of HCA after phase-correction
using RMieS correction of FIG. 10D according to aspects of the
invention;
[0069] FIG. 11B is H&E-based histopathology of the lymph node
section of FIG. 11A according to aspects of the invention;
[0070] FIG. 12A is a visual microscopic image of a section of
stained cervical image;
[0071] FIG. 12B is an infrared spectral image created from
hierarchical cluster analysis of an infrared dataset collected
prior to staining the tissue according to aspects of the
invention;
[0072] FIG. 13A is a visual microscopic image of a section of an
H&E-stained axillary lymph node section according to aspects of
the invention;
[0073] FIG. 13B is an infrared spectral image created from a
Multilayer Perceptron Networks analysis of an infrared dataset
collected prior to staining the tissue according to aspects of the
invention;
[0074] FIG. 14A is a visual image of a small cell lung cancer
tissue according to aspects of the invention.
[0075] FIG. 14B is an HCA-based spectral image of the tissue shown
in FIG. 14A according to aspects of the invention;
[0076] FIG. 14C is a registered image of the visual image of FIG.
14A and the spectral image of FIG. 14B, according to aspects of the
invention;
[0077] FIG. 14D is an example of a graphical user interface (GUI)
for the registered image of FIG. 14C according to aspects of the
invention;
[0078] FIG. 15A is a visual microscopic image of H&E-stained
lymph node tissue section according to aspects of the
invention;
[0079] FIG. 15B is a global digital staining image of section shown
in FIG. 15A, distinguishing capsule and interior of lymph node
according to aspects of the invention;
[0080] FIG. 15C is a diagnostic digital staining image of the
section shown in FIG. 15A, distinguishing capsule, metastatic
breast cancer, histiocytes, activated B-lymphocytes, and
T-lymphocytes according to aspects of the invention;
[0081] FIG. 16 is a schematic of relationship between global and
diagnostic digital staining according to aspects of the
invention;
[0082] FIG. 17A is a visual image of H&E-stained tissue section
from an axillary lymph node according to aspects of the
invention;
[0083] FIG. 17B is a SHP-based digitally stained region of breast
cancer micrometastasis according to aspects of the invention;
[0084] FIG. 17C is a SHP-based digitally stained region occupied by
B-lymphocyes according to aspects of the invention;
[0085] FIG. 17D is a SHP-based digitally stained region occupied by
histocytes according to aspects of the invention.
[0086] FIG. 18 illustrates the detection of individual cancer
cells, and small clusters of cancer cells via SHP according to
aspects of the invention;
[0087] FIG. 19A shows raw spectral data sets comprising cellular
spectra recorded from lung adenocarcinoma, small cell carcinoma,
and squamous cell carcinoma cells according to aspects of the
invention;
[0088] FIG. 19B shows corrected spectral data sets comprising
cellular spectra recorded from lung adenocarcinoma, small cell
carcinoma, and squamous cell carcinoma cells according to aspects
of the invention;
[0089] FIG. 19C shows standard spectra for lung adenocarcinoma,
small cell carcinoma, and squamous cell carcinoma according to
aspects of the invention;
[0090] FIG. 19D shows KK transformed spectra calculated from
spectra in FIG. 19C;
[0091] FIG. 19E shows PCA scores plots of the multi class data set
before EMSC correction according to aspects of the invention;
[0092] FIG. 19F shows PCA scores plots of the multi class data set
after EMSC correction according to aspects of the invention;
[0093] FIG. 20A shows mean absorbance spectra of lung
adenocarcinoma, small cell carcinoma, and squamous carcinoma,
according to aspects of the invention;
[0094] FIG. 20B shows second derivative spectra of absorbance
spectra displayed in FIG. 20A according to aspects of the
invention;
[0095] FIG. 21A shows 4 stitched microscopic R&E-stained images
of 1 mm.times.1 mm tissue areas comprising adenocarcinoma, small
cell carcinoma, and squamous cell carcinoma cells, respectively,
according to aspects of the invention;
[0096] FIG. 21B is a binary mask image constructed by performance
of a rapid reduced RCA analysis upon the 1350 cm.sup.-1-900
cm.sup.-1 spectral region of the 4 stitched raw infrared images
recorded from the tissue areas shown in FIG. 21A according to
aspects of the invention;
[0097] FIG. 21C is a 6-cluster RCA image of the scatter corrected
spectral data recorded from regions of diagnostic cellular material
according to aspects of the invention;
[0098] FIG. 22 shows various features of a computer system for use
in conjunction with aspects of the invention; and
[0099] FIG. 23 shows a computer system for use in conjunction with
aspects of the invention;
DETAILED DESCRIPTION
[0100] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which aspects of this invention
belong. Although methods and materials similar or equivalent to
those described herein can be used in the practice or testing,
suitable methods and materials are described below. All
publications, patent applications, patents, and other references
mentioned herein are incorporated by reference in their entirety.
In case of conflict, this specification, including definitions,
will control. In addition, the materials, methods, and examples are
illustrative only and not intended to be limiting.
[0101] One aspect of the invention relates to a method for
analyzing biological specimens by spectral imaging to provide a
medical diagnosis. The biological specimens may be medical
specimens obtained by surgical methods, biopsies, and cultured
samples. The method includes obtaining spectral and visual images
of biological specimens and registering the images to detect cell
abnormalities, pre-cancerous cells, and cancerous cells. The
biological specimens may include tissue or cellular samples, but
tissue samples are preferred for some applications. This method
identifies abnormal or cancerous and other disorders including, but
not limited to, lymph node, thyroid, breast, uterine, renal,
testicular, ovarian, or prostate cancer, small cell lung carcinoma,
non-small cell lung carcinoma, and melanoma, as well as
non-cancerous effects including, but not limited to, inflammation,
necrosis, and apoptosis.
[0102] One method in accordance with aspects of the invention
overcomes the obstacles discussed above in that it eliminates or
generally reduces the bias and unreliability of diagnoses,
prognosis, predictive, and theranostics that are inherent in
standard histopathological and other spectral methods. In addition,
it allows access to a spectral database of tissue types that is
produced by quantitative and reproducible measurements and is
analyzed by an algorithm that is calibrated against classical
histopathology. Via this method, for example, abnormal and
cancerous cells may be detected earlier than they can be identified
by the related art, including standard histopathological or other
spectral techniques.
[0103] A method in accordance with aspects of the invention is
illustrated in the flowchart of FIG. 3. As shown in FIG. 3, the
method generally includes the steps of acquiring a biological
section 301, acquiring a spectral image of the biological section
302, acquiring a visual image of the same biological section 303,
and performing image registration 304. The registered image may
optionally be subjected to training 305, and a medical diagnosis
may be obtained 306.
[0104] Biological Section
[0105] According to the example method of the invention shown in
FIG. 3, the step of acquiring a biological section 301 refers to
the extraction of tissue or cellular material from an individual,
such as a human or animal. A tissue section may be obtained by
methods including, but not limited to core and punch biopsy, and
excising. Cellular material may be obtained by methods including,
but not limited to swabbing (exfoliation), washing (lavages), and
by fine needle aspiration (FNA).
[0106] A tissue section that is to be subjected to spectral and
visual image acquisition may be prepared from frozen or from
paraffin embedded tissue blocks according to methods used in
standard histopathology. The section may be mounted on a slide that
may be used both for spectral data acquisition and visual
pathology. For example, the tissue may be mounted either on
infrared transparent microscope slides comprising a material
including, but not limited to, calcium fluoride (CaF.sub.2) or on
infrared reflective slides, such as commercially available "low-e"
slides. After mounting, paraffin-embedded samples may be subjected
to deparaffinization.
[0107] Spectral Image
[0108] According to aspects of the invention, the step of acquiring
a spectral image of the biological section 302 shown in FIG. 3 may
include acquiring spectral data from the biological section 308,
performing data pre-processing 310, performing multivariate
analysis 312, and creating a grayscale or pseudo-color image of the
biological section 314, as outlined in the flowchart of FIG.
3A.
[0109] Spectral Data
[0110] As set forth in FIG. 3A, spectral data from the biological
section may be acquired 401. Spectral data from an unstained
biological sample, such as a tissue sample, may be obtained to
capture a snapshot of the chemical composition of the sample. The
spectral data may be collected from a tissue section in pixel
detail, where each pixel is about the size of a cellular nucleus.
Each pixel has its own spectral pattern, and when the spectral
patterns from a sample are compared, they may show small but
recurring differences in the tissue's biochemical composition.
[0111] The spectral data may be collected by methods including, but
not limited to infrared, Raman, visible, terahertz, and
fluorescence spectroscopy. Infrared spectroscopy may include, but
is not limited to, attenuated total reflectance (ATR) and
attenuated total reflectance Fourier transform infrared
spectroscopy (ATR-FTIR). In general, infrared spectroscopy may be
used because of its fingerprint sensitivity, which is also
exhibited by Raman spectroscopy. Infrared spectroscopy may be used
with larger tissue sections and to provide a dataset with a more
manageable size than Raman spectroscopy. Furthermore, infrared
spectroscopy data may be more amenable to fully automatic data
acquisition and interpretation. Additionally, infrared spectroscopy
may have the necessary sensitivity and specificity for the
detection of various tissue structures and diagnosis of
disease.
[0112] The intensity axis of the spectral data, in general, express
absorbance, reflectance, emittance, scattering intensity or any
other suitable measure of light power. The wavelength may relate to
the actual wavelength, wavenumber, frequency or energy of
electromagnetic radiation.
[0113] Infrared data acquisition may be carried out using presently
available Fourier transform (FT) infrared imaging
microspectrometers, tunable laser-based imaging instruments, such
as quantum cascade or non-linear optical devices, or other
functionally equivalent instruments based on different
technologies. The acquisition of spectral data using a tunable
laser is described further in U.S. patent application Ser. No.
13/084,287 titled, "Tunable Laser-Based Infrared Imaging System and
Method of Use Thereof", which is incorporated herein in its
entirety by reference.
[0114] According to one method in accordance with aspects of the
invention, a pathologist or technician may select any region of a
stained tissue section and receive a spectroscopy-based assessment
of the tissue region in real-time, based on the hyperspectral
dataset collected for the tissue before staining. Spectral data may
be collected for each of the pixels in a selected unstained tissue
sample. Each of the collected spectra contains a fingerprint of the
chemical composition of each of the tissue pixels. Acquisition of
spectral data is described in WO 2009/146425, which is incorporated
herein in its entirety by reference.
[0115] In general, the spectral data includes hyperspectral
datasets, which are constructs including N=nm individual spectra or
spectral vectors (absorption, emission, reflectance etc.), where n
and m are the number of pixels in the x and y dimensions of the
image, respectively. Each spectrum is associated with a distinct
pixel of the sample, and can be located by its coordinates x and y,
where 1.ltoreq.x.ltoreq.n, and 1.ltoreq.y.ltoreq.m. Each vector has
k intensity data points, which are usually equally spaced in the
frequency or wavenumber domain.
[0116] The pixel size of the spectral image may generally be
selected to be smaller than the size of a typical cell so that
subcellular resolution may be obtained. The size may also be
determined by the diffraction limit of the light, which is
typically about 5 .mu.m to about 7 .mu.m for infrared light. Thus,
for a 1 mm.sup.2 section of tissue, about 140.sup.2 to about
200.sup.2 individual pixel infrared spectra may be collected. For
each of the N pixels of a spectral "hypercube", its x and y
coordinates and its intensity vector (intensity vs. wavelength),
are stored.
[0117] Pre-Processing
[0118] Subjecting the spectral data to a form of pre-processing may
be helpful to isolate the data pertaining to the cellular material
of interest and to remove confounding spectral features. Referring
to FIG. 3A, once the spectral data is collected, it may be
subjected to such pre-processing 310.
[0119] Pre-processing may involve creating a binary mask to
separate diagnostic from non-diagnostic regions of the sampled area
to isolate the cellular data of interest. Methods for creating a
binary mask are disclosed in WO 2009/146425, which is incorporated
by reference herein in its entirety.
[0120] A method of pre-processing, according to another aspect of
the invention, permits the correction of dispersive line shapes in
observed absorption spectra by a "phase correction" algorithm that
optimizes the separation of real and imaginary parts of the
spectrum by adjusting the phase angle between them. This method,
which is computationally fast, is based on a revised phase
correction approach, in which no input data are required. Although
phase correction is used in the pre-processing of raw
interferograms in FTIR and NMR spectroscopy (in the latter case,
the interferogram is usually referred to as the "free induction
decay, FID") where the proper phase angle can be determined
experimentally, the method of this aspect of the invention differs
from earlier phase correction approaches in that it takes into
account mitigating factors, such as Mie, RMie and other effects
based on the anomalous dispersion of the refractive index, and it
may be applied to spectral datasets retroactively.
[0121] The pre-processing method of this aspect of the invention
transforms corrupted spectra into Fourier space by reverse FT
transform. The reverse FT results in a real and an imaginary
interferogram. The second half of each interferogram is zero-filled
and forward FT transformed individually. This process yields a real
spectral part that exhibits the same dispersive band shapes
obtained via numeric KK transform, and an imaginary part that
includes the absorptive line shapes. By recombining the real and
imaginary parts with a correct phase angle between them,
phase-corrected, artifact-free spectra are obtained.
[0122] Since the phase required to correct the contaminated spectra
cannot be determined experimentally and varies from spectrum to
spectrum, phase angles are determined using a stepwise approach
between -90.degree. and 90.degree. in user selectable steps. The
"best" spectrum is determined by analysis of peak position and
intensity criteria, both of which vary during phase correction. The
broad undulating Mie scattering contributions are not explicitly
corrected for explicitly in this approach, but they disappear by
performing the phase correction computation on second derivative
spectra, which exhibit a scatter-free background.
[0123] According to aspects of the invention, pre-processing 310 as
shown in FIG. 3A may include selecting the spectral range 316,
computing the second derivative of the spectra 318, reverse Fourier
transforming the data 320, zero-filling and forward Fourier
transforming the interferograms 322, and phase correcting the
resulting real and imaginary parts of the spectrum 324, as outlined
in the flowchart of FIG. 3B.
[0124] Spectral Range
[0125] In 316, each spectrum in the hyperspectral dataset is
pre-processed to select the most appropriate spectral range
(fingerprint region). This range may be about 800 to about 1800
cm.sup.-1, for example, which includes heavy atom stretching as
well as X-H (X: heavy atom with atomic number .gtoreq.12)
deformation modes. A typical example spectrum, superimposed on a
linear background, is shown in FIG. 6A.
[0126] Second Derivative of Spectra
[0127] The second derivative of each spectrum is then computed 318
as shown in the flowchart of FIG. 3B. Second derivative spectra are
derived from original spectral vectors by second differentiation of
intensity vs. wavenumber. Second derivative spectra may be computed
using a Savitzky-Golay sliding window algorithm, and can also be
computed in Fourier space by multiplying the interferogram by an
appropriately truncated quadratic function.
[0128] Second derivative spectra may have the advantage of being
free of baseline slopes, including the slowly changing Mie
scattering background. The second derivative spectra may be nearly
completely devoid of baseline effects due to scattering and
non-resonant Mie scattering, but still contain the effects of
RMieS. The second derivative spectra may be vector normalized, if
desired, to compensate for varying sample thickness. An example of
a second derivative spectrum is shown in FIG. 6B.
[0129] Reverse Fourier Transform
[0130] As shown in 320 of the flowchart of FIG. 3B, each spectrum
of the data set is reverse Fourier transformed (FT). Reverse FT
refers to the conversion of a spectrum from intensity vs.
wavenumber domain to intensity vs. phase difference domain. Since
FT routines only work with spectral vectors the length of which are
an integer power of 2, spectra are interpolated or truncated to
512, 1024 or 2048 (NFT) data point length before FT. Reverse FT
yields a real (RE) and imaginary (IM) interferogram of NFT/2
points. A portion of the real part of such an interferogram is
shown in FIG. 7.
[0131] Zero-Fill and Forward Fourier Transform
[0132] The second half of both the real and imaginary interferogram
for each spectrum is subsequently zero-filled 322. These
zero-filled interferograms are subsequently forward Fourier
transformed to yield a real and an imaginary spectral component
with dispersive and absorptive band shapes, respectively.
[0133] Phase Correction
[0134] The real (RE) and imaginary (IM) parts resulting from the
Fourier analysis are subsequently phase corrected 324, as shown in
the flowchart of FIG. 3B. This yields phase shifted real (RE') and
imaginary (IM') parts as set forth in the formula below:
( RE ' IM ' ) = = ( cos ( .phi. ) sin ( .phi. ) - sin ( .phi. ) cos
( .phi. ) ) ( RE IM ) , ##EQU00001##
where .phi. is the phase angle.
[0135] Since the phase angle .phi. for the phase correction is not
known, the phase angle may be varied between
-.pi./2.ltoreq..phi..ltoreq..pi./2 in user defined increments, and
a spectrum with the least residual dispersive line shape may be
selected. The phase angle that produces the largest intensity after
phase correction may be assumed to be the uncorrupted spectrum, as
shown in FIG. 8. The heavy trace marked with the arrows and
referred to as the "original spectrum" is a spectrum that is
contaminated by RMieS contributions. The thin traces show how the
spectrum changes upon phase correction with various phase angles.
The second heavy trace is the recovered spectrum, which matches the
uncontaminated spectrum well. As indicated in FIG. 8, the best
corrected spectrum exhibits the highest amide I intensity at about
1655 cm.sup.-1. This peak position matches the position before the
spectrum was contaminated.
[0136] The phase correction method, in accordance with aspects of
the invention in 316-324, works well both with absorption and
derivative spectra. This approach even solves a complication that
may occur if absorption spectra are used, in that if absorption
spectra are contaminated by scattering effects that mimic a
baseline slope, as shown schematically in FIG. 9A, the imaginary
part of the forward FT exhibits strongly curved effects at the
spectral boundaries, as shown in FIG. 9B, which will contaminate
the resulting corrected spectra. Use of second derivative spectra
may eliminate this effect, since the derivation eliminates the
sloping background; thus, artifact-free spectra may be obtained.
Since the ensuing analysis of the spectral data-set by hierarchical
cluster analysis, or other appropriate segmenting or diagnostic
algorithms, is carried out on second derivative spectra anyway, it
is advantageous to carry out the dispersive correction on second
derivative spectra, as well. Second derivative spectra exhibit
reversal of the sign of spectral peaks. Thus, the phase angle is
sought that causes the largest negative intensity. The value of
this approach may be demonstrated from artificially contaminated
spectra: since a contamination with a reflective component will
always decrease its intensity, the uncontaminated or "corrected"
spectrum will be the one with the largest (negative) band intensity
in the amide I band between 1650 and 1660 cm.sup.-1.
Example 1--Operation of Phase Correction Algorithm
[0137] An example of the operation of the phase correction
algorithm is provided in FIGS. 10 and 11. This example is based on
a dataset collected from a human lymph node tissue section. The
lymph node has confirmed breast cancer micro-metastases under the
capsule, shown by the black arrows in FIG. 10A. This
photo-micrograph shows distinct cellular nuclei in the cancerous
region, as well as high cellularity in areas of activated
lymphocytes, shown by the gray arrow. Both these sample
heterogeneities contribute to large RMieS effects.
[0138] When data segmentation by hierarchical cluster analysis
(HCA) was first carried out on this example lymph node section, the
image shown in FIG. 106 was obtained. To distinguish the cancerous
tissue (dark green and yellow) from the capsule (red), and the
lymphocytes (remainder of colors), 10 clusters were necessary, and
the distinction of these tissue types was poor. In FIG. 10B, the
capsule shown in red includes more than one spectral class, which
were combined into 1 cluster.
[0139] The difficulties in segmenting this dataset can be gauged by
inspection of FIG. 10C. This plot depicts the peak frequencies of
the amide I vibrational band in each spectrum. The color scale at
right of the figure indicates that the peak occurs between about
1630 and 1665 cm.sup.-1 of the lymph node body, and between 1635
and 1665 cm.sup.-1 for the capsule. The spread of amide I frequency
is typical for a dataset heavily contaminated by RMieS effects,
since it is well-known that the amide I frequency for peptides and
proteins should occur in the range from 1650 to 1660 cm.sup.-1,
depending on the secondary protein structure. FIG. 10D shows an
image of the same tissue section after phase-correction based RMieS
correction. Within the body of the lymph node, the frequency
variation of the amide I peak was reduced to the range of 1650 to
1654 cm.sup.-1, and for the capsule to a range of 1657 to 1665
cm.sup.-1 (fibro-connective proteins of the capsule are known to
consist mostly of collagen, a protein known to exhibit a high amide
I band position).
[0140] The results from a subsequent HCA are shown in FIG. 11. In
FIG. 11A, cancerous tissue is shown in red; the outline of the
cancerous regions coincides well with the H&E-based
histopathology shown in FIG. 11B (this figure is the same as 10A).
The capsule is represented by two different tissue classes (light
blue and purple), with activated B-lymphocytes shown in light
green. Histiocytes and T-lymphocytes are shown in dark green, gray
and blue regions. The regions depicted in FIG. 11A match the visual
histopathology well, and indicate that the phase correction method
discussed herein improved the quality of the spectral
histopathology methods enormously. In an aspect, narrow band
normalization may also be used to enhance and/or improve the
quality of the image, which may be helpful for image registration
accuracy. The narrow band normalization may select features and/or
subsets of features within the broad band spectral region and apply
a weighting to the selected features.
[0141] The advantages of the pre-processing method in accordance
with aspects of the invention over previous methods of spectral
correction include that the method provides a fast execution time
of about 5000 spectra/second, and no a priori information on the
dataset is required. In addition, the phase correction algorithm
can be incorporated into spectral imaging and "digital staining"
diagnostic routines for automatic cancer detection and diagnosis in
SCP and SHP. Further, phase correction greatly improves the quality
of the image, which is helpful for image registration accuracy and
in diagnostic alignment and boundary representations.
[0142] Further, the pre-processing method in accordance with
aspects of the invention may be used to correct a wide range of
absorption spectra contaminated by reflective components. Such
contamination occurs frequently in other types of spectroscopy,
such as those in which band shapes are distorted by dispersive line
shapes, such as Diffuse Reflectance Fourier Transform Spectroscopy
(DRIFTS), Attenuated Total Reflection (ATR), and other forms of
spectroscopy in which mixing of the real and imaginary part of the
complex refractive index, or dielectric susceptibility, occurs to a
significant extent, such as may be present with Coherent
Anti-Stokes Raman Spectroscopy (CARS).
[0143] Multivariate Analysis
[0144] Multivariate analysis may be performed on the pre-processed
spectral data to detect spectral differences, as outlined in 312 of
the flowchart of FIG. 3A. In certain multivariate analyses, spectra
are grouped together based on similarity. The number of groups may
be selected based on the level of differentiation required for the
given biological sample. In general, the larger the number of
groups, the more detail that will be evident in the spectral image.
A smaller number of groups may be used if less detail is desired.
According to aspects of the invention, a user may adjust the number
of groups to attain the desired level of spectral
differentiation.
[0145] For example, unsupervised methods, such as HCA and principal
component analysis (PCA), supervised methods, such as machine
learning algorithms including, but not limited to, artificial
neural networks (ANNs), hierarchical artificial neural networks
(hANN), support vector machines (SVM), and/or "random forest"
algorithms may be used. Unsupervised methods are based on the
similarity or variance in the dataset, respectively, and segment or
cluster a dataset by these criteria, requiring no information
except the dataset for the segmentation or clustering. Thus, these
unsupervised methods create images that are based on the natural
similarity or dissimilarity (variance) in the dataset. Supervised
algorithms, on the other hand, require reference spectra, such as
representative spectra of cancer, muscle, or bone, for example, and
classify a dataset based on certain similarity criteria to these
reference spectra.
[0146] HCA techniques are disclosed in Bird (Bird et al., "Spectral
detection of micro-metastates in lymph node histo-pathology", J.
Biophoton. 2, No. 1-2, 37-46 (2009)), which is incorporated herein
in its entirety. PCA is disclosed in WO 2009/146425, which is
incorporated by reference herein in its entirety.
[0147] Examples of supervised methods for use in accordance with
aspects of the invention may be found in P. Lasch et al.
"Artificial neural networks as supervised techniques for FT-IR
microspectroscopic imaging" J. Chemometrics 2006 (hereinafter
"Lasch"); 20: 209-220, M. Miljkovic et al., "Label-free imaging of
human cells: algorithms for image reconstruction of Raman
hyperspectral datasets" (hereinafter "Miljkovic"), Analyst, 2010,
xx, 1-13, and A. Dupuy et al., "Critical Review of Published
Microarray Studies for Cancer Outcome and Guidelines on Statistical
Analysis and Reporting", JNCI, Vol. 99, Issue 2|Jan. 17, 2007
(hereinafter "Dupuy"), each of which is incorporated by reference
herein in its entirety.
[0148] Grayscale or Pseudo-Color Spectral Image
[0149] Similarly grouped data from the multivariate analysis may be
assigned the same color code. The grouped data may be used to
construct "digitally stained" grayscale or pseudo-color maps, as
set forth in 314 of the flowchart of FIG. 3A. Accordingly, this
method may provide an image of a biological sample that is based
solely or primarily on the chemical information contained in the
spectral data.
[0150] An example of a spectral image prepared after multivariate
analysis by HCA is provided in FIGS. 12A and 12B. FIG. 12A is a
visual microscopic image of a section of stained cervical image,
measuring about 0.5 mm.times.1 mm. Typical layers of squamous
epithelium are indicated. FIG. 12B is a pseudo-color infrared
spectral image constructed after multivariate analysis by HCA prior
to staining the tissue. This image was created by mathematically
correlating spectra in the dataset with each other, and is based
solely on spectral similarities; no reference spectra were provided
to the computer algorithm. As shown in FIG. 12B, an HCA spectral
image may reproduce the tissue architecture visible after suitable
staining (for example, with a H&E stain) using standard
microscopy, as shown in FIG. 12A. In addition, FIG. 12B shows
features that are not readily detected in FIG. 12A, including
deposits of keratin at (a) and infiltration by immune cells at
(b).
[0151] The construction of pseudo-color spectral images by HCA
analysis is discussed in Bird.
[0152] An example of a spectral image prepared after analysis by
ANN is provided in FIGS. 13A and 13B. FIG. 13A is a visual
microscopic image of a section of an H&E-stained axillary lymph
node section. FIG. 13B is an infrared spectral image created from
ANN analysis of an infrared dataset collected prior to staining the
tissue of FIG. 13A.
[0153] Visual Image
[0154] A visual image of the same biological section obtained in
302 may be acquired, as indicated by 303 as shown in FIG. 3. The
biological sample applied to a slide in step 301 described above
may be unstained or may be stained by any suitable well-known
method used in standard histopathology, such as by one or more
H&E and/or IHC stains, and may be coverslipped. Examples of
visual images are shown in FIGS. 12A and 13A.
[0155] A visual image of a histopathological sample may be obtained
using a standard visual microscope, such as one commonly used in
pathology laboratories. The microscope may be coupled to a high
resolution digital camera that captures the field of view of the
microscope digitally. This digital real-time image is based on the
standard microscopic view of a stained piece of tissue, and is
indicative of tissue architecture, cell morphology and staining
patterns. The digital image may include many pixel tiles that are
combined via image stitching, for example, to create a photograph.
According to aspects of the invention, the digital image that is
used for analysis may include an individual tile or many tiles that
are stitched combined into a photograph. This digital image may be
saved and displayed on a computer screen.
[0156] Registration of Spectral and Visual Images
[0157] According to one method in accordance with aspects of the
invention, once the spectral and visual images have been acquired,
the visual image of the stained tissue may be registered with a
digitally stained grayscale or pseudo-color spectral image, as
indicated in 304 of the flowchart of FIG. 3. In general, image
registration is the process of transforming or matching different
sets of data into one coordinate system. Image registration
involves spatially matching or transforming a first image to align
with a second image. The pixels in the first image and the pixels
in the second image may coincide to the same points in the
coordinate system. The images may contain different types of data,
and image registration allows the matching or transformation of the
different types of data. In an aspect, the transformation may
include a scaled rigid body transformation. It should be noted that
the transformation may include warping if staining the sample made
the sample shrink non-uniformly. Example transformation equations
that the computing system may use include the following:
u=u0+scale*(x*cos(.theta.)-y*sin(.theta.))
v=v0+scale*(x*sin(.theta.)+y*cos(.theta.)) [0158] where (u0,v0) is
a shift of the origin, .theta. is a rotation angle in radians and
scale is the scale factor, (x,y) are coordinates in the HCA image,
and (u,v) are coordinates in the H&E (visual image).
[0159] In accordance with aspects of the invention, image
registration may be performed in a number of ways. For example, a
common coordinate system may be established for the visual and
spectral images. If establishing a common coordinate system is not
possible or is not desired, the images may be registered by point
mapping to bring an image into alignment with another image. In
point mapping, control points on both of the images that identify
the same feature or landmark in the images are selected. Based on
the positions of the control points, spatial mapping of both images
may be performed. For example, at least two control points may be
used. To register the images, the control points in the visible
image may be correlated to the corresponding control points in the
spectral image and aligned together.
[0160] In an aspect, at least two control points may be used to
determine the transformation parameters of the scaled body
transformation. The transformation parameters may be selected to
minimize an error between the mapped control points in the
registered images (e.g., the overlapped images). For example, when
two control points are used to determine the transformation
parameters, two solutions for the transformation may be generated
by the computing system. The computing system may select one of the
two solutions generated based upon, for example, the orientation of
the image. However, when three control points are used to determine
the transformation parameters, a unique solution for the
transformation may be generated by the computing system. Thus, it
should be noted that more than two control points may be used by
the computing system to determine the parameters of the scaled body
transformation. In addition, as the number of control points
increase, the accuracy of the transformation may also increase
and/or improve.
[0161] In one variation according to aspects of the invention,
control points may be selected by placing reference marks on the
slide containing the biological specimen. Reference marks may
include, but are not limited to, ink, paint, and a piece of a
material, including, but not limited to polyethylene. The reference
marks may have any suitable shape or size, and may be placed in the
central portion, edges, or corners of the side, as long as they are
within the field of view. The reference mark may be added to the
slide while the biological specimen is being prepared. If a
material having known spectral patterns, including, but not limited
to a chemical substance, such as polyethylene, and a biological
substance, is used in a reference mark, it may be also used as a
calibration mark to verify the accuracy of the spectral data of the
biological specimen.
[0162] In another variation according to aspects of the invention,
a user, such as a pathologist, may select the control points in the
spectral and visual images. The user may select the control points
based on their knowledge of distinguishing features of the visual
or spectral images including, but not limited to, edges and
boundaries. For biological images such as cells and tissue, control
points may be selected from any of the biological features in the
image. For example, such biological features may include, but are
not limited to, clumps of cells, mitotic features, cords or nests
of cells, sample voids, such as alveolar and bronchi, and irregular
sample edges. The user's selection of control points in the
spectral and visual images may be saved to a repository that is
used to provide a training correlation for personal and/or
customized use. This approach may allow subjective best practices
to be incorporated into the control point selection process.
[0163] In another variation according to aspects of the invention,
software-based recognition of distinguishing features in the
spectral and visual images may be used to select control points.
The software may detect at least one control point that corresponds
to a distinguishing feature in the visual or spectral images. For
example, control points in a particular a cluster region may be
selected in the spectral image. The cluster pattern may be used to
identify similar features in the visual image. The control points
may be used to digitally correlate the pixels from the spectral
image with the pixels from the visual image. In another aspect, the
software may use morphological (e.g., shape) features in the images
to select the control points. The morphological features may come
from the shape of the specimen, the shape of the spaces between the
tissues, and/or the shape of stained regions within the tissue
(e.g., as a result of staining the biological sample, for example,
with an IHC agent). Thus, any shape that may occur in the visual
image that also occurs in the spectral image may be used to select
the control points.
[0164] The features in both images may be aligned by translation,
rotation, and scaling. Translation, rotation and scaling may also
be automated or semi-automated, for example, by developing mapping
relationships or models after selecting the features selection.
Such an automated process may provide an approximation of mapping
relationships that may then be resampled and transformed to
optimize registration, for example. Resampling techniques include,
but are not limited to nearest neighbor, linear, and cubic
interpolation.
[0165] Once the control points are aligned, the pixels in the
spectral image having coordinates P.sub.1 (x.sub.1, y.sub.1) may be
aligned with the corresponding pixels in the visual image having
coordinates P.sub.2 (x.sub.2, y.sub.2). This alignment process may
be applied to all or a selected portion of the pixels in the
spectral and visual images. Once aligned, the pixels in each of the
spectral and visual images may be registered together. By this
registration process, the pixels in each of the spectral image and
visual images may be digitally joined with the pixels in the
corresponding image. Since the method in accordance with aspects of
the invention allows the same biological sample to be tested
spectroscopically and visually, the visual and spectral images may
be registered accurately.
[0166] An identification mark such as a numerical code, bar code,
may be added to the slide to verify that the correct specimen is
being accessed. The reference and identification marks may be
recognized by a computer that displays or otherwise stores the
visual image of the biological specimen. This computer may also
contain software for use in image registration.
[0167] An example of image registration according to an aspect of
the invention is illustrated in FIGS. 14A-14C. FIG. 14A is a visual
image of a small cell lung cancer tissue sample, and FIG. 14B is
spectral image of the same tissue sample subjected to HCA. FIG. 14B
contains spectral data from most of the upper right-hand section of
the visual image of FIG. 14A. When the visual image of FIG. 14A is
registered with the spectral image of FIG. 14B, the result is shown
in FIG. 14C. As shown in FIG. 14C, the circled sections containing
spots and contours 1-4 that are easily viewable in the spectral
image of FIG. 14B correspond closely to the spots and contours
visible in the microscopic image of FIG. 14A.
[0168] Once the coordinates of the pixels in the spectral and
visual images are registered, they may be digitally stored
together. The entire images or a portion of the images may be
stored. For example, the diagnostic regions may be digitally stored
instead of the images of the entire sample. This may significantly
reduce data storage requirements.
[0169] A user who views a certain pixel region in either the
spectral or visual image may immediately access the corresponding
pixel region in the other image. For example, a pathologist may
select any area of the spectral image, such as by clicking a mouse
or with joystick control, and view the corresponding area of the
visual image that is registered with the spectral image. FIG. 14D
is an example of a graphical user interface (GUI) for the
registered image of FIG. 14C according to aspects of the invention.
The GUI shown in FIG. 14D allows a pathologist to toggle between
the visual, spectral, and registered images and examine specific
portions of interest.
[0170] In addition, as a pathologist moves or manipulates an image,
he/she can also access the corresponding portion of the other image
to which it is registered. For example, if a pathologist magnifies
a specific portion of the spectral image, he/she may access the
same portion in the visual image at the same level of
magnification.
[0171] Operational parameters of the visual microscope system, as
well as microscope magnification, changes in magnification etc.,
may be also stored in an instrument specific log file. The log file
may be accessed at a later time to select annotation records and
corresponding spectral pixels for training the algorithm. Thus, a
pathologist may manipulate the spectral image, and at a later time,
the spectral image and the digital image that is registered to it
are both displayed at the appropriate magnification. This feature
may be useful, for example, since it allows a user to save a
manipulated registered image digitally for later viewing or for
electronic transmittal for remote viewing.
[0172] Image registration may be used with a tissue section, a cell
section, and/or any other biological sample having a known
diagnosis, prognosis, and/or predictive use to extract training
spectra during a training step of a method in accordance with
aspects of the invention. During the training step, a visual image
of stained tissue may be registered with an unsupervised spectral
image, such as from HCA. Image registration may also be used when
making a diagnosis, prognosis, and/or predictive use on a tissue
section. For example, a supervised spectral image of the tissue
section may be registered with its corresponding visual image.
Thus, a user may obtain a diagnosis, prognosis, and/or predictive
use based on any point in the registered images that has been
selected.
[0173] Image registration according to aspects of the invention
provides numerous advantages over prior methods of analyzing
biological samples. For example, it allows a pathologist to rely on
a spectral image, which reflects the highly sensitive biochemical
content of a biological sample, when making analyzing biological
material. As such, it provides significantly greater accuracy in
detecting small abnormalities, pre-cancerous, or cancerous cells,
including micrometastates, than the related art. Thus, the
pathologist does not have to base his/her analysis of a sample on
his/her subjective observation of a visual image of the biological
sample. Thus, for example, the pathologist may simply study the
spectral image and may easily refer to the relevant portion in the
registered visual image to verify his/her findings, as
necessary.
[0174] In addition, the image registration method in accordance
with aspects of the invention provides greater accuracy than the
prior method of Bird (Bird et al., "Spectral detection of
micro-metastates in lymph node histo-pathology", J. Biophoton. 2,
No. 1-2, 37-46 (2009)) because it is based on correlation of
digital data, i.e. the pixels in the spectral and visual images.
Bird does not correlate any digital data from the images, and
instead relies merely on the skill of the user to visually match
spectral and visual images of adjacent tissue sections by
physically overlaying the images. Thus, the image registration
method in accordance with aspects of the invention provides more
accurate and reproducible diagnoses with regard to abnormal or
cancerous cells. This may be helpful, for example, in providing
accurate diagnosis in the early stages of disease, when indicia of
abnormalities and cancer are hard to detect.
[0175] In an aspect, image registration may automatically occur
between a spectral image and a visual image. For example, a
computing system may automatically register a spectral image and a
visual image based on features of the images, as illustrated in
FIGS. 5A-5C. In addition, a computing system may automatically
register a spectral image and a visual image based on coordinates
that are independent of image features, as illustrated in FIGS. 4A
and 4B.
[0176] Referring now to FIGS. 4A and 4B, illustrated is an example
automated method 400 for performing image registration based on
coordinates that are independent of image features, in accordance
with an aspect of the present invention. For example, the method
may be used when the spectral image and visual image are captured
using the same slide holder, such as a stage plate used with
microscope stages. The slide holder may allow the biological sample
to be placed with spatial accuracy and precision in each
microscope.
[0177] The method may include receiving coordinate positions of a
plurality of reticles on a slide holder with a biological sample in
a visual collection apparatus 402. The visual collection apparatus
may include, but is not limited to, a microscope that is capable of
capturing an image of the biological sample. In addition, the slide
holder may include a plurality of reticles marking a coordinate
location on the slide holder, as illustrated in FIG. 4C.
[0178] Referring now to FIG. 4C, illustrated is an example slide
holder 426 in accordance with an aspect of the present invention.
Slide holder 426 may include a slot 428 where the biological sample
may be inserted. Biological samples may include, but are not
limited to, cells and tissues. In addition, slide holder 426 may
also include a plurality of reticles 430, 432, and 434 marking a
position on the slide holder 426. In another aspect, the plurality
of reticles may be placed directly on the slide, marking a position
on the slide instead of the slide holder. Reticles 430, 432, and
434 may each have a coordinate location, e.g., an (x,y) coordinate.
The coordinates from each of reticles 430, 432, and 434 may define
a coordinate system that may be used during data acquisition of the
biological sample. It should be noted that at least two reticles
may be used to determine the coordinate system. For example, when
the coordinates of two reticles are used, two solutions for the
coordinate systems may be generated by the computing system. The
computing system may select one of the two solutions generated
based upon the orientation of the biological sample. For example,
the computing system may select the solution based upon the
assumption that the biological sample is not turned upside down
and/or flipped. When three reticles are used to determine the
coordinate system, a unique solution for the coordinate system may
be generated by the computing system. Thus, as the number of
reticles increase, the accuracy of the transformation may also
increase and/or improve
[0179] Referring back to FIG. 4A, the coordinate locations of each
of the reticles on the slide holder may be received from the visual
collection apparatus. In an aspect, a computing system in
communication with the visual collection apparatus may receive the
coordinate positions of the plurality of reticles on the slide
holder. For example, the visual collection apparatus (e.g., a
microscope) may be programmed to locate each of the reticles on the
slide holder by moving the microscope and/or the slide holder until
the reticle comes into view and transmitting the coordinate
locations of the reticles to the computing system. In another
aspect, a user may enter the coordinate locations of the reticles
into the computing system. For example, the user may move the
microscope and/or the slide holder until each reticle comes into
view (and may become aligned with indicators, like crosshairs,
within the microscope), and the user may enter the coordinates
displayed on the microscope into the computing system. Thus, it
should be noted that a variety of mechanisms, automated or
otherwise, may be used to capture the coordinate position of the
reticles on the slide holder and send the coordinate information to
the computing system.
[0180] The method may also include receiving a visual image of the
biological sample from the visual image collection apparatus 404.
For example, the visual image collection apparatus may transmit the
visual image of the biological sample captured by the visual image
collection apparatus to the computing system.
[0181] In addition, the method may include associating the
coordinate positions of the plurality of reticles on the slide
holder with the visual image 406 and storing the visual image
coordinate positions and the visual image 408. In an aspect, the
computing system may associate the coordinate positions of the
reticles received with the visual image received and store the
visual image coordinate positions and the visual image, for
example, in a data repository. In an aspect, the computing system
may associate the file that stores the received visual image
coordinates with the file that stores the received visual
image.
[0182] The method may further include receiving coordinate
positions of the plurality of the reticles on the slide holder with
the biological sample in a spectral image collection apparatus 410.
It should be noted that the same slide holder with the biological
sample that is used in the visual image collection apparatus may
also be used in the spectral image collection apparatus. The
computing system may also be in communication with the spectral
image collection apparatus and may receive the coordinate positions
of each of the plurality of the reticles on the slide holder
directly from the spectral image collection apparatus and/or
through a user of the spectral image collection apparatus. For
example, the spectral collection apparatus may be programmed to
locate each of the reticles on the slide holder by moving the
spectral collection apparatus and/or the slide holder until the
reticle comes into view and sending the coordinate locations of the
reticles to the computing system. A user may also enter the
coordinate locations of the reticles into the computing system.
[0183] In addition, the method may include receiving a spectral
image of the biological sample from the spectral image collection
apparatus 412. The spectral image collection apparatus may transmit
the captured spectral image of the biological sample to the
computing system.
[0184] The method may also include associating the coordinate
positions of the plurality of reticles on the slide holder with the
spectral image 414 and storing the spectral image coordinates
positions and the spectral image 416. The computing system may
associate the received spectral image coordinates with the received
spectral image. For example, the computing system may apply a label
to the file storing the spectral image coordinates associating the
file to the spectral image. It should also be noted that the
spectral image coordinates may be stored in the same file as the
spectral image.
[0185] The method may further include aligning or otherwise
associating the received visual image coordinates with the received
spectral image coordinates 418. The computing system may
automatically map the spectral image coordinates to the visual
image coordinates to create a common coordinate system between the
visual image and the spectral image.
[0186] The method may additionally include generating a registered
image aligning the received spectral image and the received visual
image based upon the alignment of the visual image coordinates and
the spectral image coordinates 420. For example, the computing
system may overlay the spectral image on the visual image using the
alignment of the visual image coordinates with the spectral image
coordinates and automatically generate a registered image. Thus,
the computing system may automatically register the spectral image
with the visual image by using coordinates that are independent of
the features from the spectral image and the visual image.
[0187] The method may optionally include storing the registered
image 422. The computing system may store the registered image in a
data repository so that a user of the computing system may access
the registered image and/or make changes to the registered
image.
[0188] In addition, the method may optionally include optimizing
the registered image 424. For example, the computing system may
apply one or more optimizations to find the best rigid body
transforms that will cause the visual image coordinate points and
the spectral image coordinate points to align or correspond. The
computing system may use one or more optimizations to improve the
accuracy of a registered image by attempting to further align the
images.
[0189] One optimization may include minimizing the distance between
the spectral image and the visual images in the overlaid images
that are mapped in the same coordinate system. The distance may be
a measure of the grayscale pixel-by-pixel errors summed over the
whole image. For example, the optimization may include:
min(p)J=sum D(p,I1,I2)
D=I2(p)-I1
[0190] where p is the same scaled rigid body transformation used
for the select points or reticle-based registration, D(p,.,.) is
the distance measure (which is applied pixel-by-pixel), and I2(p)
is the I2 image transformed by p into the same space as I1. I1 and
I2 images may be created by applying a series of transformations to
the spectral and visual images in order to get the images into the
same grayscale space because the visual pixel values may not
directly compared to the HCA or spectral pixel values.
[0191] Another optimization may include minimizing the least
squared error between the pairs of points selected in the two
images (e.g., the visual image and the spectral image). In an
aspect, the computing system may perform an optimization to
minimize the least squared error between the pairs of points
selected in the two images (e.g., the visual image and the spectral
image). For example, the optimization may include:
min p J = i , j ( x i - x j ) 2 + ( y i - y j ) 2 ##EQU00002##
[0192] where (x.sub.i, y.sub.i) are selected reference points in
the visual image, (x.sub.j, y.sub.j) are the reference points from
the spectral image after they are mapped to the visual image, and
p=[u v .lamda. .theta.] are the registration parameters.
[0193] It should be noted that various optimization settings may be
used by the computing system to provide optimization limits on the
optimizations being performed by the computing system. For example,
optimization limits may include, but are not limited to, a maximum
number of function evaluations, convergence tolerances, and/or an
upper and a lower bound on the transformation parameters. The upper
and lower bounds may be advantageous in preventing the optimization
from venturing too far outside of a desired solution.
[0194] Referring now to FIG. 5A, illustrated is an example method
500 for refining image registration based on image features in
accordance with an aspect of the present invention. The computing
system may refine an image registration when the overlay of the
visual image with the spectral image does not correspond well. For
example, the overlay of the images may display the features of the
biological sample out of alignment between the visual image and the
spectral image. In an aspect, the computing system may
automatically perform the method for refining the image
registration to align the image features from the spectral image
with the visual image as precisely as possible. It should be noted
that the computing system may also switch between a first spectral
image level and a different spectral image level, for example, if
the first spectral image level does not contain sufficient useful
information in the spectral image when the registration occurs.
[0195] The method may include scaling the spectral image 502 and
scaling the visual image 504. Scaling may be performed so that the
morphological (e.g., shape) features of interest are approximately
the same size in each image. Scaling may be based upon the ratios
between the spectral image and the visual images. For example, the
visual image may have a higher resolution than the spectral image,
and therefore, the scaling may include setting an upper and lower
bound on the images to scale the higher resolution image to a lower
resolution image. Example scaling equations that the computing
system may use include the following:
x.sub.H&E=u+.lamda.*(x.sub.HCA*cos .theta.-y.sub.HCA*sin
.theta.)
y.sub.H&E=v+.lamda.*(y.sub.HCA*sin .theta.+y.sub.HCA*cos
.theta.)
[0196] where (u,v) represents a translation, .lamda. a scale factor
and .theta. a rotation. The scaling may be applied to selected
spectral image reference points (e.g., reticle coordinates and/or
registration points selected by a user) and to map the selected
spectral image reference points to the visual image reference
points (e.g., reticle coordinates and/or registration points
selected by the user).
[0197] In an aspect, the computing system may perform an
optimization to minimize a registration distance function between
all the points in the registered images. For example, the
optimization may include:
min p J = i , j ( I ( p ) i , j - T i , j ) 2 ##EQU00003##
[0198] where image T.sub.ij is based on the visual image,
I(p).sub.ij is based on the spectral image further transformed by
the scaled rigid body transform p=[u v .lamda. .theta.] and
interpolated into the same coordinate frame as T. The sum is over
all pixels (i,j) in the images. While this optimization uses the
grayscale distance function D=I(p)-T, it should be noted that other
distance functions may be used in the optimization, such as the
normalized gradient field. The images T and I are interpolated,
filtered and converted versions of the visual and spectral images
as required by the particular distance function being used.
Different distance functions may require different conversions and
filtering of the visual and spectral images.
[0199] The method may optionally include normalizing the spectral
image 505. In an aspect, the computing system may normalize the
spectral image to improve the image features of the spectral
images. For example, when the normalized spectral image is compared
with the visual image, the features in the normalized spectral
image may appear sharper and provide a more accurate representation
of the image features.
[0200] In an aspect, the computing system may apply a weighted
normalization to the spectral image. Using a weighted normalization
on the spectral image may be beneficial, for example, because the
infrared absorption spectrum of a cell or tissue pixel is dominated
by the protein vibrations. Since proteins contribute over 60% of a
cell's dry mass, whereas nucleic acids (DNA and RNA) contribute
about 20% or less of a cell's dry mass. Therefore, the vibrations
of proteins (observed predominantly in the amide I and II regions,
between 1700 and 1500 cm.sup.-1) may be much more prominent in the
spectra than the features of nucleic acids, which may be observed
mostly in the symmetric (ca. 1090 cm.sup.-1) and antisymmetric (ca.
1230 cm.sup.-1) phosphodiester stretching vibrations. Since changes
in nucleic acid vibrational bands are frequently observed with the
onset of cancerous disease, it may be advantageous to utilize
normalization procedures that emphasize the low wavenumber spectral
region of spectra in a data set. This approach may be advantageous,
for example, when carrying out hierarchical cluster analysis (HCA)
for the initial partition of the spectral data set.
[0201] In an aspect, the weighted normalization may include a ramp
function with a value of 1 at the low wavenumber limit of the
spectrum (typically 778 cm.sup.-1) and a value of 0 at the high
wavenumber limit (typically 1800 cm.sup.-1) multiplied by the
spectral vector after standard vector normalization (e.g., "the
ramp method"). The product of this function may include a weighted
spectral vector in which the importance of the protein region is
suppressed.
[0202] In another aspect, the weighted normalization may include
region normalization. In region normalization, the spectrum S is
divided into two or more (for example 2 or 3) regions, such that
the protein and nucleic acid spectral features fall within
different regions (for example: region 1 from 1800 to 1480
cm.sup.-1, and region 2 from 1478 to 778 cm.sup.-1). The two (or
more) regions may be vector normalized separately, adding more
weight to the low intensity spectral regions. Although "region
normalization" may cause a discontinuity (for example, between 1478
and 1480 cm.sup.-1) in the spectra, this method may result in
better discrimination of normal and cancerous regions, for example,
as measured by the number of clusters required in HCA for the
discrimination of normal and cancerous regions.
[0203] The computing system may also perform the weighted
normalization on the spectral image to improve the features of the
spectral image to aid in the refinement of the registered
image.
[0204] The method may also include applying a threshold value to
the spectral image and the visual image 506 and generating a binary
spectral image and a binary visual image based on the applied
threshold value 508. The computing system may automatically select
a threshold value to apply to the spectral image and a threshold
value to apply to the visual image. In addition, the computing
system may receive the threshold values from a user of the
computing system.
[0205] Referring now to FIG. 5B, illustrated is an example GUI
interface that allows a user to select a threshold value for the
visual image 520a and the spectral image 520b. For example, the
user may use sliders to set the threshold values. As the user moves
the sliders, the tissues in both images may change color (e.g.,
black to white or white to black) and the shapes in or other
aspects of the images may become more or less visible or distinct,
for example. The user may select a threshold value for each of the
spectral image and the visual image when the tissues in both images
become the same color/shade (e.g., black or white) and common
shapes in each image may become visible without a lot of noise in
the image. It should be noted that the threshold values for the
spectral image and the visual image may be the same number and/or
may be a different number.
[0206] Referring back to FIG. 5A, the computing system may receive
the selected threshold values and generate a binary spectral image
and a binary visual image based on the applied threshold values.
For example, each pixel with a number above the threshold value may
be converted to white, while each pixel with a number below the
threshold value may be converted to black. The computing system may
map all the pixels in the spectral image and the visual image into
black and white using the threshold values for each respective
image. By generating a binary image (e.g., a black and white
image), the interstitial spaces between the tissues may be
highlighted, as well as the basic structure of the biological
sample, any morphological (e.g., shape) features in the biological
sample, and/or the shape of stained regions within the tissue
(e.g., as a result of staining the biological sample, for example,
with an IHC agent). Thus, any shape that may occur in the visual
image that also occurs in the spectral image could be
highlighted.
[0207] In addition, the computing system may display a difference
image illustrating the difference between the spectral binary image
and one or both of the visual binary image and the registered
image. Referring now to FIG. 5C, illustrated is an example GUI
screen with a difference image illustrated and a graph with points
illustrating the progress in minimizing the error in the difference
image. The difference image provides a visual indication of the
accuracy of the fit of the registered images. In an aspect, the
difference image may be as black in color as possible. If the
difference image includes frequent white spaces, this result may
indicate the presence of error in the registered image (e.g., the
overlay of the images illustrates image features out of alignment).
As illustrated in the graph, multiple iterations of the threshold
selection may occur before the difference image illustrates minimum
error (e.g., a mostly black image) in the threshold selections. It
should be noted that the iteration process may terminate if a
maximum number of iterations is reached before the error is
minimized in the difference image.
[0208] Referring back to FIG. 5A, the computing system may continue
to apply various threshold values until a minimum error value is
reached for the binary spectral image and the visual spectral image
and/or until a maximum number of iterations are reached, whichever
occurs first.
[0209] The method may also include applying a morphological closure
to the binary spectral image and the binary visual image 510. The
morphological closure may remove noise from the binary images by
smoothing any tiny dots that may appear in the binary images into
white areas. For example, the computing system may apply the
morphological closure to the binary images by adding a boundary to
the images and converting the dots to white into white areas and/or
removing small black or white dots within larger white or black
areas, respectively.
[0210] In addition, the method may include softening the edges in
the binary spectral image and the binary visual image 512. In an
aspect, the computing system may apply a Gaussian filter to blur
the ramps between the black and white edges in the binary images.
For example, the computing system may smooth across the edges to
blur the edges and make them softer, in order to improve
convergence of the optimization.
[0211] The method may further include minimizing the grayscale
difference between the binary spectral image and the binary visual
image 514. For example, the computing system may apply one or more
of the optimizations discussed above in 502-512 to minimize the
grayscale difference between the spectral image and the binary
image in order to obtain registration parameters with a better fit.
It should be noted that the optimization process may repeat until
the structures in the spectral image and the visual image are
aligned as close as possible in the registered image.
[0212] Training
[0213] A training set may optionally be developed, as set forth in
step 305 in the method provided in the flowchart of FIG. 3.
According to aspects of the invention, a training set includes
spectral data that is associated with specific diseases or
conditions, among other things. The association of diseases or
conditions to spectral data in the training set may be based on a
correlation of classical pathology to spectral patterns based on
morphological features normally found in pathological specimens.
The diseases and conditions may include, but are not limited to,
cellular abnormalities, inflammation, infections, pre-cancer, and
cancer.
[0214] According to one aspect in accordance with the invention, in
the training step, a training set may be developed by identifying a
region of a visual image containing a disease or condition,
correlating the region of the visual image to spectral data
corresponding to the region, and storing the association between
spectral data and the corresponding disease or condition. The
training set may then be archived in a repository, such as a
database, and made available for use in machine learning algorithms
to provide a diagnostic algorithm with output derived from the
training set. The diagnostic algorithm may also be archived in a
repository, such as a database, for future use.
[0215] For example, a visual image of a tissue section may be
registered with a corresponding unsupervised spectral image, such
as one prepared by HCA. Then, a user may select a characteristic
region of the visual image. This region may be classified and/or
annotated by a user to specify a disease or condition. The spectral
data underlying the characteristic region in the corresponding
registered unsupervised spectral image may be classified and/or
annotated with the disease or condition.
[0216] The spectral data that has been classified and/or annotated
with a disease or condition provides a training set that may be
used to train a supervised analysis method, such as an ANN. Such
methods are also described, for example, in Lasch, Miljkovic Dupuy.
The trained supervised analysis method may provide a diagnostic
algorithm.
[0217] A disease or condition information may be based on
algorithms that are supplied with the instrument, algorithms
trained by a user, or a combination of both. For example, an
algorithm that is supplied with the instrument may be enhanced by
the user.
[0218] An advantage of the training step according to aspects of
the invention is that the registered images may be trained against
the best available, consensus-based "gold standards", which
evaluate spectral data by reproducible and repeatable criteria.
Thus, after appropriate instrument validation and algorithm
training, methods in accordance with aspects of the invention may
produce similar results worldwide, rather than relying on
visually-assigned criteria such as normal, atypical, low grade
neoplasia, high grade neoplasia, and cancer. The results for each
cell may be represented by an appropriately scaled numeric index or
the results overall as a probability of a classification match.
Thus, methods in accordance with aspects of the invention may have
the necessary sensitivity and specificity for the detection of
various biological structures, and diagnosis of disease.
[0219] The diagnostic limitation of a training set may be limited
by the extent to which the spectral data are classified and/or
annotated with diseases or conditions. As indicated above, this
training set may be augmented by the user's own interest and
expertise. For example, a user may prefer one stain over another,
such as one or many IHC stains over an H&E stain. In addition,
an algorithm may be trained to recognize a specific condition, such
as breast cancer metastases in axillary lymph nodes, for example.
The algorithm may be trained to indicate normal vs. abnormal tissue
types or binary outputs, such as adenocarcenoma vs.
not-adenocarcenoma only, and not to classify the different normal
tissue types encountered, such as capsule, B- and T-lymphocytes.
The regions of a particular tissue type, or states of disease,
obtained by SHP, may be rendered as "digital stains" superimposed
on real-time microscopic displays of the tissue sections.
[0220] Diagnosis, Prognosis, Predictive, Thernostic
[0221] Once the spectral and visual images have been registered,
they may be used make a medical diagnosis, as outlined in step 306
in the flowchart of FIG. 3. The diagnosis may include a disease or
condition including, but not limited to, cellular abnormalities,
inflammation, infections, pre-cancer, cancer, and gross anatomical
features. In a method according to aspects of the invention,
spectral data from a spectral image of a biological specimen of
unknown disease or condition that has been registered with its
visual image may be input to a trained diagnostic algorithm, as
described above. Based on similarities to the training set that was
used to prepare the diagnostic algorithm, the spectral data of the
biological specimen may be correlated to a disease or condition.
The disease or condition may be output as a diagnosis.
[0222] For example, spectral data and a visual image may be
acquired from a biological specimen of unknown disease or
condition. The spectral data may be analyzed by an unsupervised
method, such as HCA, which may then be used along with spatial
reference data to prepare an unsupervised spectral image. This
unsupervised spectral image may be registered with the visual
image, as discussed above. The spectral data that has been analyzed
by an unsupervised method may then be input to a trained supervised
algorithm. For example, the trained supervised algorithm may be an
ANN, as described in the training step above. The output from the
trained supervised algorithm may be spectral data that contains one
or more labels that correspond to classifications and/or
annotations of a disease or condition based on the training
set.
[0223] To extract a diagnosis based on the labels, the labeled
spectral data may used to prepare a supervised spectral image that
may be registered with the visual image and/or the unsupervised
spectral image of the biological specimen. For example, when the
supervised spectral image is registered with the visual image
and/or the unsupervised spectral image, through a GUI, a user may
select a point of interest in the visual image or the unsupervised
spectral image and be provided with a disease or condition
corresponding to the label at that point in the supervised spectral
image. As an alternative, a user may request a software program to
search the registered image for a particular disease or condition,
and the software may highlight the sections in any of the visual,
unsupervised spectral, and supervised spectral images that are
labeled with the particular disease or condition. This
advantageously allows a user to obtain a diagnosis in real-time,
and also allows the user view a visual image, which he/she is
familiar with, while accessing highly sensitive spectroscopically
obtained data.
[0224] The diagnosis may include a binary output, such as an "is/is
not" type output, that indicates the presence or lack of a disease
or condition. In addition, the diagnosis may include, but is not
limited to an adjunctive report, such as a probability of a match
to a disease or condition, an index, or a relative composition
ratio.
[0225] In accordance with aspects of the method of the invention,
gross architectural features of a tissue section may be analyzed
via spectral patterns to distinguish gross anatomical features that
are not necessarily related to disease. Such procedures, known as
global digital staining (GDS), may use a combination of supervised
and unsupervised multivariate methods. GDS may be used to analyze
anatomical features including, but not limited to, glandular and
squamous epithelium, endothelium, connective tissue, bone, and
fatty tissue.
[0226] In GDS, a supervised diagnostic algorithm may be constructed
from a training dataset that includes multiple samples of a given
disease from different patients. Each individual tissue section
from a patient may be analyzed as described above, using spectral
image data acquisition, pre-processing of the resulting dataset,
and analysis by an unsupervised algorithm, such as HCA. The HCA
images may be registered with corresponding stained tissue, and may
be annotated by a pathologist. This annotation step, indicated in
FIGS. 15A-C, allows the extraction of spectra corresponding to
typical manifestation of tissue types or disease stages and states,
or other desired features. The resulting typical spectra, along
with their annotated medical diagnosis, may subsequently be used to
train a supervised algorithm, such as an ANN, that is specifically
suited to detect the features it was trained to recognize.
[0227] According to the GDS method, the sample may be stained using
classical stains or immuno-histochemical agents. When the
pathologist receives the stained sample and inspects it using a
computerized imaging microscope, the spectral results may be
available to the computer controlling the visual microscope. The
pathologist may select any tissue spot on the sample and receive a
spectroscopy-based diagnosis. This diagnosis may overlay a
grayscale or pseudo-color image onto the visual image that outlines
all regions that have the same spectral diagnostic
classification.
[0228] FIG. 15A is a visual microscopic image of H&E-stained
lymph node tissue section. FIG. 15B shows a typical example of
global discrimination of gross anatomical features, such as capsule
and interior of lymph node. FIG. 15B is a global digital staining
image of section shown in FIG. 15A, distinguishing capsule and
interior of lymph node.
[0229] Areas of these gross anatomical features, which are
registered with the corresponding visual image, may be selected for
analysis based on more sophisticated criteria in the spectral
pattern dataset. This next level of diagnosis may be based on a
diagnostic marker digital staining (DMDS) database, which may be
solely based on SHP results, for example, or may contain spectral
information collected using immuno-histochemical (IHC) results. For
example, a section of epithelial tissue may be selected to analyze
for the presence of spectral patterns indicative of abnormality
and/or cancer, using a more diagnostic database to scan the
selected area. An example of this approach is shown schematically
in FIG. 15C, which utilizes the full discriminatory power of SHP
and yields details of tissue features in the lymph node interior
(such as cancer, lymphocytes, etc.), as may be available only after
immune-histochemical staining in classical histopathology. FIG. 15C
is a DMDS image of section shown in FIG. 15A, distinguishing
capsule, metastatic breast cancer, histiocytes, activated
B-lymphocytes and T-lymphocytes.
[0230] The relationship between GDS and DMDS is shown by the
horizontal progression marked in dark blue and purple,
respectively, in the schematic of FIG. 16. Both GDS and DMDS are
based on spectral data, but may include other information, such as
IHC data. The actual diagnosis may also be carried out by the same
or a similarly trained diagnostic algorithm, such as a hANN. Such a
hANN may first analyze a tissue section for gross anatomical
features detecting large variance in the dataset of patterns
collected for the tissue (the dark blue track). Subsequent
"diagnostic element" analysis may be carried out by the hANN using
a subset of spectral information, shown in the purple track. A
multi-layer algorithm in binary form may be implemented, for
example. Both GDS and DMDS may use different database subsections,
shown as Gross Tissue Database and Diagnostic Tissue Database in
FIG. 16, to arrive at the respective diagnoses, and their results
may be superimposed on the stained image after suitable image
registration.
[0231] According to an example method in accordance with aspects of
the invention, a pathologist may provide certain inputs to ensure
that an accurate diagnosis is achieved. For example, the
pathologist may visually check the quality of the stained image. In
addition, the pathologist may perform selective interrogation to
change the magnification or field of view of the sample.
[0232] The method according to aspects of the invention may be
performed by a pathologist viewing the biological specimen and
performing the image registration. Alternatively, since the
registered image contains digital data that may be transmitted
electronically, the method may be performed remotely.
[0233] Methods may be demonstrated by the following non-limiting
examples.
Example 2--Lymph Node Section
[0234] FIG. 17 shows a visual image of an H&E-stained axillary
lymph node section measuring 1 mm.times.1 mm, containing a breast
cancer micrometastasis in the upper left quadrant. FIG. 17B is a
SHP-based digitally stained region of breast cancer
micrometastasis. By selecting, for example, by clicking using a
cursor controlled mouse, in the general area of the
micrometastasis, a region that was identified by SHP to be
cancerous is highlighted in red as shown in FIG. 17B. FIG. 17C is a
SHP-based digitally stained region occupied by B-lymphocyes. By
pointing toward the lower right corner, regions occupied by
B-lymphocyte are marked in light blue, as shown in FIG. 17C. FIG.
17D is a SHP-based digitally stained region that shows regions
occupied by histocytes, which are identified by the arrow.
[0235] Since the SHP-based digital stain is based on a trained and
validated repository or database containing spectra and diagnoses,
the digital stain rendered is directly relatable to a diagnostic
category, such as "metastatic breast cancer," in the case of FIG.
17B. The system may be first used as a complementary or auxiliary
tool by a pathologist, although the diagnostic analysis may be
carried out by SHP. As an adjunctive tool, the output may be a
match probability and not a binary report, for example. FIG. 18
shows the detection of individual and small clusters of cancer
cells with SHP.
Example 3--Fine Needle Aspirate Sample of Lung Section
[0236] Sample sections were cut from formalin fixed paraffin
embedded cell blocks that were prepared from fine needles aspirates
of suspicious legions located in the lung. Cell blocks were
selected based on the criteria that previous histological analysis
had identified an adenocarcinoma, small cell carcinoma (SCC) or
squamous cell carcinoma of the lung. Specimens were cut by use of a
microtome to provide a thickness of about 5 .mu.m and subsequently
mounted onto low-e microscope slides (Kevley Technologies, Ohio,
USA). Sections were then deparaffinized using standard protocols.
Subsequent to spectroscopic data collection, the tissue sections
were hematoxylin and eosin (H&E) stained to enable
morphological interpretations by a histopathologist.
[0237] A Perkin Elmer Spectrum 1/Spotlight 400 Imaging Spectrometer
(Perkin Elmer Corp, Shelton, Conn., USA) was employed in this
study. Infrared micro-spectral images were recorded from 1
mm.times.1 mm tissue areas in transflection
(transmission/reflection) mode, with a pixel resolution of 6.25
.mu.m.times.6.25 .mu.m, a spectral resolution of 4 cm.sup.-1, and
the co-addition of 8 interferograms, before Norton-Beer apodization
(see, e.g., Naylor, et al. J Opt. Soc. Am., A24:3644-3648 (2007))
and Fourier transformation. An appropriate background spectrum was
collected outside the sample area to ratio against the single beam
spectra. The resulting ratioed spectra were then converted to
absorbance. Each 1 mm.times.1 mm infrared image contains
160.times.160, or 25,600 spectra.
[0238] Initially, raw infrared micro-spectral data sets were
imported into and processed using software written in Matlab
(version R2009a, Mathworks, Natick, Mass., USA). A spectral quality
test was performed to remove all spectra that were recorded from
areas where no tissue existed, or displayed poor signal to noise.
All spectra that pass the test were then baseline off-set
normalized (subtraction of the minimal absorbance intensity across
the entire spectral vector), converted to second derivative
(Savitzy-Golay algorithm (see, e.g., Savitzky, et al. Anal. Chem.,
36:1627 (1964)), 13 smoothing points), cut to only include
intensity values recorded in the 1350 cm.sup.-1-900 cm.sup.-1
spectral region, and finally vector normalized.
[0239] Processed data sets were imported into a software system and
HCA performed using the Euclidean distance to define spectral
similarity, and Ward's algorithm (see, e.g., Ward, J Am. Stat.
Assoc., 58:236 (1963)) for clustering. Pseudo-color cluster images
that describe pixel cluster membership, were then assembled and
compared directly with H&E images captured from the same
sample. HCA images of between 2 and 15 clusters, which describe
different clustering structures, were assembled by cutting the
calculated HCA dendrogram at different levels. These cluster images
were then provided to collaborating pathologists who confirmed the
clustering structure that best replicated the morphological
interpretations they made upon the H&E-stained tissue.
[0240] Infrared spectra contaminated by underlying base line
shifts, unaccounted signal intensity variations, peak position
shifts, or general features not arising from or obeying LambertBeer
law were corrected by a sub-space model version of EMSC for Mie
scattering and reflection contributions to the recorded spectra
(see B. Bird, M. Miljkovi and M. Diem, "Two step resonant Mie
scattering correction of infrared micro-spectral data: human lymph
node tissue", J. Biophotonics, 3 (8-9) 597-608 (2010)). Initially,
1000 recorded spectra for each cancer type were pooled into
separate data sets from the infrared images presented in FIG.
19A-19F.
[0241] These data sets were then searched for spectra with minimal
scattering contributions, a mean for each cancer type was
calculated to increase signal to noise, and KK transforms were
calculated for each cell type, as shown in FIG. 19A and FIG. 19B.
FIG. 19A shows raw spectral data sets comprising cellular spectra
recorded from lung adenocarcinoma, small cell carcinoma, and
squamous cell carcinoma cells. FIG. 19B shows corrected spectral
data sets comprising cellular spectra recorded from lung
adenocarcinoma, small cell carcinoma, and squamous cell carcinoma
cells, respectively. FIG. 19C shows standard spectra for lung
adenocarcinoma, small cell carcinoma, and squamous cell
carcinoma.
[0242] A sub space model for Mie scattering contributions was
constructed by calculating 340 Mie scattering curves that describe
a nuclei sphere radius range of 6 .mu.m-40 .mu.m, and a refractive
index range of 1.1-1.5, using the Van de Hulst approximation
formulae (see, e.g., Brussard, et al., Rev. Mod. Phys., 34:507
(1962)). The first 10 principal components that describe over 95%
of the variance composed in these scattering curves, were then used
in a addition to the KK transforms for each cancer type, as
interferences in a 1 step EMSC correction of data sets. The EMSC
calculation took approximately 1 sec per 1000 spectra. FIG. 19D
shows KK transformed spectra calculated from spectra in FIG. 19C.
FIG. 19E shows PCA scores plots of the multi class data set before
EMSC correction. FIG. 19F shows PCA scores plots of the multi class
data set after EMSC correction. The analysis was performed on the
vector normalized 1800 cm.sup.-1-900 cm.sup.-1 spectral region.
[0243] FIG. 20A shows mean absorbance spectra of lung
adenocarcinoma, small cell carcinoma, and squamous carcinoma,
respectively. These were calculated from 1000 scatter corrected
cellular spectra of each cell type. FIG. 20B shows second
derivative spectra of absorbance spectra displayed in FIG. 20A. In
general, adenocarcinoma and squamous cell carcinoma have similar
spectral profiles in the low wavenumber region of the spectrum.
However, the squamous cell carcinoma displays a substantially low
wavenumber shoulder for the amide I band, which has been observed
for spectral data recorded from squamous cell carcinoma in the oral
cavity (Papamarkakis, et al. (2010), Lab. Invest., 90:589-598). The
small cell carcinoma displays very strong symmetric and
anti-symmetric phosphate bands that are shifted slightly to higher
wavenumber, indicating a strong contribution of phospholipids to
the observed spectra.
[0244] Since the majority of sample area is composed of blood and
non-diagnostic material, the data was pre-processed to only include
diagnostic material and correct for scattering contributions. In
addition, HCA was used to create a binary mask and finally classify
the data. This result is shown in FIGS. 21A-21C. FIG. 21A shows 4
stitched microscopic R&E-stained images of 1 mm.times.1 mm
tissue areas comprising adenocarcinoma, small cell carcinoma, and
squamous cell carcinoma cells, respectively. FIG. 21B is a binary
mask image constructed by performance of a rapid reduced RCA
analysis upon the 1350 cm.sup.-1-900 cm.sup.-1 spectral region of
the 4 stitched raw infrared images recorded from the tissue areas
shown in FIG. 21A. The regions of diagnostic cellular material and
blood cells are shown. FIG. 21C is a 6-cluster RCA image of the
scatter corrected spectral data recorded from regions of diagnostic
cellular material. The analysis was performed on the 1800
cm.sup.-1-900 cm.sup.-1 spectral region. The regions of squamous
cell carcinoma, adenicarcinoma, small cell carcinoma, and diverse
desmoplastic tissue response are shown. Alternatively, these
processes can be replaced with a supervised algorithm, such as an
ANN.
[0245] The results presented in the Examples above show that the
analysis of raw measured spectral data enables the differentiation
of SCC and non-small cell carcinoma (NSCC). After the raw measured
spectra are corrected for scattering contributions, adenocarinoma
and squamous cell carcinoma according to methods in accordance with
aspects of the invention, however, the two subtypes of NSCC, are
clearly differentiated. Thus, these Examples provide strong
evidence that this spectral imaging method may be used to identify
and correctly classify the three main types of lung cancer.
[0246] FIG. 22 shows various features of an example computer system
100 for use in conjunction with methods in accordance with aspects
of invention, including, but not limited to image registration and
training. As shown in FIG. 22, the computer system 100 may be used
by a requestor 101 via a terminal 102, such as a personal computer
(PC), minicomputer, mainframe computer, microcomputer, telephone
device, personal digital assistant (PDA), or other device having a
processor and input capability. The server module may comprise, for
example, a PC, minicomputer, mainframe computer, microcomputer, or
other device having a processor and a repository for data or that
is capable of accessing a repository of data. The server module 106
may be associated, for example, with an accessible repository of
disease based data for use in diagnosis.
[0247] Information relating to a diagnosis, for example, via a
network, 110, such as the Internet, for example, may be transmitted
between the analyst 101 and the server module 106. Communications
may be made, for example, via couplings 111, 113, such as wired,
wireless, or fiberoptic links.
[0248] Aspects of the invention may be implemented using hardware,
software or a combination thereof and may be implemented in one or
more computer systems or other processing systems. In one
variation, aspects of the invention are directed toward one or more
computer systems capable of carrying out the functionality
described herein. An example of such a computer system 200 is shown
in FIG. 23.
[0249] Computer system 200 includes one or more processors, such as
processor 204. The processor 204 is connected to a communication
infrastructure 206 (e.g., a communications bus, cross-over bar, or
network). Various software aspects are described in terms of this
exemplary computer system. After reading this description, it will
become apparent to a person skilled in the relevant art(s) how to
implement the aspects of invention using other computer systems
and/or architectures.
[0250] Computer system 200 can include a display interface 202 that
forwards graphics, text, and other data from the communication
infrastructure 206 (or from a frame buffer not shown) for display
on the display unit 230. Computer system 200 also includes a main
memory 208, preferably random access memory (RAM), and may also
include a secondary memory 210. The secondary memory 210 may
include, for example, a hard disk drive 212 and/or a removable
storage drive 214, representing a floppy disk drive, a magnetic
tape drive, an optical disk drive, etc. The removable storage drive
214 reads from and/or writes to a removable storage unit 218 in a
well-known manner. Removable storage unit 218, represents a floppy
disk, magnetic tape, optical disk, etc., which is read by and
written to removable storage drive 214. As will be appreciated, the
removable storage unit 218 includes a computer usable storage
medium having stored therein computer software and/or data.
[0251] In alternative variations, secondary memory 210 may include
other similar devices for allowing computer programs or other
instructions to be loaded into computer system 200. Such devices
may include, for example, a removable storage unit 222 and an
interface 220. Examples of such may include a program cartridge and
cartridge interface (such as that found in video game devices), a
removable memory chip (such as an erasable programmable read only
memory (EPROM), or programmable read only memory (PROM)) and
associated socket, and other removable storage units 222 and
interfaces 220, which allow software and data to be transferred
from the removable storage unit 222 to computer system 200.
[0252] Computer system 200 may also include a communications
interface 224. Communications interface 224 allows software and
data to be transferred between computer system 200 and external
devices. Examples of communications interface 224 may include a
modem, a network interface (such as an Ethernet card), a
communications port, a Personal Computer Memory Card International
Association (PCMCIA) slot and card, etc. Software and data
transferred via communications interface 224 are in the form of
signals 228, which may be electronic, electromagnetic, optical or
other signals capable of being received by communications interface
224. These signals 228 are provided to communications interface 224
via a communications path (e.g., channel) 226. This path 226
carries signals 228 and may be implemented using wire or cable,
fiber optics, a telephone line, a cellular link, a radio frequency
(RF) link and/or other communications channels. In this document,
the terms "computer program medium" and "computer usable medium"
are used to refer generally to media such as a removable storage
drive 214, a hard disk installed in hard disk drive 212, and
signals 228. These computer program products provide software to
the computer system 200. Aspects of the invention are directed to
such computer program products.
[0253] Computer programs (also referred to as computer control
logic) are stored in main memory 208 and/or secondary memory 210.
Computer programs may also be received via communications interface
224. Such computer programs, when executed, enable the computer
system 200 to perform the features in accordance with aspects of
the invention, as discussed herein. In particular, the computer
programs, when executed, enable the processor 204 to perform such
features. Accordingly, such computer programs represent controllers
of the computer system 200.
[0254] In a variation where aspects of the invention are
implemented using software, the software may be stored in a
computer program product and loaded into computer system 200 using
removable storage drive 214, hard drive 212, or communications
interface 224. The control logic (software), when executed by the
processor 204, causes the processor 204 to perform the functions as
described herein. In another variation, aspects of the invention
are implemented primarily in hardware using, for example, hardware
components, such as application specific integrated circuits
(ASICs). Implementation of the hardware state machine so as to
perform the functions described herein will be apparent to persons
skilled in the relevant art(s).
[0255] In yet another variation, aspects of the invention are
implemented using a combination of both hardware and software.
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