U.S. patent application number 14/314900 was filed with the patent office on 2014-10-16 for quantitative, multispectral image analysis of tissue specimens stained with quantum dots.
The applicant listed for this patent is Ventana Medical Systems, Inc.. Invention is credited to Ned C. Haubein, G. Scott Lett, Gary Pestano.
Application Number | 20140310635 14/314900 |
Document ID | / |
Family ID | 39733102 |
Filed Date | 2014-10-16 |
United States Patent
Application |
20140310635 |
Kind Code |
A1 |
Lett; G. Scott ; et
al. |
October 16, 2014 |
Quantitative, Multispectral Image Analysis of Tissue Specimens
Stained with Quantum Dots
Abstract
A biological sample such as a tissue section is stained with one
or more quantum dots and possibly other fluorophores (total number
of fluorophores N). A camera coupled to a microscope generates an
image of the specimen at a plurality of different wavelengths
within the emission spectral band of the N fluorophores. An
analysis module calculates coefficients C1 . . . CN at each pixel
which are related to the concentration of each of the individual
fluorophores. Morphological processing instructions find biological
structures. A display module displays quantitative analysis results
to the user. The quantitative analysis display includes histograms
of the biological structures, scatter plots of fluorophore
concentrations, statistical data, spectral data, image data and
still others.
Inventors: |
Lett; G. Scott; (Hightstown,
NJ) ; Haubein; Ned C.; (Yardley, PA) ;
Pestano; Gary; (Lafayette, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ventana Medical Systems, Inc. |
Tucson |
AZ |
US |
|
|
Family ID: |
39733102 |
Appl. No.: |
14/314900 |
Filed: |
June 25, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13454205 |
Apr 24, 2012 |
8798394 |
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14314900 |
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11999914 |
Dec 6, 2007 |
8244021 |
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13454205 |
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60876493 |
Dec 20, 2006 |
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Current U.S.
Class: |
715/771 |
Current CPC
Class: |
G01N 2021/6421 20130101;
G06F 3/04847 20130101; G01N 21/6428 20130101; G06F 3/04842
20130101; G01N 2021/6439 20130101; G01N 21/6458 20130101; G01N
2021/6423 20130101 |
Class at
Publication: |
715/771 |
International
Class: |
G06F 3/0484 20060101
G06F003/0484 |
Claims
1-34. (canceled)
35. A workstation for quantitative analysis of a type of tissue
specimen containing cells or cellular components stained with a
plurality of fluorophores, comprising: a display for displaying
quantitative analysis of the specimen and one or more images of the
tissue specimen; a processing unit; and a memory storing software
instructions for execution by the processing unit and multispectral
image data acquired from the stained tissue specimen with the aid
of a at least one of a microscope and a camera, wherein the
software instructions present on the display a user-selectable area
for identifying, in the one or more images of the specimen, at
least one of the plurality of fluorophores applied to the specimen,
a user-selectable area for indicating whether the tissue specimen
is to be analyzed using a reference spectrum for autofluorescence
corresponding to the type of the tissue specimen, and a user
selectable area for selecting the colors to represent, in the one
or more images, the at least one the plurality of fluorophores
present in the specimen for display purposes, and wherein the color
selected for the at least one of the plurality of fluorophores is
different from a color of the at least one of the plurality of
fluorophores that was selected.
36. The apparatus of claim 35, wherein the software instructions
further provide a feature allowing a user to select via the display
a segment of the one or more images of the specimen, and wherein
the software instructions generate a displaying of quantitative
results for the selected segment of the one or more images of the
tissue specimen.
37. A network server generating quantitative analysis data for a
remote workstation for quantitative analysis of a type of a tissue
specimen containing cells or cellular components stained with a
plurality of fluorophores, the workstation having a display, the
network server comprising: a processing unit; and a memory storing
software instructions for execution by the processing unit and
multispectral image data acquired from the stained specimen with
the aid of at least one of a microscope and a camera, wherein the
software instructions present on the display a user-selectable area
for identifying, in one or more images of the specimen, at least
one of the plurality of fluorophores applied to the specimen, a
user-selectable area for indicating whether the specimen is to be
analyzed using a reference spectrum for autofluorescence
corresponding to the type of the tissue specimen, and a user
selectable area for selecting the colors to represent, in the one
or more images, the at least one the plurality of fluorophores
present in the specimen for display purposes, and wherein the color
selected for the at least one of the plurality of fluorophores is
different from a color of the at least one of the plurality of
fluorophores that was selected.
38. A method for interfacing with a user interface for quantitative
analysis of a type of tissue specimen stained with a plurality of
fluorophores, and wherein the tissue specimen is imaged after being
stained, comprising: visually displaying, on a display, a
user-selectable area that allows a user to identify at least one of
the plurality of fluorophores applied to the tissue specimen;
visually displaying, on the display, a user-selectable area that
allows a user to select whether an image of the tissue specimen is
analyzed using a reference spectrum for autofluorescence
corresponding to the tissue type of the tissue specimen; and
visually displaying, on the display, a user selectable area for
selecting the colors to represent, in the image, the plurality of
fluorophores present in the specimen, and wherein a different color
is selected for each of the plurality of fluorophores.
Description
PRIORITY
[0001] This application in a continuation of U.S. application Ser.
No. 11/999,914 filed Dec. 6, 2007, pending, which claims priority
benefits under 35 U.S.C. .sctn.119(e) to prior U.S. provisional
application Ser. No. 60/876,493 filed Dec. 20, 2006, the contents
of which are incorporated by reference herein.
BACKGROUND
[0002] This invention relates to the field of systems and methods
for analysis of biological specimens such as tissue sections,
blood, cell cultures and the like. More particularly, this
invention relates to a system, method and apparatus for analysis of
images of biological specimens which are stained with one or more
fluorophores, at least one of which is a nano-crystalline
luminescent semiconductor material known in the art as a "quantum
dot." This invention also relates to methods of presentation of
quantitative data resulting from such analysis to a user.
[0003] It is known in the art that biological specimens, such as
tissue sections from human subjects, can be treated with a stain
containing an organic fluorophore conjugated to an antibody which
binds to protein, protein fragments, or other targets in the
specimen. The stained specimen is then illuminated with light and
the stain fluoresces. A digital camera attached to a microscope is
then used to capture an image of the specimen. The areas where the
fluorophore/antibody combination became bound to the target of
interest (e.g., proliferation protein produced by cancerous cells)
appears as colored regions in the image of the specimen, with the
color of the area dictated by the fluorescence spectrum of the
fluorophore applied to the specimen. In addition to the visible
spectrum, the fluorescence signal may be detected in the infra-red
or ultra-violet regions, depending on emission spectrum of the
particular fluorophore. A stain containing two or more fluorophores
can also be applied to the specimen. These methods have a variety
of uses, including diagnosis of disease, assessment of response to
treatment, and development of new drugs to fight disease.
[0004] More recently, quantum dots have been developed as a stain
material for biological staining and imaging applications. The use
of quantum dots poses several advantages over traditional organic
fluorophores for use in biological staining applications. These
advantages include narrow emission band peaks, broad absorption
spectra, intense signals, and strong resistance to bleaching or
other degradation.
[0005] Prior art references disclosing quantum dots and their
application to biochemical imaging applications include U.S. Pat.
Nos. 6,322,901, 5,990,749, and 6,274,323. Representative image
capture and analysis systems and related methods are disclosed in
the U.S. Pat. Nos. 6,215,892 and 6,403,947 and published PCT
applications WO 00/31534, WO 00/17808 and WO 98/43042. Other prior
art of interest includes US Patent Application Publication US
2001/0033374 A1; US Patent Application Publication 2002/0001080 A1;
Fountaine et al., Multispectral imaging of clinically relevant
cellular targets in tonsil and lymphoid tissue using semiconductor
quantum dots, Modern Pathology (2006) 1-11, and Huth et al.,
Fourier Transformed Spectral Bio-Imaging for Studying the
Intracellular Fate of Liposomes, Cytometry Part A, vol. 57A pp.
10-21 (2004). The entire content of the above-cited references are
incorporated by reference herein.
SUMMARY
[0006] The following embodiments and aspects thereof are described
and illustrated in conjunction with systems, tools and methods
which are meant to be exemplary and illustrative, not limiting in
scope. All questions regarding scope of the invention are to be
determined with reference to the appended claims and claims
hereafter introduced into the application.
[0007] In a first aspect, a system is disclosed for analysis of a
biological specimen. The specimen may, for example, take the form
of a tissue section obtained from a human or animal subject. The
specimen may be living cellular tissue, frozen cells, tumor cells,
blood, throat culture, or other; the type or nature of specimen is
not particularly important. Typically, the specimen is mounted on a
slide for analysis. The analysis may be for purposes of
identification and study of the sample for presence of
proliferation proteins or tumor cells, or for other purposes such
as genomic DNA detection, messenger RNA detection, protein
detection, or other. The biological specimen has between 1 and N
discrete fluorophore(s) applied to the specimen, the fluorophores
including at least one quantum dot. For example, the specimen may
be treated with 2, 3 or 5 different quantum dots (N=2, 3 or 5 in
these examples). One or more of the fluorophores applied to the
specimen may be organic fluorophores.
[0008] The system includes a microscope and attached digital camera
capturing images of the specimen. Each image is composed of a
plurality of pixels corresponding to the individual picture
elements (pixels) of the digital camera. The camera captures an
image of the specimen at a plurality of discrete wavelengths. The
number of wavelengths (M herein) may be 5, 10, 20 or more. The
wavelengths include discrete wavelengths at which the 1 . . . N
fluorophores produce a luminescent response to incident light. A
data set representing two dimensional pixel data at M wavelengths
is referred to herein occasionally as an "image cube."
[0009] The system further includes a workstation which includes a
processing unit executing software instructions which performing
certain processing steps on the images generated by the camera.
These processing steps include:
[0010] a) an unmixing process, which processes the plurality of
images in conjunction with reference spectral data associated with
the 1 . . . N fluorophores and responsively calculates coefficients
C.sub.1 . . . C.sub.N at each pixel location, wherein the
coefficients C.sub.1 . . . C.sub.N are related to the
concentrations of the 1 . . . N fluorophores present in the sample
at each pixel location;
[0011] b) at least one morphological processing process identifying
at least one biological structure in the specimen;
[0012] c) a quantitative analysis process calculating fluorophore
concentrations for the biological structures identified by process
b) from the coefficients C.sub.1 . . . C.sub.N; and
[0013] d) a display process for displaying the results of the
quantitative analysis process c) on a display associated with the
workstation.
[0014] A variety of display tools are disclosed by which a user may
interact with the system and obtain displays of quantitative
results from the specimen. In one embodiment, the biological
structures which are identified by the morphological processing
process are cells or cellular components. The morphological
processing process measures the size of the biological structures,
and counts the number of biological structures identified in the
specimen. The results of the quantitative analysis process are
presented as a histogram of the number of biological structures
sorted by size of the biological structures. The histograms may
also include histograms of the size distribution of cells having a
positive signal for each of the 1 . . . N fluorophores applied to
the specimen.
[0015] In another embodiment, the display process includes a
feature allowing a user to select a segment of an image of the
specimen displayed on the display (e.g., a region of the sample
having a high concentration of cells with a high fluorescent
signal) and the display process displays quantitative results for
the selected segment of the image. As a further enhancement, the
quantitative results are displayed as a plot of concentration of
one quantum dot as a function of concentration of a second quantum
dot for cells positive for both quantum dots. Such a plot can
visually be represented as a scatter plot. Scatter plots can be
displayed for either the entire image or any selected sub-segment
of the slide.
[0016] In another embodiment, the coefficients C.sub.1 . . .
C.sub.N are scaled to absolute concentrations of the fluorophores
in the specimen (e.g., nanomols per liter, number of quantum dots
per cell, or other system of units). Furthermore, the plots of
concentration of fluorophores can be expressed in units of absolute
concentrations.
[0017] The display of the quantitative results may include display
of an image of the specimen on the same display. The image can be
constructed from one or more of the M images, or, more preferably,
from one or more of the coefficients C.sub.1 . . . C.sub.N. It will
be recalled that the coefficients are obtained by the unmixing
processes and are known for each pixel. For example, if the user is
studying a histogram of the size distribution of cells have
positive signal for a quantum dot fluorophore whose emission
spectrum peaks at 625 nm, the display may simultaneously show an
image of the specimen with the image generated from the coefficient
C.sub.i which corresponds to the 625 nm quantum dot fluorophore. In
other words, the image masks (omits) the signal contribution from
all other fluorophores which may be present in the sample and only
reveals the signal from the 625 nm fluorophore. The quantitative
results may further include statistical data for the segment of the
image selected by the user.
[0018] In one embodiment the display process further provides a
tool by which color intensity for one or more selected fluorophores
can be selectively weighted by the user to thereby change the
appearance of the image on the display. For example, the user may
wish to view an image of the specimen that reveals the distribution
of 605 nm and 625 nm quantum dots in the specimen. When such image
is displayed, a tool is presented by which a user can selectively
weight (or attenuate), either the 605 nm quantum dot signal or the
625 nm quantum dot signal. The weighting may be used for example to
strengthen a weak fluorophore signal and allow the user to more
readily perceive the distribution of the fluorophore in the
biological structures (e.g., cells) in the specimen.
[0019] The display process may combine the various analytical
features and provide a variety of different tools for analyzing the
specimen. For example, the display process may include processes
for displaying i) an image of the specimen constructed from one or
more of the coefficients C.sub.1 . . . C.sub.N (either an image of
the entire specimen or some sub-segment of the specimen); ii) a
histogram of biological structures identified in the image in i)
sorted by size of the biological structures, for at least at least
one of the fluorophores applied to the sample; and iii) one or more
scatter plots of concentration of one of the fluorophores as a
function of concentration of one of the other fluorophores, for
biological structures having a positive signal for both
fluorophores. These features may be combined with the display of
additional statistical data, tools for selection of portions of an
image conducting further quantitative analysis, and still other
features.
[0020] In still another embodiment, the display process includes a
feature by which a user may select a portion of a scatter plot,
histogram, or other visualization of the quantitative data and
conduct further quantitative analysis on the portions of the
specimen corresponding to the selected portion of the scatter plot,
histogram or other visualization. For example, a user may select
the portion of a histogram corresponding to larger cells with
relatively high concentrations of a particular fluorophore (e.g.
625 nm quantum dot). The display process creates a new display
which displays additional quantitative data for the larger cells in
the histogram which were selected by the user. Such quantitative
data may take a variety of forms, such has a new scatter plot
showing the concentration of the 605 nm quantum dot as a function
of the concentration of the 625 nm quantum dot, for the cells which
correspond to the portion of the histogram selected by the
user.
[0021] Additionally, the display process may display an image of
the specimen with the biological structures associated with the
selected portion of the histogram, scatter plot or other
visualization, with the biological structures highlighted, e.g. in
a contrasting color. The image can be constructed from the
concentration coefficient corresponding to the 625 nm quantum dot,
the 605 quantum dot, other fluorophore present in the sample, e.g.,
autofluorescence, combination thereof, or other.
[0022] In yet another aspect of this disclosure, a method is
provided for analysis of a biological specimen in which between 1 .
. . N quantum dots are applied to the specimen. The method includes
the steps of:
[0023] (a) capturing a set of images of the specimen with a camera
coupled to a microscope at M different wavelengths, where M is an
integer greater than 2, the images arranged as an array of
pixels;
[0024] (b) determining, from the set of M images, coefficients
C.sub.1 . . . C.sub.N for each pixel, wherein the coefficients
C.sub.1 . . . C.sub.N are related to the concentrations of the 1 .
. . N quantum dots present in the specimen imaged by each
pixel;
[0025] (c) morphologically processing an image constructed from one
or more of the coefficients C.sub.1 . . . C.sub.N to identify cells
or cellular components in the specimen,
[0026] (d) conducting a quantitative analysis of cells or cellular
components identified in step (c) from the coefficients C.sub.1 . .
. C.sub.N; and
[0027] (e) displaying the results of the quantitative analysis
process (d) on a display of a workstation.
[0028] The quantitative analysis and displaying steps may
incorporate one or more of the quantitative analysis and display
features highlighted above in the discussion of the system aspect
of this invention.
[0029] In still another aspect of this disclosure, the invention
can be characterized as biological specimen analysis apparatus
taking the form of a machine readable storage medium (e.g., hard
disk, CD, or other medium) which contains a set of software
instructions for execution by a processing unit, e.g., a computer
workstation. The processing unit has access to an image cube of a
specimen stained with one or more quantum dots and imaged with a
camera coupled to a microscope. The image cube may be a set of M
images of the sample taken at M different wavelengths, where M is
an integer greater than 2. The images are arranged as an array of
pixels. The set of images can be stored locally on the processing
unit, obtained over a network, or also stored on the
machine-readable storage medium. The instructions comprise a set of
instructions for:
[0030] (a) determining from the set of M images coefficients
C.sub.1 . . . C.sub.N for each pixel, wherein the coefficients
C.sub.1 . . . C.sub.N are related to the concentrations of the one
or more quantum dots present in the specimen imaged by each
pixel;
[0031] (b) morphologically processing an image of the specimen to
identify cells or cellular components in the specimen,
[0032] (c) conducting a quantitative analysis of the specimen
including calculating quantum dot concentrations for the cells or
cellular components identified in step (b) from the coefficients
C.sub.1 . . . C.sub.N; and
[0033] (d) generating data for display of the results of the
quantitative analysis process (c) on a display associated with the
processing unit.
[0034] As with the method aspect of the invention, the software
instructions may incorporate one or more of the quantitative
analysis and display features highlighted above in the discussion
of the system aspect of this invention.
[0035] The image analysis, quantitative analysis and display
methods are preferably designed to be used with a variety of
commercially available imaging platforms, staining systems and
workstations. Accordingly, in one possible embodiment the software
instructions can be provided as a separate product that enables
existing imaging equipment and computer workstations to practice
the invention, without necessitating purchase of expensive new
hardware. Thus, the software instructions stored on a machine
readable medium (e.g., CD) have their own special utility and
advantage.
[0036] In addition to the exemplary aspects and embodiments
described above, further aspects and embodiments will become
apparent by reference to the drawings and by study of the following
detailed descriptions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0037] FIG. 1 is block diagram of a system for analyzing a
biological specimen. The system includes a digital camera, a
microscope, and a workstation having a display and a memory storing
software instructions for processing images of the specimen
captured by the camera.
[0038] FIG. 2 is a more detailed block diagram of the workstation
of FIG. 1, showing software modules which are stored in memory of
the workstation.
[0039] FIG. 3 is a flow chart showing a sequence of processing
steps performed by the initialization and set-up module of FIG.
2.
[0040] FIG. 4 is a flow chart showing a sequence of processing
steps performed by the analysis module of FIG. 2.
[0041] FIG. 5 is an illustration of a set of images (M=22) of a
slide containing a biological specimen, each of the images captured
by the camera of FIG. 1 at a discrete wavelength; data from the set
of images of FIG. 5 are referred to herein occasionally as an
"image cube."
[0042] FIG. 6 is a graph of the reference emission spectra of seven
quantum dot fluorophores, one or more of which are applied to the
specimen. The spectra of FIG. 6 are stored in the workstation and
applied to the image cube of FIG. 5 in a spectral unmixing
algorithm in order to calculate coefficients C.sub.1 . . . C.sub.N
at each pixel location. The coefficients C.sub.1 . . . C.sub.N are
related to the concentrations of the 1 . . . N fluorophores
(quantum dots) present in the sample at each pixel location.
[0043] FIG. 7 is a typical image of the biological specimen in
which the luminescent emission from multiple quantum dots
contribute to the image and aid in highlighting cells or other
cellular structures such as nuclei or cell membranes. The image of
FIG. 7 can be generated from one or more the coefficients C.sub.1 .
. . C.sub.N.
[0044] FIG. 8 is an illustration of a display presented on the
workstation showing the user selecting the fluorophores which are
present in the image, assigning an informative label to that
fluorophore, and selecting a color to use to represent the
individual fluorophores.
[0045] FIG. 9 is another image of the biological specimen, composed
of two of the coefficients C.sub.1 . . . C.sub.N.
[0046] FIG. 10 is an illustration of a display presented on the
workstation showing a feature by which a user can weight (i.e.,
enhance or attenuate) the contribution of one fluorophore or
another in the generation of the image of FIG. 9. Note from FIG. 10
that one of the fluorophores may consist of autofluorescence from
the specimen.
[0047] FIG. 11 is an illustration of a display on the workstation
showing a set of quantitative data which is presented to the user
for the specimen, including histograms, scatter plots, statistical
data and image data.
[0048] FIG. 12 is an illustration of a display on the workstation
showing two separate images of the specimen, one from
autofluorescence and another from a quantum dot emitting at 625 nm,
and spectral data for a selected point or region in the images.
[0049] FIG. 13 is an illustration of two histograms showing
quantitative data and showing a feature by which a user can select
a sub-population of cells in one of the histograms and perform
additional quantitative and qualitative analysis on the selected
sub-population of cells.
[0050] FIG. 14 is an illustration of a display of the workstation
showing a scatter plot providing additional quantitative analysis
of the selected sub-population of cells selected by the user in
FIG. 13.
[0051] FIG. 15 is an illustration of a display presented on the
workstation showing an image of the specimen and a feature by which
a user can select a discrete sub-region of the image and have
additional quantitative analysis performed on the selected
sub-region.
[0052] FIG. 16 is an illustration of a display presented on the
workstation showing two images of the same portion of the specimen,
one showing the autofluorescence and the other the luminance from a
625 nm quantum dot, and spectra for the region of the specimen
selected in the procedure shown in FIG. 15.
[0053] FIG. 17 is an illustration of a display presented on the
workstation showing three images of the same region of the
specimen, one image showing the autofluorescence signal from the
specimen, one showing the signal from a 605 nm quantum dot, and one
showing the signal from a 655 nm quantum dot. FIG. 17 also shows
the spectra for the region of the specimen represented in the three
images.
[0054] FIG. 18 is another illustration of a display presented on
the workstation showing quantitative data from the specimen in the
form of histograms, scatter plots, statistical data, and image
data.
DETAILED DESCRIPTION
[0055] System and Software Overview
[0056] FIG. 1 is a block diagram of a system for analysis of a
biological specimen 10. The specimen 10 may, for example, take the
form of a tissue section obtained from a human or animal subject,
such as a formalin-fixed, paraffin-embedded tissue sample. The
specimen may be living cellular tissue, frozen cells, tumor cells,
blood, throat culture, or other; the type or nature of specimen is
not particularly important.
[0057] Typically, the specimen is mounted on a slide 18 or other
device for purposes of imaging by a camera system platform 22.
Computer analysis of images of the specimen is performed in a
workstation 34 in accordance with the present disclosure. The
analysis may be for purposes of identification and study of the
sample for presence of proteins, protein fragments or other markers
indicative of cancer or other disease, or for other purposes such
as genomic DNA detection, messenger RNA detection, protein
detection, detection of viruses, detection of genes, or other.
[0058] The biological specimen 10 is stained by means of
application of a stain containing one or more different
fluorophore(s). The number N of fluorophores that are applied to
the specimen can vary, but will typically be between 2 and say 10.
The fluorophores may comprise one or more nano-crystalline
semiconductor fluorophores (i.e., quantum dots) 12, each producing
a peak luminescent response in a different range of wavelengths.
Quantum dots are described in the patent and technical literature,
see for example U.S. Pat. Nos. 6,322,901, 5,990,749, and 6,274,323.
The term "quantum dot" is intended to be broadly read to encompass
such structures generally. Quantum dots, including conjugated
quantum dots, are commercially available from Invitrogen Corp.,
Evident Technologies, and others.
[0059] For example, the specimen 10 may be treated with 2, 3 or 5
different quantum dots (N=2, 3 or 5 in this example), for example
quantum dots which produce a peak luminescent response at 525, 600
and 625 nm. One or more of the fluorophores applied to the specimen
may be organic fluorophores 14 (e.g., DAPI, Texas Red), which are
well known in the art. Thus, the system of FIG. 1 can be used with
a specimen which is stained with just quantum dots, with quantum
dots in combination with conventional organic fluorophores, or just
conventional organic fluorophores. It is noted that quantum dots
have several important advantages over conventional organic
fluorophores. In practice, the quantum dots or other fluorophores
are conjugated to an antibody, which is designed to bind to a
target in the specimen, such as a protein.
[0060] In typical practice, the specimen is processed in an
automated staining/assay platform 16 which applies a stain
containing quantum dots and/or organic fluorophores to the
specimen. There are a variety of commercial products on the market
suitable for use as the staining/assay platform, one example being
the Discovery.TM. product of the assignee Ventana Medical Systems,
Inc.
[0061] After preliminary tissue processing and staining in the
platform 16, the slide 18 containing the specimen 10 is supplied to
a camera system platform 22. The platform 22 includes a light
source for illuminating the specimen 10 at wavelengths intended to
produce a luminescent response from the fluorophores applied to the
specimen. In the case of quantum dots, the light source may be a
broad spectrum light source. Alternatively, the light source may
comprise a narrow band light source such as a laser. The camera
platform also includes a microscope 24 having one or more objective
lenses and a digital imager (camera) 26 which is coupled to the
microscope in order to record high resolution, magnified digital
images of the specimen. As will be explained below, the specimen 10
is imaged by the camera 26 at a plurality of different wavelengths.
In order to capture images at a plurality of different wavelengths,
the camera platform 22 includes a set of spectral filters 28. Other
techniques for capturing images at different wavelengths may be
used. The camera 26 may take the form of a charge-coupled device
imager sensitive to light in a band covering the luminescent
response spectra of the fluorophores, e.g., between 400 and 900 nm.
Camera platforms suitable for imaging stained biological specimens
are known in the art and commercially available from companies such
as Zeiss, Canon, Applied Spectral Imaging, and others, and such
platforms are readily adaptable for use in the system, methods and
apparatus of this invention.
[0062] The camera 26 images the specimen at a plurality (M) of
discrete wavelengths and responsively generates an image of the
specimen at each of the M wavelengths. Each of the images is
composed of a plurality of pixels corresponding to the individual
picture elements (pixels) in the digital imager 26. The wavelengths
at which the specimen is imaged includes wavelengths at which the 1
. . . N fluorophores present in the sample produce a luminescent
response to incident light. For example, suppose the specimen is
stained with two quantum dots, having a peak luminescent response
at 625 nm and 605 nm. Suppose further that the nominal (reference)
spectra of such quantum dots has a Gaussian distribution with
appreciable response between 575 and 750 nm (see for example the
reference quantum dot spectra in FIG. 6 and the discussion below).
Therefore, the camera 26 is operated to image the specimen at say
10, 15, or 20 different wavelengths between 575 and 750 nm. As an
example, the camera (and any attendant spectral filters 28) is
operated so as to capture images of the specimen at 575, 600, 625,
650, 675, 700, 725 and 750 nm, such wavelengths overlapping the
reference spectra of the 605 and 625 nm quantum dot fluorophores at
wavelengths where the fluorophores produce a significant
luminescent response, M=8 in this example.
[0063] The data resulting from a set of M images of the specimen
(one taken at each of the M wavelengths) is referred to herein as
an "image cube". Referring again to FIG. 1, the image cube is
supplied to the workstation 34, either via a cable connection
between the camera imaging platform 22 and the workstation 34
(indicated at 46) or via a computer network 30 connecting the
camera imaging platform 22 to the workstation 34 or using any other
medium that is commonly used to transfer digital information
between computers. The image cube can also be supplied over the
network 30 to a network server 32 or database for storage and later
retrieval by the workstation 34. The workstation 34 includes a
central processing unit 36 and a memory 36, user input devices in
the form of a keyboard 40 and mouse 42, and a display 44. As will
be explained in the following discussion, the processor 36 executes
program instructions loaded in to the memory 38 which perform
analysis of the image cube, morphological processing of the images
or image data derived from such images, quantitative analysis, and
display of quantitative results to a user operating the workstation
34.
[0064] FIG. 2 is a more detailed illustration of the workstation of
FIG. 1 showing in greater detail certain software modules and data
stored in the memory 38. The memory includes three main software
instruction modules, namely an initialization and set-up module 50
(steps of which are shown in FIG. 3), an analysis module 52 (steps
of which are shown in FIG. 4) and a display module 54, the
operation of which will be described in conjunction with FIGS.
7-18. The memory 38 further stores certain other data which is used
or operated on by the modules 50, 52 and 54, namely run-time
libraries 60, configuration files 62, and reference spectral data
64 for the 1 . . . N fluorophores which are applied to the sample.
An example of the reference spectra 64 for an assay using seven
different quantum dots (Qdot 1 . . . Qdot 7) is shown in FIG. 6.
The reference spectra 64 are used in the spectral unmixing
algorithm in the analysis module 52, as will be explained in
greater detail below. The spectra 64 of FIG. 6 are offered by way
of example only to show that different quantum dots have different
spectra, including a different wavelength of peak luminescent
response and different peak intensity under the same illumination
conditions. The spectral data 64 will thus vary depending on the
particular quantum dots that are used in an assay. The spectral
data of FIG. 6 can be obtained from the manufacturer of the quantum
dots or alternatively obtained by testing examples of the quantum
dots using appropriate equipment.
[0065] The memory 38 further stores the image cube 70 comprising
the M images of the spectrum, taken at M different wavelengths. The
memory further stores a list 72 of the wavelengths at which the
specimen was imaged, and a list 74 of the exposure times at each of
the wavelengths. The image cube and lists 72 and 74 are inputs to
the analysis module 52.
[0066] The memory further stores the calculated concentration
coefficients C.sub.1 . . . C.sub.N (item 80). The coefficients are
outputs from the analysis module 52 and are used by quantitative
analysis routines in the module 52. The module 52 further produces
as additional output quantitative data 82 which is stored in
memory. The concentration coefficients 80 and quantitative data 82
are used by the display module 54 to display the quantitative data
to the user in a convenient and user-friendly fashion as will be
explained below.
[0067] One aspect of the processing performed by the analysis
module 52 in the workstation 34 is a spectral unmixing process by
which the plurality of images in the image cube 70 are processed
with reference spectral data 64 associated with the 1 . . . N
fluorophores (FIG. 6) in order to produce an estimate of
coefficients C.sub.1 . . . C.sub.N at each pixel location. The
coefficients C.sub.1 . . . C.sub.N are related to the
concentrations of the 1 . . . N fluorophores present in the sample
at each pixel location. The coefficients C.sub.1 . . . C.sub.N can
be scaled to absolute fluorophore concentrations. The coefficients
also can be scaled in arbitrary units and used to represent
concentration in terms of illumination intensity, either relative
or absolute.
[0068] The term "pixel location" in the context of the coefficients
C.sub.1 . . . C.sub.N will be understood to refer to the individual
locations in each of the images which also corresponds to the
individual pixels of the digital camera 26. For example, if the
digital camera 26 is constructed as an array of pixels arranged in
1 . . . i rows and 1 . . . j columns of pixels, each pixel of the
camera imaged the specimen M times.
[0069] For each pixel, the spectral unmixing process calculates
coefficients C.sub.1 . . . C.sub.N which relate to the
concentration of each fluorophore to all of the M images for that
pixel. When applied to the entire image cube, the spectral unmixing
process determines, in an overall sense, the relative contributions
of each of the 1 . . . N fluorophores present in the sample to the
resulting images, and in particular their concentrations, either in
relative terms or with appropriate scaling in absolute terms. The
spectral unmixing process performs such calculations for each of
the pixels. A variety of unmixing processes can be used, and a
linear spectral unmixing process as described in Huth et al.,
Fourier Transformed Spectral Bio-Imaging for Studying the
Intracellular Fate of Liposomes, Cytometry Part A, vol. 57A pp.
10-21 (2004) is considered preferred. This process will be
discussed in greater detail below.
[0070] The workstation further includes software instructions as
part of the analysis module 52 which performs at least one
morphological process in order to identifying one or more
biological structures in the specimen. Such structures can be whole
cells (indicated at 20 in FIG. 1), or cellular components such as
cell membranes, nuclei, cytosol, mitochondria, genes, DNA
fragments, RNA, messenger RNA entities, or other, and are
identified by shape or other characteristics which can be
determined by using known morphological or similar image processing
techniques. The morphological processing to identify such
structures can be performed on any one of the images, all of the
images, or more preferably an image constructed from one or more of
the identified coefficients C.sub.1 . . . C.sub.N. In still another
possible variation, the specimen is stained with Hematoxylin and
Eosin (H and E) and the morphological process identifies the
biological structures of interest from an image of the sample
stained with H and E.
[0071] A variety of morphological processing techniques are known
to persons skilled in the art which can be used to identify the
biological structures in an image of the sample. Examples include a
multi-scale approach, such as described in Kriete, A et al.,
Automated quantification of quantum-dot-labeled epidermal growth
factor receptor internalization via multiscale image segmentation,
Journal of Microscopy, v. 222(1) 22-27 (April 2006); an active
contour (snake) approach, described in Kass, A. Witkin, and D.
Terzopoulos. Snakes: Active contour models. International, Journal
of Computer Vision, 1:321-332, 1988; a level set approach,
described in J. A. Sethian, Level Set Methods: Evolving Interfaces
in Geometry, Fluid Mechanics, Computer Vision and Materials
Sciences. Cambridge Univ. Press, 1996; a contour closure approach
described in Mahamud, S et al., Segmentation of Multiple Salient
Closed Contours from Real Images, IEEE Transactions On Pattern
Analysis And Machine Intelligence, Vol. 25, No. 4, April 2003, and
a Watershed approach (currently used in the illustrated
embodiment), described in Vincent, L. et al., Watersheds in digital
spaces: An efficient algorithm based on immersion simulations, IEEE
Transactions on Pattern Analysis and Machine Intelligence v. 13(6)
June 1991 pp. 583-598, see also the review article on Watershed:
Roerdink, I. and Meijster A., The Watershed Transform: Definitions,
Algorithms and Parallelization Strategies", Fundamenta Informatica
v. 41, 2001, IOS Press pp. 187-228. Other techniques can be used,
including those disclosed in the following papers: Thouis R. Jones
et al., Voroni-Based Segmentation of Cells on Image Manifolds, in
CVBIA, ser. Lecture Notes in Computer Science, Y. Liu et al. Eds.,
vol. 3765 Springer-Verlag, 2005 pp. 535-543; the poster paper of
Thouis R. Jones et al., Methods for High-Content, High-Throughput
Image-Based Cell Screening, Proceedings of MIAAB 2006 available
on-line at www.broad.mit.edu/.about.thouis/MIAABPoster.pdf; and
Gang Lin et al., A Hybrid 3D Watershed Algorithm Incorporating
Gradient Cues and Object Models for Automatic Segmentation aof
Nuclei in Confocal Image Stacks, Cytometry Part A, 56A:23-26
(2003).
[0072] Once the biological structures are identified in the
specimen using these processes, a routine in the analysis module 52
counts the structures in the entire specimen, counts the structures
positive for each of the fluorophores applied to the specimen,
measures their size, and stores the location in the image of each
of such structures. The storage of such quantitative data is
represented in FIG. 2 at 82.
[0073] Part of the analysis module 52 includes a quantitative
analysis process which, among other things, calculates the
fluorophore concentrations for the biological structures 20 (FIG.
1) for each of the fluorophores, using the coefficients C.sub.1 . .
. C.sub.n which were obtained in the spectral unmixing process. For
example, for each of the identified biological structures 20 (e.g.,
cells), the quantitative analysis sums the total fluorophore
concentrations for each of the N fluorophores for those pixels
representing the biological structures. Consider, for example, a
specimen containing cancer cells stained with six quantum dots
conjugated to antibodies which are designed to attach to six
different proliferation proteins which may be found in the
specimen. Consider further that two of the quantum dots (605 nm and
625 nm) were bound to the cells in the specimen but the remaining
four quantum dots were not bound to any cells in the specimen. The
morphological processing processes identify all cells in the
specimen which produced a non-zero luminescent response for the 605
and 625 quantum dots. (A threshold other than zero could of course
be specified, such as 10 or 30 on a scale of 0-255 with 8-bit
quantization of the signal level from the camera 26.) The pixels
coordinates for such cells are identified. The values of
coefficients C.sub.i, C.sub.j associated with the 605 and 625
quantum dots are recorded for such pixel coordinates, and
optionally scaled to intensities, absolute concentrations, or other
value. The resulting quantitative data, along with pixel addresses
for the cells, is stored in memory in the workstation.
[0074] The quantitative analysis module may also calculate other
statistics for the sample, including (a) counts of the number of
cells; (b) counts of the number of cells with positive signal for
each of the fluorophores; c) sorting the cells into histograms
organized by size, presence of one or more fluorophores or other
characteristic; (d) calculating mean, median and standard deviation
of cell sizes; (d) measurements of fluorophore concentration
(intensity) for the identified biological structures, and still
others. Such additional quantitative data is also represented in
FIG. 2 at 82.
[0075] The workstation further includes a display process or module
54 for displaying the results of the quantitative analysis process
c) on a display 44 associated with the workstation. A variety of
methods and tools for display of quantitative data from the
specimen are contemplated and will be described with reference to
FIG. 11-18 in the following discussion. As one example, as shown in
FIG. 11, the results of the quantitative analysis process are
presented as a histogram of the number of biological structures
sorted by size of the biological structures, for each of the 1 . .
. N fluorophores applied to the specimen.
[0076] In another embodiment described later in conjunction with
FIGS. 15 and 16, the display process includes a feature allowing a
user to select a segment of an image of the specimen displayed on
the display (e.g., a region of cells having a particularly high
fluorescent signal) and the display process displays quantitative
results for the selected segment of the image. As another example,
as shown in FIGS. 11 and 14, the quantitative results that are
displayed can take the form of a plot of concentration of one
fluorophore (e.g. 605 quantum dot) as a function of concentration
of a second fluorophore (e.g., 625 quantum dot) for those cells
positive for both fluorophores, either in a selected segment of the
image, or in an overall image. Such a plot can visually be
represented as a scatter plot, for example as shown in FIG. 18.
[0077] In another embodiment, the coefficients C.sub.1 . . .
C.sub.N are scaled to absolute concentrations of the fluorophores
in the specimen (e.g., nanomols per cubic micron, number of quantum
dots per cell, or other system of units). Furthermore, the plots of
concentration of fluorophores, or the histograms, or other reports
or formats of quantitative data for the specimen, can be expressed
in terms of absolute concentrations of fluorophores present in the
specimen, or selected portions of the specimen.
[0078] As shown for example in FIGS. 11 and 18, the display of the
quantitative results may include display of an image of the
specimen on the same display. The image can be constructed from one
or more of the M images. More preferably, the image that is
displayed simultaneous with the quantitative data is constructed
from one or more of the coefficients C.sub.1 . . . C.sub.N, it
being recalled that such coefficients are obtained in the unmixing
processes and are determined for each pixel. For example, if the
user is studying a histogram of the size distribution of cells have
positive signal for a 625 nm quantum dot fluorophore, the display
may simultaneously show an image of the specimen with the image
generated from the coefficient C.sub.i which corresponds to the 625
nm quantum dot fluorophore. Such image may be of the entire
specimen, or a selected portion of the specimen. In other words,
the image masks (omits) the luminescent response from all the other
fluorophores which may be present in the specimen, and only reveals
the contribution of the 625 nm fluorophore. The quantitative
results may further include statistical data for the segment of the
image selected by the user.
[0079] In a still further example, the display process provides a
tool by which color intensity for one or more selected fluorophores
can be selectively weighted by the user to thereby change the
appearance of the image on the display. For example, the user may
wish to view an image of the specimen that reveals the distribution
of cells positive for the 605 nm and 625 nm quantum dots in the
specimen. When such image is displayed, a tool is presented by
which a user can selectively weight (or attenuate), either the 605
nm quantum dot signal or the 625 nm quantum dot signal. The
weighting may be used for example to strengthen a weak fluorophore
signal and allow the user to more readily perceive the distribution
of the fluorophore in the biological structures (e.g., cells) in
the specimen.
[0080] The display process may combine the various analytical
features and provide a variety of different tools for analyzing the
specimen. For example, the display process may includes processes
for displaying i) an image of the specimen constructed from one or
more of the coefficients C.sub.1 . . . C.sub.N for each pixel; ii)
a histogram of biological structures identified in the image in i)
sorted by size of the biological structures, for at least at least
one of the fluorophores applied to the sample; and iii) one or more
scatter plots of concentration of one of the fluorophores as a
function of concentration of one of the other fluorophores, for
cells or other biological structures positive for both
fluorophores. An example of such as display is shown in FIGS. 11
and 18. These features may be combined with the display of
additional statistical data, tools for selection of portions of an
image conducting further quantitative analysis, and still other
features.
[0081] In still another embodiment, the display process further
includes a feature by which a user may select a portion of a
scatter plot or a histogram, e.g., by drawing a box around the
portion of the histogram or scatter plot using a mouse, and conduct
further quantitative analysis on the portions of the specimen
(e.g., particular cells) corresponding to the selected portion of
the scatter plot or histogram. For example, a user may select the
portion of a histogram corresponding to larger cells with
relatively high concentrations of a particular fluorophore (e.g.
625 nm quantum dot). The display process creates a new display
which displays additional quantitative data for the larger cells in
the histogram which were selected by the user. Such quantitative
data may take a variety of forms, such has a new scatter plot
showing the concentration of the 605 nm quantum dot as a function
of the concentration of the 625 nm quantum dot, for the cells which
correspond to the portion of the histogram selected by the user.
Additionally, the display process may display an image of the
specimen with the biological structures associated with just the
cells in the selected portion of the histogram presented in the
image and highlighted, e.g., in a contrasting color. Such image
could be constructed from the concentration coefficient C.sub.i
corresponding to the 625 nm quantum dot, the 605 quantum dot, other
fluorophore present in the sample, autofluorescence, combination
thereof, or other.
[0082] The above-described software processes will now be described
in greater detail with reference to FIGS. 3-18. As an initial step,
the specimen is processed and stained with one ore more
fluorophores (e.g., up to N quantum dots, where N may for example
be 2, 5, 10 or more), and then imaged at the M wavelengths as
described above. The resulting image cube, list of wavelengths and
exposure times at each wavelength are stored in the memory 38 of
the workstation 34.
[0083] FIG. 5 is an illustration of an example of one image cube
70, consisting of twenty-two discrete images 150, 152, 154 . . . of
the specimen at different wavelengths (M=22). The data representing
the image cube 70 is obtained by the camera 26 of FIG. 1. The
wavelengths .lamda..sub.1 . . . .lamda..sub.M are selected so that
they overlap the portions of the reference spectra for the
fluorophore(s) applied to the sample where there is a significant
luminescent response from the fluorophore(s). For example, image
150 is an image at 505 nm, image 152 is an image of the specimen at
515 nm, image 154 is an image of the specimen at 525 nm, and the
remaining 19 images obtained at 10 nm increments up to 715 nm. The
22 wavelengths overlap substantially the reference spectra for Qdot
1 . . . Qdot 5 of the reference spectra of FIG. 6.
[0084] The camera 26 (FIG. 1) can use any convenient filtering
technique to acquire the spectral images forming the image cube,
including the use of physical filters 28, e.g., Liquid Crystal
Display (LCD) spectral filters, or other types of filters.
[0085] Each image is captured for an appropriate exposure interval,
depending on the sensitivity of the camera and the intensity of the
illumination source. Such exposure interval may for example be from
between 100 milliseconds and 5 seconds per sampled wavelength. The
exposure interval at each wavelength is stored and supplied to the
workstation.
[0086] A. Initialization and Set-Up Module 50 (FIG. 3)
[0087] The initialization and set up module 50, analysis module 52
and display processes 54 of FIG. 2 are part of an application which
is launched when the user clicks on an icon associated with the
application on the desktop of the workstation display. The
initialization and set-up module 50 is invoked when the application
is launched, indicated at 100 in FIG. 3. The initialization and
set-up module performs initial tasks that do not require user
involvement. The details of the module are not particularly
pertinent to the present invention and therefore many details are
omitted for the sake of brevity. Basically, with reference to FIG.
3, the module 50 includes a step 102 which loads run-time libraries
60 (FIG. 2) which are stored in memory, and which may include image
processing subroutines and other code modules ancillary to the
operation of the system. At step 104, the module 104 loads
configuration files, which may contain data pertinent to the
particular imaging system being used, data pertinent to the
specimen being analyzed, and other configuration files. At step
106, the library of reference spectra data (FIG. 2, 64) for the
known fluorophores is loaded. At step 108, a initial screen is
presented on the display 44 to the user that allows the user to
interact with the application, and take initial steps to view the
quantitative data, such as select a specimen image set for
processing, e.g., using a drop-down menu or other tool, select
colors for individual fluorophores, identify fluorophores which
were applied to the sample, view images of the specimen, view
quantitative data, etc.
[0088] B Analysis Module 52 (FIG. 4)
[0089] The analysis module 52 of FIG. 2 will be described in
greater detail in conjunction with the flow chart of FIG. 4 and
FIGS. 5, 6 and 8. FIG. 4 shows a sequence of individual
sub-routines or steps (processing instructions) which are performed
in the module 52 in order to extract quantitative data from the
images of the specimen for display on the workstation.
[0090] At step 110, the user is presented with a screen on the
workstation by which they identify the fluorophores which were
applied to the specimen, e.g., by checking a box or by means of
selection from a drop-down list. An example is shown in FIG. 8.
FIG. 8 shows a display 202 presented on the workstation display
which shows a list 204 of quantum dots or other organic
fluorophores, and the user checks the box next to the name of the
fluorophore to indicate that it was applied to the specimen. The
user checks the box next to autofluorescence to indicate that the
user wishes to analyze the sample using a reference spectrum for
autofluorescence appropriate for the tissue type being studied.
Autofluorescence refers to naturally occurring fluorescence from
molecules present in the sample. Analysis of the specimen to
extract autofluorescence data makes use of a separate file
containing autofluorescence spectra. The spectra in this file are
computed by separately examining a sample that contains no external
(added) fluorophores, and optimizing one or more reference spectra
to optimally represent the spectral information collected from this
sample. Subsequently, autofluorescence is treated in a manner
identical to other fluorophores in the system, i.e., the
fluorophores added to the specimen.
[0091] At step 112, the user selects the labels and colors to use
for the individual fluorophores present in the specimen for display
purposes. With reference to FIG. 8, the user is provided with a
tool 206 on the display 202 by which the user can select a color
for each of the fluorescence types present in the sample for use in
display in an image of the specimen. For example, the user checks
on the box "set color" next to the fluorophore and toggles through
a sequence of colors to apply to the selected fluorophore. For
example for autofluorescence the user can select blue, for the 605
nm quantum dot fluorophore "Qdot 605" the user can select red, and
for the 655 quantum dot fluorophore "Qdot 655" the use can select a
third color, e.g. yellow.
[0092] At step 114, with reference to FIGS. 2 and 4, the module 52
loads the image cube 70, and the list 72 of wavelengths and the
list 74 of exposure times, described above in the context of FIG.
5.
[0093] At step 116, an optional exposure compensation operation is
performed on the M images in order to normalize the response over
the range of wavelengths. It is not unusual for different
fluorophores to produce fluorescent signals of significantly
different peak intensity. For example, "QDot 655" produces a much
more intense signal than "QDot 525" when excited using the same
light source, resulting in an image cube that is dominated by the
"QDot 655" spectrum. To compensate for this situation and achieve
optimal signals from both fluorophores, the auto-expose feature
common on camera platforms can be used to automatically choose the
duration for which the camera is exposed to the sample at each
wavelength. This results in signal output levels that are similar
across all wavelengths, but necessitates that a correction be
applied during the quantitative analysis because the fluorescent
signal intensity is a function of the exposure time, with longer
exposure times resulting in higher signal intensity. One model that
describes the dependence of signal intensity on exposure time is
the linear model in which intensity scales linearly with respect to
exposure time, as described in Y. Garini, A. Gil, I. Bar-Am, D.
Cabib, and N. Katzir, Signal to Noise Analysis of Multiple Color
Fluorescence Imaging Microscopy, Cytometry vol. 35 pp. 214-226
(1999). Pixels with an intensity signal equal to the maximum signal
produced by the camera may not behave according to this model and
should be either ignored or treated separately. The details of this
treatment are discussed in the `Constraints` portion of the Further
examples and implementation details section.
[0094] At step 118, the process 52 performs additional image
pre-processing functions, including subtraction of background
signals (which may not be uniform across all the M images and which
may be device-dependent), and application of other corrections for
imperfections or noise in the spectral filters, the microscope or
camera optics, variation in incident light output, and camera
response. These details are not considered particularly pertinent
and so a further discussion is omitted for the sake of brevity.
[0095] At step 120, the module 52 performs an interpolation of the
fluorophore reference spectra (64 in FIG. 2) to the M sampled
wavelengths in the image cube. This operation results in a matrix
of illumination intensity at each wavelength, for each of the
fluorophores, matrix I in equation (1) below. Each column in the
matrix represents the spectral intensity value for one fluorophore,
at the M wavelengths. Thus, each column has M rows. There are N
columns in the matrix, one per fluorophore. The interpolation
algorithm also corrects the reference spectra for exposure time,
just like in the exposure compensation operation at step 116.
[0096] At step 122, the analysis module 52 performs a spectral
unmixing process on the M images in the image cube. A variety of
spectral unmixing processes are known in the art and considered to
be suitable for this calculation. In one method, this spectral
unmixing process multiplies a Moore-Penrose pseudo-inverse of the
matrix I (indicated by [I].sup.-1 in equation (1)) by a vector of
the total fluorescence intensity at each of the M wavelengths
(vector S in equation (1)) to calculate a vector of concentration
coefficients C.sub.1 . . . C.sub.N. Methods for calculation of a
Moore-Penrose pseudo inverse of an n.times.m matrix are known in
the art. This operation is represented in equation (1) as
follows:
[ c 1 c 2 c N ] = [ I 1 ( .lamda. 1 ) I 2 ( .lamda. 1 ) I N (
.lamda. 1 ) I 1 ( .lamda. 2 ) I 2 ( .lamda. 2 ) I 1 ( .lamda. M ) I
N ( .lamda. M ) ] - 1 .times. [ S ( .lamda. 1 ) S ( .lamda. 2 ) S (
.lamda. M ) ] ( 1 ) ##EQU00001##
[0097] This operation of equation 1 is performed for each pixel
location, yielding a vector of coefficients C.sub.1 . . . C.sub.N
for each pixel. The coefficients C.sub.1 . . . C.sub.N are related
to the concentrations of the 1 . . . N fluorophores present in the
sample at each pixel location. The coefficients C.sub.1 . . .
C.sub.N can be scaled to absolute concentrations, or considered
representative of relative intensity, or relative concentration for
the 1 . . . N fluorophores, e.g., on a scale of 0-255 in an 8-bit
quantization of image intensities.
[0098] At step 124, the analysis module performs one or more
morphological processing process to identify biological structures
that may be present in the specimen, such as cells, cell membranes,
nuclei, viruses, or other. Such processes basically identify the
cells or other structures by identifying patterns and shapes
present in an image of the specimen, e.g., closed curves of a
certain size. The image upon which the morphological processing
operates may one of the M images, a composite of two or more of the
M images, an image constructed from one or more of the coefficients
C.sub.1 . . . C.sub.N, a bright field image of the specimen, or
other. In a preferred embodiment, the morphological processing is
performed on an image constructed from the coefficients C.sub.1 . .
. C.sub.N. The morphological processing step was described
previously.
[0099] At step 126, a quantitative analysis is performed for the
biological structures, e.g., cells or nuclei, that are identified
by the step 124. Such quantitative analysis was described
previously.
[0100] At step 128, the resulting quantitative data is stored in
the memory 38 of the workstation for use by the display module or
process 54.
[0101] C. Display Module 54 (FIG. 2)
[0102] The operation of the display module will be described in
conjunction with FIGS. 7 and 9-18. Basically, this module generates
data for display of images of the specimen and quantitative data to
the user on the display of the workstation. A variety of different
tools and methods for display of quantitative data will be
described.
[0103] FIG. 7 shows an image of the specimen which is displayed in
a display 200 on the workstation. The image may be of the whole
slide or a portion thereof. The image is typically multi-colored,
with the fluorophore response from each of the fluorophores given a
different color, as a result of the user interacting with the
screen of FIG. 8. The image shown in FIG. 7 is preferably generated
by the coefficients C.sub.1 . . . C.sub.N which were calculated in
the spectral unmixing process.
[0104] FIG. 9 is a second image of a specimen. The image 210 of
FIG. 9 is generated from one of the coefficients C.sub.1, along
with autofluorescence signal. In this instance, the coefficient
C.sub.1 corresponds to a quantum dot conjugated to an antibody
which binds to a protein on the surface of the cell membrane.
Hence, in the image, the fluorescence signal due to coefficient
C.sub.i appears as loops, corresponding to the shape of the cell
membrane. The display process gives the user the opportunity to add
in to the image the fluorescence signal from the other fluorophores
which are present in the sample. When two fluorophores are present,
one signal may be much stronger than the other. Therefore it is
preferable to be able to adjust the fluorophore balance as an aid
to visualization of the data. In FIG. 10, the user is presented
with a display 220 with a portion 222 showing two fluorescence
components in the sample, in this example autofluorescence and Qdot
625. The display includes a slider bar 224 by which the user can
attenuate or enhance each of the fluorophores. For example, the
user has enhanced the Qdot 625 signal by a scaling factor of 6,
which is shown in the box 226. The user can also use the boxes 228
to reset the color of the fluorophores. When the user clicks on the
Close button 230, the settings are saved and the image of both
fluorophores with the new colors selection and weighting is
displayed on the display of the workstation.
[0105] Recall from the previous discussion of FIG. 8 that the user
assigns a color to each fluorophore for display purposes, and FIG.
10 shows that the user can later change that color. For instance,
Qdot 525 might be assigned to yellow which in RGB space is (255,
255, 0)--i.e., the mixture of pure red and pure green. In general,
fluorophore species has color (red.sub.i, green.sub.i, blue.sub.i).
Once the source data image cube containing the overlapping spectra
is separated into individual spectra (coefficients C.sub.1 . . .
C.sub.N), the intensity for each fluorophore at each pixel is known
(on a scale of 0-255). To generate an image that shows multiple
fluorophores mixed together, the colors are linearly mixed in RGB
space. Other equivalent approaches are available for merging
together, however linear mixing is used in this implementation.
When displaying the image showing the contribution from all the
fluorophores (an image constructed from all of the coefficients
(C.sub.1 . . . C.sub.N), the software takes the amount of red
contributed from each fluorophore and computes the overall
intensity- or concentration-weighted red value for each pixel. This
is accomplished by taking the intensity of each fluorophore i (or
in equation below "intens.sub.i") times the red component of that
fluorophore red; (or in the equation below "red.sub.i"), summing
this operation over all fluorophores.
red = i ( intens i * red i ) j red j ( 2 ) ##EQU00002##
A similar calculation is performed for the green and blue
contributions.
[0106] The interface of FIG. 10 allows the user to artificially
enhance one fluorophore over another, and achieves this by adding a
multiplier in front of the intensity value. Thus, if the user wants
to enhance fluorophore k by a factor of 2 (perhaps because it is
being hidden by another, brighter fluorophore), the term in
numerator of the above equation when i=k would be
(2*intens.sub.k*red.sub.k). This multiplicative approach is simply
one implementation of enhancement; other methods are possible.
[0107] FIG. 11 shows an example of a display of quantitative data
for the specimen which is presented on the display of the
workstation. The display 250 includes several histograms 252, 254
and 256, several scatter plots 260 and 262, and an image of the
specimen 266. The histogram 252 show the size distribution of all
cells in the specimen. The histogram 254 shows the distribution of
cells showing positive autofluorescence signal, sorted by cell
size. The histogram 256 shows the distribution of cells showing
positive for Qdot 655, sorted by cell size. The scatter plot 260
plots points which indicate the relative intensity (or
concentration) of one fluorophore as a function of the intensity
(or concentration) of one of the other fluorophores, for cells or
other biological structures which are positive for both
fluorophores. For example, in the scatter plot 260, the plot shows
the amount of Qdot 625 as a function of the amount of
autofluorescence, which indicates that the cells producing
relatively low autofluorescence signals also had relatively high
signal from the Qdot 625 fluorophore (area 258).
[0108] The user is able to select any portion of the histograms
252, 254, 256 or scatter plots 260, 262 and conduct further
quantitative analysis on the selected portion of the histogram. For
example, the user has drawn a box 257 about a certain population of
cells in the histogram C (plot 256). The box contains the larger
cells in the histogram. The scatter plot 262 (plot E) shows the
intensity of Qdot 625 signals as a function of the autofluorescence
signal, for the sub-population of cells selected in box 257 in the
histogram 256. The image 266 shows the specimen, with the selected
cells 264 from the scatter plot 262 highlighted. For example, the
larger cells indicated by the box 257 in the histogram are shown in
a contrasting color (appearing as white in FIG. 11).
[0109] The display of FIG. 11 further includes additional
statistical output data 270, such as data showing the intensity or
concentration of each of the fluorophores for each of the
biological structures, mean, median and standard deviation
statistics, and so forth. The user scrolls down using the slider
bars 274 and 272 to view all of the statistical data.
[0110] FIG. 12 shows another screen display presented on the
workstation. The display includes two images 300 and 302, each of
which shows the signal from one of the fluorophores present in the
sample. In this example, image 300 shows the autofluorescence
signal and the image 302 shows the Qdot 625 signal. The display
also includes spectral data 304 for a portion of the image that is
selected by the user. For example, the user can click on or select
a point or region in the image 300 or 302, and the spectra data 304
shows the relative fluorescence data for autofluorescence, the 625
quantum dot, experimental data 312 and model sum 314. The model sum
is the set of values S.sub.model(.lamda.) from equation (3)
computed based on the I matrix and computed coefficients C.sub.1 .
. . C.sub.N. The experimental data are the S(.lamda.) values that
were used in equation (1) to compute the C coefficients. The region
306 allows the user to select which of the sets of data to present
in the spectrum plot 304.
[ S model ( .lamda. 1 ) S model ( .lamda. 2 ) S model ( .lamda. M )
] = [ I 1 ( .lamda. 1 ) I 2 ( .lamda. 1 ) I N ( .lamda. 1 ) I 1 (
.lamda. 2 ) I 2 ( .lamda. 2 ) I 1 ( .lamda. M ) I N ( .lamda. M ) ]
.times. [ C 1 C 2 C N ] ( 3 ) ##EQU00003##
[0111] FIG. 13 shows another example of the display of quantitative
data. The display shows a histogram 254 showing the distribution of
cell sizes for cells positive for autofluorescence, and a histogram
256 showing the distribution of cell sizes for cells positive for
Qdot 625 signal. The display allows a user to select a region in
the histogram 256 showing the cell size distribution for those
cells that have a positive Qdot 625 signal. Here, the user has
indicated the region by drawing the box 257 around a group of
cells. This action pops up a dialog box 330 that asks the user what
they would like to do with the selected points: show them
highlighted on the image (332), or duplicate another of the plots
showing only the cells that have been selected at 257. In FIG. 13,
the user in indicating that they want to re-plot Plot-D from FIG.
11 (the intensity plot) with only the points selected in Plot-C
(257 in FIG. 13) included. The result is shown in FIG. 14 as a new
plot 340, with the scatter plot showing the data points 342
corresponding to the subpopulation of cells from 257, showing Qdot
625 intensity as a function of autofluorescence for cells having a
non-zero response to both fluorescence signals.
[0112] Next, the points which were selected are shown as
highlighted cells on a merged image 350 as shown in FIG. 15. The
cellular objects shown in white in FIG. 15 are the cells selected
in the histogram 256 by the box 257 (FIG. 14). The image of FIG. 15
is constructed from the autofluorescence signal and the coefficient
C.sub.i corresponding to the 625 nm quantum dot.
[0113] Additionally, the user can note where the interesting cells
are on the merged image of FIG. 15, by drawing a region of interest
(ROI) 352 on the merged image window with the mouse. The user is
able to inspect spectral information or other quantitative data for
that region 352 in a visual results window, shown in FIG. 16. When
you select a region on an image as shown in FIG. 15, the curves in
the plot on the bottom of the visual results window (FIG. 16) are
updated. This technique may take advantage of data linking and data
brushing, described in further detail below.
[0114] FIG. 16 shows an example of an image 350 having two
components from two different fluorophores. In this example there
is autofluorescence image 300 and the 625 nm Qdot image 302.
[0115] The tools menu is expanded in FIG. 16 to show the
sub-options. Brightness/Contrast 356 launches a tool that allows
the user to vary these quantities in the merged image, and Color
Equalizer launches the tool shown in FIG. 10 and described
previously to allow the user to enhance one or more fluorophores
for display purposes only. FIG. 16 also shows another example of
the spectra in the region 352 selected in FIG. 15, including the
autofluorescence spectrum 308, the 625 quantum dot spectrum 310,
the experimental data 312 and the model sum 314.
[0116] FIG. 17 shows another example of the display of images from
the specimen. In the example of FIG. 17, there are three images,
namely an autofluorescence image 370, a 605 quantum dot image
constructed from the coefficient C.sub.i for the 605 fluorophore,
and a 625 quantum dot image constructed from the coefficient for
the 625 fluorophore. The spectra 308, 376 and 310 for the
fluorophores are shown in the plot below the images. The images
370, 372 and 374 can be images of the entire specimen, or images of
only a portion of the specimen, e.g., a sub-region selected by the
user as shown in FIG. 15.
[0117] FIG. 18 is another example of a display 400 showing a
combination of scatter plots, histograms, image, and statistical
data for a specimen. The scatter plot 402 plots intensity of the
605 nm Qdot as a function of autofluorescence for cells having a
positive signal for both types of fluorescence. The
autofluorescence and 605 Qdot values are plotted on a scale of 0 to
255 for convenience, and the scatter plot 402 is zoomed in to
magnify the region containing data. The units for the X and Y axis
of the scatter plots can be scaled to absolute concentration values
using appropriate scaling factors.
[0118] Similarly, the scatter plot 410 shows the distribution of
the Qdot 655 values as a function of the Qdot 605 values, for cells
positive for both fluorophores. The scaling factors on the X and Y
axes 412 and 414 could be relative intensity or concentration or
absolute intensity or concentration.
[0119] The statistical output 422 presents statistical data for all
of the identified objects in the specimen, including the area or
size of the objects, and the intensity values for each object.
Additional statistics on the objects, such as mean, median and
standard deviation values can be presented as well.
Further Examples and Implementation Details
[0120] Z-Stack Imaging
[0121] Optical sectioning is the technique of optically imaging
"slices" of a three-dimensional sample by changing the focal plane
in the vertical direction and taking images at each plane. See D.
A. Agard, "Optical Sectioning Microscopy: Cellular Architecture in
Three Dimensions," Annual. Reviews in Biophysics and
Bioengineering, vol. 13, pp. 191-219, 1984. S. Joshi and M. I.
Miller, "Maximum a Posteriori Estimate with Good's Roughness for
Three-Dimensional Optical-Sectioning Microscopy," Journal of
Optical Society of America, vol. 10, no. 5, pp. 1078-1085,
1993.
[0122] The camera of FIG. 1 can use this technique to operate at
multiple depths of field (i.e., focus settings in the Z direction,
into the tissue sample), and at each depth of field the M images
are obtained. The resulting data set includes an image cube at each
depth. From this data set, three dimensional quantitative data from
the specimen is obtained. When the user is presented with the
quantitative data, the user is given an option to select the depth
of field they wish to view and analyze. Additional, quantitative
data is obtained for a three dimensional volume of the tissue
section. Such quantitative data is presented to the user.
[0123] The separate 2-dimensional images can be registered
mathematically into a 3-dimensional representation of the cells and
tissues. One additional application of optical sectioning is to
improve resolution of the 2-dimensional images. See Enhanced
Resolution from Three-dimensional Imaging: W. A. Carrington, R. M.
Lynch, E. D. Moore, G. Isenberg, K. E. Fogarty, and F. S. Fay,
"Superresolution Three-Dimensional Images of Fluorescence in Cells
with minimal Light Exposure," Science, vol. 268, pp. 1483-1487,
Jun. 9, 1995. The concepts of data linking/brushing have been
extended to 3-dimensional representations. See Pak Chung Wong, R.
D. Bergeron, "Brushing techniques for exploring volume datasets,"
vis, p. 429, Eighth IEEE Visualization 1997 (VIS'97), 1997. This
invention further extends them to include 3-dimension spectral
images in the domain of fluorescence and concentration.
[0124] Constraints
[0125] The image analysis software uses constraint algorithms to
handle errors in the data. For example, assume that quantitative
analysis module is analyzing a pixel for presence of fluorophores
X, Y, and Z. If the module encounters a pixel that has a negative
value Y (a non-physical situation), the module re-analyzes that
pixel for fluorophores X & Z only, and uses the new results for
X and Z, and sets Y to zero. Also, if any fluorophore results in a
concentration that exceeds the maximum that can be detected by the
camera, the algorithm sets it to the maximum allowable value. Other
approaches to handling anomalous results, such as omitting such
pixels from the analysis, are possible, and the algorithm described
above is one possible embodiment.
[0126] Data-Linking/Brushing
[0127] Multidimensional data linking and data brushing are
well-accepted means for interacting with high-dimensional data. See
Interactive Data Exploration with Multiple Views (Data Linking and
Data Brushing): Sigmar-Olaf Tergan (Editor), Tanja Keller (Editor),
Knowledge and Information Visualization: Searching for Synergies
(Lecture Notes in Computer Science) Springer-Verlag, Berlin, 1998.
The analysis module of this disclosure extends the concepts of data
linking and data brushing to the case where one view of the data is
in the form of images of cells in culture or tissue. The image can
be either a rendering of the raw fluorescence, or a synthetic
image, e.g. images generated from the concentration coefficients as
explained above.
[0128] Intelligent Whole Slide Imaging
[0129] In systems used in current practice, imaging of biological
specimens is typically performed on multiple samples arranged on a
slide. Tissue microarrays, for example, are paraffin blocks
containing as many as 1000 tissue samples arranged on a slide in a
rectangular fashion. Slides are prepared, using manual operations
or automated devices, by taking thin slices of the paraffin
material and mounting each slice on a slide. See Battifora, H. The
multitumor (sausage) tissue block: novel method for
immunohistochemical antibody testing. Lab Invest 1986, 55:244-248;
Battifora, H. et al., The checkerboard tissue block. An improved
multitissue control block. Lab Invest 1998, 63:722-724; Kononen J,
et al., Tissue microarrays for high-throughput molecular profiling
of tumor specimens. Nat Med 1998, 4:844-847.
[0130] Image acquisition is typically done interactively. The
pathologist or technician images a whole slide at low resolution
and selects regions containing tissue samples to view at higher
resolution. The selected regions of interest are then imaged at a
higher resolution (magnification), for example if the pathologist
wishes to record images containing specific cell types (classified
by e.g. tissue type or normal vs. cancer) at high resolution for
later processing or review by a specialist.
[0131] In this approach, only small, manually-selected regions are
imaged at high resolution, and detailed analyses of specific
regions of interest are typically done at a later time. Because of
the large number of samples on a slide, and because this process is
user intensive and time-consuming, some samples may not be imaged
at the same time, and the sample may have degraded during the
delay. Further, the experience and ability of the pathologist or
researcher may affect which regions are selected, resulting in
irreproducible and possibly erroneous results.
[0132] One solution to these problems is to record a single image
at high resolution. For example, DMetrix, Inc. has developed a
scanning system utilizing an array of microscopes and cameras (see
e.g. U.S. Patent application publication 2004/0101210 "Miniaturized
microscope array digital slide scanner"). This approach suffers
from two drawbacks. The first is that the data storage requirements
for whole slide imaging at high resolution are prohibitive for many
applications. For example, the DMetrix system records a single 12
gigabit grayscale image. While this technique could be used with
the methods of this invention, if 100 or more images were to be
collected at different wavelengths, it would result in an image
cube of 1.2 terabytes or more for a single slide, and hundreds of
terabytes for the three-dimensional (Z-stack, multiple depth of
field) imaging applications mentioned elsewhere in this document.
The second drawback with the existing approach is that standard
cameras cannot take high resolution images of a whole slide at once
because they lack sufficient field of view and the imaging arrays
in the cameras are not large enough.
[0133] One method of approaching the goal of an intelligent
approach to whole slide imaging is to generate a low resolution
image of the entire slide (or possibly a low resolution image cube
of the entire slide) and then use the automated image segmentation
and classification features of the morphological processing
processes to identify important regions on a slide (e.g.,
biological structures with a high signal for one or more
fluorophores) and then collect, preferably automatically, a
high-resolution image cube of only these important regions on a
slide. In one embodiment, the high resolution image cube of the
important regions of the slide is collected shortly after the low
resolution image is obtained, e.g., a few minutes later, after the
morphological processing steps have been performed and the
important areas of the slide identified. However, quantum dot
fluorophores are less subject to degradation then organic
fluorophores and in some embodiments the high resolution image cube
can be obtained later on.
[0134] The procedure followed in this automated approach includes
the following steps. [0135] 1) Acquire one or more low-resolution
images, or alternatively an image cube, of the entire slide. [0136]
2) Using automated image segmentation and classification algorithms
(i.e., morphological processing processes as described above),
areas of the slide which contain regions of interest such as tissue
spots are identified from the one or more low resolution image, the
image cube or an image derived from the image cube (e.g., an image
constructed from concentration coefficients). Such locations are
flagged. The locations of such tissue spots could be either
referenced to pixel locations in the low resolution image or XY
coordinates of the motion stage that moves the slide relative to
the camera and microscope optics during acquisition of the low
resolution image. [0137] 3) Acquire higher resolution images of the
tissue spots at multiple wavelengths, basically acquiring an image
cube of the important areas of the slide. The location coordinates
from step 2) are used to position the correct portions of the slide
in the field of view of the camera microscope. The microscope has a
higher magnification objective lens in place for higher resolution
imaging. While the higher resolution image of the tissue spots are
obtained, the camera system records at the same time metadata
summarizing each image, e.g. a slide identifier, a tissue sample
identifier, image magnification and the image location (or slide
location). [0138] 4) If camera limitations, storage requirements or
other constraints prevent high resolution acquisition of entire
tissue spots, the morphological processing further processes the
tissue spots to identify smaller regions of interest within the
tissue spots using automated image segmentation and classification
algorithms. Such smaller regions of interest could be regions
containing cells, cellular components, genes, DNA fragments,
messenger RNA entities, viruses, or whatever other structures are
of interest in the given assay. Alternatively, such smaller regions
could be only those regions where one or more fluorophore signal is
present. [0139] 5) Acquire and record high-resolution spectral
images (image cubes) for each region of interest.
[0140] The coefficients C.sub.1 . . . C.sub.N are calculated for
each pixel imaging the one or more regions of interest or tissue
spots in the high resolution image cube. A quantitative analysis of
the regions of interest or tissue spots is performed as explained
previously, including calculating fluorophore concentrations for
biological structures in the tissue spots or regions of interest
from the coefficients C.sub.1 . . . C.sub.N The quantitative
analysis of the regions or interest or tissue spots proceeds as
described above. The display of the results of the quantitative
analysis results and images of the regions of interest and tissue
spots proceeds using the examples described previously.
[0141] The process of automated selection of regions of interest
(steps 3-5) can be repeated an arbitrary number of times, with
multiple intermediate resolutions sampled before the reaching the
required resolution. This requires that the image analysis software
system have control of the field of view and magnification of the
microscope, and control of the digital camera as well. Many
commercially available setups provide programming interfaces that
permit this type of software control of the microscope and camera.
This approach provides for completely automated analysis of a whole
slide, as well as automated imaging of a whole slide for later
interactive viewing and data exploration.
[0142] A similar approach can be used for other types of supports
for biological specimens besides slides, such as, for example,
multiwell plates containing cultured cells.
[0143] Equivalent Imaging Methods
[0144] The data representing the image cube (pixel signal level for
rows, columns and at M different wavelengths) can be obtained in a
variety of different orders. For example, we have described
generating a two-dimensional image of the specimen at M
wavelengths. Alternatively, one could use a camera such as shown in
U.S. Pat. No. 5,926,283 which captures data at multiple wavelengths
for one row/column of an image and then collects the data for the
other rows/columns. The end result is still a cube having
rows/columns/wavelengths, with the information having been
collected and/o stored in a different order.
[0145] As another example, the images making up the image cube
could be captured with a so-called line-scan type digital imager in
which the pixels of the camera are arranged in a 1.times.N linear
array of pixels. In this type of imager, a row of image data is
obtained and then relative movement between the imager and the
slide occurs, then a second row of image data is obtained, and so
forth, until the entire slide is imaged. To obtain the image cube,
rows of image data obtained at one wavelength are obtained
sequentially to image the two-dimensional entire specimen or slide
at the first wavelength. Then, a different spectral filter is
placed in front of the camera and rows of image data are obtained
sequentially at a second wavelength. A different filter is placed
in front of the camera and rows of image data are obtained
sequentially at a third wavelength, The process continues until
images of the slide at all the M wavelengths have been
obtained.
[0146] Software Product for Generic Workstations
[0147] The software described above, including the initialization
and set-up module, analysis module, and the display module, can be
loaded on a disk or other machine readable medium and provided as a
stand alone product for commercially available workstations in
order to upgrade the workstation to function as described
herein.
[0148] In one embodiment, the instructions include a set of
instructions for: (a) determining from a set of M images the
coefficients C.sub.1 . . . C.sub.N for each pixel described above;
(b) morphologically processing an image of the specimen to identify
cells or cellular components in the specimen, (c) conducting a
quantitative analysis of the specimen including calculating quantum
dot concentrations for the cells or cellular components identified
in step (b) from the coefficients C.sub.1 . . . C.sub.N; and (d)
generating data for display of the results of the quantitative
analysis process (c) on a display associated with the processing
unit.
[0149] Additionally, the software including the initialization and
set-up module, analysis module, and the display module, can be
loaded on a network server and executed by a processing unit in the
network server. In this case, the user interacts with the software
via a client application running on separate computing platform,
e.g., a personal computer or workstation, which is coupled to the
network that includes the network server. For example, the software
may include a Web interface to allow a remote client to access and
view displays of the quantitative data and images of the specimen,
and interact with it as described above, but the software processes
for calculation the coefficients, morphologically processing an
image of the specimen, conducting the quantitative analysis and
generating display data are executed in the network server.
[0150] Quantum Dots
[0151] As used in the claims the term "quantum dot" is intended to
be read broadly to cover luminescent semi-conductor nanocrystals
generally, including CdSe nanoparticles as well as CdTe or other
luminescent semi-conductor nanoparticles. Such particles may take
any geometric form, including spherical, rod, wires, or other. Gold
particles may also be used.
[0152] All references and literature cited above are specifically
incorporated by reference herein.
[0153] While a number of exemplary aspects and embodiments have
been discussed above, those of skill in the art will recognize
certain modifications, permutations, additions and sub-combinations
thereof as being present in the disclosure. It is therefore
intended that the following appended Claims and claims hereafter
introduced are interpreted to include all such modifications,
permutations, additions and sub-combinations as are within their
true spirit and scope.
* * * * *
References