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WO2009006696A1 - Procédé de pathologie - Google Patents

Procédé de pathologie Download PDF

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Publication number
WO2009006696A1
WO2009006696A1 PCT/AU2008/001014 AU2008001014W WO2009006696A1 WO 2009006696 A1 WO2009006696 A1 WO 2009006696A1 AU 2008001014 W AU2008001014 W AU 2008001014W WO 2009006696 A1 WO2009006696 A1 WO 2009006696A1
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Prior art keywords
tissue
image
images
spectrally distinct
tissue sample
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PCT/AU2008/001014
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English (en)
Inventor
Samantha Gallagher
Louis Dagenais
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Molecular Discovery Systems Ltd
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Molecular Discovery Systems Ltd
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Priority claimed from AU2007903737A external-priority patent/AU2007903737A0/en
Application filed by Molecular Discovery Systems Ltd filed Critical Molecular Discovery Systems Ltd
Publication of WO2009006696A1 publication Critical patent/WO2009006696A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6486—Measuring fluorescence of biological material, e.g. DNA, RNA, cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/97—Determining parameters from multiple pictures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N2021/6417—Spectrofluorimetric devices
    • G01N2021/6423—Spectral mapping, video display
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G01N2021/6439—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" with indicators, stains, dyes, tags, labels, marks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
    • G01N21/6458—Fluorescence microscopy
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10064—Fluorescence image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro

Definitions

  • the present invention relates generally to processing for visualisation and analysis of anatomical pathology images, i.e. digital pathology. More particularly, the present invention relates to systems, methods, and computer program products for the analysis of pathology images for clinical applications such as diagnosis, disease staging, and surgical planning as well as for drug discovery applications such as target and biomarker validation.
  • This is an example of translational medicine where the clinic meets the research lab, where technologies, information and samples used to diagnose and treat patients are applied to the search for new therapeutic treatments.
  • Histopathology is a labour-intensive process requiring the preparation of biopsy material including fixation, freezing, sectioning and staining followed by analysis and interpretation. Together these process steps have traditionally led to turnaround times of days rather than hours. More importantly, at present, the analysis and interpretation of pathology samples is an inexact science requiring highly skilled professionals. The detection of cancerous cells and other diseased tissue, for example, is dependent upon trained cytotechnologists or pathologists examining thousands of cells under the microscope looking for evidence of aberrant cells and/or tissue structures. This screening is tedious, error-prone and greatly dependent on the skills of the cytotechnologists or pathologist. Between the world-wide trends of aging populations, increased incidence rate of cancer and the corresponding increase in routine oncology screening, there can only be added pressure to increase the throughput and accuracy of histopathology analysis .
  • Drug discovery systems also have a requirement for the histological examination of animal studies involving for example whole body sectioning of organs and tissues . These studies represent a huge number of sections for processing by a pathologist.
  • image-capturing technology has been used to digitise histopathology samples, which can then be stored or sent electronically.
  • the images and any analysis are generated on a report using the computer associated with or locally connected to the imaging device.
  • the images are then burned to a portable storage medium such as, for example, a DVD and sent back to the pathologist with the original slides.
  • the pathologist can view the images and is given the option to supplement or alter the report using their professional interpretation.
  • tissue microarrays Another area of pathology that could be further assisted by automation is the analysis of tissue microarrays .
  • Standard full-face slides contain relatively large pieces of tissue from a single source whereas tissue microarrays comprise one or more specimen slides that may contain hundreds of individual tissues for one or multiple different biological specimens arranged in a row-column matrix style.
  • Tissue microarrays consist of paraffin blocks in which up to 1000 separate tissue cores may be assembled in array fashion to allow simultaneous histological staining, imaging and analysis.
  • a hollow needle is used to remove tissue cores as small as 0.6 mm in diameter from regions of interest providing clinical biopsies or tumour samples.
  • the core biopsies are then fixed or frozen and the tissue cores inserted in a recipient paraffin block in a precisely spaced, array pattern. Sections from this block maybe cut using a microtome, mounted on a microscope slide and then analysed or stained/probed then analyzed by any method of standard histological/biochemical analysis.
  • Tissue microarrays allow staining and analysis of hundreds of samples on a slide rather than the traditional one sample/patient per slide. This throughput and ability to simultaneously test many different samples (normal vs diseased, range of tumor grading and patient prognosis, tissues from different organs) makes tissue microarrays of great interest for drug discovery applications.
  • tissue microarrays assist in the capturing of data, the interpretation and analysis is still largely manual. Indeed, at present much of the screening is by manual tissue scoring, which is inherently subjective and imprecise. It is at best a semi-quantitative method based on a manual score using a four-point scale: negative, weak positive, strong positive, or no data.
  • the Scanscope System by Aperio technologies, Vista, Calif, digitizes an entire image by applying linear detector technology used in fax machines. Furthermore, Aperio uses a pyramid compression scheme whereby the original image is compressed a number of times, yielding a plurality of images, each one with smaller number of image pixels and representing a lower image resolution. Trestle, with its MedScan product employs area scanning. Applied Imaging' s Ariol imaging and analysis system can image both calorimetric and fluorescently labelled samples.
  • a method of analyzing a pathology tissue sample comprising the steps of:
  • a plurality of spectrally distinct digital image of the tissue sample are obtained.
  • the step of obtaining at least one spectrally distinct digital image comprises the steps of obtaining a primary digital image of the tissue sample, and subsequently performing color deconvolution on the primary image so as to produce at least one spectrally distinct secondary image.
  • the fluorescent material may comprise a plurality of sets of fluophores, each fluophore being arranged to emit light within a specific emission frequency band when illuminated with light within a specific excitation frequency band.
  • quantum dots may be used instead of standard fluophores, the main differences being the sensitivity of quantum dots to a broad range of excitation wavelengths, the relatively narrow emission wavelength bandwidth and much improved efficiency (brighter signal) and durability (no photobleaching) .
  • the or each spectrally distinct image may be obtained by capturing a filtered digital image from a sample, with each image corresponding to a different band of spectral frequencies.
  • the bands of spectral frequencies may include visible frequencies or invisible frequencies such as infra red frequencies or ultra violet frequencies.
  • mis-registration or distortion may be present (e.g. small position, shape or size changes of cells acquired at different time points, slight mis-registrations of images acquired at different wavelengths due to spectral response of microscope optics, aligning overlapping areas of adjacent image tiles) .
  • Larger differences in position, orientation or even resolution must be compensated for when trying to match images of different modalities (e.g. fluorescence, transmitted light) , images acquired over multiple acquisition sessions, or images acquired by different imaging systems .
  • Maintz and Viergever provides a survey of the range of medical image registration techniques that can be used to measure and correct for any spatial distortions between images of the same sample. These techniques were classified by the intrinsic (e.g anatomical or geometric landmarks, rigid or deformable segmentation models) or extrinsic (e.g. invasive stereotactic frames or inserted fiducials, non-invasive molds or natural fiducials) nature of the technique, the nature of the transformation (e.g. rigid, affine, projection, curve) , the domain of transformation (e.g. local, global), by the degree of user interaction required (automatic, semi-automatic or significant user interaction) , the range of imaging modalities involved (monomodal, multimodal, modality to model, patient to modality) .
  • intrinsic e.g anatomical or geometric landmarks, rigid or deformable segmentation models
  • extrinsic e.g. invasive stereotactic frames or inserted fiducials, non-invasive molds or natural fiducial
  • Global linear affine transformation techniques such as described by Shi and Tomasi (1994) would be appropriate to correct for sub-pixel inaccuracies between a range of spectral image channels, or to align the edges of overlapping adjacent image tiles.
  • Global linear affine transformations based on more sophisticated intrinsic landmarks if not external fiducial markers would be required to align images of the same histopathology slide imaged by different instruments and/or imaging modalities.
  • Global transformations based on deformable models would be required to align images of the same histopathology slide where the cover slip has been removed, the tissue been further processed, or any situations where local geometric distortions may have been introduced.
  • the method further comprises the step of performing stereological techniques on the spectrally distinct images, wherein techniques such as counting frame and optical dissector methods are used to ensure that objects are accounted for a single time and without any bias related to their shape, orientation or size,- fractionator sampling approaches are used to yield unbiased measurements from statistical sampling of a subset of the total image area; or unbiased estimates of 3 dimensional information (number, length, surface or volume) are obtained from simpler measurements made on 2D microscope sections.
  • stereological techniques such as counting frame and optical dissector methods are used to ensure that objects are accounted for a single time and without any bias related to their shape, orientation or size
  • - fractionator sampling approaches are used to yield unbiased measurements from statistical sampling of a subset of the total image area; or unbiased estimates of 3 dimensional information (number, length, surface or volume) are obtained from simpler measurements made on 2D microscope sections.
  • the or each image may be decomposed to produce a plurality of spatial tiles. This decomposition is done in order to permit and/or accelerate the process of identifying cells and/or other structures within the images in a process known as segmentation.
  • segmentation When dealing with very large image files, it is either impossible or inefficient to have the entire content of the image file copied from the hard disk into the operating memory (RAM) of the computer in order to perform image processing. It is therefore necessary to break up the larger image into a series of contiguous or overlapping image tiles.
  • spatial tiling creates a series of image tiles as separate files on the computer hard drive, whereas in other virtual tiling embodiments, individual image tiles are extracted from the original image file individually on an as-needed basis and each image tile exists only temporarily within the computer's working RAM memory .
  • the spatial tiling step consists rather in identifying the position and spatial extent of the individual smaller tissue sections (cores) on the slide and creating physical or virtual tiles centered on each identified tissue core. Spot finding techniques developed for the analysis of DNA or protein microarrays can be applied to relatively low resolution versions of the image to identify the location and spatial extent of these cores .
  • tissue map Once a tissue map has been produced it is generally outputted for quantification.
  • the tissue map from a test subject is compared to a tissue map made from normal tissue and/or from known abnormal tissue.
  • the present invention provides a method of performing pathology comprising the steps of:
  • the present invention provides a method of diagnosing diseased tissue comprising the steps of:
  • tissue map from said images in order to characterize tissue features and thereby classify tissue type and condition; and (vii) comparing the tissue map from said subject suspected of a disease with a tissue map from control tissue; wherein the comparison between the two tissue maps is diagnostic of said disease.
  • the control tissue may be normal tissue and/or known abnormal tissue.
  • pathology refers to the examination of organs, tissues, cells and bodily fluids in order to determine the state of a tissue sample, for example so as to diagnose disease.
  • pathology also refers to the sub- disciplines including cytopathology, molecular pathology, immunohistochemistry, fluorescent in-situ hybridization, Chroraogenic in-situ hybridization and tissue microarrays .
  • pathology also refers to the process by which samples including tissue samples or sections are obtained from subjects and processed by standard techniques.
  • tissue sample includes tissues, cells and bodily fluids taken from a subject. These samples are then processed to produce tissue sections or tissue microarray core sections, Cytospins and smears.
  • the sections may be paraffin sections, frozen sections or resin sections or any other form of sections used in pathology.
  • the cytospins may be created from bodily fluid or lavage of for example a tube, duct or sinus containing the fluid, plus cells and the fluids spun down onto a slide.
  • the smears may be created from cell/tissue scrapes and either spun onto a slide, or spread on a slide.
  • subject or “individual” are used interchangeably herein to refer to any member of the subphylum cordata, including, without limitation, humans and other primates, including non-human primates such as chimpanzees and other apes and monkey species; farm animals such as cattle, sheep, pigs, goats and horses; domestic mammals such as dogs and cats; laboratory animals including rodents such as mice, rats and guinea pigs; birds, including domestic, wild and game birds such as chickens, turkeys and other gallinaceous birds, ducks, geese, and the like.
  • the terms do not denote a particular age. Thus, both adult and newborn individuals are intended to be covered.
  • the methods described herein are intended for use in any of the above vertebrate species, since the cellular and sub-cellular systems of all of these vertebrates operate similarly.
  • tissue samples obtained from mammals such as humans, as well as those mammals of economical importance and/or social importance to humans, for instance, carnivores other than humans (such as cats and dogs), swine (pigs, hogs, and wild boars), ruminants (such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels), and horses.
  • carnivores other than humans such as cats and dogs
  • swine pigs, hogs, and wild boars
  • ruminants such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels
  • tissue samples obtained from birds including the treatment of those kinds of birds that are endangered, kept in zoos, as well as fowl, and more particularly domesticated fowl, eg., poultry, such as turkeys, chickens, ducks, geese, guinea fowl, and the like, as they are also of economical importance to humans.
  • domesticated fowl eg., poultry, such as turkeys, chickens, ducks, geese, guinea fowl, and the like
  • tissue samples obtained from livestock including, but not limited to, domesticated swine (pigs and hogs), ruminants, horses, poultry, and the like.
  • digital image is intended to define an image obtained by an imaging apparatus whose output is an image in digital format, i.e. digital cameras, scanner devices, Nuclear Magnetic Resonance imaging apparatus, ultrasound imaging apparatus and other imaging apparatus .
  • digitised image is related to images obtained both by fully digital systems such as digital cameras and scanners as well as by substantially analog systems arranged to produce an analog image which is subsequently digitised, for example by means of a device known as scanners, regardless of whether the latter is a hardware devices, i.e. a device for reading an image which is physically printed on a medium, or a software device, i.e. designed to sample an image provided in the form of set of signals and to turn it into a digital signal.
  • a hardware devices i.e. a device for reading an image which is physically printed on a medium
  • a software device i.e. designed to sample an image provided in the form of set of signals and to turn it into a digital signal.
  • a digital image is composed of a plurality of pixels, each pixel having an associated brightness condition representative of different grey scale tones and, in color images, a color condition representative of different colors.
  • the digital images are obtained using a camera such as a digital camera.
  • the digital image may be stored in a variety of digital image formats including bit-map, joint pictures expert group (JPEG) , graphics interchange format (GIF), etc.
  • JPEG joint pictures expert group
  • GIF graphics interchange format
  • the present invention is not limited to these digital image formats and other digital formats can also be used to implement the invention.
  • the digital images are typically obtained by magnifying the sectioned tissue with a microscope or other magnifying device and capturing a digital image of the magnified sectioned tissue (e.g., groupings of plural magnified cells, etc.) with a camera (e.g., digital camera) or a scanner.
  • a camera e.g., digital camera
  • a primary digital image is first obtained using transmitted light (e.g. bright field, dark field, phase contrast, differential interference contrast) and multiple spectrally distinct secondary digital images then obtained from the primary digital image.
  • transmitted light e.g. bright field, dark field, phase contrast, differential interference contrast
  • multiple spectrally distinct secondary digital images from the primary digital image is to process the primary digital image using "spectral deconvolution” or “spectral image deconvolution” .
  • Methods for spectral deconvolution include techniques such as S PLISMA, linear spectral unmixing, multivariate curve resolution, clustering techniques, principal component analysis (PCA) , non-negative matrix factorization (NMF) and independent component analysis (ICA) .
  • PCA principal component analysis
  • NMF non-negative matrix factorization
  • ICA independent component analysis
  • Spectral deconvolution algorithms readily correct for the broadband emission filters. Such algorithms are available in free or Shareware based applications such as the Landini ImageJ plug-in based on Ruifrok methodology
  • ICA analysis further refines the approach by adopting a stochastic image formation model where each dye stains the tissue independently of all of the other dyes. This yields similar eigenvector/eigenvalues as PCA, but where the eigenvectors are not necessarily orthogonal to each other as in PCA analysis.
  • multiple spectrally distinct digital images are obtained directly from the tissue sample by incorporating fluorescent material into the tissue sample, illuminating the tissue sample with light of one or more bands of specific wavelengths so as to excite specific fluorescent material in the tissue sample, and capturing a plurality of respective digital images .
  • filters may be used to generate spectrally distinct images for capture by a suitable digital image capturing device.
  • the fluorescent material may comprise fluophores, quantum dots or FRET.
  • the illuminating light may be polarized so that a measure of molecular orientation and mobility can be obtained.
  • spectrally distinct images may be obtained by taking advantage of inherent fluorescent properties of tissue and/or organelles .
  • tissue and/or organelles can be caused to fluoresce naturally without the need to add fluorescent material into the tissue sample.
  • the spectrally distinct images may be obtained using multispectral imaging techniques by capturing multiple digital images from a sample, for example using suitable filters, with each image corresponding to a different band of spectral frequencies.
  • the bands of spectral frequencies may include visible frequencies or invisible frequencies such as infra red frequencies or ultra violet frequencies. Images produced using non-visible frequencies may be processed so that information contained in the images is represented using visible colors.
  • a captured digital image is also decomposed to produce a plurality of spatial tiles.
  • Decomposition comprises the step of segmenting or separating a digital image into overlapping or non- overlapping images .
  • the spectrally distinct digital images may be processed using stereological processing techniques to enable production of unbiased measurements, estimation of more complex measurement through relatively simple measurements (e.g. obtaining 3D information for 2D measurements), and allow image tiles to be analysed independently and results from each of these image tiles to be combined without introducing any statistical bias (e.g. counting objects twice, bias based on object size, location or orientation) .
  • stereological processing techniques to enable production of unbiased measurements, estimation of more complex measurement through relatively simple measurements (e.g. obtaining 3D information for 2D measurements), and allow image tiles to be analysed independently and results from each of these image tiles to be combined without introducing any statistical bias (e.g. counting objects twice, bias based on object size, location or orientation) .
  • image tiles may be chosen to overlap in order to facilitate an unbiased means of detecting cells or larger structures within the images.
  • a counting frame is a rectangle that is slightly smaller than the overlapping image tile, where its left, top, right, and bottom edges delineate the non-overlapping region within the image tile. Any objects found to be touching the top or left edges of the counting frame are excluded from any downstream analysis, whereas any objects completely within the counting frame or extending past the bottom and right counting frame edges but not touching the edges of the image tile are included in downstream analysis and measurement.
  • a different approach would be to combine stereological counting frame and fractionator based sampling algorithms to yield unbiased measurement through sampling a representative sample of non-adjacent image tiles.
  • a level of representative sampling e.g. 5% of total tissue area
  • a random systematic sampling algorithm is used to locate a number of tiles within the tissue area.
  • the random element is introduced by the use of random estimation to select the starting location of the first image tile, and the systematic element is introduced by locating all subsequent image tiles at regular intervals across the entire image area so as to uniformly sample the entire set image area.
  • the counting frame guards against any bias due to size and shape of cells where the fractionator technique guards against any bias regarding image tile location.
  • another stereological technique comprising an optical dissector algorithm can be used to process a set of images that represent a plurality of z locations within an original 3D volume of tissue.
  • the optical dissector technique is another counting technique where each z-location image is processed sequentially from one end of the z-axis to the other, and where objects are counted only once when they are first detected within a z location image. That is, objects detected at a given z- location are not counted if in the previous z- location image, a similar object was found in the same x-y 2D location.
  • the optical dissector method allows unbiased counting of objects through a 3D volume.
  • the digital image is further processed by additional stereology techniques i.e. a user traces the boundary of a feature or areas of interest from cross sectional digital images and features such as the type of cells present is calculated by a computer using a pixel counting method, for example.
  • additional stereology techniques i.e. a user traces the boundary of a feature or areas of interest from cross sectional digital images and features such as the type of cells present is calculated by a computer using a pixel counting method, for example.
  • More advanced known techniques that may be employed may be point-counting stereology based on a fixed-grid stereologic approach, a random-grid stereologic approach, or a grid-point counting stereological method.
  • Systematic sampling of unbiased counting frames can be achieved using a semiautomatic stereology system (Stereolnvestigator, MicroBrightField, Inc . ) .
  • the spectrally distinct images are analysed in order to produce information indicative of the state of the associated tissue sample using any suitable analysis algorithm.
  • Analysis algorithms include counting dot type algorithms such as CISH (chromegenic in-situ hybridization) or FISH (fluorescent in-situ hybridization), staining intensity measurement type algorithms such as IHC, spatial pattern detection algorithms, sub-cellular localisation algorithms, and so on.
  • any suitable image analysis algorithm is envisaged, the important aspect being that the spectrally distinct digital images are processed so as to produce information indicative of the state of a tissue sample, for example for pathology purposes.
  • tissue map represents the set of quantification and classification characteristics of image features that the image analysis framework herein described is designed to enable. This tissue map may be represented by numerical results displayed in tabular form or through a number of image visualization techniques be displayed along with the original image .
  • the original image is displayed in its original form, whereas in others, a new color image is created by the combination of spectrally deconvolved image components.
  • Image visualization techniques include the use of pseudo-color image components that can be mixed with the original image through a variety of color blending algorithm or through their display as color overlays on top of the original image. These components may be displayed statically or may be displayed in a time sequence so as to allow the visualization of a large number of results. Alternatively, icons, glyphs or textual annotations may be projected onto the original image display.
  • the tissue map numerical results can include direct measurement of intensity and morphometric characteristics of areas of the images, metrics that correlate with protein concentrations, metrics that correspond to the number of copies of chromosomes and genes within individual cells, metrics that correlate with progression of tumors classically assessed visually by pathologists using Gleason scoring techniques, as well as classifications of image areas into physiologically relevant categories useful for highlighting and describing characteristic progression of disease such as cancer.
  • the tissue map can also comprise agglomerated metrics that provide overall scores, classifications, diagnoses and/or prognoses for any set of regions within the complete set of analyzed images.
  • the tissue map may include overall metrics for a given donor tissue sample which may or not be associated with patient diagnosis and/or prognosis.
  • EXAMPLE 1 an Aperio transmitted light scanner was used to image a slide of breast cancer tissue where diaminobenzidene (DAB) -labelled antigen staining is used to localize the expression of Her2 oncogene proteins and hematoxylin is used to stain cellular nuclei. Due to the nature of the Her2 oncogene protein localisation, DAB staining will be characteristic of a membrane stain. The aim is to establish the Her2 status of the patient which has prognostic implications in order to select from a variety of treatment options.
  • DAB diaminobenzidene
  • This image is a 24 bit color image with 3 color components (red, green, blue) that represents a mixture of 2 pure color stains, one brown (DAB) and one blue (hematoxylin) .
  • Color deconvolution must therefore be performed in order to yield estimate of the original independent stain images. Due to the apriori knowledge of the stain's spectral characteristics, it could be possible to apply a pre-computed color transformation, alternately these color characteristics could either be sampled from the image or may be estimated from the entire ensemble of color image pixels .
  • the overlap between adjacent tiles is chosen to match the representative size of cells within the tissue sample, and a stereological counting frame is used to make sure that each and every cell within the tissue sample is counted a single time.
  • a representative set of image tiles is chosen through a fractionator sampling algorithm.
  • the measured IHC staining intensity could then be displayed through the use of pseudo-color overlays on a cell by cell basis, by displaying a pseudo color image where the DAB intensity, the hematoxylin intensity and the IHC score are mapped to red, blue and green respectively.
  • the image has already been decomposed into spatial tiles with an appropriate degree of overlap.
  • the exact degree of overlap is unknown due to the lack of precision of the microscope x-y stage positioning.
  • the exact overlap can be determined on an adjacent tile pairwise basis through the use of an image matching algorithm.
  • a straight- forward cross-correlation algorithm restricted to the known degree of stage imprecision will easily identify the degree of overlap within a single pixel precision.
  • Each image tile can then be processed using a stereological counting frame process so as to quantify each and every cell within the tissue area without any duplication or bias with respect to cell size, location or orientation.
  • a FISH quantification algorithm is called which detects the spatial location of all cell nuclei on the Hoechst channel as well as all spots in the FISH channel co- located within each segmented nuclear area.
  • the gene duplication is estimated either through the counting of the number of spots (1 spot per gene copy, 2 copies represent the normal situation) , or through an area measurement which can lead to an estimated oncogene count.
  • EXAMPLE 3 In this example, the previous 2 examples are combined whereas the Her2 status is measured both by IHC staining quantification of chromogenic stains in transmitted light images acquired with the Aperio slide scanner system and by FISH staining quantification of fluophores in fluorescent image acquired with the GE Healthcare IN Cell 1000 imaging system.
  • the set of GE IN Cell 1000 image tiles are carefully aligned using the same techniques outlined in example 2 and an overall combined lower resolution view of the entire field of view is created. A similar resolution view of the Aperio image is also created.
  • the user is asked to outline the tissue area outline on both a low magnification view of the transmitted light Aperio image as well as a low power view of the total field of view of the IN Cell 1000 tiled images. This outline is used to roughly align the images across the two modalities. Fine registration is done through the detection of nuclei position (hematoxylin staining of transmitted light images, Hoechst staining of fluorescent image) and the use of constellation matching algorithms of the resulting implicit image landmarks. As the tissue is physically located on a glass slide, a global linear transformation (shift, rotation, scaling) is sufficient to register the two image modalities. Virtual image tiles are defined on the Aperio images so as to match the location and size of the relevant IN Cell 1000 physical image tiles. Image quantification proceeds as previously described on these images.
  • the image visualization question becomes more complex.
  • the Her2 status is represented by a textual annotation where the numerical grading is shown in green when the two results agree and the two differing gradings are shown in red when there is a mismatch.
  • the concordance between the two measurements could be displayed through the use pseudo-colors in order to visualize any potential spatial patterns.
  • H+E represents the most widely used form of histological staining where the hematoxylin blue stains cellular nuclei and the eosin pink stains cytoplasm and connective tissue in general.
  • This type of color image is used by pathologist to grade the progression of cancer through observations of a variety of characteristic cancerous tissue spatial pattern of which the formation and progression of invasive ductal in-situ carcinoma (DCIS) .
  • DCIS invasive ductal in-situ carcinoma
  • image deconvolution and tiling is done in a similar way as in example 1, accounting of course for the blue vs. pink colors of the H and E stains.
  • the analysis algorithm in this situation detects the presence of ducts within the breast tissue and provides a tissue map metric that correlates with the progression of DCIS tumor formation. This metric can be displayed along with the image in similar ways as in example 1.
  • EXAMPLE 5 In this example, a tissue microarray slide containing a number of healthy and diseased breast cancer cores is imaged with H+E staining in transmitted light using the Aperio slide scanner system and imaged with both transmitted light and fluophores with the GE IN Cell 1000 imager.
  • the fluophores represent a number of different breast cancer biomarkers, both well-known ones such as ER, PR and Her-2 as well as a number of unvalidated potential biomarkers .
  • Image deconvolution is performed on the Aperio images in order to separate the H and E stain images and produce a plurality of spectrally distinct images.
  • the IN Cell 1000 image tiles are aligned with respect to each other and an overall lower resolution view is generated, as well as a lower resolution view of the Aperio images.
  • global linear registration is accomplished between the two vendor images through cross-correlation of lower resolution versions of the Aperio image and the transmitted light component GE image.
  • a second finer global linear registration is accomplished through the same cross -correlation techniques but done with a representative sample of high resolution images.
  • the location and spatial extent of the individual cores is automatically detected by a core finding algorithm.
  • Virtual image tiles corresponding to these breast cancer tissue cores are defined on Aperio and GE images.
  • the complete set of image components (H + E image stains as well as the set of biomarker fluophores) is then processed on a tile by tile basis.
  • the location and spatial extent of nuclear areas are found through segmentation of the hematoxylin image.
  • An estimated cytoplasmic area is generated through the morphological dilation of the nuclear masks. Within these nuclei areas and estimated surrounding cytoplasm areas, the intensity and spatial distribution of the variety of biomarker fluophores are quantified.
  • the intensity and location of staining of the potential and known biomarker signals are extracted and correlations are calculated among these results as well as with the known diagnostic and prognostic outcomes of the patient histories associated with the tissue microarray cores. Statistically significant correlations could lead to the validation of the potential biomarkers as diagnostic or prognostic predictors of disease, as well as identify potential drug targets on which to focus drug discovery efforts .
  • the spatial location and distribution of the individual fluophores can be compared against the characteristic spatial patterns of known sub-cellular compartments (e.g. Golgi apparatus, mitochondria, cytokines, ...) in order to identify the sub-cellular location of the fluophores for each cell.
  • This information collated across many cells will allow the scientist to make some initial inferences as to the mechanism of action or pathway of the drug target. Information which can be used to further inform the drug discovery process.
  • tissue map may form part of the tissue map, which can then be visualized with or without the original or processed images using a variety of techniques (e.g. labels, color coding).

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Abstract

La présente invention porte d'une manière générale sur un traitement pour une visualisation et une analyse d'images d'anatomie pathologique, à savoir une pathologie numérique. Plus précisément, la présente invention propose un procédé pour une pathologie numérique qui consiste : (i) à obtenir au moins une image numérique distincte de façon spectrale de l'échantillon de tissu ; et (ii) à préparer une carte de tissu à partir de ladite ou desdites images afin de caractériser des particularités de tissu et ainsi de classer le type et l'état du tissu.
PCT/AU2008/001014 2007-07-10 2008-07-10 Procédé de pathologie Ceased WO2009006696A1 (fr)

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US9588099B2 (en) 2003-09-23 2017-03-07 Cambridge Research & Intrumentation, Inc. Spectral imaging
US8879812B2 (en) 2003-09-23 2014-11-04 Cambridge Research & Instrumentation, Inc. Spectral imaging of biological samples
US8063385B2 (en) 2009-05-29 2011-11-22 General Electric Company Method and apparatus for ultraviolet scan planning
CN102449529A (zh) * 2009-05-29 2012-05-09 通用电气公司 用于紫外线扫描规划的方法和装置
CN102449529B (zh) * 2009-05-29 2014-08-20 通用电气公司 用于紫外线扫描规划的方法和装置
WO2010138063A1 (fr) * 2009-05-29 2010-12-02 General Electric Company Procédé et dispositif de planification de balayage ultraviolet
US10192099B2 (en) 2011-09-27 2019-01-29 Board Of Regents Of The University Of Texas System Systems and methods for automated screening and prognosis of cancer from whole-slide biopsy images
JP2013228305A (ja) * 2012-04-26 2013-11-07 Shimadzu Corp 測定プログラムおよび測定装置
JP2014185931A (ja) * 2013-03-22 2014-10-02 Shimadzu Corp 測定プログラムおよび放射能測定システム
CN105283904A (zh) * 2013-06-03 2016-01-27 文塔纳医疗系统公司 图像自适应的生理上可信的颜色分离
US10242248B2 (en) 2013-06-03 2019-03-26 Ventana Medical Systems, Inc. Image adaptive physiologically plausible color separation
US10055551B2 (en) 2013-10-10 2018-08-21 Board Of Regents Of The University Of Texas System Systems and methods for quantitative analysis of histopathology images using multiclassifier ensemble schemes
US9990719B2 (en) 2014-09-05 2018-06-05 Universidad Politécnica De Valencia Method and system for generating multiparametric nosological images
ES2537153A1 (es) * 2014-09-05 2015-06-02 Universitat Politècnica De València Método y sistema de generación de imágenes nosológicas multiparamétricas
WO2016034749A1 (fr) * 2014-09-05 2016-03-10 Universidad Politecnica De Valencia Procédé et système de génération d'images nosologiques multiparamétriques
US20200193140A1 (en) * 2017-08-24 2020-06-18 Nano Global Detection of Biological Cells or Biological Substances
US11645835B2 (en) 2017-08-30 2023-05-09 Board Of Regents, The University Of Texas System Hypercomplex deep learning methods, architectures, and apparatus for multimodal small, medium, and large-scale data representation, analysis, and applications
CN110441264A (zh) * 2019-07-22 2019-11-12 上海集成电路研发中心有限公司 一种基于压缩感知的频域多分辨率成像装置及方法
CN110441264B (zh) * 2019-07-22 2022-04-01 上海集成电路研发中心有限公司 一种基于压缩感知的频域谱成像装置及方法
CN113092382A (zh) * 2021-03-16 2021-07-09 上海卫星工程研究所 傅里叶变换光谱仪星上数据无损压缩方法及系统
CN115115662A (zh) * 2022-07-01 2022-09-27 重庆邮电大学 一种基于知识与数据融合驱动的多模态医学影像目标分割方法
CN116246019A (zh) * 2023-02-27 2023-06-09 上海迪派生物科技有限公司 一种病理切片的3d重建方法、装置、设备及介质
CN116246019B (zh) * 2023-02-27 2024-01-05 上海迪派生物科技有限公司 一种病理切片的3d重建方法、装置、设备及介质

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