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WO2014181123A1 - Appareil et procédé pour traiter les images d'échantillons de tissus - Google Patents

Appareil et procédé pour traiter les images d'échantillons de tissus Download PDF

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Publication number
WO2014181123A1
WO2014181123A1 PCT/GB2014/051426 GB2014051426W WO2014181123A1 WO 2014181123 A1 WO2014181123 A1 WO 2014181123A1 GB 2014051426 W GB2014051426 W GB 2014051426W WO 2014181123 A1 WO2014181123 A1 WO 2014181123A1
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WO
WIPO (PCT)
Prior art keywords
image
tumour
tissue
boundary
region
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/GB2014/051426
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English (en)
Inventor
Jonathon TUNSTALL
Peter Hamilton
Yinhai Wang
David Mccleary
James Diamond
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Philips DCP Belfast Ltd
Original Assignee
PathXL Ltd
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Filing date
Publication date
Application filed by PathXL Ltd filed Critical PathXL Ltd
Priority to EP14728603.3A priority Critical patent/EP2994882A1/fr
Publication of WO2014181123A1 publication Critical patent/WO2014181123A1/fr
Priority to US14/937,559 priority patent/US9946953B2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/02—Devices for withdrawing samples
    • G01N1/04—Devices for withdrawing samples in the solid state, e.g. by cutting
    • G01N1/06—Devices for withdrawing samples in the solid state, e.g. by cutting providing a thin slice, e.g. microtome
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/30—Staining; Impregnating ; Fixation; Dehydration; Multistep processes for preparing samples of tissue, cell or nucleic acid material and the like for analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • 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
    • G06T2207/10024—Color 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
    • G06T2207/20021—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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • 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
    • 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
    • G06T2207/30096—Tumor; Lesion

Definitions

  • the present invention relates to image processing, in particular to systems, methods, apparatus, computer program products and servers for processing an image of a tissue section of a tissue sample.
  • tumour tissue for analysis is obtained by cutting a thin tissue section from a formalin fixed, paraffin embedded (FFPE) tissue block known to contain tumour and non-tumour tissue, and preparing the tissue section on a glass histology slide.
  • FFPE formalin fixed, paraffin embedded
  • the tissue section will usually be cut with a thickness of approximately 5 ⁇ or any other appropriate thickness to allow the outline of tissue structures to be made out by viewing the slide under a standard laboratory microscope.
  • a method of marking tumour regions of the tissue section comprises the pathologist viewing the slide using a standard laboratory microscope and, based on subjective assessment, for example based on memory or on visual comparison with a look-up chart, identifying regions of the tissue section appearing to correspond to model tumour structures, and indicating boundaries of the regions via a manual annotation with a marker pen on the glass coverslip.
  • a sequential tissue section preferably having a thickness in the range indicated above, is cut from the tissue block and prepared on a second histology slide.
  • tissue is manually scraped, using a scalpel, from a region of the sequential tissue section contained within an area corresponding to an annotated region.
  • a computer implemented image processing method of identifying a tissue boundary of a tumour region of a tissue sample, the tissue sample containing non-tumour regions and at least one tumour region, to enable excision of at least a portion of a tumour region from the tissue sample by cutting along the tissue boundary comprising: obtaining an image of a tissue section of the tissue sample; identifying at least one image property of the image; comparing the image property with classification data; based on the comparison, classifying a region of the image as a tumour region representing a tumour region in the tissue sample or a non-tumour region representing a non-tumour region in the tissue sample; and if the region of the image is classified as a tumour region, identifying a boundary of the region of the image; and using the boundary to identify a tissue boundary of the tumour region of the tissue sample represented by the region of the image.
  • the at least one image property comprises one or any combination of image properties from the group comprising: a statistical moment, a moment invariant feature, a features derived from a grey level co-occurrence matrix, a spectral feature or a morphological feature.
  • the at least one image property comprises at least one image property of each of a plurality of different colour components of the image concatenated together to provide a feature vector.
  • the method comprises classifying the region of the image based on a comparison between the feature vector and the classification data.
  • the classification data comprises a subset of data sets selected from a set of first model image data sets indicative of tumour tissue and from a set of second model image data sets indicative of non-tumour tissue.
  • the step of identifying a boundary of the region of the image comprises: generating a two-state map of the image by representing regions classified as tumour regions using a first state and by representing regions classified as non-tumour regions using a second state.
  • the method comprises applying a smoothing algorithm to the boundary to provide a smoothed template for cutting along the tissue boundary.
  • applying the smoothing algorithm comprises applying a forward frequency-domain transform and an inverse frequency-domain transform.
  • applying the smoothing algorithm comprises representing the image boundary as a sequence of transition indicators indicating the direction of a transition between pixels on the image boundary, and smoothing the sequence of transition indicators.
  • the method comprises displaying the two-state map combined with a probability data map indicating the probability that regions of the sample comprise tumour.
  • the method comprises updating the classification data using a supervised learning method.
  • the method comprises determining whether the sample comprises tumour tissue, and only identifying the boundary in the event that it is determined that the sample comprises tumour.
  • a first of the colour component corresponds to a first indicator used to stain the tissue section
  • a second of the colour component corresponds to a second indicator used to stain the tissue section
  • a third of the colour component is a greyscale colour component
  • the binary classification data a set of first model image data sets indicative of tumour tissue and a set of second model image data sets indicative of non-tumour tissue;
  • the methods comprise cutting along the tissue boundary, wherein the step of cutting along the tissue boundary comprises cutting along a tissue boundary in the tissue section or cutting along a tissue boundary in a subsequent tissue section of the tissue sample.
  • a macrodissection apparatus comprising: a receiver for receiving data representing a boundary in the image; and a controller for guiding a cutting of the tissue sample along a path based on the data representing the boundary.
  • the classification data comprises an association between stored feature vectors and a tumour tissue class, and a non-tumour tissue class.
  • the feature vectors each comprise at least one image property derived from at least one of a plurality of colour components of the image.
  • the image server is configured to update the classification data based on the received data and feedback from the user.
  • an apparatus for guiding in vitro dissection of a tissue sample comprising: a processor configured to obtain an image of a tissue section of the tissue sample, to identify at least one image property of the image, to compare the image property with classification data, and, based on the comparison, to determine whether a region of the sample comprises tumour;
  • the processor is configured so, in the event that the sample comprises tumour, the processor identifies a boundary of the tumour region of the image; and provides the image boundary to guide dissection of tumour from the sample.
  • the apparatus comprises a display for displaying the boundary as a template to guide dissection of the sample.
  • the apparatus may be configured to perform any of the methods described herein.
  • Figure 1 schematically shows a macrodissection system for excising tumour tissue from a tissue sample
  • Figure 2 shows a flow chart illustrating a method of using the macrodissection system of Figure 1 ;
  • Figure 3 shows a flow chart illustrating a computer implemented method of processing an image to guide dissection of a tissue sample suspected of comprising tumour tissue.
  • Figure 1 shows a macrodissection system 60, comprising a tissue sample 2, a histology slide 10 carrying a tissue section 4 taken from the tissue sample 2, an imager 50, a computer 20, a controller 30, and a cutting device 40.
  • the imager 50 is arranged to obtain a digital image of the tissue section 4 on the histology slide 10 and to provide data representing the image to the computer 20.
  • the computer 20 is configured to receive the data from the imager 50, to run an algorithm on the data to generate a result, and to provide the result to the controller 30.
  • the controller 30 is configured to control a cutting operation of the cutting device 40 based on the result.
  • the tissue sample 2 comprises tissue block suspected of containing at least one tumour region 6, and containing non-tumour regions 8.
  • the tissue section 4 is a section cut from the tissue sample 2, having a thickness in the range of approximately 5 ⁇ , or 10 to 40 ⁇ , although the skilled practitioner will understand that another thickness could be chosen as appropriate.
  • a sequential tissue section (not shown) is a further slice cut from the tissue sample 2 at a location exposed by taking the first tissue section 4. The thickness of the sequential tissue section need not be the same as that of the first tissue section.
  • the histology slide 10 is a standard glass laboratory slide or any suitable equivalent for providing a flat transparent surface for receiving and displaying the tissue section 4.
  • the imager 50 is configured to generate an image of the tissue section 4 on the histology slide 10 and to provide data representing the image to computer 20.
  • the computer 20 comprises memory 24 and a processor 22.
  • the memory 24 is configured to receive and store data from the imager 50.
  • the processor 22 is coupled to access image data stored in the memory 24, to implement an image processing algorithm on the data to classify a region of the image as a tumour region and identify a boundary of the region (see, for example, Figure 3), to output data representing the boundary to the controller 30 and optionally to send a copy of the data to the memory 24 for later use.
  • Figure 2 shows a flow chart illustrating a method of operation of the system of Figure 1.
  • the histology slide 10 is prepared 201 by cutting the tissue section 4 form the tissue sample 2 using a laboratory microtome.
  • the tissue section 4 is placed in the slide 10, stained with Haematoxylin and Eosin laboratory stain and covered with a glass cover slip.
  • An image of the tissue section 4 is obtained 202 by imaging the cover slide 10 with the imager 26.
  • Data representing the image is provided to the memory 24 of the computer 20.
  • the processor 22 retrieves the image data from the memory 24 and analyses the data to classify 203 a region of the image as representing a tumour region of the tissue section. Classifying the region comprises comparing at least one property of the image data with classification data (see, for example Figure 3).
  • the processor 22 identifies 204 a boundary of the region to enable identification of a tumour boundary in a sequential tissue section.
  • Data representing the identified boundary is output to the controller 30.
  • the data may be displayed on a user interface.
  • Data representing the region and the boundary are stored to non-volatile memory for later user reference and/or for updating the classification data using a learning method.
  • the controller 30 receives the data representing the boundary and guides the cutting device 40 to cut 205 in the sequential tissue section along a path based on the boundary, by using the boundary as a template.
  • Figure 3 shows a flow chart illustrating a computer implemented method of processing an image to guide dissection of a tissue sample suspected of comprising tumour.
  • the processor 22 obtains 1000 from memory 24 an image of a section through the tissue sample.
  • the section can be imaged from a microscope slide stained using Haemotoxylin and Eosin.
  • the processor obtains 1001 a first component of the image data corresponding to the Eosin stain.
  • the processor PPP then selects a threshold to apply to the first component of the image data that divides the eosin image data into two groups.
  • the processor is configured to select a threshold that reduces, for example minimises, the variance of the data values within each group.
  • the processor 1000 then applies the threshold value to the first component of the image data to generate a mask.
  • the processor then applies the mask generated from the first component to segment 1002 the image data into two groups.
  • Image data in the first group is identified as tissue, and image data in the second group is identified as background.
  • the processor then partitions 1004 the first group of image data, relating to tissue, into tiles.
  • the image data for each tile comprises image data relating to that tile at a series of different resolutions
  • the data for the tile at each different resolution comprises a plurality of blocks each representing at least a portion of the tile at a different effective magnification. Different magnifications may be achieved by providing equivalent pixel numbers for different sized spatial regions, or a different number of pixels for equally sized spatial regions.
  • the processor For each tile, at each resolution level, the processor obtains 1005 three components of the image data, a first component corresponding to the Eosin stain, a second component corresponding to the Haemotoxylin stain, and a third grey scale component.
  • the first and second components may be obtained by applying a colour deconvolution method to the image data.
  • the grey scale image data comprises a greyscale version of the image data in the tile.
  • the processor selects at least one property to be determined based on the colour component image data in the tile. The properties to be determined are selected based on the colour component so different properties can be determined for different colour components.
  • the properties are selected from the list comprising texture, statistical moments, such as centroids, averages, variances, higher order moments, moment invariant, frequency domain features, features derived from the grey level co-occurrence matrix, and morphological features, such as average nuclear size and/or shape, nuclear concentration in a spatial region, and high level spatial relationships between image objects, which may be derived from Delaunay Triangulation, Voronoi diagram and/or a minimal expanding tree algorithm which treats each cell nucleus as a vertex.
  • the processor determines 1006 the selected image properties for each of the three components of the tile, and concatenates the image properties together to provide a feature vector.
  • the processor then obtains from memory a subset of the stored classification data.
  • the classification data comprises a first set of model image feature vectors associated with tumour tissues, and a second set of model image feature vectors associated with non-tumour tissue.
  • the processor selects from amongst the first plurality of feature vectors (tumour type) from the classification data, and the second plurality of feature vectors (non-tumour type) from the classification data to provide a subset (e.g. less than all of the set). This provides a subset of model feature vectors.
  • the processor compares 1008 the concatenated feature vector from the tile with the selected subset of the classification data, and based on the comparison, the processor classify 1010 the tile as belonging to one of two states - tumour or non-tumour.
  • the processor is configured to combine 1012 the tiles to provide a two state map (e.g. binary) identifying tumour, and non-tumour regions of the tissue with the tissue/non-tissue mask generated by the segmentation 1002 to provide a spatial map of the image data which classifies regions of the image into one of three states e.g. background, tumour tissue, and non-tumour tissue.
  • the processor is further configured to identify 1014 a boundary between regions in the three state map
  • the processor is configured to identify an initial boundary based on an edge detection algorithm, encode the resulting boundary, and smooth the boundary by reducing the contribution of high spatial frequency components to the boundary.
  • the processor then obtains a probability estimate based on comparing the feature vectors of tiles in tissue regions of the image with the selected subset of model image data to assign a probability to each tile.
  • the processor then displays the resulting probability estimate as a colour map, overlaid with the smoothed boundary data, to provide a user with an estimate of the location of tumour and non-tumour regions in the image.
  • a tumour may contain patterns of cell growth which contain, but are not limited to, any of dysplasia, neoplasia, carcinoma in-situ and cancerous tissue, or any combination thereof. It will be appreciated by the skilled addressee in the context of the present disclosure that the disclosure could equally apply to other diseases which are capable of morphological identification.
  • the computer 20 is represented as a desk-top PC. It will be appreciated that any other type of computer or server could be used.
  • the processor 22 of Figure 1 may be a standard computer processor, but it will be understood that the processor could be implemented in hardware, software, film ware or any combination thereof as appropriate for implementing the image processing method described herein.
  • the memory 24 of the computer 20 of Figure 1 may be configured to store data received from the imager 50, results generated by the processor 22, and classification data for classifying tumour and non-tumour regions.
  • Non-volatile memory may be provided for storing the classification data.
  • Further non-volatile memory may be provided for storing the image data so that the image data for a plurality of tissue samples may be uploaded and stored in memory until such time as the processor 22 has capability or an instruction to process it.
  • the memory 24 may comprise a buffer or an on-chip cache. Further non-volatile memory may be provided for storing results of the image processing method for later user reference and/or for updating the classification data using a learning method.
  • the controller 30 is configured to receive the output from the computer 20 and to control the cutting device 40 to cut tissue from a sequential tissue section based on the processor output.
  • the controller 30 comprises any suitable means for guiding a cutting device along a predetermined path.
  • the controller could comprise a processor coupled to a machine driven scalpel or laser.
  • the controller 30 is configured to provide and/or display a template based on the processor output to guide cutting, for example automated or manual cutting along a path corresponding to a boundary shown on the template or to scrape tissue with a spatula from inside the boundary.
  • Tumour regions such as the tumour schematically illustrated by region 6 of Figure 1 , are tissue regions containing abnormal patterns of growth, which may include, but are not limited to, any of dysplasia, neoplasia, carcinoma in-situ and cancerous tissue or any combination thereof.
  • the non-tumour regions 8 may also contain tumour tissue, but in a lower concentration than present in their tumour regions 6, as will be understood by those skilled in the art.
  • the tissue block may be a formalin fixed, paraffin embedded tissue block, or a tissue block prepared in any other suitable way.
  • the cutting device 40 may be a bladed instrument for cutting in a tissue section.
  • the device could comprise a laser for dissecting tissue or a blunt instrument, such as a spatula, for scraping tissue from the slide.
  • the imager 50 may comprise any suitable image generating means, including, but not limited to, an analogue or digital camera and a digital slide scanning system, in which an image is reconstructed following acquisition of image tiles or raster lines.
  • Obtaining the image data may comprise retrieving it from non-volatile memory, or from RAM, or from an on chip-cache, ADC or buffer.
  • the image data in memory may be derived from data stored elsewhere in the apparatus, or received over a communications link such as a network, or obtained from an imager such as a microscope.
  • the section of tissue can be stained using Haemotoxylin and Eosin, or with any other appropriate histological stain.
  • the description above makes reference to separating the image data into components corresponding to the particular stains. As will be appreciated, other coloured stains may be used, and the image data may be separated into components corresponding to the stains used.
  • the components may comprise colour channels, which may be separated using a colour deconvolution method. However, other types of colour component, separated by other kinds of methods may also be used.
  • Obtaining 1001 the first component corresponding to the eosin stain may comprise obtaining the intensity of eosin stain using colour deconvolution method.
  • the second component corresponding to the Haemotoxylin stain may be similarly obtained.
  • the segmentation by masking may be based on a single component of the image data, such as the first (eosin) component as described above, or from one of the other components, or from the original image data, or from a combination of one or more of these.
  • a predefined image mask may be used.
  • the threshold used to provide the mask can also be predefined rather than being based on the variance of the data values within each group.
  • the threshold may be selected based on user input, for example the processor may be configured to determine the threshold (e.g. based on intra-group variances) and then to adjust the threshold based on input from a user.
  • Segmentation may be performed to provide a mask at each of a plurality of resolution levels, or segmentation may be performed at one resolution (e.g. the native resolution of the images) and then up-sampled or down-sampled (e.g. by smoothing) to provide masks at different resolutions.
  • one resolution e.g. the native resolution of the images
  • up-sampled or down-sampled e.g. by smoothing
  • the image data for each tile may comprise image data relating to that tile at at least one resolution. Where different resolutions are used these may be provided by images collected at differing levels of magnification, for example as described in relation to Figure 3. In some possibilities, images at different resolutions for a given tile may be obtained by down- sampling, e.g. smoothing, or by image interpolation. This approach may be used in combination with differing magnification levels. Image tiles of the same image region having different resolutions described above may comprise the same number of pixels, or a different number of pixels covering the same spatial region of the image. Different classification data may be applied to the image data relating to different resolutions.
  • the processor obtains 1005 three colour components, one corresponding to an eosin colour channel and another corresponding to a Haemotoxylin colour channel as obtained using a colour decomposition method, as well as one grey scale colour channel obtained directly from the original RGB coloured image.
  • the processor then continues to step 1006 of the method as described above.
  • tiles classified as representing tumour regions may be assigned a posterior probability of corresponding to a tumour region of the tissue sample, based on a selected threshold level. For example, when classifying the tile as tumour or non-tumour, a threshold level of 0.5 (50%) may be applied.
  • the probability estimate used to generate the colour map be obtained by updating the posterior probability data.
  • the processor subset of the stored classification data may be selected at random, for example in a Monte-Carlo type approach.
  • selecting may comprise selecting a predefined, or user selected, subset of classification data.
  • the classification data comprises data (e.g. feature vectors) relating to known tissue types and/or known tumour types, and selecting the subset of classification data may comprise selecting classification data based on the tissue type of the sample from which the imaged section of tissue was derived.
  • the classification data may be derived from a supervised learning model in which the classification data comprises a feature vector derived from an image of a tissue sample, and an indication of whether that feature vector relates to tumour or non-tumour image data.
  • the processor may be configured to obtain input from a user confirming whether a region of the image comprises tumour tissue and to store one or more feature vectors from that region of the image in memory with the classification data. This may enable the operation of the method to be adapted or tuned to operation in particular types of tissue.
  • the boundary may be encoded in the form of a sequence of transition indicators indicating the direction of a transition between two consecutive boundary pixels, e.g. a step to the right, a step diagonally right and up, a step up etc.
  • Encoding the boundary in this way may provide as many transition indicator elements as there are boundary pixels.
  • the position of the boundary in the image can then be encoded using the coordinates of the first pixel in the sequence.
  • any other type of encoding of a line in a 2D plane may be used.
  • the processor may be configured to control a cutting tool as described above with reference to Figure 1 and Figure 2 to dissect the tumour tissue from a sample (e.g the sample from which the tissue section was derived) based on the boundary.
  • the functionality of the computer and/or the processor may be provided by digital logic, such as field programmable gate arrays, FPGA, application specific integrated circuits, ASIC, a digital signal processor, DSP, or by software loaded into a programmable processor.
  • a computer implemented image processing method of identifying a tissue boundary of a first tissue type region of a tissue sample comprising at least one region of a first tissue type, and a second tissue type, to enable excision of at least a portion of the region of the first tissue type from the tissue sample by cutting along the tissue boundary
  • the method comprising: obtaining an image of a tissue section of the tissue sample; identifying at least one image property of the image; comparing the image property with classification data; based on the comparison, classifying a region of the image as the first tissue type or the first tissue type; and if the region of the image is classified as a first tissue type, identifying a boundary of the first tissue type in the image; using the boundary to identify a region of the first tissue type in the tissue sample.
  • Other examples and variations are within the scope of the disclosure, as set out in the appended claims.

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Abstract

L'invention concerne un procédé de traitement d'image mis en œuvre par ordinateur, permettant d'identifier la frontière de tissu d'une région tumorale d'un échantillon de tissu, l'échantillon de tissu contenant des régions non tumorales et au moins une région tumorale, pour permettre l'excision d'au moins une partie d'une région tumorale de l'échantillon de tissu en coupant le long de la frontière de tissu, le procédé consistant à : obtenir une image d'une section de tissu de l'échantillon de tissu ; identifier au moins une propriété d'image de l'image ; comparer la propriété d'image à des données de classification ; d'après le résultat de la comparaison, classifier une région de l'image comme région tumorale représentant une région tumorale dans l'échantillon de tissu ou comme région non tumorale représentant une région non tumorale dans l'échantillon de tissu ; et, si la région de l'image est classifiée comme région tumorale, identifier une frontière de la région de l'image ; et utiliser la frontière pour identifier une frontière de tissu de la région tumorale de l'échantillon de tissu représenté par la région de l'image.
PCT/GB2014/051426 2013-05-10 2014-05-09 Appareil et procédé pour traiter les images d'échantillons de tissus Ceased WO2014181123A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP14728603.3A EP2994882A1 (fr) 2013-05-10 2014-05-09 Appareil et procédé pour traiter les images d'échantillons de tissus
US14/937,559 US9946953B2 (en) 2013-05-10 2015-11-10 Apparatus and method for processing images of tissue samples

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB1308460.3A GB2513916B (en) 2013-05-10 2013-05-10 Identifying a Tissue Boundary of a Tumour Region of a Tissue Sample
GB1308460.3 2013-05-10

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