SYSTEM FOR PROCESSING A WHOLE SLIDE IMAGE, WSI, OF A BIOPSY
20230081707 · 2023-03-16
Inventors
- LIONEL MICHEL BLANCHET (EINDHOVEN, NL)
- LAURIE BAX (EINDHOVEN, NL)
- CARLOS SÀNCHEZ SÀNCHEZ C (EINDHOVEN, NL)
- DUYGU BUYUKAYDIN (EINDHOVEN, NL)
- WEN-YANG CHU (HASSELT, BE)
- ANJA VAN GESTEL (EINDHOVEN, NL)
Cpc classification
International classification
Abstract
A system and method is provided for processing a whole slide image, WSI, of a biopsy such as a core needle biopsy or a vacuum assisted biopsy. A skeleton of the shape of the detected tissue is created and a skeleton path is determined. An image is generated comprising a line representing the skeleton path with different line representations along the line representing different tissue pathologies. A pathology summary for the overall line is also prepared. This provides the information desired by a pathologist in a most convenient format and representation.
Claims
1. A system for processing a whole slide image, WSI, of a biopsy, comprising a processor which is adapted to: detect tissue present in the WSI relating to one biopsy; create a skeleton of the shape of the detected tissue; select a skeleton path as the longest continuous path through the tissue; determine tissue pathologies of the detected tissue; and generate an image comprising: a line representing the skeleton path with different line representations along the line representing different tissue pathologies; and a pathology summary for the overall line, wherein the pathology summary comprises a biopsy length and a fraction of a line occupied by a particular type of tissue pathology.
2. The system as claimed in claim 1, wherein the different representations comprise different colors or line thickness.
3. (canceled)
4. The system as claimed in claim 3, wherein the particular type of tissue pathology is tumor tissue.
5. The system as claimed in claim 1, wherein generating the line comprises overlaying the detected tissue pathologies over the skeleton path, and selecting a representative tissue pathology for a point along the skeleton path.
6. The system as claimed in claim 5, wherein the representative tissue pathology is selected to be tumor tissue if tumor tissue is present at or perpendicular to the respective point along the line.
7. The system as claimed in claim 1, comprising a first machine learning algorithm (ML1) for tissue detection.
8. The system as claimed in claim 7, comprising one or more second machine learning algorithms (ML2) for detecting tissue pathologies.
9. The system as claimed in claim 1, comprising a display for displaying the image, with the pathology summary alongside the line.
10. A computer-implemented method for processing a whole slide image, WSI, of a biopsy, comprising: detecting tissue present in the WSI relating to one biopsy; creating a skeleton of the shape of the detected tissue; selecting a skeleton path as the longest continuous path through the tissue; determining tissue pathologies of the detected tissue; and generating an image comprising: a line representing the skeleton path with different line representations along the line representing different tissue pathologies; and a pathology summary for the overall line, wherein the pathology summary comprises a biopsy length and a fraction of the line occupied by a particular type of tissue pathology.
11. The method as claimed in claim 10, comprising generating an image with different colors of the line representing different tissue pathologies.
12. (canceled)
13. The method as claimed in claim 10, comprising generating the image by overlaying or juxtaposing the determined tissue pathologies over the skeleton path, and selecting an associated tissue pathology for a point along the skeleton path.
14. The method as claimed in claim 10, comprising displaying the image, with the pathology summary alongside the line.
15. A non-transitory computer readable medium comprising a computer program such that when said program is run on a computer, the computer: detects tissue present in the WSI relating to one biopsy; creates a skeleton of the shape of the detected tissue; selects a skeleton path as the longest continuous path through the tissue; determines tissue pathologies of the detected tissue; and generates an image comprising: a line representing the skeleton path with different line representations along the line representing different tissue pathologies; and a pathology summary for the overall line, wherein the pathology summary comprises a biopsy length and a fraction of the line occupied by a particular type of tissue pathology.
16. The computer readable medium of claim 15, wherein the computer generates an image with different colors of the line representing different tissue pathologies.
17. The computer readable medium of claim 15, wherein the computer generates the image by overlaying or juxtaposing the determined tissue pathologies over the skeleton path, and selecting an associated tissue pathology for a point along the skeleton path.
18. The computer readable medium of claim 15, wherein the computer displays the image, with the pathology summary alongside the line.
19. The computer readable medium of claim 15, wherein when the computer generates the line the computer overlays the detected tissue pathologies over the skeleton path, and selects a representative tissue pathology for a point along the skeleton path.
20. The computer readable medium of claim 19, wherein the representative tissue pathology is selected to be tumor tissue if tumor tissue is present at or perpendicular to the respective point along the line.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:
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DETAILED DESCRIPTION OF THE EMBODIMENTS
[0052] The invention will be described with reference to the Figures.
[0053] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.
[0054] The invention provides a system and method for processing a whole slide image, WSI, of a biopsy such as a core needle biopsy or a vacuum assisted biopsy. A skeleton of the shape of the detected tissue is created and a skeleton path is determined. An image is generated comprising a line representing the skeleton path with different line representations along the line representing different tissue pathologies. A pathology summary for the overall line is also prepared. This provides the information desired by a pathologist in a most convenient format and representation.
[0055] The invention relates to elongate biopsies wherein pathology information at different positions along the elongate length of the biopsy is of interest. As one example of the type of biopsy to which the invention may be applied,
[0056] The steps carried out in the processing of the WSI will be discussed. These steps are carried out as image analysis steps of the WSI.
[0057] The first step is to detect all tissue present on the WSI. This creates an area outline 12 as shown in the right image in
[0058] For the identified biopsies, a skeleton 20 of the biopsy is identified as shown in
[0059] The skeleton is then trimmed, to remove any branches of the skeleton that differ from the longest possible path. The left image of
[0060] The skeleton path 32 thus defines the full extent of the biopsy.
[0061] The left image of
[0062] The relevant pathology or pathologies in each sample (i.e. within the area outline 12) is then identified.
[0063] The left image of
[0064] The left image of
[0065] The result is a line 60 representing the skeleton path but with different line representations along the line representing different tissue pathologies. For example, the regions 62 may correspond to areas of tumor tissue (e.g. indicated red) whereas the other regions are free of tumor tissue (e.g. indicated green).
[0066] The determination of whether tumor tissue or normal tissue is represented along the line may be based on a most sensitive detection approach, by which any tumor tissue detected along a path perpendicular to a point along the line 60 results in that point indicating tumor tissue. If there are multiple pathologies being detected, a weighting scheme may determine which pathology is identified at the point along the line 60.
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[0068] This example has only two classes (tumor or normal), but there may be more.
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[0071] The line 60 is overlaid over the WSI. It has different colors or different thickness (or other differences such as markers, dot patterns etc.) along its length to represent the different tissue pathologies. In this example the area outline 12 is also represented.
[0072] The pathology summary is displayed alongside the line, for example tumor lengths 92 for individual tumor portions along the line length as well as an overall summary 94 (such as in
[0073] The pathologist may zoom in to inspect a particular area of the WSI. The line 60 may then, for example, disappear so that the inspection is not obstructed.
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[0075] The image is displayed on a display device 102. It includes the original WSI with the addition of a line representing the skeleton path with different line representations along the line representing different tissue pathologies. A pathology summary for the overall line is also displayed.
[0076] A first machine learning algorithm MLA1 is for example used for the tissue detection step. Such algorithms are known.
[0077] By way of example, reference is made to the article “tissueloc: Whole slide digital pathology image tissue localization” of Pingjun Chen et. al., Department of Biomedical Engineering, University of Florida DOI: 10.21105/joss.01148.
[0078] One or more second machine learning algorithms MLA2 are for example used for detecting tissue pathologies.
[0079] By way of example, reference is made to the article “Clinical-grade computational pathology using weakly supervised deep learning on whole slide images” of Gabriele Campanella et. al., Nature Medicine volume 25, pages 1301-1309(2019).
[0080] Further reference is made to the article “Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study”, of Wouter Bulten et. al., DOI:https://doi.org/10.1016/S1470-2045(19)30739-9.
[0081] Algorithms are also well known for skeleton identification from shapes in images. EP 2 772 882 for example discloses the creation of a skeleton from a biopsy image as well as a length determination from the skeleton.
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[0083] in step 110, detecting tissue present in the WSI relating to one biopsy;
[0084] in step 112, creating a skeleton of the shape of the detected tissue;
[0085] in step 114, selecting a skeleton path as the longest continuous path through the tissue;
[0086] in step 116, determining tissue pathologies of the detected tissue; and
[0087] in step 118, generating an image for display.
[0088] The image is generated by creating in step 120 a line representing the skeleton path with different line representations along the line representing different tissue pathologies and in step 122 creating a pathology summary for the overall line.
[0089] As discussed above, the system makes use of a processor to perform the data and image processing. The processor can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. The processor typically employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. The processor may be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.
[0090] Examples of circuitry that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).
[0091] In various implementations, the processor may be associated with one or more storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform the required functions. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor.
[0092] Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality.
[0093] The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
[0094] A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. (optional)
[0095] If the term “adapted to” is used in the claims or description, it is noted the term “adapted to” is intended to be equivalent to the term “configured to”.
[0096] Any reference signs in the claims should not be construed as limiting the scope.