Method of storing and retrieving digital pathology analysis results

Patent metadata

Field	Value
Publication number	US-11568657-B2
Application number	US-202016892075-A
Country	US
Kind code	B2
Filing date	Jun 3, 2020
Priority date	Dec 6, 2017
Publication date	Jan 31, 2023
Grant date	Jan 31, 2023

Abstract

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

First claim

The invention claimed is:

1. A system for deriving data corresponding to irregularly-shaped cells from an image of a biological sample comprising at least one stain, the system comprising: (i) one or more processors, and (ii) a memory coupled to the one or more processors, the memory to store computer-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
(a) deriving one or more feature metrics from the image;
(b) generating a plurality of sub-regions within the image, each sub-region having pixels with similar characteristics, the characteristics selected from color, brightness, and/or texture;
(c) computing a series of representational objects that correspond to a set of sub-regions of the generated plurality of sub-regions; wherein:
each representational object of the series of representational objects (i) identifies a particular cell type, and (ii) defines an outline of a corresponding sub-region of the set of sub-regions; and
each sub-region of the set of sub-regions identifies an amount of stain that exceeds a threshold value; and

(d) associating the derived one or more feature metrics from the image with calculated coordinates of each of the series of representational objects.

2. The system of claim 1 , wherein generating plurality of sub-regions comprises deriving superpixels.

3. The system of claim 2 , wherein the superpixels are derived using one of a graph-based approach or a gradient-ascent-based approach.

4. The system of claim 2 , wherein the superpixels are derived by (i) grouping pixels with local k-means clustering; and (ii) using a connected components algorithm to merge small isolated regions into nearest large superpixels.

5. The system of claim 1 , wherein the particular cell type includes a fibroblast or a macrophage.

6. The system of claim 1 , wherein each representational object of the series of representational objects is further identified by a corresponding seed point.

7. The system of claim 1 , wherein the operations further comprise storing the derived one or more feature metrics and associated calculated representational object coordinates in a database.

8. The system of claim 1 , wherein the one or more derived feature metrics comprise at least one expression score selected from percent positivity, an H-score, and a staining intensity.

9. The system of claim 1 , wherein data corresponding to irregularly-shaped cells is derived for a region-of-interest within the image.

10. The system of claim 9 , wherein the region-of-interest is an area of the image annotated by a medical professional.

11. A non-transitory computer-readable medium storing instructions for analyzing data associated with biological objects having irregular shapes, the instructions comprising:
(a) instructions for deriving one or more feature metrics from an image of a biological sample, the biological sample comprising at least one stain;
(b) instructions for partitioning the image into a series of sub-regions by grouping pixels having similar characteristics, the characteristics selected from color, brightness, and/or texture;
(c) instructions for computing a plurality of representational objects that correspond to a set of sub-regions of the series of sub-regions;
each representational object of the plurality of representational objects (i) identifies a particular cell type, and (ii) defines an outline of a corresponding sub-region of the set of sub-regions; and
each sub-region of the set of sub-regions identifies an amount of stain that exceeds a threshold value; and

(d) instructions for associating the derived one or more feature metrics from the image with calculated coordinates of each of the plurality of representational objects.

12. The non-transitory computer-readable medium of claim 11 , wherein the partitioning of the image into the series of sub-regions comprises computing superpixels.

13. The non-transitory computer-readable medium of claim 12 , wherein the superpixels are computed using one of a normalized cuts algorithm, an agglomerative clustering algorithm, a quick shift algorithm, a turbopixel algorithm, or simple linear iterative clustering algorithm.

14. The non-transitory computer-readable medium of claim 12 , wherein the superpixels are generated using simple iterative clustering, and wherein a superpixel size parameter is set to between 40 pixels and 400 pixels, and wherein a compactness parameter is set to between 10 to 100.

15. The non-transitory computer-readable medium of claim 12 , wherein the superpixels are computed by (i) grouping pixels with local k-means clustering; and (ii) using a connected components algorithm to merge small isolated regions into nearest large superpixels.

16. The non-transitory computer-readable medium of claim 11 , wherein the biological sample is stained with at least FAP, and wherein the derived one or more feature metrics include at least one of a FAP staining intensity or a FAP percent positivity.

17. The non-transitory computer-readable medium of claim 16 , wherein an average FAP percent positivity is calculated for all pixels within a sub-region.

18. The non-transitory computer-readable medium of claim 16 , wherein an average FAP staining intensity is calculated for all pixels within a sub-region.

19. The non-transitory computer-readable medium of claim 11 , wherein each representational object of the plurality of representational objects is further identified by a corresponding seed point.

20. The non-transitory computer-readable medium of claim 11 , further comprising instructions for storing the derived one or more feature metrics and associated calculated representational object coordinates in a database.

21. The non-transitory computer-readable medium of claim 20 , further comprising instructions for projecting stored information onto the image of the biological sample.

Assignees

• Ventana Med Syst Inc

Inventors

• Bredno Joerg
• Lorsakul Auranuch

Classifications

• G16H10/40

  for data related to laboratory analysis, e.g. patient specimen analysis · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G01N2015/1497

  Particle shape · CPC title

• G06K9/622

  Physics · mapped topic

• G06V2201/03

  Recognition of patterns in medical or anatomical images · CPC title