Systems and methods for processing electronic images of slides for a digital pathology workflow
A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.
1. A computer-implemented method for processing electronic images associated with a pathology specimen, the method comprising:
receiving one or more cases associated with a pathology specimen at a digital storage device;
receiving a selection of a case of the one or more cases;
partitioning the case into a plurality of parts, and partitioning a part of the plurality of parts into a plurality of slides;
applying a machine learning model to generate an interactive visualization for the plurality of slides, the machine learning model having been trained by processing a plurality of training images;
determining image features associated with the plurality of slides;
determining at least one report for the image features;
aggregating a plurality of feature instances of a reportable feature, the reportable feature corresponding to at least one of the images features, into one group;
aggregating the at least one report for the image features into a part report; and
aggregating at least one part report into a case report.
2. The computer-implemented method of claim 1 , wherein the image features comprise a classified and/or labeled area or focus of a suspicious tissue, an observation of the suspicious tissue, and/or a feature instance.
3. The computer-implemented method of claim 2 , wherein the image features comprise a diagnostic and/or anatomic characteristic unique to a reportable feature, and associated metadata.
4. The computer-implemented method of claim 2 , further comprising:
displaying at least one thumbnail of the image feature in a gallery view covering reportable features; and
selecting a thumbnail to jump to the image feature.
5. The computer-implemented method of claim 2 , further comprising:
displaying the group of the at least one feature instance together; and
outputting a visualization of the group with associated context areas.
6. The computer-implemented method of claim 1 , further comprising:
determining a relationship between two reportable features; and
detailing the relationship on the case report.
7. The computer-implemented method of claim 1 , further comprising:
identifying at least one metric associated with a reportable feature; and
including the at least one metric in the case report.
8. The computer-implemented method of claim 1 , further comprising:
moving between a selection of a visualization of a first feature instance or an aggregation of feature instances and a selection of a visualization of a second feature instance or an aggregation of feature instances on a slide; and
displaying the selection to a user.
9. The computer-implemented method of claim 8 , wherein a visualization of a feature instance comprises one or more focus areas.
10. The computer-implemented method of claim 1 , wherein the report for the image features, the part report, and/or the case report is editable.
11. The computer-implemented method of claim 2 , wherein the feature instance is editable.
12. A system for using at least one machine learning model to process electronic images associated with a pathology specimen, the system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
receiving one or more cases associated with a pathology specimen at a digital storage device;
receiving a selection of a case of the one or more cases;
partitioning the case into a plurality of parts, and partitioning a part of the plurality of parts into a plurality of slides;
applying a machine learning model to generate an interactive visualization for the plurality of slides, the machine learning model having been trained by processing a plurality of training images;
determining image features associated with the plurality of slides;
determining at least one report for the image features;
aggregating at least one feature instance of a reportable feature into one group;
aggregating the at least one report for the image features into a part report; and
aggregating at least one part report into a case report.
13. The system of claim 12 , wherein the image features comprise a classified and/or labeled area or focus of a suspicious tissue, an observation of the suspicious tissue, and/or a feature instance.
14. The system of claim 13 , wherein the image features comprise a diagnostic and/or anatomic characteristic unique to a reportable feature, and associated metadata.
15. The system of claim 13 , wherein the operations further comprise:
displaying at least one thumbnail of the image feature in a gallery view covering reportable features; and
selecting a thumbnail to jump to the image feature.
16. The system of claim 13 , wherein the operations further comprise:
displaying the group of the at least one feature instance together; and
outputting a visualization of the group with associated context areas.
17. The system of claim 12 , wherein the operations further comprise:
determining a relationship between two reportable features; and
detailing the relationship on the case report.
18. A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a method of processing electronic images associated with a pathology specimen, the method comprising:
receiving one or more cases associated with a pathology specimen at a digital storage device;
receiving a selection of a case of the one or more cases;
partitioning the case into a plurality of parts, and partitioning a part of the plurality of parts into a plurality of slides;
applying a machine learning model to generate an interactive visualization for the plurality of slides, the machine learning model having been trained by processing a plurality of training images;
determining image features associated with the plurality of slides;
determining at least one report for the image features;
aggregating at least one feature instance of a reportable feature into one group;
aggregating the at least one report for the image features into a part report; and
aggregating at least one part report into a case report.
19. The computer-readable medium of claim 18 , wherein the image features comprise a classified and/or labeled area or focus of a suspicious tissue, an observation of the suspicious tissue, and/or a feature instance.
20. The computer-readable medium of claim 18 , wherein the method further comprises:
moving between a selection of a visualization of a first feature instance or an aggregation of feature instances and a selection of a visualization of a second feature instance or an aggregation of feature instances on a slide; and
displaying the selection to a user.