Systems and methods for processing images to prepare slides for processed images for digital pathology
Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.
1. A computer-implemented method for processing an electronic image corresponding to a specimen, the method comprising:
receiving a target electronic image of a slide corresponding to a target specimen, the target specimen comprising a tissue sample from a patient;
applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies, the training images comprising either or both of images of human tissue and images that are algorithmically generated; and
based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared using immunohistochemistry.
2. The computer-implemented method of claim 1 , wherein determining deficiencies comprises determining a likelihood that the additional slide is to be prepared based on specimen information of the target specimen, and
in response to the likelihood being greater than or equal to a predetermined amount, automatically ordering the additional slide to be prepared.
3. The computer-implemented method of claim 1 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test.
4. The computer-implemented method of claim 1 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test, and
wherein the diagnosis that automatically initiates the additional test comprises additional test is automatically initiated in response to a diagnosis of any one or any combination of lung adenocarcinoma, breast carcinoma, endometrioid adenocarcinoma, colonic adenocarcinoma, adenocarcinoma in other tissues, sarcomas, prognostic biomarkers, suspicious lesions, amyloid presence, and/or fungal organisms.
5. The computer-implemented method of claim 1 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a morphology that automatically triggers a genetic test.
6. The computer-implemented method of claim 1 , wherein the ordering the additional slide comprises ordering a new stain to be prepared for the slide corresponding to the target specimen.
7. The computer-implemented method of claim 1 , wherein the ordering the additional slide comprises ordering a recut for the slide corresponding to the target specimen.
8. The computer-implemented method of claim 1 , further comprising outputting an alert on a display indicating that the additional slide is being prepared.
9. A system for processing an electronic image corresponding to a 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 a target electronic image of a slide corresponding to a target specimen, the target specimen comprising a tissue sample from a patient;
applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies, the training images comprising either or both of images of human tissue and images that are algorithmically generated; and
based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared using immunohistochemistry.
10. The system of claim 9 , wherein determining deficiencies comprises determining a likelihood that the additional slide is to be prepared based on specimen information of the target specimen, and
in response to the likelihood being greater than or equal to a predetermined amount, determining to automatically order the additional slide to be prepared.
11. The system of claim 9 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test.
12. The system of claim 9 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test, and
wherein the diagnosis that automatically initiates the additional test comprises any one or any combination of lung adenocarcinoma, breast carcinoma, endometrioid adenocarcinoma, colonic adenocarcinoma, adenocarcinoma in other tissues, sarcomas, prognostic biomarkers, suspicious lesions, amyloid presence, and/or fungal organisms.
13. The system of claim 9 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a morphology that automatically triggers a genetic test.
14. The system of claim 9 , wherein the ordering the additional slide comprises ordering a new stain to be prepared for the slide corresponding to the target specimen.
15. The system of claim 9 , wherein the ordering the additional slide comprises ordering a recut for the slide corresponding to the target specimen.
16. The system of claim 9 , further comprising outputting an alert on a display indicating that the additional slide is being prepared.
17. A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform a method for processing an electronic image corresponding to a specimen, the method comprising:
receiving a target electronic image of a slide corresponding to a target specimen, the target specimen comprising a tissue sample from a patient;
applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies, the training images comprising either or both of images of human tissue and images that are algorithmically generated; and
based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared using immunohistochemistry.
18. The non-transitory computer-readable medium of claim 17 , wherein determining deficiencies comprises determining a likelihood that the additional slide is to be prepared based on specimen information of the target specimen, and
in response to the likelihood being greater than or equal to a predetermined amount, determining to automatically order the additional slide to be prepared.
19. The non-transitory computer-readable medium of claim 17 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test.
20. The non-transitory computer-readable medium of claim 17 , wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test, and
wherein the diagnosis that automatically initiates the additional test comprises any one or any combination of lung adenocarcinoma, breast carcinoma, endometrioid adenocarcinoma, colonic adenocarcinoma, adenocarcinoma in other tissues, sarcomas, prognostic biomarkers, suspicious lesions, amyloid presence, and/or fungal organisms.