Systems and methods to process electronic images to produce a tissue map visualization
Systems and methods are disclosed for analyzing an image of a slide corresponding to a specimen, the method including receiving at least one digitized image of a pathology specimen; determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type; determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a value for each pixel; and suppressing, based on the value for each pixel, one or more non-salient regions of the digitized image.
1. A computer-implemented method for analyzing an image of a slide corresponding to a specimen, the method comprising:
receiving at least one digitized image of a pathology specimen;
determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient feature comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type;
determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a diagnostic value for each pixel;
suppressing, based on the diagnostic value for each pixel, one or more non-salient regions of the digitized image; and
normalizing the salient region overlay to obtain a variable, the salient region overlay is represented by a binary segmentation of the digitized image indicating if each pixel has or does not have the salient feature present.
2. The computer-implemented method of claim 1 , further comprising converting the salient region overlay into a tissue map.
3. The computer-implemented method of claim 1 , wherein the salient region overlay is represented by a set of super-pixels associated with a score or a probability for the salient feature being present or absent.
4. The computer-implemented method of claim 1 , wherein detecting a salient feature uses image processing techniques, AI, and/or machine learning on the digitized image to produce a tissue visualization.
5. The computer-implemented method of claim 1 , wherein the digitized image comprises related case information, patient information and information from a clinical system.
6. The computer-implemented method of claim 1 , further comprising alerting a user when the salient region overlay is available.
7. The computer-implemented method of claim 1 , wherein the salient region overlay is resized to a same size as the digitized image.
8. The computer-implemented method of claim 1 , further comprising developing a pipeline to archive a plurality of processed images.
9. The computer-implemented method of claim 1 , wherein the salient region overlay is represented by a semantic segmentation of the digitized image indicating a score or probability for each pixel.
10. A system for analyzing an image of a slide 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 at least one digitized image of a pathology specimen;
determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient feature comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type;
determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a diagnostic value for each pixel; and
suppressing, based on the diagnostic value for each pixel, one or more non-salient regions of the digitized image; and
normalizing the salient region overlay to obtain a variable, the salient region overlay is represented by a binary segmentation of the digitized image indicating if each pixel has or does not have the salient feature present.
11. The system of claim 10 , further comprising converting the salient region overlay into a tissue map.
12. The system of claim 10 , wherein the salient region overlay is represented by a set of super-pixels associated with a score or a probability for the salient feature being present or absent.
13. The system of claim 10 , wherein detecting a salient feature uses image processing techniques, AI, and/or machine learning on the digitized image to produce a tissue visualization.
14. The system of claim 10 , wherein the digitized image comprises related case information, patient information, and information from a clinical system.
15. The system of claim 10 , further comprising developing a pipeline to archive prospective patient data.
16. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for analyzing an image of a slide corresponding to a specimen, the method comprising:
receiving at least one digitized image of a pathology specimen;
determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient feature comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type;
determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a diagnostic value for each pixel;
suppressing, based on the diagnostic value for each pixel, one or more non-salient regions of the digitized image; and
normalizing the salient region overlay to obtain a variable, the salient region overlay is represented by a binary segmentation of the digitized image indicating if each pixel has or does not have the salient feature present.
17. The non-transitory computer readable medium of claim 16 , further comprising converting the salient region overlay into a tissue map.