System and method for pathologic feature detection and quantification

A non-transitory computer-readable medium storing instructions that when executed perform operations comprising:

• identifying a set of images of an eye of a person comprising an en face optical coherence tomography (OCT) image and an OCT Brightness-scan (B-scan);
• evaluating the en face OCT image using a first machine learning model to generate an en face feature segmentation indicative of one or more areas, of the en face OCT image, corresponding to a first pathological feature;
• evaluating the OCT B-scan using a second machine learning model to generate a B-scan feature segmentation indicative of one or more areas, of the OCT B-scan, corresponding to the first pathological feature;
• and
• generating an enhanced pathological feature segmentation indicative of the first pathological feature based upon the en face feature segmentation and the B-scan feature segmentation.

A computing device comprising:

• a processor;
• and
• memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:
• identifying a set of images of an eye of a person comprising an en face optical coherence tomography (OCT) image and an OCT Brightness-scan (B-scan);
• evaluating the en face OCT image using a first machine learning model to generate an en face feature segmentation indicative of one or more areas, of the en face OCT image, corresponding to a first pathological feature;
• evaluating the OCT B-scan using a second machine learning model to generate a B-scan feature segmentation indicative of one or more areas, of the OCT B-scan, corresponding to the first pathological feature;
• and
• generating an enhanced pathological feature segmentation indicative of the first pathological feature based upon the en face feature segmentation and the B-scan feature segmentation.