Predicting response to anti-vascular endothelial growth factor therapy with computer-extracted morphology and spatial arrangement features of leakage patterns on baseline fluorescein angiography in diabetic macular edema

An apparatus comprising:

• a processor;
• a memory configured to store a two-dimensional (2D) fluorescein angiography (FA) image of a region of interest (ROI) demonstrating diabetic macular edema (DME), where the image is associated with a patient, where the image has a plurality of pixels, a pixel having an intensity;
• an input/output (I/O) interface;
• a set of circuits;
• and
• an interface that connects the processor, the memory, the I/O interface, and the set of circuits, the set of circuits comprising:
• an image acquisition circuit configured to access a 2D FA image of an ROI demonstrating DME, where the image is associated with a patient, where the image has a plurality of pixels, a pixel having an intensity;
• a leakage localization circuit configured to:
• generate a leakage localization mask based on the 2D FA image, where the leakage localization mask includes a plurality of leakage patches;
• a graph-network generation circuit configured to:
• generate a set of leakage graphs based on the leakage localization mask, where a node of a member of the set of leakage graphs is a member of the plurality of leakage patches;
• and
• generate a set of graph-network features based on the set of leakage graphs;
• a morphology circuit configured to:
• extract a set of morphological features from the FA image based on the leakage localization mask;
• a DME classification circuit configured to:
• compute a probability that the patient is a non-rebounder based on the set of graph-network features and the set of morphological features;
• and
• generate a classification of the patient as a non-rebounder or rebounder based, at least in part, on the probability;
• and
• a display circuit configured to displaying the classification and to optionally display at least one of the probability, the set of graph-network features, the set of morphological features, a member of set of leakage graphs, or the image.

A non-transitory computer-readable storage device storing computer-executable instructions that when executed by a computer control the computer to perform a method, the method comprising:

• accessing a pre-anti-vascular endothelial growth factor (anti-VEGF) two-dimensional (2D) ultra-wide field fluorescein angiography (UWFA) mage of a region of interest (ROI) demonstrating diabetic macular edema (DME), where the image is associated with a patient, the image having a plurality of pixels, a pixel having an intensity;
• generating a leakage localization mask based on the UWFA image, where the leakage localization mask includes a plurality of leakage patches;
• generating a set of leakage graphs based on the leakage localization mask, where the set of leakage graphs includes a minimum spanning tree (MST) graph, where a node of the MST graph is a member of the plurality of leakage patches;
• generating a set of graph-network features based on the set of leakage graphs, where the set of graph-network features includes an edge length disorder of MST feature;
• extracting a set of morphological features from the UWFA image based on the leakage localization mask, where the set of morphological features includes at least one of an area feature, a distance to N nearest neighbors feature, or a disorder of variance of distance to N nearest neighbors feature;
• providing the set of graph-network features and the set of morphological features to a quadratic discriminant analysis (QDA) machine learning classifier configured to distinguish non-rebounders from rebounders in DME based on the set of graph-network features and the set of morphological features;
• receiving, from the machine learning classifier, a probability that the patient is a non-rebounder, where the machine learning classifier computes the probability based on the set of graph-network features and the set of morphological features;
• generating a classification of the patient as a non-rebounder or rebounder based, at least in part, on the probability;
• generating a personalized DME treatment plan based, at least in part, on the classification;
• and
• displaying the classification and optionally displaying at least one of the probability, the set of graph-network features, the set of morphological features, a member of the set of leakage graphs, or the UWFA image.