Machine Learning-Based Retinal Neovascularization Localization in En Face Optical Coherence Tomography Angiography Images Using Vessel Features

Abstract

Retinal neovascularization (RNV) is a pathological ocular disorder marked by the development of abnormal blood vessels within the retinal tissue, leading to potential complications such as hemorrhage and vision loss. It can be visualized using Optical Coherence Tomography Angiography (OCTA), a specialized imaging tool that allows for the detailed analysis of retinal blood vessels. However, identifying RNVs within OCTA images poses challenges including variability in patterns and sizes, overlapping vascular networks, complex morphology, noise and artifacts, low contrast, depth variability, and interpatient variability. This study addressed this challenge by extracting vessel density, bifurcation points, and vessel thinness features to pinpoint regions of interest. Our systematic approach led to selecting potential regions using a support vector machine (SVM). Notably, our method achieved a localization precision of 90.33% in two datasets comprising 69 en face OCTA images, demonstrating a superior F1-score than the state-of-the-art methods, which used VNet and the weighted combination of feature maps by 44.19% and 57.46%, respectively.