Abstract Objectives Diabetic Retinopathy (DR) causes major vision loss, requiring precise segmentation of retinal vessels and the Foveal Avascular Zone (FAZ). Accurate structural masks enable quantitative biomarkers that support early diagnosis and long-term monitoring. Methods We propose a Retinal Graph Neural Network (RGNNNet) for OCTA segmentation. It combines multi-scale feature extraction with a graph representation, where node relations derive from an affinity matrix of feature maps. A symmetric normalization strategy stabilizes graph propagation and integrates local–global vascular context. A hybrid Dice–Focal loss refines fine-structure segmentation. Results On OCTA-500, RGNNNet achieved superior Dice and IoU to existing methods. For FAZ, it attained Dice values of 96.78 % (6 mm) and 98.02 % (3 mm), and maintained 0.915 on ROSE-0 without retraining. It outperformed baselines by 1–3 % Dice for other classes and remained lightweight (0.83 M params, 11.25 ms per 400 × 400 image). Conclusions By coupling residual feature learning with graph-based relational reasoning, RGNNNet provides accurate structure-specific masks that can serve as a foundation for downstream biomarker extraction. Its compact design and stable generalization highlight its potential for large-scale ophthalmic screening and integration into clinical workflows.
Raja et al. (2026) studied this question.