Generative Neural Framework for Micro-Vessels Classification

The morphological abnormalities in the retinal blood vessel have a close association with cerebrovascular, cardio-vascular, and systemic diseases. It makes the retinal artery/vein (A/V) classification salient for clinical decision-making. The existing methods find it challenging to correctly classify A/V with non-uniform brightness and vessel thickness, especially at the bifurcation and endpoints. To avoid these problems and increase precision, AV-Net is proposed. It uses the context information and performs data fusion to improve A/V classification. Specifically, the AV-Net offers a module that fuses local and global vessel information for creating a weight map to constrain the A/V features. It helps suppress the background-prone features and improve region extraction at the bifurcation and endpoints. In addition, to improve model robustness, the AV-Net uses a multiscale-feature module that captures coarse and fine details.