Retinal Vascular

Retinal vascular analysis focuses on automatically segmenting and classifying retinal blood vessels from images, aiming to improve the efficiency and accuracy of diagnosing various ocular and systemic diseases. Current research heavily utilizes deep learning models, such as U-Net variations, Swin Transformers, and ResNets, often incorporating innovative loss functions optimized for vascular features like density and tortuosity to enhance segmentation accuracy and topological correctness. These advancements enable quantitative analysis of vascular morphology, providing objective biomarkers for disease detection and potentially improving patient care through earlier and more accurate diagnoses. Open-source toolkits are emerging to facilitate wider adoption and standardization of these methods.