Retinal Vessel

Retinal vessel analysis focuses on automatically segmenting and classifying retinal blood vessels from images (fundus photography, OCTA) to aid in the diagnosis of various eye and systemic diseases. Current research heavily utilizes deep learning, employing architectures like U-Net, ResNet, and transformers (e.g., Swin Transformer) within sophisticated model ensembles to improve segmentation accuracy and robustness across diverse image qualities and pathologies. These advancements enable more precise quantification of vascular features (e.g., branching angles, vessel density, tortuosity), leading to improved diagnostic capabilities and potentially streamlining clinical workflows.