Retinal Blood Vessel Segmentation

Retinal blood vessel segmentation aims to automatically identify and delineate blood vessels in retinal images, facilitating early diagnosis of various eye diseases. Current research emphasizes improving segmentation accuracy and robustness using deep learning models, particularly U-Net variations and novel architectures incorporating graph convolutional networks, attention mechanisms, and multi-modal data fusion. These advancements address challenges like class imbalance, limited training data, and cross-dataset generalization, ultimately improving the efficiency and accuracy of disease detection and monitoring. The resulting automated analysis holds significant potential for streamlining clinical workflows and enhancing the quality of ophthalmological care.