Retinal Vessel Segmentation

Retinal vessel segmentation, the automated identification of blood vessels in retinal images, aims to improve the efficiency and accuracy of diagnosing various eye and systemic diseases. Current research heavily utilizes deep learning, employing architectures like U-Net and its variations, along with novel convolutional techniques and diffusion models, to address challenges such as segmenting small vessels and handling variations in image quality and modality (e.g., fundus photography, OCTA). Improved segmentation accuracy and robustness across different imaging modalities are key goals, with a focus on developing methods that generalize well to unseen data and minimize the need for extensive manual annotation. This work has significant implications for ophthalmology and related fields, enabling faster and more accurate disease detection and monitoring.