Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Over four decades of retinal vessel segmentation (RVS) research (1982–2025) are synthesized, tracing developments across classical image processing, traditional machine learning, and deep learning paradigms. A cross‐paradigm taxonomy is introduced that links computational methods with clinical interpretability and model generalization, addressing gaps identified in earlier surveys. A comparative meta‐analysis of 428 studies catalogs preprocessing and postprocessing techniques, segmentation architectures, and loss functions and assesses reported performance on benchmark datasets (DRIVE, STARE, CHASE_DB1, HRF) using 20 standardized evaluation metrics. Architectural trends, including residual‐enhanced U‐Nets, attention‐guided models, graph convolutional networks, and transformer‐based architectures (SGAT‐Net, MTPA‐UNet), correlate with measurable improvements in vessel continuity, topology preservation, and clinical reliability. Emerging interpretability strategies, including attention mapping, saliency visualization, and feature attribution, are examined for their ability to enhance clinical transparency and trust in automated RVS systems. Persistent challenges include cross‐dataset generalization, annotation scarcity, and limited clinical deployment of explainable models. A forward‐looking roadmap emphasizes domain adaptation, semi‐supervised and weakly‐supervised learning, and efficient architectures for clinical translation. By integrating quantitative evidence with a clinically grounded taxonomy, a novel perspective on RVS is provided, serving as a comprehensive reference for research and clinical adoption in precision ophthalmology.