Research on Retinal Vessel Segmentation Algorithm Based on Multiple Attention Mechanisms

Abstract

Retinal vessel segmentation a critical diagnostic procedure for the early screening of ophthalmic and systemic diseases is undergoing a profound transformation driven by the integration of advanced deep learning and adaptive attention mechanisms. Conventional segmentation approaches, constrained by localized receptive fields, uniform feature weighting, and susceptibility to pathological artifacts, are being superseded by attention-augmented architectures capable of dynamically recalibrating vascular responses, modeling long-range topological dependencies, suppressing lesion-induced noise, and preserving microcapillary continuity. This research examines the synergistic deployment of multiple attention modules across the segmentation pipeline from local channel-spatial refinement to global self-attention modeling evaluating both their representational advantages and computational trade-offs. Drawing on systematic experimental validation across the DRIVE and STARE datasets, the paper identifies optimal fusion strategies specifically a serial SE–Attention Gate pipeline and a bottleneck-deployed lightweight MHSA block and assesses their impact on segmentation fidelity and hardware efficiency. A comparative analysis against mainstream CNN and Transformer baselines is presented. The paper proposes a Structured MultiAttention Fusion Framework and offers architectural guidelines for researchers and clinicians seeking to deploy efficient, interpretable models in resource-constrained environments. Findings confirm that the proposed architecture delivers substantial and measurable improvements in Dice coefficient, sensitivity, vascular connectivity, and inference efficiency, establishing a robust foundation for automated ophthalmic diagnostics.