C2AM-Unet: Coordinate and Channel Attention Mixing Flexible Architecture for Retinal Vessel Segmentation

Abstract

Accurate retinal Microvascular segmentation demands a balanced combination of anatomical fidelity and hemodynamic relevance. However, existing methods fall short in preserving critical structures such as capillary junctions and bifurcations, thus limiting clinical applications and causing fragmentation. To address these limitations, we propose DFMS-Net, a novel dualfield segmentation framework that synergistically integrates geometric-field modeling--achieved through the Spatial Pathway Extractor (SPE) and Transformer-based Topology Interaction (TTI) for preserving structural continuity--and functional-field optimization--realized by the Semantic Attention Amplification (SAA) module for enhancing semantic visibility--via a unified Dual-Field Hemo-dynamic Attention (DFHA) mechanism. This core module enables joint enhancement of vessel continuity, accurate resolution of complex branching patterns, and recovery of low-contrast capillaries, all under physiological guidance. By co-optimizing geometric and functional cues within a unified attention learning paradigm, DFMS-Net produces segmentations that are both morphologically accurate and hemodynamically plausible. Furthermore, we propose two specialized variants using a streamlined Double SPE Attention for vessel continuity refinement. To address the directionality-dependent nature of structural damage in retinal ischemia and glaucoma, we propose Variant-1--a streamlined architecture that emphasizes dual-stage directional refinement to enhance trajectory coherence and improve the detection of topological disruptions, while Variant-2 supports high-resolution analysis of capillary dropout in early diabetic retinopathy through detailed microvascular recovery. Extensive experiments on retinal (DRIVE, STARE) and coronary angiography (DCA1, CHUAC) datasets demonstrate that DFMS-Net achieves state-of-theart performance. Meanwhile, its strong generalization capability offers a promising foundation for diagnosing both retinal and cardiovascular diseases. The code will be available at https://github.com/699zjl/DFMS-Net-new.