A high-resolution network with adaptive spatial channel fusion for retinal vessel segmentation

Accurate segmentation of retinal vessels is critical for the diagnosis of ophthalmic diseases. However, this task is made challenging by two issues: vast-scale variations from major arteries to fine capillaries often lead to a fractured vessel topology, and low-contrast boundaries corrupted by noise frequently result in segmentation ambiguity. To address these challenges, we propose an adaptive spatial channel fusion high-resolution network (ASCF-HRNet). The proposed architecture has two synergistic innovations: first, to preserve the topological integrity of the vascular network against vast-scale variations, we propose a spatial semantic enhancement (SSE) block that replaces standard convolutions with parallel multi-scale kernels and spatial attention; and second, to resolve segmentation ambiguity at low-contrast boundaries, we design a channel feature enhancement (CFE) block. Strategically integrated prior to each upsampling operation, the features were purified by performing a semantics-aware refinement that prevented the propagation of background noise and redundant information. Extensive experiments on the DRIVE, CHASE_DB1, and STARE datasets demonstrate that ASCF-HRNet achieves leading AUC scores of 0.9880, 0.9899, and 0.9828, and highly competitive F1-scores of 0.8263, 0.8119, and 0.7781. The results demonstrate that our proposed ASCF-HRNet achieves a superior segmentation performance, particularly in preserving vascular topology and ensuring boundary fidelity.