A retinal vessel segmentation network with multi-scale feature extraction and cross-layer attention fusion (MAF-Net)
Accurate segmentation of retinal vessels is a key task in fundus image analysis and is crucial for the early screening and diagnosis of various ophthalmic and systemic diseases. However, existing methods still struggle to simultaneously address scale variation, contextual fusion, and the recovery of directional structures when dealing with retinal vessels, which easily leads to missed detection of thin vessels and structural discontinuities. To tackle these issues, we propose a retinal vessel segmentation network, MAF-Net. First, we design an adaptive multi-scale dilated residual block in the encoder, which performs branch-wise adaptive selection and reweighting over parallel dilated branches to dynamically adjust scale responses, thereby enabling adaptive modeling of vessel features with different calibers. Second, to enhance the network’s ability to understand context, we introduce a windowed hierarchical cross-scale attention fusion module at the bottleneck layer, which enables cross-scale interaction of multi-level features within local windows, thereby efficiently aggregating contextual information while reducing computational complexity. Finally, in the skip connections, we propose a semantically guided tri-axis fusion module, which extracts directional information along the horizontal, vertical, and spatial axes, and uses progressively guided features from lower-resolution layers to generate a spatial confidence map for gated selection, thereby suppressing shallow noise and reducing discontinuities and adhesion. On three public datasets, DRIVE, STARE, and CHASE-DB1, MAF-Net achieves accuracies of 97.14%, 97.99%, and 97.82%, respectively. These results indicate that MAF-Net improves vessel structure recovery while better preserving vascular connectivity and structural consistency.