DSF-Net: dual selective fusion network with spatial-frequency domain encoding for retinal vessel segmentation

Abstract

Retinal vessel segmentation is a fundamental task in quantitative fundus image analysis. However, existing methods still face challenges in segmenting thin and complex vessels because local details and global contextual information are often insufficiently integrated. To address this issue, we propose a Dual Selective Fusion Network (DSF-Net) for retinal vessel segmentation. The proposed network consists of a Dual-Branch Encoder (DB-Encoder), a Pinwheel-based Local Attention (PLA) module, and a Dual Selective Fusion Transformer Block (DSFTB). The DB-Encoder jointly models spatial- and frequency-domain information to capture both fine vessel details and global contextual patterns. The PLA module enhances local perception and boundary sensitivity through asymmetric multidirectional convolutions and Sobel edge priors. The DSFTB integrates Multi-scale Feature Attention (MSFA) and token-selective Global Feature Attention (GFA) to enable adaptive feature fusion and long-range dependency modeling. Experiments conducted on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that DSF-Net achieves competitive overall performance compared with existing methods. In particular, the proposed method produces more accurate and structurally coherent segmentation results, especially for thin and complex vessels. These findings indicate that the combined modeling of local detail, frequency-aware representation, and global contextual dependency is effective for retinal vessel segmentation. DSF-Net provides a robust framework for improving vessel continuity and boundary delineation in fundus images. The source code is available at: https://github.com/liang050629/DSF-Net .