<p>Accurate retinal vessel segmentation is crucial for early clinical diagnosis and effective disease treatment guidance. Due to the large scale variation and complex structure of retinal vessels, common U-shaped networks fail to capture distinct and representative features. Furthermore, the continuous downsampling leads to loss of spatial features. To address these challenges, an adaptive multi-scale feature extraction and fusion network with deep supervision (AMFEF-Net) is proposed for retinal vessel segmentation. First, a structured residual module that integrates local and global information to preserve spatial features is built via residual connection. A multi-scale features aggregated attention module is then designed to obtain high-level feature representations of multi-scale vessels. A feature fusion module is utilized to guide the fusion of features at different levels, which exploits the complementary of high-level and low-level features. Additionally, multi-scale deep supervisionis used to learn hierarchical representations from multi-scale aggregated feature maps. Ablation and comparison study on three public datasets (DRIVE, CHASE_DB1, and STARE) are performed. Results demonstrate AMFEF-Net’s superior segmentation performance, particularly in the segmentation of tiny vessels and the extraction of the whole vascular network.</p>