A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation

The structure and function of retinal vessels play a crucial role in diagnosing and treating various ocular and systemic diseases. Therefore, the accurate segmentation of retinal vessels is of paramount importance to assist a clinical diagnosis. U-Net has been highly praised for its outstanding performance in the field of medical image segmentation. However, with the increase in network depth, multiple pooling operations may lead to the problem of crucial information loss. Additionally, handling the insufficient processing of local context features caused by skip connections can affect the accurate segmentation of retinal vessels. To address these problems, we proposed a novel model for retinal vessel segmentation. The proposed model is implemented based on the U-Net architecture, with the addition of two blocks, namely, an MsFE block and MsAF block, between the encoder and decoder at each layer of the U-Net backbone. The MsFE block extracts low-level features from different scales, while the MsAF block performs feature fusion across various scales. Finally, the output of the MsAF block replaces the skip connection in the U-Net backbone. Experimental evaluations on the DRIVE dataset, CHASEDB1 dataset, and STARE dataset demonstrated that MsAF-UNet exhibited excellent segmentation performance compared with the state-of-the-art methods.