Abstract: Retinal vessel segmentation is critical in the diagnosis and treatment planning of many ocular diseases. Accurate segmentation of vascular features from retinal images remains particularly challenging for complex retinal structures as well as low-contrast fundus structures. A Joint Attention and Trans-former Network (JAT-Net) based on Joint Attention and Transformer for retinal vessel segmentation is proposed, which focuses on encoding local detail features with joint attention to channel information and location information of encoding stage features. To achieve more accurate segmentation, the ability to model long-distance contextual information and spatial dependencies is enhanced by Transformer. Retinal vessel segmentation experiments were performed on the DRIVE and CHASE datasets with accuracies of 0.970 6 and 0.977 4, F1 scores of 0.843 3 and 0.815 4.

Key words: retinal vessel segmentation, convolutional neural network, attentional mechanism, Transformer