联合注意力和Transformer的视网膜血管分割网络

视网膜血管分割在许多眼科疾病诊断和治疗方面至关重要。对复杂的视网膜结构及低对比度眼底图像来说，准确地分割视网膜图像的血管特征仍具有挑战性。联合注意力和Transformer的视网膜血管分割网络JAT-Net，通过对编码阶段特征的通道信息和位置信息联合关注增强编码局部细节特征，利用Transformer增强对长距离上下文信息和空间依赖关系建模的能力。在DRIVE和CHASE数据集上进行视网膜血管分割实验，其准确率分别为0.970 6和0.977 4，F1分数分别为0.843 3和0.815 4,在视网膜血管分割方面表现不错。

Retinal vessel segmentation networkwith joint attention and Transformer

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.