CFFANet: category feature fusion and attention mechanism network for retinal vessel segmentation

Retinal vessel segmentation is a computer-aided diagnostic method for ophthalmic disease analysis. Owing to the complex structure of the retinal vasculature, it is difficult for the segmentation network to capture effective features, and the semantic gap between different layers of features leads to insufficient feature fusion and thus makes segmentation difficult. In this paper, we propose a new segmentation network called CFFANet. Firstly, to capture accurate and sufficient global and local features, we design a Multi-scale Residual Pooling Module. In addition, a Category Feature Fusion Module is proposed to fuse category features at different stages to reduce the semantic gap between layers. Finally, a Frequency Channel Fusion Cross Attention Module is incorporated to reduce redundant semantic information during feature fusion. We conducted experiments on the DRIVE, CHASEDB1 and STARE datasets. The Dice and MIoU scores of the CFFANet network on the above datasets are 83.0, 82.9, 84.2, 84.1, and 84.1, 84.5. The ablation experiments also validate the effectiveness of the main modules in the network. The experiments show the value of the method in retinal vessel segmentation tasks.