DCU-net: a deformable convolutional neural network based on cascade U-net for retinal vessel segmentation

To further improve retinal vessel segmentation accuracy, we propose a deformable convolutional neural network based on cascade U-Net for retinal vessel segmentation: DCU-Net. The overall structure of DCU-Net is composed of two U-Net. We introduce deformable convolution to build a feature extraction module, which enhances the modeling ability of the model for vessel deformation. For improving the efficiency of information transfer between U-Net models, we use a residual channel attention module to connect U-Net. DCUNet achieves excellent results on public datasets. On DRIVE and CHASE_DB1 datasets, the Acc reaches 0.9568, 0.9664, respectively, the AUC reaches 0.9810, and 0.9872, respectively. From the experimental results, the residual channel attention module and residual deformable convolution module greatly improve the retinal vessel segmentation accuracy. The comprehensive performance of our method is better than that of some state-of-the-art methods.