A Deformable Network with Attention Mechanism for Retinal Vessel Segmentation

The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research. To overcome the limitation that traditional U-shaped vessel segmentation networks fail to extract features in fundus image sufficiently, we propose a novel network (DSeU-net) based on deformable convolution and squeeze excitation residual module. The deformable convolution is utilized to dynamically adjust the receptive field for the feature extraction of retinal vessel. And the squeeze excitation residual module is used to scale the weights of the low-level features so that the network learns the complex relationships of the different feature layers efficiently. We validate the DSeU-net on three public retinal vessel segmentation datasets including DRIVE, CHASEDB1, and STARE, and the experimental results demonstrate the satisfactory segmentation performance of the network.