ADD-Net:Attention U-Net with Dilated Skip Connection and Dense Connected Decoder for Retinal Vessel Segmentation

Retinal vessel segmentation is an essential step in the diagnosis of many diseases. Due to the large number of capillaries and complex branch structure, efficient and accurate segmentation of fundus vessels faces a huge challenge. In this paper, we propose an improved U-shape network, aiming at the problem of complex vessel segmentation, especially those thin, obscure ones. Firstly, we propose a new attention module, including channel attention and spatial attention, to build the connection between channels and learn to focus on those crucial representations. Secondly, we improve the skip connection by adding dilated convolutions, which can not only coping with the problem of semantic gap between the low-dimension and high-dimension features but also extract rich context information in encoder. Finally, the idea of dense connection is adopted in the decoder to fuse the feature representations with low computation cost and parameters. Experimental results show that our method could efficiently obtain the accurate segmentation image and achieve state-of-the-art performance on the public datasets DRIVE and CHASE_DB1.