Residual U-Net for Retinal Vessel Segmentation

In recent years, the influence of deep learning on retinal vessel segmentation has grown rapidly. Most of the available deep learning based methods use relatively shallow structures. However, due to the limited representative capacity, shallow networks will restrain deep learning models to segment both vessel and non-vessel pixels accurately. In this paper, we propose a residual U-Net for retinal vessel segmentation. Our network has several advantages. First, the network uses a new residual block structure. In the new structure, batch normalization layers are placed before the activation unit to achieve better performance and accelerate the convergence. Also, a dropout layer is utilized in the structure to alleviate over-fitting problems. Second, the depth of the network is increased by adding more residual blocks and strong dropouts which then allow the network to extract features better. Fundus images from the publicly available DRIVE and STARE datasets are used to evaluate the proposed network. Experimental result shows that the proposed modified residual U-Net has better performance than existing state-of-the-art algorithms.