ILU-Net: Inception-Like U-Net for retinal vessel segmentation

Abstract

High-precision segmentation of retinal vessels is a critical procedure for clinicians to analyze retinal diseases. However, previous U-Net based segmentation methods are lost in the preservation of capillaries and vascular structures of complex bifurcation. To solve these problems, this paper proposes a novel U-Net based network, named Inception-Like U-Net (ILU-Net), including the following key points: First, two different types of inception-like block (Down-sampling Inception Blocks (DIB) in encoder and up-sampling Inception Blocks (UIB) in decoder) replace the two convolution operations in U-Net to learn the characteristics from different sizes of receptive field. Second, a novel skip connection strategy is proposed and can better connect low-level features to high-level features for reproducing more micro-scale vascular structures. Third, the segmentation performance is further improved by using the combination of two activation functions (ReLU and ELU). Qualitative and quantitative results of experiments on the public DRIVE dataset demonstrate that the proposed ILU-Net approach is effective and even better than the state-of-the-arts in preserving capillaries and complex bifurcation structures.