Vessel-Net: Retinal vessel segmentation under multi-path supervision

Abstract

Due to the complex morphology of fine vessels, it remains challenging for most of existing models to accurately segment them, particularly the capillaries in color fundus retinal images. In this paper, we propose a novel and lightweight deep learning model called Vessel-Net for retinal vessel segmentation. First, we design an efficient inception-residual convolutional block to combine the advantages of the Inception model and residual module for improved feature representation. Next, we embed the inception-residual blocks inside a U-like encoder-decoder architecture for vessel segmentation. Then, we introduce four supervision paths, including the traditional supervision path, a richer feature supervision path, and two multi-scale supervision paths to preserve the rich and multi-scale deep features during model optimization. We evaluated our Vessel-Net against several recent methods on two benchmark retinal databases and achieved the new state-of-the-art performance (i.e. AUC of 98.21%/98.60% on the DRIVE and CHASE databases, respectively). Our ablation studies also demonstrate that the proposed inception-residual block and the multi-path supervision both can produce impressive performance gains for retinal vessel segmentation.