RVSeg-Net: An Efficient Feature Pyramid Cascade Network for Retinal Vessel Segmentation

Abstract

Accurate retinal vessel segmentation plays a critical role in the diagnosis of many relevant diseases. However, it remains a challenging task due to (1) the great scale variation of retinal vessels, (2) the existence of a large number of capillaries in the vascular network, and (3) the interactions of the retinal vessels and other structures in the images, which easily results in the discontinuities in the segmentation results. In addition, limited training data also often prohibit current deep learning models from being efficiently trained because of the overfitting problem. In this paper, we propose a novel and efficient feature pyramid cascade network for retinal vessel segmentation to comprehensively address these challenges; we call it RVSeg-Net. The main component of the proposed RVSeg-Net is a feature pyramid cascade (FPC) module, which is capable of capturing multi-scale features to tackle scale variations of retinal vessels and aggregating local and global context information to solve the discontinuity problem. In order to overcome the overfitting problem, we further employ octave convolution to replace the traditional vanilla convolution to greatly reduce the parameters by avoiding spatial redundancy information. We conducted extensive experiments on two mainstream retinal vessel datasets (DRIVE and CHASE_DB1) to validate the proposed RVSeg-Net. Experimental results demonstrate the effectiveness of the proposed method, outperforming start-of-the-art approaches with much fewer parameters.