LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation

Abstract

Retinal vessel segmentation is an essential part of ocular disease diagnosis. However, due to complex vascular structure, large-scale variations of retinal vessels, as well as inefficiency of vessel segmentation speed, accurate and fast automatic vessel segmentation for retinal images is still technically challenging. To tackle these issues, we present a lightweight multi-scale-aware network (LMSA-Net) for retinal vessel segmentation. The network leverages the encoder-decoder structure that was used in U-Net. In the encoder, we propose a ghosted sandglass residual (GSR) block, aiming at greatly reducing the parameters and computational cost while obtaining richer semantic information. After that, a multi-scale feature-aware aggregation (MFA) module is designed to perceive multi-scale semantic information for effective information extraction. Then, a global adaptive upsampling (GAU) module is proposed to guide the effective fusion of high- and low-level semantic information in the decoder. Experiments are conducted on three public datasets, including DRIVE, CHASE_DB1, and STARE. The experimental results indicate the effectiveness of the LMSA-Net, which can achieve better segmentation performance than other state-of-the-art methods.