Retinal Vessel Segmentation with Pixel-wise Adaptive Filters

TL;DR

This paper introduces a novel, efficient retinal vessel segmentation method using pixel-wise adaptive filters and a response cue erasing strategy, outperforming existing methods with a compact model.

Contribution

The paper proposes a lightweight multi-scale residual similarity gathering module and a response cue erasing strategy for improved retinal vessel segmentation.

Findings

Outperforms state-of-the-art methods on DRIVE, CHASE_DB1, and STARE datasets.

Maintains a compact model structure while achieving high accuracy.

Code is publicly available for reproducibility.

Abstract

Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast. Previous methods generally refine segmentation results by cascading multiple deep networks, which are time-consuming and inefficient. In this paper, we propose two novel methods to address these challenges. First, we devise a light-weight module, named multi-scale residual similarity gathering (MRSG), to generate pixel-wise adaptive filters (PA-Filters). Different from cascading multiple deep networks, only one PA-Filter layer can improve the segmentation results. Second, we introduce a response cue erasing (RCE) strategy to enhance the segmentation accuracy. Experimental results on the DRIVE, CHASE_DB1, and STARE datasets demonstrate that our proposed method outperforms state-of-the-art methods while maintaining a compact structure. Code is available at https://github.com/Limingxing00/Retinal-Vessel-Segmentation-ISBI20222.