Retinal Vessel Segmentation with Pixel-wise Adaptive Filters

• URL: http://arxiv.org/abs/2202.01782v1
• Date: Thu, 3 Feb 2022 14:40:36 GMT
• Title: Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
• Authors: Mingxing Li, Shenglong Zhou, Chang Chen, Yueyi Zhang, Dong Liu, Zhiwei Xiong
• Abstract summary: We propose two novel methods to address the challenges of retinal vessel segmentation. First, we devise a light-weight module, named multi-scale residual similarity gathering (MRSG), to generate pixel-wise adaptive filters (PA-Filters) Second, we introduce a response cue erasing (RCE) strategy to enhance the segmentation accuracy.
• Score: 47.8629995041574
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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