  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.
