Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
This work addresses the problem of inefficient and time-consuming retinal vessel segmentation for medical imaging applications, offering an incremental improvement over existing methods.
The paper tackles the challenge of accurate retinal vessel segmentation by proposing a light-weight module for pixel-wise adaptive filters and a response cue erasing strategy, achieving state-of-the-art performance on DRIVE, CHASE_DB1, and STARE datasets while maintaining a compact structure.
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.