论文标题
具有像素的自适应过滤器的视网膜血管分割
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
论文作者
论文摘要
由于视网膜血管的复杂质地和低成像对比度，准确的视网膜血管分割具有挑战性。以前的方法通常是通过级联的多个深层网络来完善分割结果，这些网络耗时且效率低下。在本文中，我们提出了两种解决这些挑战的新方法。首先，我们设计了一个轻巧的模块，称为多尺度剩余相似性收集（MRSG），以生成像素自适应过滤器（PA-FILTERS）。与级联多个深网不同，只有一个PA过滤器层可以改善分割结果。其次，我们引入了响应提示擦除（RCE）策略，以提高分割精度。驱动器，chase_db1和凝视数据集的实验结果表明，我们所提出的方法在保持紧凑的结构的同时优于最先进的方法。代码可在https://github.com/limingxing00/tinal-vessel-segentation-isbi20222获得。
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.
