  Blood vessel segmentation is crucial for many diagnostic and research
applications. In recent years, CNN-based models have leaded to breakthroughs in
the task of segmentation, however, such methods usually lose high-frequency
information like object boundaries and subtle structures, which are vital to
vessel segmentation. To tackle this issue, we propose Boundary Enhancement and
Feature Denoising (BEFD) module to facilitate the network ability of extracting
boundary information in semantic segmentation, which can be integrated into
arbitrary encoder-decoder architecture in an end-to-end way. By introducing
Sobel edge detector, the network is able to acquire additional edge prior, thus
enhancing boundary in an unsupervised manner for medical image segmentation. In
addition, we also utilize a denoising block to reduce the noise hidden in the
low-level features. Experimental results on retinal vessel dataset and
angiocarpy dataset demonstrate the superior performance of the new BEFD module.
