BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation
This work addresses boundary enhancement for vessel segmentation in medical imaging, which is incremental as it builds on existing encoder-decoder architectures.
The paper tackled the problem of losing high-frequency information like boundaries in CNN-based vessel segmentation by proposing a Boundary Enhancement and Feature Denoising (BEFD) module, which improved performance on retinal vessel and angiocarpy datasets.
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.