BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation

TL;DR

This paper introduces BEFD, a module that enhances boundary detection and reduces noise in vessel segmentation networks, significantly improving accuracy in medical image analysis.

Contribution

The paper proposes the BEFD module that integrates boundary enhancement and feature denoising into CNNs for improved vessel segmentation performance.

Findings

BEFD improves boundary accuracy in vessel segmentation.

The module enhances performance on retinal and angiocarpy datasets.

Experimental results show superior segmentation accuracy with BEFD.

Abstract

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.