BDFNet: Boundary-Assisted and Discriminative Feature Extraction Network for COVID-19 Lung Infection Segmentation

Hui Ding, Qirui Niu, Yufeng Nie, Yuanyuan Shang, Nianzhe Chen, Rui Liu

Abstract: The coronavirus disease (COVID-19) pandemic has affected billions of lives around the world since its first outbreak in 2019. The computed tomography (CT) is a valuable tool for the COVID-19 associated clinical diagnosis, and deep learning has been extensively used to improve the analysis of CT images. However, owing to the limitation of the publicly available COVID-19 imaging datasets and the randomness and variability of the infected areas, it is challenging for the current segmentation methods to achieve satisfactory performance. In this paper, we propose a novel boundary-assisted and discriminative feature extraction network (BDFNet), which can be used to improve the accuracy of segmentation. We adopt the triplet attention (TA) module to extract the discriminative image representation, and the adaptive feature fusion (AFF) module to fuse the texture information and shape information. In addition to the channel and spatial dimensions that are mainly used in previous models, the cross channel-special context is also obtained in our model via the TA module. Moreover, fused hierarchical boundary information is integrated through the application of the AFF module. According to experiments conducted on two publicly accessible COVID-19 datasets, COVID-19-CT-Seg and CC-CCII, BDFNet performs better than most cutting-edge segmentation algorithms in six widely used segmentation metrics.