Deep Residual W-Unit Learning with Semantic Embedding for Automatic Pulmonary CT Artery-Vein Separation

Hao Qi, Ming Wu, Sunkui Ke, Xiangxing Chen, Hui-Qing Zeng, Yinran Chen, Xiongbiao Luo

Abstract: Automatic segmentation of pulmonary arteries and veins in CT has great clinical significance. Because the growth range of a single vessel is vast, and the arteries and veins have barely identical intensity values on CT and grow very close to or even interleaved, accurate segmentation of them requires intricate vascular texture information and long-distance vascular trunk information as the basis for artery and vein classification. In order to meet these two requirements simultaneously, we design a residual W-Unit, which concatenated two U-shaped structures. It allows the network to become deeper and improve the receptive field for global information while preserving the detailed features of the vessels. And we design a semantic embedding module using cross-attention, which enhances the expression of bronchial features and assists in further utilizing features. It explicitly leverages the anatomical knowledge of parallel growth between arteries and bronchi. Then we combine RWUs and SEMs to construct a concise network to extract and fuse the features with detailed information from different network depths and receptive fields. Finally, we use a post-processing scheme to reduce spatial inconsistency. We validated our networks on 40 training sets and 17 test sets, and the experimental results show that our networks outperform current segmentation methods.