Dual-Path Model for Pulmonary Artery Segmentation

Abstract

The pulmonary artery (PA) is a multi-level vascular system composed of the main pulmonary artery (mPA) and the branch pulmonary arteries (bPA). Accurate segmentation of the PA is of significant importance for the diagnosis of diseases such as pulmonary embolism. However, since the vascular features of the mPA and the bPA are very different, the existing holistic PA segmentation methods will cause the network to pay too much attention to the mPA with significant features and ignore the bPA with weak features, resulting in the imbalance of PA segmentation. To address this issue, we propose a Dual-Path Pulmonary Artery Segmentation Model, which employs two separate paths to learn the features of the mPA and bPA, thereby enhancing the network’s ability to learn features of the bPA. Additionally, we have designed a Skeleton-Optimized Feature Learning Mechanism that optimizes the topological structure of the PA through skeletal guidance, reducing fragmentation and false positives. We conducted experiments on the public dataset provided by PARSE2022, and the results demonstrate that our model indeed improves the segmentation accuracy of the bPA while maintaining the segmentation effectiveness of the mPA. Furthermore, the Skeleton-Optimized Feature Learning Mechanism plays a significant role in reducing vascular fragmentation and false positives.