Coronary CT angiography (CCTA) scans are widely used for diagnosis of coronary artery diseases. An accurate and automatic vessel labeling algorithm for CCTA analysis can significantly improve the diagnostic efficiency and reduce the clinicians'manual efforts. In this paper, we propose a simple vessel labeling method based on the Point Transformer, which only needs the coronary artery segmentation. Specifically, firstly, the coronary segmentation is transformed to point cloud. Then, these points are fed into the hierarchical transformer blocks to obtain the multi-level features, including local and global features. Finally, the network output the semantic classification points and map them to centerline labeling. This method is only based on the structure of coronary segmentation and need not other features, so it is easy to generalize to other vessel labeling tasks, e.g., head and neck vessel labeling. To evaluate the performance of our proposed method, CCTA scans of 53 subjects are collected in our experiment. The experimental results demonstrate the efficacy of this approach.

翻译：冠状动脉CT血管造影（CCTA）扫描被广泛用于冠状动脉疾病的诊断。在CCTA分析中，精确且自动化的血管标注算法能够显著提升诊断效率并减少临床医生的人工操作。本文提出一种基于Point Transformer的简单血管标注方法，该方法仅需冠状动脉分割结果。具体而言，首先将冠状动脉分割结果转换为点云数据；随后将这些点输入分层Transformer模块以获取包含局部与全局特征的多层次特征；最后网络输出语义分类点并将其映射至中心线标注。该方法仅依赖冠状动脉分割的结构信息，无需其他特征，因此易于推广至其他血管标注任务，例如头颈部血管标注。为评估所提方法的性能，我们收集了53例受试者的CCTA扫描数据。实验结果表明了该方法的有效性。