Topology-Preserving Automatic Labeling of Coronary Arteries via
  Anatomy-aware Connection Classifier
Zhixing Zhang Ziwei Zhao Dong Wang Shishuang Zhao Yuhang Liu Jia Liu Liwei Wang
Automatic labeling of coronary arteries is an essential task in the practical
diagnosis process of cardiovascular diseases. For experienced radiologists, the
anatomically predetermined connections are important for labeling the artery
segments accurately, while this prior knowledge is barely explored in previous
studies. In this paper, we present a new framework called TopoLab which
incorporates the anatomical connections into the network design explicitly.
Specifically, the strategies of intra-segment feature aggregation and
inter-segment feature interaction are introduced for hierarchical segment
feature extraction. Moreover, we propose the anatomy-aware connection
classifier to enable classification for each connected segment pair, which
effectively exploits the prior topology among the arteries with different
categories. To validate the effectiveness of our method, we contribute
high-quality annotations of artery labeling to the public orCaScore dataset.
The experimental results on both the orCaScore dataset and an in-house dataset
show that our TopoLab has achieved state-of-the-art performance.