Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier
This work addresses the need for accurate artery labeling in cardiovascular disease diagnosis, offering an incremental improvement by explicitly using anatomical topology that was previously underexplored.
The paper tackled the problem of automatic labeling of coronary arteries by incorporating anatomical connection priors into a new framework called TopoLab, achieving state-of-the-art performance on public and in-house datasets.
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.