Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier

TL;DR

This paper introduces TopoLab, a novel framework for automatic coronary artery labeling that explicitly incorporates anatomical connection knowledge, leading to improved accuracy in medical diagnosis.

Contribution

The study presents a new anatomy-aware connection classifier and hierarchical feature extraction strategies, enhancing artery labeling by leveraging prior topology information.

Findings

Achieved state-of-the-art performance on orCaScore dataset.

Validated effectiveness on in-house dataset.

Contributed high-quality artery annotations to public dataset.

Abstract

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.