A Neural Syntax Parser for Coronary Artery Anatomical Labeling in Coronary CT Angiography

Abstract

Automated anatomical labeling plays a crucial part for computer-assisted diagnostic systems targeting coronary artery diseases. Established from medical practice, the labeling conventions intrinsically carry profound prior knowledge about the results, indicating what outcome is favorable and what is illegal. However, the prior has been largely neglected by existing works. Drawing inspiration from syntax parsing in the NLP domain, we propose a neural stochastic grammar parser for anatomical labeling. Our method captures the essential parental and sibling dependencies between vessel segments, incorporates structural prior in a principled and interpretable manner, while retaining the learning capabilities of deep models. Experiments show encouraging results both for the robustness and accuracy of our method.