Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
This work addresses the need for automated artery labeling to reduce manual effort and aid in identifying geometric risk factors for vascular disease, representing a strong specific gain in the medical imaging domain.
The authors tackled the problem of automatically labeling intracranial arteries with anatomical names, which is challenging due to variations in the vessels, and achieved a node labeling accuracy of 97.5% and 63.8% of scans correctly labeled for all Circle of Willis nodes on a testing set of 105 scans.
Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.