  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.
