  Intracranial arteries are critical blood vessels that supply the brain with
oxygenated blood. Intracranial artery labels provide valuable guidance and
navigation to numerous clinical applications and disease diagnoses. Various
machine learning algorithms have been carried out for automation in the
anatomical labeling of cerebral arteries. However, the task remains challenging
because of the high complexity and variations of intracranial arteries. This
study investigates a novel graph convolutional neural network with deep feature
fusion for cerebral artery labeling. We introduce stacked graph convolutions in
an encoder-core-decoder architecture, extracting high-level representations
from graph nodes and their neighbors. Furthermore, we efficiently aggregate
intermediate features from different hierarchies to enhance the proposed
model's representation capability and labeling performance. We perform
extensive experiments on public datasets, in which the results prove the
superiority of our approach over baselines by a clear margin.
