  Automated segmentation of intracranial arteries on magnetic resonance
angiography (MRA) allows for quantification of cerebrovascular features, which
provides tools for understanding aging and pathophysiological adaptations of
the cerebrovascular system. Using a convolutional autoencoder (CAE) for
segmentation is promising as it takes advantage of the autoencoder structure in
effective noise reduction and feature extraction by representing high
dimensional information with low dimensional latent variables. In this report,
an optimized CAE model (Y-net) was trained to learn a 3D segmentation model of
intracranial arteries from 49 cases of MRA data. The trained model was shown to
perform better than the three traditional segmentation methods in both binary
classification and visual evaluation.
