Segmentation and Anatomical Annotation of Cerebral Arteries in Non-Angiographic MRI.

The assessment of cerebrovascular disease benefits from the availability of different neuroimaging modalities, each providing different aspects to the vasculature and surrounding brain parenchyma. Segmented vasculature may improve cross-modal alignment but is difficult to annotate in sequences without vessel contrast. We propose a novel method to segment cerebral arteries in three non-angiographic sequences: T1w, T2w and PDw. Our method further predicts annotations for four anatomical regions, which can be used to mask specific parts of the vascular network or analyze the topology. For our experiments, we annotated arterial vessels and anatomical regions in 2,218 scans of the IXI dataset using a novel automatic method. We used the nnU-Net framework to train models in a 5-fold cross validation and to predict on a separate test-set. Our results suggest that vascular structures can be segmented and annotated in the examined MRI sequences with reasonable quality. The approach may potentially be used to study vascular diseases, when trained on pathological images. We share our ground-truth and models to encourage future experiments.