Brain arterial-blood-vessels can carry important information regarding cerebrovascular pathogenesis. Due to non-invasiveness, 3D-time-of-flight MR-angiography is widely used to depict arteries in clinical-exams. However, deemed limited for qualitative assessment in clinical setting. Although several post-processing approaches exist such as tool-based-manual segmentation and diameter-marking or deep-learning-based segmentations--these require huge-time and experienced-eyes or large manually-segmented training-sets, making performance variable. Since brain-artery-annotations are anatomically-inspired, anatomical-confidant-points identified with a Bayesian approach can be used to annotate major-brain-arteries, which circumvents the need for large-dataset from learning requirement. The approach will allow clinical evaluations of major-arteries and biomarker identifications for cerebrovascular pathogenesis with limited resource and time.
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