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Abstract #0169

BayesTract: Automated machine learning based brain artery segmentation, anatomical prior annotation and feature-extraction in MR Angiography

Abrar Faiyaz1, Nhat Hoang1, Alan Finkelstein1, Jianhui Zhong1, Marvin Doyley1, Henry Wang2, Md Nasir Uddin1,2, and Giovanni Schifitto1,2
1University of Rochester, Rochester, NY, United States, 2University of Rochester Medical Center, Rochester, NY, United States


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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