Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

ABSTRACT: Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorpor

SUBMITTER: Hong SW 

PROVIDER: S-EPMC9957982 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature