Intracranial atherosclerosis is a major cause of stroke worldwide. Vessel wall quantitative measurement is an essential tool for plaque analysis, while manual vessel wall segmentation is time-consuming and costly. In this study, we proposed a fully automated vessel wall segmentation framework for intracranial arteries using only 3D black-blood MRI, in which 3D lumen segmentation and skeletonization were applied to locate the arteries of interest for further 2D vessel wall segmentation. It achieved high segmentation performance for both normal (DICE=0.941) and stenotic (DICE=0.922) vessel wall and provided a promising tool for quantitative intracranial atherosclerosis analysis in large population studies.
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