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Abstract #2406

A fully automated framework for intracranial vessel wall segmentation based on 3D black-blood MRI

Jiaqi Dou1, Hao Liu1, Qiang Zhang1, Dongye Li2, Yuze Li1, Dongxiang Xu3, and Huijun Chen1
1Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, China, 2Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China, 3Department of Radiology, University of Washington, Seattle, WA, United States

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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