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
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
This study proposed a fully automated vessel wall
segmentation framework for intracranial arteries using only 3D black-blood MRI,
and it achieved high segmentation performance
for both normal (DICE=0.941) and stenotic (DICE=0.922) arteries.
Figure 2
Three
examples
of the 3D lumen segmentation results with the manual labels as reference. An automatic skeletonization algorithm was used on the predicted
binary segmentation to extract the centerline (Red lines) of the intracranial
arteries.