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Abstract #1062

Automated Morphology Analysis of Intracranial and Extracranial Vessel Wall Using Convolutional Neural Network

Liwen Wan1, Na Zhang1, Lei Zhang1, Shi Su1, Cheng Wang1, Baochang Zhang1, Hao Peng1, Haoxiang Li1, Dong Liang1, Xin Liu1, and Hairong Zheng1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Intracranial and extracranial atherosclerotic disease are major causes of ischemic stroke. Manual analyses of intracranial and extracranial artery vessel wall are time consuming and experience dependent. The purpose of this study was to develop an automated method to analyze 3D intra- and extracranial arterial vessel wall images, including vessel centerline tracking, vessel straightened reformation, vessel wall segmentation based on CNN, and morphological quantification. In conclusion, the proposed method facilitates the large-scale quantitative analysis of vessel wall, and is promising in promoting the clinical applications of MR vessel wall imaging.

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