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Abstract #3158

Automatic analysis of carotid vessel wall in MR black blood images using custom convolutional trajectories

Shuai Shen1,2, Wenjing Xu1, Hongbing Ma3, Xiaoyi Lv2, Guanxun Cheng4, Liwen Wan1, Lei Zhang1, Ye Li1, Dong Liang1, Xin Liu1, Hairong Zheng1, and Na Zhang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2College of Software, xinjiang University, Xinjiang, China, 3Department of Electronic Engineering, Tsinghua University, Beijing, China, 4Department of Radiology, Peking university shenzhen hospital, Beijing, China


Atherosclerotic plaque is a major cause of ischemic stroke. Some arterial morphological features obtained from MR vessel wall images show great potential for identifying high-risk plaques. Deep learning has now been applied to the automatic segmentation of vessel walls to accurately and efficiently measure arterial morphological features. However, the accuracy of the existing segmentation methods is not yet high enough for clinical practical applications. This study proposed a new segmentation framework with custom convolutional trajectories for automatic segmentation of arterial vessel wall and the framework improved the accuracy of vessel wall segmentation.

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