Abstract #2642

Deep Learning for MR Vessel Wall Imaging: Automated Detection of arterial vessel wall and plaque

Wenjing Xu^1,2,3,4, Xiong Yang⁵, Yikang Li^1,3,4, Jin Fang⁶, Guihua Jiang⁶, Shuheng Zhang⁵, Yanqun Teng⁵, Xiaomin Ren⁵, Lele Zhao⁵, Jiayu Zhu⁵, Qiang He⁵, Hairong Zheng^1,3,4, Xin Liu^1,3,4, and Na Zhang^1,3,4

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²Faculty of Information Technology, Beijing University of Technology, Beijing, China, ³Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁴CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁵Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China, ⁶Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou, China

The rupture of unstable or vulnerable atherosclerotic plaque is the major cause of ischemic stroke. Manual analysis of the vessel wall and plaque is labor-intensive and experience dependent. The purpose of this study is to develop an automatic method to segment the vessel wall and lumen contour for quantitative measurement. In this work, a CNN architecture for fully automated segmentation of arterial lumen and vessel wall on MR vessel wall images was developed and evaluated on ischemic stroke patients. In conclusion, we proposed a good performance automatic analysis method for the vessel wall and is important for plaque analysis.

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