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Abstract #1509

Deep Learning-Based Automatic Segmentation of Arterial Plaques in MR Vessel Wall Images

Long Yang1,2, Xiong Yang3, Zhenhuan Gong3, Yufei Mao3, Guanxun Cheng4, Ke Wu3, Cheng Li3, Ye Li1, Dong Liang1, Xin Liu1, Hairong Zheng1, Zhanli Hu1, and Na Zhang1
1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2School of Computer Sciences, University Sains Malaysia, Penang, Malaysia, 3Department of Image Advanced Analysis of HSW BU, Shanghai United Imaging Healthcare Co., Shanghai, China, 4Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China

Synopsis

Keywords: Vessel Wall, Atherosclerosis, atherosclerotic plaque, morphological quantitative assessmentManual segmentation of atherosclerotic plaque for quantitative assessment is a time-consuming process. In this study, a convolutional neural network based automatic segmentation method named Vessel-Segnet was proposed for quantitative evaluation of lumen, vessel wall and plaque based on MR vessel wall images. The proposed method achieved the best segmentation performance with the highest dice similarity coefficient and the lowest average surface distance among six models. In terms of morphological quantitative evaluation, the proposed method achieved excellent agreement with manual method. Overall, the proposed method can quickly and accurately realize the segmentation of lumen, vessel wall and plaque for quantitative evaluation.

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