Wenjing Xu1,2,3,4, Xiong Yang5, Yikang Li1,3,4, Jin Fang6, Guihua Jiang6, Shuheng Zhang5, Yanqun Teng5, Xiaomin Ren5, Lele Zhao5, Jiayu Zhu5, Qiang He5, Hairong Zheng1,3,4, Xin Liu1,3,4, and Na Zhang1,3,4
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
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
we proposed a good performance automatic analysis method for the vessel wall and is important for plaque analysis.
Figure 1: The architecture of the 2DVet network.
Figure 4: The representative images and segmentation results from two clinical cases. Case A and Case B represent the images with anterior circulation and posterior circulation, respectively.