Shuai Shen1,2,3,4, Xiao Liu5, Zhuyuerong Li5, Tao Jiang5, 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, 2College of Software, Xinjiang University, Urumqi, 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, 5Department of radiology, Beijing Chao-Yang hospital, Capital medical university, beijing, China
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 2College of Software, Xinjiang University, Urumqi, 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, 5Department of radiology, Beijing Chao-Yang hospital, Capital medical university, beijing, China
The study verifies the
effectiveness of using neural networks to segment cerebral artery plaques. Both
models can effectively complete the segmentation of atherosclerosis. In
addition, all parameters of V-net are higher than U-net, and experiments show
that V-net is more stable.
Figure 1 Representative images of the segmentation results of the two deep
learning models (U-net and V-net).
Table 1 the three quantitative indicators of the model to reflect the accuracy of the results.