Abstract #2634

Automatic segmentation of middle cerebral artery plaque based on deep learning

Shuai Shen^1,2,3,4, Xiao Liu⁵, Zhuyuerong Li⁵, Tao Jiang⁵, 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, 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

At present, deep learning has gradually been applied to the field of plaque segmentation. However, the existing work is mainly used for the processing of 2D images. In this study, we trained a 3D network model to automatically segment the middle cerebral artery plaques based on 3D images and compared the accuracy with 2D network model. Magnetic resonance vessel wall imaging (MR-VWI) data from 102 patients were used for training. The results showed that all quantitative accuracy indicators of V-net were higher than U-net, and experiments showed that V-net was more stable.

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