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.