Semantic Segmentation of 3D Tooth Model Based on GCNN for CBCT Simulated Mouth Scan Point Cloud Data

The 3 D data acquisition,reconstruction and accurate segmentation of teeth are of great significance for diagnosis and treatment planning in dentistry.A 3 D tooth model was reconstructed based on the cone-beam-CT(CBCT),and an improved GCNN network was proposed to improve the semantic segmentation of each tooth in the 3 D tooth model.Accurate semantic segmentation of individual tooth can be achieved by deep learning of the fine local details as well as rough global structure of each tooth.The framework of the improved network consists of two parts:(1)an example segmentation network,which is used to obtain the general shape and relative position information of each tooth;(2)a fine-grained segmentation network,which is used to learn the fine details of individual tooth.A penalty mechanism for mis-assigned labels was further used to improve the accuracy of tooth segmentation.The results showed that the end-to-end deep learning framework used in this study can achieve accurate segmentation in the 3 D tooth.The mean intersection over union(MIoU)score of the proposed improved GCNN network achieves 0.91,which was much better than that of PointNet++(MIoU:0.78)and GACNet(MIoU:0.88).