Abstract

Info

Title:: Classification of teeth in CBCT images using deep learning with multi-view projection

Author(s):: LIU Muran1; 2; TAN Minhui2; ZHANG Yu1; 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; 2. School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China

Keywords:: deep learning; cone beam computed tomography; tooth identification; multi-view projection

Abstract:: Objective To address the issue that current methods for classifying teeth in cone beam computed tomography (CBCT) images overly rely on precise segmentation and lack utilization of tooth morphology and positional information, a tooth classification method based on multi-view projection and Transformer architecture is proposed for accurately classifying teeth from CBCT images across all age groups, including pediatric cases, into 52 categories. Methods The coarseto-fine tooth classification task was accomplished after enhancing the utilization of spatial positional information of the teeth by incorporating multi-view projection, integrating Transformer architecture, and combining semantic segmentation with instance segmentation. The two-digit notation system of the Federation Dentaire Internationale was adopted for classifying the teeth in CBCT images, and evaluating the effectiveness of multi-view fusion. Results The improved method effectively classified and numbered both permanent and deciduous teeth, achieving a tooth-level classification accuracy of 0.982. Conclusion The tooth classification method based on multi-view projection and Transformer architecture successfully integrates tooth category and positional information, and improves the accuracies of tooth classification and numbering, providing a more precise foundation for the formulation of personalized treatment schemes.