Automatic Tooth labeling After Segmentation using Prototype-based Meta-learning

Digital dentistry has been upgraded traditional dentistry by realizing the complete process linking preliminary image processing tasks to orthodontic diagnosis and treatment planning. Automatic tooth labeling from dental images involves identifying a specific tooth class using deep learning algorithms. Existing methods used for tooth labeling often use incomplete tooth models, with only crown information obtained by oral scanners or X-ray radiographs. Even with CBCT images, the problem of misclassification with a change in tooth orientation still appears. Consequently, they could not perform well for the varying test samples with similar teeth or thoes with missing teeth. We propose a tooth labeling method based on complete tooth models using a few-shot classification based on a meta-learning approach. To resolve the problem of intra-class similarities, we designed an attention mechanism based on matrix decomposition inspired by HamNet. We validate the proposed method on the CBCT dataset with augmented tooth models. The results demonstrate that our approach performs better than cutting edge methods like PointNet, PointCNN, PointNet++, DGCNN, HCNN, and TSegNet in terms of accuracy with a significant improvement of 11.46%, 7.8%, 6.16%, 4.48%, 5.06%, and 5.08% respectively. Therefore, with validated data applicability, automated tooth labeling will be applied in digital dentistry with much excellence and feasibility.