TAPoseNet: Teeth Alignment Based on Pose Estimation via Multi-scale Graph Convolutional Network

Qingxin Deng, Xunyu Yang, Minghan Huang, Landu Jiang, Dian Zhang

Abstract: Teeth alignment plays an important role in orthodontic treatment. Automating the prediction of teeth alignment target can significantly aid both doctors and patients. Traditional methods often utilize rule-based approach or deep learning method to generate teeth alignment target. However, they usually require extra manual design by doctors, or produce deformed teeth shapes, even fail to address severe misalignment cases. To tackle the problem, we introduce a pose prediction model which can better describe the space representation of the tooth. We also consider geometric information to fully extracted features of teeth. In the meanwhile, we build a multi-scale Graph Convolutional Network(GCN) to characterize the teeth relationships from different levels (global, local, intersection). Finally the target pose of each tooth can be predicted and so the teeth movement from the initial pose to the target pose can be obtained without deforming teeth shapes. Our method has been validated in clinical orthodontic treatment cases and shows promising results both qualitatively and quantitatively.