A Collaborative Learning Framework With Coupling Graph Transformers for 3D Tooth Segmentation

Automatic segmentation of 3D dental models into individual teeth is an important step in orthodontic computer-aided design (CAD) systems. However, most existing methods rely on single-view dental models and ignore the intrinsic relationships between upper and lower dental models, hindering the handling of complex tooth structures. In this paper, a collaborative learning framework with coupling graph Transformers (CGT-CLF) is proposed for automatic tooth segmentation on 3D dental models. The framework collaboratively learns geometric features of both upper and lower dental models, capturing their interactivity and complementarity by facilitating interaction between graph-Transformer encoders to improve segmentation of complex and diverse teeth. Specifically, CGT-CLF consists of three key components as follows: First, a graph embedding-based boundary perception module (GEBPM) is developed to aggregate fine-grained geometric features within the neighborhood graph domain, enhancing the network's ability to perceive and distinguish intricate tooth boundaries. Then, coupling geometric Transformers are designed to capture the intrinsic relationships of pair-wise dental models by promoting the exchange of relevant information to gain a comprehensive understanding of the overall tooth structure, allowing for better identification of adjacent teeth with similar appearances. Finally, a collaborative cross-scale feature fusion (CCFF) strategy is utilized to obtain interactive and complementary information by modeling the inter-relationships between dual-stream features. Experimental results on a clinical dental model dataset demonstrate that the proposed CGT-CLF framework outperforms state-of-the-art methods, delivering superior segmentation performance.