Tooth segmentation from dental meshes is a fundamental step in clinical applications such as computer-aided orthodontics and dental implantation. Compared with mature image segmentation, deep learning-based mesh segmentation research is currently in a high-speed development stage. This study follows a dual-flow personalized feature learning scheme based on meshes and researches high-resolution mesh segmentation problems for clinical needs, proposing a dual-flow deep learning architecture called Position Shape Network (PSNet). Its basic idea includes continuously adjusting the feature map size in the network layer to enhance the model’s generalization ability and designing a reasonable branch structure to personalize the learning of position attributes represented by coordinates and shape attributes represented by surface perimeter area. In addition, it is proposed that the resolution of the validation set should be determined by comprehensively analyzing and simplifying errors to ensure the credibility of the model evaluation. Under this evaluation system, PSNet was compared with relevant authoritative methods in experiments, and the results verified the rationality and efficiency of the method and viewpoint proposed in this paper.
Qi-qin et al. (2026) studied this question.