SemiTooth: a Generalizable Semi-supervised Framework for Multi-Source Tooth Segmentation

• URL: http://arxiv.org/abs/2603.11616v1
• Date: Thu, 12 Mar 2026 07:15:48 GMT
• Title: SemiTooth: a Generalizable Semi-supervised Framework for Multi-Source Tooth Segmentation
• Abstract summary: tooth structure segmentation for Cone-Beam Computed Tomography (CBCT) has made significant progress in recent years.<n> challenges arise from the obtainment difficulty of full-annotated data, and the acquisition variability of multi-source data.<n>We propose SemiTooth, a generalizable semi-supervised framework for multi-source tooth segmentation.
• Score: 17.16459372606428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of artificial intelligence, intelligent dentistry for clinical diagnosis and treatment has become increasingly promising. As the primary clinical dentistry task, tooth structure segmentation for Cone-Beam Computed Tomography (CBCT) has made significant progress in recent years. However, challenges arise from the obtainment difficulty of full-annotated data, and the acquisition variability of multi-source data across different institutions, which have caused low-quality utilization, voxel-level inconsistency, and domain-specific disparity in CBCT slices. Thus, the rational and efficient utilization of multi-source and unlabeled data represents a pivotal problem. In this paper, we propose SemiTooth, a generalizable semi-supervised framework for multi-source tooth segmentation. Specifically, we first compile MS3Toothset, Multi-Source Semi-Supervised Tooth DataSet for clinical dental CBCT, which contains data from three sources with different-level annotations. Then, we design a multi-teacher and multi-student framework, i.e., SemiTooth, which promotes semi-supervised learning for multi-source data. SemiTooth employs distinct student networks that learn from unlabeled data with different sources, supervised by its respective teachers. Furthermore, a Stricter Weighted-Confidence Constraint is introduced for multiple teachers to improve the multi-source accuracy.Extensive experiments are conducted on MS3Toothset to verify the feasibility and superiority of the SemiTooth framework, which achieves SOTA performance on the semi-supervised and multi-source tooth segmentation scenario.

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