Search papers, labs, and topics across Lattice.
The paper introduces SemiTooth, a semi-supervised learning framework for tooth segmentation in multi-source CBCT data, addressing the challenge of limited labeled data and domain-specific variations across institutions. They compile a new dataset, MS3Toothset, comprising CBCT data from three sources with varying annotation levels. SemiTooth employs a multi-teacher, multi-student architecture with a stricter weighted-confidence constraint to improve accuracy, achieving state-of-the-art performance in semi-supervised multi-source tooth segmentation.
Achieve state-of-the-art tooth segmentation by teaching multiple student networks on unlabeled multi-source data, each supervised by its own teacher, and enforcing stricter confidence constraints.
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.
