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

TL;DR

SemiTooth is a semi-supervised learning framework that effectively utilizes multi-source and unlabeled CBCT data for improved tooth segmentation, addressing data annotation and domain variability challenges in clinical dentistry.

Contribution

The paper introduces SemiTooth, a novel multi-teacher, multi-student semi-supervised framework with a new multi-source dataset for tooth segmentation.

Findings

Achieves state-of-the-art performance on multi-source semi-supervised tooth segmentation.

Demonstrates effective utilization of unlabeled multi-source CBCT data.

Improves accuracy through a Stricter Weighted-Confidence Constraint.

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.