MICCAI 2023 STS Challenge: A retrospective study of semi-supervised approaches for teeth segmentation

Yaqi Wang, Yifan Zhang, Xiaodiao Chen, Shuai Wang, Dahong Qian, Fan Ye, Feng Xu, Hongyuan Zhang, Ruilong Dan, Qianni Zhang, Xingru Huang, Zhao Huang, Jun Liu, Zhiwen Zheng, Chengyu Wu, Yunxiang Li, Zhi Li, Zhean Ma, Weiwei Cui, Shan Luo et al. (17 additional authors not shown)

Abstract: Computer-aided diagnosis greatly enhances personalized treatment planning and diagnostic efficiency by providing accurate dental anatomy through teeth segmentation. However, it still constrained by the scarcity of high-quality annotated dental datasets. To address this issue, this paper presents a dataset combining both 2D panoramic X-rays with over 6500 images and 3D CBCT with over 580 volumes (88,500+ slices) to support the Semi-supervised Teeth Segmentation (STS) Challenge, which includes partially meticulous annotations and covers all age groups. Moreover, multi-phase semi-supervised teeth segmentation algorithms and high-confidence pseudo-labels refinement strategies were proposed by competitors during this challenge. Algorithms were verified on this proposed dataset and good segmentation performance were achieved, over 93+ and 80+ Dice score were obtained for top three 2D and 3D participants, demonstrating the high quality of this proposed dataset. This paper also summarizes the diverse methods employed by the top-ranking teams in the MICCAI 2023 STS Challenge. Our dataset is publicly accessible through Zenodo (https://zenodo.org/records/10597292), and the participants’ code is hosted on GitHub (https://github.com/ricoleehduu/STS-Challenge).

External IDs:doi:10.1016/j.patcog.2025.112049