STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
<|reference_start|>STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation: Computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the 2D panoramic X-ray image efficiently detects invisible caries, impacted teeth and supernumerary teeth in children, while the 3D dental cone beam computed tomography (CBCT) is widely used in orthodontics and endodontics due to its low radiation dose. However, there is no open-access 2D public dataset for children's teeth and no open 3D dental CBCT dataset, which limits the development of automatic algorithms for segmenting teeth and analyzing diseases. The Semi-supervised Teeth Segmentation (STS) Challenge, a pioneering event in tooth segmentation, was held as a part of the MICCAI 2023 ToothFairy Workshop on the Alibaba Tianchi platform. This challenge aims to investigate effective semi-supervised tooth segmentation algorithms to advance the field of dentistry. In this challenge, we provide two modalities including the 2D panoramic X-ray images and the 3D CBCT tooth volumes. In Task 1, the goal was to segment tooth regions in panoramic X-ray images of both adult and pediatric teeth. Task 2 involved segmenting tooth sections using CBCT volumes. Limited labelled images with mostly unlabelled ones were provided in this challenge prompt using semi-supervised algorithms for training. In the preliminary round, the challenge received registration and result submission by 434 teams, with 64 advancing to the final round. This paper summarizes the diverse methods employed by the top-ranking teams in the STS MICCAI 2023 Challenge.<|reference_end|>
@article{wang2024sts,
    title={STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised
  tooth segmentation},
    author={Yaqi Wang, Yifan Zhang, Xiaodiao Chen, Shuai Wang, Dahong Qian, Fan
  Ye, Feng Xu, Hongyuan Zhang, Qianni Zhang, Chengyu Wu, Yunxiang Li, Weiwei
  Cui, Shan Luo, Chengkai Wang, Tianhao Li, Yi Liu, Xiang Feng, Huiyu Zhou,
  Dongyun Liu, Qixuan Wang, Zhouhao Lin, Wei Song, Yuanlin Li, Bing Wang,
  Chunshi Wang, Qiupu Chen, Mingqian Li},
    journal={arXiv preprint arXiv:2407.13246},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13246},
    primaryClass={cs.CV}
}