  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.
