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

Le résumé fourni par la source

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 6,500 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 ).

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