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Teeth localization, segmentation, and labeling from intra-oral 3D scans areessential tasks in modern dentistry to enhance dental diagnostics, treatmentplanning, and population-based studies on oral health. However, developingautomated algorithms for teeth analysis presents significant challenges due tovariations in dental anatomy, imaging protocols, and limited availability ofpublicly accessible data. To address these challenges, the 3DTeethSeg'22challenge was organized in conjunction with the International Conference onMedical Image Computing and Computer Assisted Intervention (MICCAI) in 2022,with a call for algorithms tackling teeth localization, segmentation, andlabeling from intraoral 3D scans. A dataset comprising a total of 1800 scansfrom 900 patients was prepared, and each tooth was individually annotated by ahuman-machine hybrid algorithm. A total of 6 algorithms were evaluated on thisdataset. In this study, we present the evaluation results of the 3DTeethSeg'22challenge. The 3DTeethSeg'22 challenge code can be accessed at:https://github.com/abenhamadou/3DTeethSeg22_challenge

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge