3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
This addresses the problem of automating dental analysis for diagnostics and treatment planning, but it is incremental as it focuses on benchmarking existing methods on a new dataset.
The paper presents the 3DTeethSeg'22 challenge, which tackled teeth localization, segmentation, and labeling from intra-oral 3D scans, resulting in the evaluation of 6 algorithms on a dataset of 1800 scans from 900 patients.
Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg'22 challenge. The 3DTeethSeg'22 challenge code can be accessed at: https://github.com/abenhamadou/3DTeethSeg22_challenge