3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Abstract
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
Cite
@article{arxiv.2305.18277,
title = {3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge},
author = {Achraf Ben-Hamadou and Oussama Smaoui and Ahmed Rekik and Sergi Pujades and Edmond Boyer and Hoyeon Lim and Minchang Kim and Minkyung Lee and Minyoung Chung and Yeong-Gil Shin and Mathieu Leclercq and Lucia Cevidanes and Juan Carlos Prieto and Shaojie Zhuang and Guangshun Wei and Zhiming Cui and Yuanfeng Zhou and Tudor Dascalu and Bulat Ibragimov and Tae-Hoon Yong and Hong-Gi Ahn and Wan Kim and Jae-Hwan Han and Byungsun Choi and Niels van Nistelrooij and Steven Kempers and Shankeeth Vinayahalingam and Julien Strippoli and Aurélien Thollot and Hugo Setbon and Cyril Trosset and Edouard Ladroit},
journal= {arXiv preprint arXiv:2305.18277},
year = {2023}
}
Comments
29 pages, MICCAI 2022 Singapore, Satellite Event, Challenge