3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Achraf Ben-Hamadou Oussama Smaoui Ahmed Rekik Sergi Pujades Edmond Boyer Hoyeon Lim Minchang Kim Minkyung Lee Minyoung Chung Yeong-Gil Shin Mathieu Leclercq Lucia Cevidanes Juan Carlos Prieto Shaojie Zhuang Guangshun Wei Zhiming Cui Yuanfeng Zhou Tudor Dascalu Bulat Ibragimov Tae-Hoon Yong Hong-Gi Ahn Wan Kim Jae-Hwan Han Byungsun Choi Niels van Nistelrooij Steven Kempers Shankeeth Vinayahalingam Julien Strippoli Aurélien Thollot Hugo Setbon Cyril Trosset Edouard Ladroit
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