3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2023

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

Sergi Pujades
Edmond Boyer
Wan Kim
  • Function : Author

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

Dates and versions

hal-04397404 , version 1 (16-01-2024)

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Attribution - NonCommercial - ShareAlike

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Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik, Sergi Pujades, Edmond Boyer, et al.. 3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge. 2023. ⟨hal-04397404⟩
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