Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge

doi:10.1109/TMI.2024.3523096

	Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge
	Federico Bolelli 1; Luca Lumetti 1; Shankeeth Vinayahalingam 2; Mattia Di Bartolomeo 3; Arrigo Pellacani 4; Kevin Marchesini 1; Niels van Nistelrooij 2,5; Pieter van Lierop 2; Tong Xi 2; Yusheng Liu 6; Rui Xin 6; Tao Yang 6; Lisheng Wang 6; Haoshen Wang7 ; Chenfan Xu7 ; Zhiming Cui7 ; Marek Wodzinski 8,9; Henning Müller 9,10; Yannick Kirchhoff 11,12,13; Maximilian R. Rokuss 11,12; Klaus Maier-Hein 13,14; Jaehwan Han 15; Wan Kim 15; Hong-Gi Ahn 15; Tomasz Szczepański 16; Michal K. Grzeszczyk 16; Przemyslaw Korzeniowski 16; Vicent Caselles-Ballester 17; Xavier Paolo Burgos-Artizzu 18; Ferran Prados Carrasco 17,19; Stefaan Berge’2; Bram van Ginneken 20; Alexandre Anesi 21; Costantino Grana 1
	2025-04-01
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING (IF:8.9[JCR-2023],11.3[5-Year])
ISSN	1558-254X
卷号	44 期号:4
发表状态	已发表
DOI	10.1109/TMI.2024.3523096
摘要	In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, resulting in a lack of comparative evaluation studies on a common benchmark. To address this scientific gap and encourage deep learning research in the field, the ToothFairy challenge was organized within the MICCAI 2023 conference. In this context, a public dataset was released to also serve as a benchmark for future research. The dataset comprises 443 CBCT scans, with voxel-level annotations of the IAC available for 153 of them, making it the largest publicly available dataset of its kind. The participants of the challenge were tasked with developing an algorithm to accurately identify the IAC using the 2D and 3D-annotated scans. This paper presents the details of the challenge and the contributions made by the most promising methods proposed by the participants. It represents the first comprehensive comparative evaluation of IAC segmentation methods on a common benchmark dataset, providing insights into the current state-of-the-art algorithms and outlining future research directions. Furthermore, to ensure reproducibility and promote future developments, an open-source repository that collects the implementations of the best submissions was released.
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来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/510467
专题	生物医学工程学院信息科学与技术学院_硕士生生物医学工程学院_PI研究组_崔智铭组生物医学工程学院_硕士生
作者单位	1.Department of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, Italy 2.Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands 3.Department of Oral and Maxillofacial Sciences, Sapienza University of Rome, Rome, Italy 4.Maternity and Infant Department, Unit of Dentistry and Maxillo-Facial Surgery, Surgery, Dentistry, University of Verona, Verona, Italy 5.Department of Oral and Maxillofacial Surgery, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany 6.Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China 7.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China 8.Department of Measurement and Electronics, AGH University of Kraków, Kraków, Poland 9.Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Delémont, Switzerland 10.Medical Faculty, University of Geneva, Geneva, Switzerland 11.Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 12.Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany 13.HIDSS4Health-Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany 14.Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany 15.Osstem Implant Company Ltd., Seoul, Republic of Korea 16.Sano Centre for Computational Medicine, Kraków, Poland 17.eHealth Center, Universitat Oberta de Catalunya, Barcelona, Spain 18.Movumtech S.L., Bajos, Spain 19.Department of Medical Physics and Biomedical Engineering, and the Queen Square MS Centre, Department of Neuroinflammation, Center for Medical Image Computing, University College London, London, U.K. 20.Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands 21.Department of Medical and Surgical Sciences for Children and Adults, Cranio and Maxillo-Facial Unit, University of Modena and Reggio Emilia, Modena, Italy
推荐引用方式 GB/T 7714	Federico Bolelli,Luca Lumetti,Shankeeth Vinayahalingam,et al. Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2025,44(4).
APA	Federico Bolelli.,Luca Lumetti.,Shankeeth Vinayahalingam.,Mattia Di Bartolomeo.,Arrigo Pellacani.,...&Costantino Grana.(2025).Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge.IEEE TRANSACTIONS ON MEDICAL IMAGING,44(4).
MLA	Federico Bolelli,et al."Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge".IEEE TRANSACTIONS ON MEDICAL IMAGING 44.4(2025).