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

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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Publication Details

Journal
IEEE Transactions on Medical Imaging
Published
2024-12-25
DOI
https://doi.org/10.1109/tmi.2024.3523096
Citations
9
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
4.44

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article

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

Maximilian Rokuss, Costantino Grana, Marek Wodziński, Przemysław Korzeniowski et al.
9 citations
IEEE Transactions on Medical Imaging
Dental Radiography and Imaging
4.44
article

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

Maximilian Rokuss, Costantino Grana, Marek Wodziński, Przemysław Korzeniowski, Pieter van Lierop, Klaus Maier‐Hein, Federico Bolelli, Stefaan Bergé, Bram van Ginneken, Xavier P. Burgos-Artizzu, Michal K. Grzeszczyk, Ferrán Prados, Mattia Di Bartolomeo, Henning Müller, Wan Kim, Hong-Gi Ahn, Arrigo Pellacani, Luca Lumetti, Alexandre Anesi, Tong Xi, Niels van Nistelrooij, Shankeeth Vinayahalingam, Yannick Kirchhoff, Zhiming Cui, Lisheng Wang, Tomasz Szczepański, Tao Yang, Rui Xin, Jae-Hwan Han, Yusheng Liu, Chenfan Xu, Haoshen Wang, Kevin Marchesini, Vicent Caselles Ballester
article en
9 citations

Abstract

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.

IEEE Transactions on Medical ImagingVol. 44(4)
University of Verona (IT), University of Modena and Reggio Emilia (IT), Universitat Oberta de Catalunya (ES), Radboud University Nijmegen (NL), HES-SO University of Applied Sciences and Arts Western Switzerland (CH), German Cancer Research Center (DE), Shanghai Jiao Tong University (CN), Heidelberg University (DE), Radboud University Medical Center (NL), Helmholtz Zentrum München (DE), ShanghaiTech University (CN), Vicomtech (ES), Kraftanlagen (Germany) (DE), Osstem Implant (South Korea) (KR), AGH University of Krakow (PL), Sapienza University of Rome (IT)
Università Degli Studi di Modena e Reggio Emila, European Commission, Radboud Universiteit, Fundacja na rzecz Nauki Polskiej, Radboud Universitair Medisch Centrum, Infrastruktura PL-Grid, Berlin Institute of Health, European Regional Development Fund, Academic Computer Centre Cyfronet, AGH University of Science and Technology, H2020 Environment
Openalex Percentile: Top 5%
Dental Radiography and Imaging
4.44
DOIOpen Access PDFOpenAlex
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