TY  - JOUR
ID  - 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
T1  - Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge
A1  - Bolelli, Federico
A1  - Lumetti, Luca
A1  - Wang, Lisheng
A1  - Wodzinski, Marek
A1  - Müller, Henning
A1  - Maier-Hain, Klaus
A1  - Ginneken, Bram van
A1  - Grana, Costantino
JA  - IEEE Transactions on Medical Imaging
Y1  - 2025
VL  - Volume 44, Issue : 4, Arpil 2025
SP  - 1890
EP  - 1906
SN  - https://pubmed.ncbi.nlm.nih.gov/
UR  - https://ieeexplore.ieee.org/document/10816445
M2  - doi: https://doi.org/10.1109/TMI.2024.3523096
KW  - 3D Network
KW  - 3D U-Net Author Keywords Segmentation
KW  - algorithms
KW  - Annotation Process
KW  - Annotations
KW  - Benchmark testing
KW  - Challenge Participants
KW  - Common Benchmark
KW  - computed tomography MeSH Terms Cone-Beam Computed Tomography
KW  - Cone-beam Computed Tomography Images
KW  - Cone-beam Computed Tomography Volume
KW  - Data Augmentation
KW  - Deep Learning
KW  - Deep Neural Network
KW  - Dice Loss
KW  - Dice Similarity Coefficient
KW  - Domain Dataset
KW  - EEE Keywords Three-dimensional displays
KW  - Final Ranking
KW  - Focal Loss
KW  - Humans
KW  - image segmentation
KW  - Imaging
KW  - Inferior Alveolar Canal
KW  - Inferior Alveolar Nerve
KW  - Intersection Over Union
KW  - Irrigation
KW  - Mandible
KW  - Mandibular Canal
KW  - Medical Experts
KW  - Mental Foramen
KW  - Neural Network
KW  - Panoramic Radiographs
KW  - Private Dataset
KW  - Proposals Index Terms Inferior Alveolar
KW  - Public Datasets
KW  - semi-supervised learning
KW  - Sparse Labeling
KW  - Statistical Shape Model
KW  - Surgery
KW  - Teeth
KW  - Three-Dimensional
KW  - tooth
KW  - training
KW  - Training Data
KW  - Training Set
KW  - X-ray imaging
N2  - 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.
ER  -