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Related Experiment Video

Updated: May 10, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Published on: April 8, 2020

Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge.

Federico Bolelli¹, Luca Lumetti¹, Niels van Nistelrooij²

¹Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Italy.

Medical Image Analysis

|May 8, 2026

Summary

This summary is machine-generated.

ToothFairy2 established a benchmark for segmenting maxillofacial cone-beam computed tomography (CBCT) structures. While large structures were well-segmented, teeth numbering and fine structures remain challenging for automated methods.

Keywords:

CBCT Multi-class segmentation Tooth ToothFairy

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Last Updated: May 10, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Published on: April 8, 2020

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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Area of Science:

Medical Imaging
Computer Vision
Dental Diagnostics

Background:

Cone-beam computed tomography (CBCT) is crucial for dento-maxillofacial diagnostics.
Comprehensive multi-structure segmentation in CBCT is time-consuming, hindering research.
Automated segmentation methods are needed for efficient analysis of maxillofacial CBCT data.

Purpose of the Study:

To introduce ToothFairy2, a challenge for multi-structure segmentation in maxillofacial CBCT.
To establish a benchmark dataset and standardized evaluation protocol for segmentation tasks.
To assess the performance of automated methods on various maxillofacial structures, including teeth and their numbering.

Main Methods:

Development of the ToothFairy2 challenge with 530 CBCT volumes and 42 annotated classes.
Standardized voxel-wise multi-class segmentation evaluation protocol.
Analysis of tooth detection and FDI numbering capabilities, alongside ranking stability.

Main Results:

High performance achieved for large structures like jawbones and pharynx.
Challenges identified in segmenting maxillary sinuses, dental restorations, and fine structures due to class imbalance and artifacts.
Assigning correct FDI numbers for teeth proved more difficult than delineating teeth themselves.

Conclusions:

ToothFairy2 provides a valuable benchmark for advancing automated segmentation in maxillofacial CBCT.
Further research is needed to address challenges in segmenting complex and artifact-prone regions.
The released data and code will facilitate the development of robust, clinically relevant segmentation tools.