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Toward accurate tooth segmentation from computed tomography images using a hybrid level set model.

Yangzhou Gan¹, Zeyang Xia², Jing Xiong³

¹Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.

Medical Physics

|January 8, 2015

Summary

This study introduces an accurate and efficient method for segmenting teeth in computed tomography (CT) images, crucial for creating 3D dental models for orthodontic treatment. The developed technique demonstrates high accuracy and speed in segmenting various tooth types.

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Area of Science:

Medical Imaging
Computational Anatomy
Orthodontics

Background:

Three-dimensional (3D) models of teeth are vital for orthodontic diagnosis and treatment planning.
Accurate tooth segmentation from computed tomography (CT) images is a critical prerequisite for generating these 3D models.
Existing segmentation methods may lack the required accuracy or efficiency for clinical application.

Purpose of the Study:

To develop an accurate and efficient method for segmenting individual tooth contours from dental CT images.
To enable the automated generation of precise 3D dental models for improved orthodontic workflows.

Main Methods:

A hybrid level set model was developed for automatic 2D tooth segmentation on transverse CT slices.
A manual initialization step was used to select seed points for each tooth on a starting slice.
Tooth contour propagation was employed to initialize the level set function automatically for subsequent slices.
The method was validated using cone beam CT (CBCT) images from 18 subjects, with quantitative assessment using volume overlap and surface distance metrics.

Main Results:

The proposed method achieved high accuracy across different tooth types, with Dice Similarity Coefficients (DSC) ranging from 88.82% ± 2.14% for incisors to 94.12% ± 1.38% for molars.
Quantitative metrics demonstrated strong performance, including low average symmetric surface distances (ASSD) around 0.27-0.30 mm.
The average computation time for segmenting one subject's CBCT images was 7.25 ± 0.73 minutes, indicating efficiency.
The method showed significant accuracy improvements compared to two state-of-the-art segmentation techniques.

Conclusions:

The presented tooth segmentation method provides an accurate and efficient solution for processing dental CT images.
This technique facilitates the creation of detailed 3D dental models, supporting enhanced orthodontic diagnosis and treatment planning.