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Teeth

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
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The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...

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Related Experiment Video

Updated: Jun 24, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Fine structural human phantom in dentistry and instance tooth segmentation.

Atsushi Takeya¹, Keiichiro Watanabe¹, Akihiro Haga²

¹Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.

Scientific Reports

|June 1, 2024

Summary

This summary is machine-generated.

Researchers developed a dental phantom and used virtual imaging to train a deep-learning model for precise tooth segmentation in cone-beam computed tomography (CBCT) scans, achieving high accuracy on clinical data.

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Last Updated: Jun 24, 2025

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Area of Science:

Biomedical Engineering
Medical Imaging
Computer Vision

Background:

Accurate tooth segmentation is crucial for dental diagnostics and treatment planning.
Traditional methods can be time-consuming and prone to variability.
Advancements in medical computer vision offer potential for automated segmentation.

Purpose of the Study:

To develop a fine structural human phantom for dental imaging applications.
To evaluate the effectiveness of medical computer vision techniques for tooth segmentation using this phantom.
To assess the performance of a deep-learning model trained with virtual imaging data on clinical cone-beam computed tomography (CBCT) data.

Main Methods:

A human phantom was designed for dental applications.
A virtual cone-beam computed tomography (CBCT) system generated over 170,000 training datasets by varying phantom properties and imaging parameters.
A deep-learning (DL) model was trained on these virtual datasets for tooth segmentation.

Main Results:

The DL-based tooth segmentation model demonstrated robust performance.
The model achieved a Dice similarity coefficient exceeding 0.87 when compared to manual contouring on clinical CBCT data.
High agreement was observed between the automated segmentation and manual segmentation.

Conclusions:

Virtual imaging techniques are practically useful in dentistry.
Medical computer vision, particularly deep learning, shows significant potential for enhancing precision and efficiency in dental imaging.
The developed segmentation model offers a reliable tool for automated tooth segmentation in CBCT analysis.