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Tooth Anatomy

Tooth Anatomy

The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
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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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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Clinical tooth segmentation based on local enhancement.

Jipeng Wu¹, Ming Zhang², Delong Yang^3,1

¹Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Frontiers in Molecular Biosciences

|October 28, 2022

Summary

This summary is machine-generated.

This study introduces a new local contextual enhancement model for segmenting teeth in dental cone-beam computed tomography (CBCT) images. This automated method improves accuracy and efficiency for dentists, aiding in early detection of issues like dental caries in children.

Keywords:

ASPP CNN decoder local enhancement tooth segmentation

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Area of Science:

Dentistry
Medical Imaging
Computer Vision

Background:

Accurate observation of human tooth arrangements, particularly for detecting pediatric dental caries, is challenging with the naked eye.
Cone-beam computed tomography (CBCT) aids in dental imaging, but manual measurements are time-consuming and subjective.
Automated tooth segmentation is crucial for efficient and reproducible dental diagnostics.

Purpose of the Study:

To develop a fast and accurate tooth segmentation algorithm for clinical dental CBCT images.
To address the limitations of manual measurements in CBCT analysis.
To enhance the clinical significance of automated dental image analysis.

Main Methods:

Proposed a novel local contextual enhancement model tailored for dental CBCT images.
Integrated the local enhancement model into an encoder-decoder framework.
Conducted extensive experiments to validate the proposed method's performance.

Main Results:

The developed local contextual enhancement model demonstrated effectiveness in segmenting teeth from dental CBCT data.
The encoder-decoder framework integrated with the model provided accurate and efficient segmentation.
Experimental validation confirmed the method's potential for clinical application.

Conclusions:

The proposed local contextual enhancement model offers a significant advancement in automated tooth segmentation for dental CBCT.
This automated approach can reduce dentists' workload and improve diagnostic accuracy, especially for pediatric dental caries.
The method holds promise for enhancing clinical workflows in dentistry.