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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

1.1K
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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Updated: Sep 18, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Tooth image segmentation and root canal measurement based on deep learning.

Ziqing Chen1, Qi Liu1, Jialei Wang2

  • 1School of Biomedical Engineering, Sichuan University, Chengdu, China.

Frontiers in Bioengineering and Biotechnology
|June 24, 2025
PubMed
Summary
This summary is machine-generated.

An automated method for tooth segmentation and root canal measurement using cone beam computed tomography (CBCT) images achieves high accuracy. This AI-driven approach offers objective, efficient results for clinical guidance in endodontic procedures.

Keywords:
Attention U-netCBCTV-Netdeep learningroot canal measurementtooth instance segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Dentistry
  • Endodontics

Background:

  • Accurate tooth segmentation and root canal measurement are crucial for endodontic diagnosis and treatment planning.
  • Manual measurements can be time-consuming and prone to inter-observer variability.
  • Cone beam computed tomography (CBCT) provides detailed 3D anatomical data for dental applications.

Purpose of the Study:

  • To develop an automated method for tooth segmentation and root canal measurement using CBCT images.
  • To provide objective, efficient, and accurate measurements for clinical guidance.
  • To assist clinicians in root canal diagnosis grading, instrument selection, and preoperative planning.

Main Methods:

  • Utilized Attention U-Net for tooth descriptor recognition and region of interest (ROI) cropping.
  • Applied an integrated deep learning method for automated tooth segmentation.
  • Implemented position correction and automatic measurement of root canal lengths and angles.

Main Results:

  • Achieved a segmentation Dice coefficient of 96.33% and Jaccard coefficient of 92.94%.
  • Demonstrated a low relative error of 3.42% for root canal length measurement, surpassing existing methods.
  • Clinicians recognized the positive effect of automated correction on measurement accuracy.

Conclusions:

  • The proposed automated segmentation method shows high performance and accuracy.
  • The automated measurement system provides a low relative error compared to manual measurements.
  • This AI-driven approach offers a valuable tool for enhancing clinical applications in endodontics.