Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

9.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
9.6K
Tooth Anatomy01:21

Tooth Anatomy

3.0K
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...
3.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Secondary Structure-Encoded Control of Lipid Assembly by Radially Amphiphilic Peptides.

Journal of the American Chemical Society·2026
Same author

Coarse-to-fine prior-guided attention network for multi-structure segmentation on dental panoramic radiographs.

Physics in medicine and biology·2023
Same author

Design optimization of a periodic microstructured array anode for hard x-ray grating interferometry.

Physics in medicine and biology·2019
Same author

Identified trans-splicing of YELLOW-FRUITED TOMATO 2 encoding the PHYTOENE SYNTHASE 1 protein alters fruit color by map-based cloning, functional complementation and RACE.

Plant molecular biology·2019
Same author

Value of SATB2, ISL1, and TTF1 to differentiate rectal from other gastrointestinal and lung well-differentiated neuroendocrine tumors.

Pathology, research and practice·2019
Same author

Rock salt type NiO assembled on ordered mesoporous carbon as peroxidase mimetic for colorimetric assay of gallic acid.

Talanta·2019

Related Experiment Video

Updated: Apr 13, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

9.3K

Automatic jawbone structure segmentation on dental CBCT images via deep learning.

Yuan Tian1, Jin Hao2, Mingzheng Wang3

  • 1Angelalign Technology Inc., No. 500 Zhengli Road, Yangpu District, Shanghai, 200433, China. tianyuan@angelalign.com.

Clinical Oral Investigations
|November 28, 2024
PubMed
Summary

This study presents a deep learning system for segmenting jawbone structures in cone beam computed tomography (CBCT) scans. The system accurately segments mandibular and maxillary bones, offering efficiency for dental workflows.

Keywords:
Artificial intelligenceCancellous bone segmentationCone beam computed tomographyCortical bone segmentationDeep learningJawbone segmentation

More Related Videos

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

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

Published on: August 5, 2021

1.7K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

771

Related Experiment Videos

Last Updated: Apr 13, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

9.3K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

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

Published on: August 5, 2021

1.7K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

771

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dental Anatomy

Background:

  • Accurate segmentation of jawbone structures is crucial for digital dentistry.
  • Current segmentation methods can be time-consuming and labor-intensive.

Purpose of the Study:

  • To develop and evaluate a two-stage deep learning system for automatic segmentation of mandibular and maxillary cortical and cancellous bone.
  • To assess the system's performance using metrics like Dice Similarity Coefficient (DSC) and Average Symmetric Surface Distance (ASSD).

Main Methods:

  • A dataset of 155 CBCT scans was used.
  • A two-stage deep learning model was implemented for automatic segmentation.
  • Segmentation performance was evaluated against ground truth and compared with manual segmentation.

Main Results:

  • The system demonstrated high segmentation accuracy with average DSC values ranging from 86.14% to 96.83%.
  • Average ASSD values were below 0.41 mm, indicating precise boundary delineation.
  • Manual refinement showed high overlap with automatic segmentation (DSCs > 98.8%, ASSDs < 0.1 mm).

Conclusions:

  • The developed deep learning system provides an accurate and efficient method for jawbone segmentation on CBCT images.
  • This technology holds significant potential for enhancing digital clinical workflows, aiding in diagnosis and treatment planning.