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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Community-based tuberculosis screening with computer-aided detection technology alone and in combination with point-of-care C-reactive protein testing: a paired screen-positive trial.

The Lancet. Infectious diseases·2026
Same author

Assessment of modifications to a blind-sweep ultrasound protocol for improved lower-uterus imaging by novice operators.

Scientific reports·2026
Same author

The evaluation of osteoconductive WE43 magnesium as a therapeutic approach for nonunion treatment : a mouse model.

Bone & joint research·2026
Same author

Influence of maxillomandibular skeletal discrepancy on the position and course of the retromandibular vein in patients with jaw deformities.

Journal of stomatology, oral and maxillofacial surgery·2026
Same author

Applying artificial intelligence to assess the impact of orthognathic treatment on gender-affirming facial features.

European journal of orthodontics·2026
Same author

Artificial intelligence for keratosis characterization and identification of lichenoid lesions in histological samples of oral leukoplakia.

Virchows Archiv : an international journal of pathology·2026

Related Experiment Video

Updated: Jun 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.

Tabea Flügge1, Shankeeth Vinayahalingam2,3,4, Niels van Nistelrooij1,2

  • 1Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203 Berlin, Germany.

Dento Maxillo Facial Radiology
|November 26, 2024
PubMed
Summary

An artificial intelligence model was developed for tooth segmentation in magnetic resonance (MR) scans, achieving high accuracy. This automated method shows moderate to high effectiveness for MR scans, particularly those without dental artefacts.

Keywords:
artificial intelligencejawmagnetic resonance imagingmedical image processingtooth

More Related Videos

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
11:03

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

9.3K

Related Experiment Videos

Last Updated: Jun 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
11:03

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

9.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dental Anatomy

Background:

  • Accurate tooth segmentation in magnetic resonance (MR) scans is crucial for dental diagnostics and treatment planning.
  • Manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI) model for automated tooth segmentation in MR scans.
  • To assess the accuracy and effectiveness of the AI model compared to manual segmentation.

Main Methods:

  • Utilized the nnU-Net framework to train an AI model on 16 patient MR datasets.
  • Employed T1-weighted 3D-SPACE sequences for MR imaging.
  • Evaluated model performance using precision, sensitivity, Dice-Sørensen coefficient, and Hausdorff distance 95% (HD95) on 4 independent datasets.

Main Results:

  • The AI model achieved a precision of 0.867, sensitivity of 0.926, and Dice-Sørensen coefficient of 0.895.
  • The mean Hausdorff distance 95% (HD95) was reported as 0.91 mm.
  • Model accuracy was reduced in scans with dental restorations due to image artefacts.

Conclusions:

  • An automated AI method for tooth segmentation in MR scans was successfully developed.
  • The model demonstrates moderate to high effectiveness, especially in MR scans free from artefacts.
  • Further refinement may be needed to address challenges posed by dental restorations and image artefacts.