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

You might also read

Related Articles

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

Sort by
Same author

Deep Learning-Assisted Three-Dimensional Segmentation of Vertebrobasilar Artery Calcification in Cone Beam Computed Tomography.

Journal of imaging informatics in medicine·2026
Same author

DenseViT-OCT: A Hybrid CNN-Transformer Architecture with Multi-Scale Dense Feature Aggregation for Automated Epiretinal Membrane Severity Classification.

Tomography (Ann Arbor, Mich.)·2026
Same author

Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.

Diagnostics (Basel, Switzerland)·2026
Same author

Automatic Infant Movement Assessment Using Pose-LBP Features and a Cost-Sensitive Subspace kNN Ensemble.

Bioengineering (Basel, Switzerland)·2026
Same author

An Explainable Plane-Wise ConvNet Approach for Detecting Femoral Head Osteonecrosis from Magnetic Resonance Images.

Bioengineering (Basel, Switzerland)·2026
Same author

Explainable Neutrosophic Knowledge Distillation Model for Ocular Disease Classification Using Ultra-Wide Field Fundus Images.

Bioengineering (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 5, 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

Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based

Salih Taha Alperen Özçelik1, Hüseyin Üzen2, Abdulkadir Şengür3

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering, Bingöl University, Bingöl 12000, Turkey.

Diagnostics (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered deep learning model for precise tooth segmentation in dental X-rays, improving diagnostic accuracy. The Squeeze and Excitation Inception Block-based Encoder-Decoder network achieved high performance in identifying teeth regions.

Keywords:
attention gateencoder–decodersqueeze and excitationtooth labellingtooth segmentation

More Related Videos

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

4.3K
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

766

Related Experiment Videos

Last Updated: Jun 5, 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
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

4.3K
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

766

Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Image Analysis
  • Deep Learning for Dental Applications

Background:

  • Dental disorders affect billions globally, necessitating early diagnosis for effective treatment.
  • Manual tooth segmentation and numbering are prone to errors, hindering precise dental disease diagnosis.
  • Artificial intelligence (AI), particularly deep learning, offers automated, fast, and accurate solutions for dental image processing.

Purpose of the Study:

  • To develop and evaluate an AI-based method for automated teeth segmentation in panoramic X-ray images.
  • To improve the accuracy and efficiency of tooth identification for better dental diagnostics.
  • To address the limitations of manual tooth segmentation in clinical settings.

Main Methods:

  • Proposed the Squeeze and Excitation Inception Block-based Encoder-Decoder (SE-IB-ED) network for teeth segmentation.
  • Utilized InceptionV3 for encoding and a custom decoder with attention mechanisms for feature integration.
  • Annotated a dataset of 313 panoramic radiographs using the FDI system, with SAM for automated labeling and manual correction.

Main Results:

  • The SE-IB-ED network achieved a 92.65% F1-score, 86.38% mIoU, and 92.84% accuracy.
  • Demonstrated superior performance compared to state-of-the-art models in teeth segmentation.
  • Achieved high precision (92.49%) and recall (99.92%) in segmenting teeth regions.

Conclusions:

  • The proposed SE-IB-ED network shows significant potential for accurate segmentation of teeth and backgrounds in panoramic X-rays.
  • Automated segmentation can enhance diagnostic workflows and treatment planning in dentistry.
  • Deep learning models offer a robust solution for complex dental image analysis tasks.