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 Experiment Video

Updated: Sep 8, 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

2.9K

Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.

Juan Zhang1,2, Gaoqiang Jiang1,2, Zhongwen Li3

  • 1National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.

Pattern Recognition
|August 20, 2025
PubMed
Summary

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

Laser-Based Micro/Nano Additive Manufacturing of Conductive Structures on Transparent Substrates: Technical Approaches, Challenges, and Future Prospects.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets.

BMC ophthalmology·2025
Same author

Adaptive boundary-enhanced Dice loss for image segmentation.

Biomedical signal processing and control·2025
Same author

Parameter Optimization for Laser Peen Forming on 6005A-T6 Aluminum Alloy Plates to Enhance the Constrained Deformation of Integral Stiffened Plates.

Materials (Basel, Switzerland)·2024
Same author

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.

Frontiers in physiology·2024
Same author

In Situ Synthesis of (M:Nb,Ta)C/Ni35 Composite Coating Cladded on 40Cr Steel.

Materials (Basel, Switzerland)·2021
Same journal

Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection.

Pattern recognition·2026
Same journal

LDM-Morph: Latent diffusion model guided deformable image registration.

Pattern recognition·2026
Same journal

Variable Priority for Unsupervised Variable Selection.

Pattern recognition·2026
Same journal

A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery.

Pattern recognition·2025
Same journal

Multi-graph Graph matching for coronary artery semantic labeling in invasive coronary angiograms.

Pattern recognition·2025
Same journal

A graph transformer-based foundation model for brain functional connectivity network.

Pattern recognition·2025
See all related articles
This summary is machine-generated.

This study introduces dual-decoder mutual teaching (DDMT), a new semi-supervised learning method for medical image segmentation. DDMT significantly reduces annotation effort by effectively using limited labeled data and abundant unlabeled data.

Area of Science:

  • Medical image analysis
  • Deep learning
  • Computer vision

Background:

  • Accurate medical image segmentation is crucial for clinical applications.
  • Manual pixel-level annotation of medical images is time-consuming and labor-intensive.
  • Deep learning models require large annotated datasets for optimal performance.

Purpose of the Study:

  • To develop a novel semi-supervised segmentation method to reduce manual annotation effort.
  • To improve the stability and shape consistency of deep learning models in segmentation tasks.
  • To achieve promising segmentation performance with limited labeled and abundant unlabeled images.

Main Methods:

  • Introduced dual-decoder mutual teaching (DDMT), a semi-supervised segmentation method.
  • Incorporated smoothed exponential moving average (sEMA) for enhanced model stability.
Keywords:
Exponential moving averageImage segmentationMean teacherMutual teachingSemi-supervised learning

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.3K

Related Experiment Videos

Last Updated: Sep 8, 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

2.9K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.3K
  • Integrated shape consistency constraint (SCC) for consistent shape learning across decoders.

    • Main Results:

    • DDMT demonstrated promising segmentation performance on limited labeled data.
    • The method consistently outperformed state-of-the-art semi-supervised learning methods.
    • Experiments on left atrium, pancreas, and optic disc datasets validated DDMT's effectiveness.

    Conclusions:

    • DDMT offers an effective solution for reducing manual annotation in medical image segmentation.
    • The proposed method enhances model stability and shape consistency.
    • DDMT shows significant potential for clinical applications requiring accurate image segmentation.