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

XL-MS and De Novo Protein Design Identified a Common Motif for TREM2 Binding.

bioRxiv : the preprint server for biology·2026
Same author

CFG-MambaNet: Contextual and Frequency-Guided Mamba Network for medical image segmentation.

NPJ digital medicine·2026
Same author

Moving Through Genetic Alterations and PD-L1 Expression in High-Grade Fetal Adenocarcinoma of the Lung: A Case Report and Literature Review.

International medical case reports journal·2025
Same author

Dual-Student Adversarial Framework With Discriminator and Consistency-Driven Learning for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2025
Same author

Correction: PEG-poly(amino acid)s-encapsulated tanshinone IIA as potential therapeutics for the treatment of hepatoma.

Journal of materials chemistry. B·2025
Same author

Design of a Superhydrophobic Photothermal Shape-Memory Material Based on Carbon-Nanotubes-Doped Resin for Anti-Icing/De-Icing Applications.

Materials (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jun 29, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.0K

SDMI-Net: Spatially Dependent Mutual Information Network for semi-supervised medical image segmentation.

Di Gai1, Zheng Huang2, Weidong Min1

  • 1School of Mathematics and Computer Science, Nanchang University, Nanchang, 330031, China; Institute of Metaverse, Nanchang University, Nanchang, 330031, China.

Computers in Biology and Medicine
|April 6, 2024
PubMed
Summary

This study introduces a novel dual-teacher method for semi-supervised medical image segmentation, improving deep learning models using unlabeled data by focusing on block-level consistency and uncertainty estimation for better accuracy.

Keywords:
Consistency learningMedical image segmentationMutual informationSemi-supervised learningSpatially dependent

More Related Videos

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
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.4K

Related Experiment Videos

Last Updated: Jun 29, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.0K
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
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Semi-supervised learning in medical image segmentation uses limited labeled data with abundant unlabeled data to train deep models.
  • Voxel-level consistency learning methods are hindered by low-confidence voxels and ignore spatial correlations between voxels.
  • Existing methods struggle with efficiently and accurately segmenting medical images due to these limitations.

Purpose of the Study:

  • To develop a robust semi-supervised medical image segmentation method that overcomes the limitations of voxel-level consistency learning.
  • To enhance the spatial dependence between neighboring voxels for more reliable segmentation.
  • To achieve state-of-the-art performance in medical image segmentation tasks.

Main Methods:

  • Proposed a dual-teacher affine consistent uncertainty estimation to filter high-uncertainty voxels, promoting reliable voxel-level learning.
  • Introduced a spatially dependent mutual information module to maximize mutual information between local voxel blocks for block-level consistency.
  • Implemented and validated the method on the Left Atrial Segmentation Challenge and BraTS-2019 datasets.

Main Results:

  • The proposed method effectively filters out uncertain voxels, improving the reliability of the segmentation process.
  • Block-level consistency learning significantly enhances the spatial dependence between neighboring voxels.
  • Achieved state-of-the-art quantitative and qualitative results on benchmark medical image segmentation datasets.

Conclusions:

  • The dual-teacher affine consistent uncertainty estimation and spatially dependent mutual information module offer a superior approach to semi-supervised medical image segmentation.
  • This method addresses key limitations of previous techniques, leading to improved segmentation accuracy and robustness.
  • The approach demonstrates significant potential for advancing deep learning applications in medical image analysis.