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

Beyond gray matter: unveiling the critical role of white matter in Alzheimer's disease.

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Age-inappropriate white matter injury reveals hidden cerebral small vessel disease burden and mortality risk.

Cerebral circulation - cognition and behavior·2026
Same author

Integrated model based on ultrasound attenuation and metabolic biomarkers for noninvasive assessment of hepatic fat fraction categories in MASLD: a QCT-referenced study.

Frontiers in physiology·2026
Same author

DPF-EHDNet: a differential-path and structurally enhanced network for thyroid ultrasound segmentation.

Frontiers in medicine·2026
Same author

Association of imaging-defined brain age with disease severity and adverse outcomes in CADASIL.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Resting-state fMRI-based perfusion-timing analysis in cerebral small vessel disease: biomarker potential and mechanistic implications.

NeuroImage·2026

Related Experiment Video

Updated: Sep 30, 2025

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

9.0K

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network.

Kai Niu1, Xueyan Li2,3, Li Zhang4

  • 1Department of Otorhinolaryngology Head and Neck Surgery, the First Hospital of Jilin University, Changchun, China.

Quantitative Imaging in Medicine and Surgery
|March 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces QBrain, a novel deep learning framework that enhances the reliability of brain MRI segmentation across different scanners. QBrain improves quantitative analysis for individual patient brain tissue assessment.

Keywords:
Magnetic resonance imaging (MRI)brain, segmentationdeep learninggenerative adversarial network (GAN)

More Related Videos

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

3.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

538

Related Experiment Videos

Last Updated: Sep 30, 2025

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

9.0K
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

3.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

538

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Inconsistent contrast properties in MRI scans from different scanners hinder quantitative analysis.
  • Developing reliable automatic brain image segmentation is crucial for multi-scanner data integration.

Purpose of the Study:

  • To develop an automatic brain image segmentation model for reliable analysis of MR images acquired on different scanners.
  • To improve the robustness and accuracy of quantitative brain tissue analysis across diverse imaging hardware.

Main Methods:

  • Utilized the spatially localized atlas network tiles-27 (SLANT-27) deep learning model for segmentation.
  • Developed the QBrain framework integrating a generative adversarial network (GAN) image transfer module with SLANT-27.
  • Trained and tested QBrain on a multi-center dataset (1,917 3D T1-weighted MR images) and an interscan dataset (48 participants across 3 scanners).

Main Results:

  • QBrain demonstrated superior reliability and robustness in segmentation compared to SLANT-27 and FreeSurfer (FS).
  • The GAN image transfer module in QBrain reduced mean segmentation error across scanner pairs by up to 2.01%.
  • QBrain improved intra-scanner variability (0.9-1.67%) compared to FS (2.47-4.32%).

Conclusions:

  • The QBrain method enhances the reliability of whole-brain automatic structural segmentation across multiple MRI scanners.
  • QBrain offers a suitable alternative for quantitative comparative brain tissue analysis in individual patients.
  • Integration of GANs with deep learning segmentation models improves cross-scanner MRI analysis.