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: Jun 19, 2026

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

Interactive surface-guided segmentation of brain MRI data.

Konstantin Levinski1, Alexei Sourin, Vitali Zagorodnov

  • 1Nanyang Technological University, Singapore.

Computers in Biology and Medicine
|November 6, 2009
PubMed
Summary
This summary is machine-generated.

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

Predicting vulnerability to sleep deprivation using diffusion model parameters.

Journal of sleep research·2014
Same author

New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance.

PloS one·2013
Same author

Image-driven haptic simulation of arthroscopic surgery.

Studies in health technology and informatics·2013
Same author

Haptic editing of MRI brain data.

Studies in health technology and informatics·2012
Same author

Adverse Associations between Visceral Adiposity, Brain Structure, and Cognitive Performance in Healthy Elderly.

Frontiers in aging neuroscience·2011
Same author

Component analysis approach to estimation of tissue intensity distributions of 3D images.

IEEE transactions on medical imaging·2010
Same journal

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Computers in biology and medicine·2026
Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026
Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
See all related articles

This study introduces a 3D approach to correct errors in automated brain MRI segmentation, significantly reducing correction time by utilizing the brain surface and highlighting potential issues.

Area of Science:

  • Medical Imaging and Image Analysis
  • Neuroimaging
  • Computational Anatomy

Background:

  • Magnetic Resonance Imaging (MRI) segmentation extracts semantic information from volumetric data.
  • Automated brain MRI segmentation, often performed using tools like FreeSurfer, can produce errors requiring manual correction on individual 2D slices.
  • Current error correction methods are time-consuming and may not fully leverage volumetric context.

Purpose of the Study:

  • To develop and present a novel approach for correcting segmentation errors in 3D modeling space for brain MRI data.
  • To reduce the time and effort required for manual segmentation error correction.
  • To improve the efficiency of the segmentation refinement process by utilizing volumetric context.

Main Methods:

  • The proposed method operates in 3D modeling space, directly addressing segmentation errors.

More Related Videos

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

  • It actively utilizes the estimated brain surface, even if imperfectly generated by automated pipelines (e.g., FreeSurfer).
  • The approach incorporates heuristic editing support and automatic visual highlighting of potential error locations.

    • Main Results:

    • The 3D approach allows simultaneous correction across multiple slices by leveraging volumetric context.
    • Heuristic editing support and automatic error highlighting substantially reduce the time needed for segmentation correction.
    • The method enables more efficient and potentially more accurate refinement of automated brain segmentation results.

    Conclusions:

    • The developed software tool offers an effective solution for correcting errors in brain MRI segmentation.
    • Working in 3D space and utilizing the brain surface improves efficiency and accuracy compared to slice-by-slice editing.
    • This approach has the potential to significantly streamline neuroimaging analysis workflows.