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

Bridging Global Attention and Local Hierarchies: A Robust Hybrid Ensemble Framework With Multi-Perspective Explainability for Automated HER2-IHC Scoring.

Technology in cancer research & treatment·2026
Same author

Should we explore CBD during cholecystectomy in a patient undergone successful ERCP clearance of stones? a new recommendation.

Updates in surgery·2026
Same author

Infection following foot and ankle surgery : a subanalysis of data captured from the UK Foot and Ankle Thromboembolism (FATE) audit.

The bone & joint journal·2026
Same author

Novel pyridine-based chalcone analogs and triple negative breast cancer: potential therapy & molecular pathways.

Scientific reports·2026
Same author

Explainable Split-Learning-Based Framework for Accurate Pulmonary Nodule Classification.

Bioengineering (Basel, Switzerland)·2026
Same author

Correction: Developmental and angiogenic safety profiles of novel pyridine-based chalcone.

Frontiers in pharmacology·2026

Related Experiment Video

Updated: Feb 18, 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

9.5K

A fast stochastic framework for automatic MR brain images segmentation.

Marwa Ismail1, Ahmed Soliman1, Mohammed Ghazal1,2

  • 1Bioengineering Department, University of Louisville, Louisville, KY, United States of America.

Plos One
|November 15, 2017
PubMed
Summary
This summary is machine-generated.

This study presents a novel framework for segmenting brain structures in 3D MR images across different life stages. The method improves accuracy by adapting shape priors using visual characteristics and advanced modeling for better MR image segmentation.

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.6K
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

12.7K

Related Experiment Videos

Last Updated: Feb 18, 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

9.5K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.6K
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

12.7K

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Accurate segmentation of brain structures in Magnetic Resonance (MR) images is crucial for understanding brain development and disease.
  • Existing segmentation tools struggle with variations across life stages and image inhomogeneities, particularly in infant MRIs.

Purpose of the Study:

  • To introduce a novel, adaptive framework for segmenting white matter, gray matter, and cerebrospinal fluid in 3D MR brain images.
  • To enhance segmentation accuracy by integrating shape priors with visual appearance characteristics and advanced spatial modeling.
  • To specifically address challenges in segmenting infant MRIs due to significant signal inhomogeneity.

Main Methods:

  • A segmentation framework utilizing a shape prior adapted during processing based on first- and second-order MR image appearance characteristics.
  • Modeling empirical grey level distribution using a linear combination of discrete Gaussians (LCDG) with positive and negative components.
  • Employing a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model, incorporating third- and fourth-order families with a second-order model, to handle MR image inhomogeneity.

Main Results:

  • The proposed framework demonstrated superior segmentation performance on 102 3D MR brain scans.
  • Evaluation using Dice coefficient, 95-percentile modified Hausdorff distance, and absolute brain volume difference showed significant improvements.
  • The approach outperformed current open-source segmentation tools in accuracy.

Conclusions:

  • The developed framework offers a robust and accurate method for brain structure segmentation across diverse life stages.
  • The adaptive shape prior and advanced spatial modeling effectively address challenges in MR image segmentation, especially for infant data.
  • This work provides a valuable tool for neuroimaging research and clinical applications requiring precise brain segmentation.