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

Cardio amyloid-artificial intelligence: advanced multi-modal screening for transthyretin cardiac amyloidosis in severe aortic stenosis patients.

European heart journal. Digital health·2026
Same author

Deep-learning-based Optimization of the Under-sampling Pattern in MRI.

IEEE transactions on computational imaging·2026
Same author

A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI.

Communications biology·2026
Same author

TractoMFormer: A novel streamline-level tractography analysis framework for group classification using deep graph and multi-scale ViT.

NeuroImage·2026
Same author

BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia.

Scientific data·2026
Same author

Study of sex differences in the whole brain white matter using diffusion MRI tractography and suprathreshold fiber cluster statistics.

NeuroImage. Clinical·2026

Related Experiment Video

Updated: Jun 13, 2026

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

Consistency Clustering: A Robust Algorithm for Group-wise Registration, Segmentation and Automatic Atlas Construction

Ulas Ziyan1, Mert R Sabuncu, W Eric L Grimson

  • 1Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.

International Journal of Computer Vision
|May 6, 2010
PubMed
Summary

This study introduces consistency clustering, an automated method for creating probabilistic white-matter atlases from diffusion MRI data. The algorithm enhances accuracy by rejecting outliers and denoising, enabling precise analysis of brain structures.

More Related Videos

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

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
13:26

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Related Experiment Videos

Last Updated: Jun 13, 2026

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

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

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
13:26

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) is crucial for studying white-matter tracts.
  • Accurate white-matter atlases are essential for analyzing brain structure and function.
  • Existing atlas construction methods can be manual, time-consuming, or lack robustness.

Purpose of the Study:

  • To develop an automated algorithm for constructing probabilistic white-matter atlases.
  • To improve the accuracy and robustness of atlas creation using diffusion MRI data.
  • To enable precise analysis of white-matter bundles in specific populations.

Main Methods:

  • An integrated registration and clustering algorithm named "consistency clustering" was developed.
  • The algorithm formulates atlas creation as a maximum likelihood problem solved via a generalized Expectation Maximization (EM) framework.
  • Outlier rejection and denoising strategies were incorporated to enhance map sharpness.

Main Results:

  • The consistency clustering algorithm successfully constructed probabilistic white-matter atlases from synthetic and real diffusion MRI data.
  • The method demonstrated stability against initialization variations.
  • The resulting atlas accurately labeled novel subjects, showing its utility for population-specific analysis.

Conclusions:

  • Consistency clustering provides a viable tool for fully automated white-matter atlas construction for specific subpopulations.
  • The generated probabilistic atlases facilitate diffusion measurements in a common coordinate system.
  • This approach aids in identifying pathology-related changes and developmental trends in white matter.