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

Loss of ASIC1A-dependent inhibitory neuron activity in basolateral amygdala is associated with increased CO <sub>2</sub> -evoked jumping.

bioRxiv : the preprint server for biology·2026
Same author

A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.

NPJ digital medicine·2026
Same author

A DNA Methylation-based algorithm Improves Lung Cancer risk prediction in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Lung cancer (Amsterdam, Netherlands)·2026
Same author

A Two-Track Model of Huntington's Disease Pathology: Striatal Atrophy Mediates Maladaptive Immune Dysregulation.

International journal of molecular sciences·2026
Same author

Global functional connectivity of cognitive control networks predicts task-switching performance in older adults.

Cortex; a journal devoted to the study of the nervous system and behavior·2026
Same author

Methylation Biomarker of Chronic Heavy Alcohol Consumption (HAC), but Not Acute HAC, Predicts All-Cause Mortality in Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Genes·2026
Same journal

Synaptic micromechanics and brain softening as a mechanobiological hypothesis for Alzheimer's disease.

Frontiers in neuroscience·2026
Same journal

The relationship between healthy sleep patterns and the risk of scoliosis: a large prospective cohort study.

Frontiers in neuroscience·2026
Same journal

Dynamic functional reorganization in post-stroke aphasia: a state-of-the-art fMRI review from disease evolution to intervention.

Frontiers in neuroscience·2026
Same journal

Correction: Case Report: A possible novel adult-onset, progressive MAO-A hypofunction.

Frontiers in neuroscience·2026
Same journal

Respiratory modulation of neurophysiology and symptoms in athletes with sports-related concussion: a randomized crossover trial.

Frontiers in neuroscience·2026
Same journal

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;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

Preliminary analysis using multi-atlas labeling algorithms for tracing longitudinal change.

Regina E Y Kim1, Spencer Lourens2, Jeffrey D Long3

  • 1Department of Psychiatry, University of Iowa Iowa City, IA, USA.

Frontiers in Neuroscience
|August 4, 2015
PubMed
Summary
This summary is machine-generated.

Multi-atlas labeling tools show promise for neuroimaging biomarkers in neurodegenerative disease research. MALF generally outperformed other methods in accuracy and reliability for subcortical segmentation, though BRAINSCut showed higher reliability in specific regions.

Keywords:
brain MRIlongitudinal data analysismachine learningmulti-atlas label fusionmulticenter studyvalidation

More Related Videos

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM
09:25

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

Published on: May 8, 2020

11.5K
Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

713

Related Experiment Videos

Last Updated: Apr 6, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;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
Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM
09:25

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

Published on: May 8, 2020

11.5K
Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

713

Area of Science:

  • Neuroimaging
  • Biomarker Discovery
  • Neurodegenerative Diseases

Background:

  • Multicenter longitudinal neuroimaging is crucial for developing consistent biomarkers for neurodegenerative diseases and aging.
  • Reliable tools are essential for rare disease studies to ensure data consistency across multiple collection sites and increase study power.
  • Multi-atlas labeling algorithms are increasingly popular for brain image segmentation due to their power and potential for accuracy.

Purpose of the Study:

  • To evaluate the performance of multi-atlas labeling tools for subcortical brain region segmentation.
  • To compare the accuracy, multicenter reliability, and longitudinal reliability of two multi-atlas tools (MABMIS, MALF) and one machine-learning tool (BRAINSCut).
  • To assess segmentation performance using in-vivo neuroimaging data from the Traveling Human Phantom (THP) and PREDICT-HD databases.

Main Methods:

  • Comparison of three automated segmentation approaches: MABMIS, MALF (multi-atlas labeling), and BRAINSCut (machine-learning).
  • Evaluation metrics included Dice Similarity Coefficient (DSC) and intraclass correlation (ICC) for accuracy, Coefficient of Variance (CV) for multicenter reliability, and volume trajectory smoothness and Akaike Information Criterion (AIC) for longitudinal reliability.
  • Utilized in-vivo neuroimaging datasets: Traveling Human Phantom (THP) and PREDICT-HD.

Main Results:

  • MALF generally demonstrated superior performance, exhibiting higher accuracy (DSC, ICC), better multicenter reliability (lower CV), and smoother longitudinal trajectories (lower AIC).
  • BRAINSCut showed higher reliability for the accumbens region, but its validity metrics (DSC < 0.7, ICC < 0.7) were questionable.
  • For the caudate nucleus, BRAINSCut offered slightly better accuracy, while MALF provided a significantly smoother longitudinal trajectory.

Conclusions:

  • Multi-atlas labeling methods can enhance overall brain image segmentation quality for neuroimaging research.
  • The choice of segmentation approach can significantly impact results, necessitating careful consideration based on specific research objectives.
  • While MALF generally performed best, variations in performance across different subcortical regions highlight the need for cautious selection of tools in neuroimaging studies.