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 Videos

Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution.

Marcel Bosc1, Fabrice Heitz, Jean Paul Armspach

  • 1Laboratoire des Sciences de l'Image de l'Informatique et de la Télédetection (LSIIT) UMR-7005 CNRS, 67400, Illkirch, France. bosc@lsiit.u-strasbg.fr

Neuroimage
|October 22, 2003
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

Faster decline of very prodromal dementia with Lewy bodies when amyloid positive.

Alzheimer's & dementia (Amsterdam, Netherlands)·2025
Same author

Cognitive and neuroimaging outcome of very prodromal dementia with Lewy bodies.

GeroScience·2025
Same author

Sex estimation from coxal bones using deep learning in a population balanced by sex and age.

International journal of legal medicine·2024
Same author

Prodromal characteristics of dementia with Lewy bodies: baseline results of the MEMENTO memory clinics nationwide cohort.

Alzheimer's research & therapy·2022
Same author

Emotional experience is increased and emotion recognition decreased in multiple sclerosis.

Scientific reports·2021
Same author

Spatially Constrained Online Dictionary Learning for Source Separation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same journal

Decoding neuronal criticality firing patterns for large brain based EEG models.

NeuroImage·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
See all related articles

This study presents an automated system for detecting subtle changes in MRI scans, improving disease progression assessment. The advanced image processing method outperforms human experts in identifying lesion evolution in multiple sclerosis (MS) patients.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Neurology

Background:

  • Assessing disease evolution using serial MRI scans is crucial but challenging.
  • Manual analysis of 3D MRI data for disease progression is time-consuming and prone to errors.
  • Existing automatic change detection methods struggle with MRI artifacts, leading to unreliability.

Purpose of the Study:

  • To develop and evaluate an automated image processing system for reliable detection of subtle changes in serial MRI scans.
  • To overcome limitations of manual analysis and standard automatic methods in detecting disease evolution.
  • To improve the accuracy and efficiency of monitoring diseases like multiple sclerosis (MS).

Main Methods:

  • A multiresolution deformable image matching technique to correct for registration errors and anatomical deformations.

Related Experiment Videos

  • A nonlinear intensity normalization method combined with statistical hypothesis testing for robust change detection.
  • Optional exploitation of multimodal MRI data to further reduce false positive rates.

    • Main Results:

    • The automated system demonstrated high performance in detecting lesion evolution in 3D multimodal MR images of MS patients.
    • Receiver operating characteristics (ROC) analysis confirmed the system's effectiveness.
    • The system successfully identified subtle lesion changes missed by human experts.

    Conclusions:

    • The developed automatic image processing system reliably detects subtle changes in serial MRI scans.
    • This automated approach surpasses human expert performance in identifying disease progression, particularly small, subtle changes.
    • The system offers a significant advancement for monitoring diseases like multiple sclerosis (MS).