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

Integrated graph cuts for brain MRI segmentation.

Zhuang Song1, Nicholas Tustison, Brian Avants

  • 1Penn Image Computing and Science Lab, University of Pennsylvania, USA. songz@seas.upenn.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 16, 2007
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

Adaptive Riemannian optimization for multi-scale diffeomorphic matching.

Nature communications·2026
Same author

Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy.

Brain communications·2026
Same author

Realistic PET image synthesis from MRI for automated inference of brain atrophy and Alzheimer's.

iScience·2026
Same author

Deep Computational Anatomy via Latent-Aligned Multiview Normalizing Flows.

bioRxiv : the preprint server for biology·2026
Same author

Contusions bias cortical thickness estimates after traumatic brain injury: A TRACK-TBI study.

NeuroImage. Clinical·2026
Same author

Text-Image Co-Alignment for Weakly Supervised Polyp Segmentation.

IEEE transactions on medical imaging·2026
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

This study introduces a novel graph-based brain MRI segmentation method. It improves accuracy by integrating tissue priors, boundary data, and adaptive inhomogeneity correction, outperforming existing techniques.

Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Image Analysis

Background:

  • Brain MRI segmentation is crucial for neurological disorder diagnosis and treatment planning.
  • Existing segmentation techniques face challenges with image intensity variations and complex anatomical structures.
  • Accurate segmentation is essential for quantitative analysis of brain morphology and function.

Purpose of the Study:

  • To develop and validate a novel graph-based framework for enhanced brain MRI segmentation.
  • To integrate multiple sources of information, including image intensity, tissue priors, and local boundary information, into a unified segmentation approach.
  • To address the challenge of MRI inhomogeneity by incorporating an adaptive correction mechanism.

Main Methods:

  • A graph-based framework was employed, integrating image intensity, tissue priors, and local boundary information into edge weight calculations.

Related Experiment Videos

  • An adaptive inhomogeneity correction was implemented by dynamically adjusting edge weights based on intermediate inhomogeneity estimations.
  • The method was validated using simulated brain MRIs and real neonatal brain MRI datasets.

    • Main Results:

    • The proposed method demonstrated superior performance compared to the iterated conditional modes (ICM) segmentation method on simulated brain MRIs.
    • On real neonatal brain MRI data, the method achieved good overlap with manual segmentations performed by human experts.
    • The integration of diverse information sources and adaptive correction significantly improved segmentation accuracy and robustness.

    Conclusions:

    • The novel graph-based approach offers a robust and accurate solution for brain MRI segmentation, outperforming traditional methods.
    • The integration of tissue priors, boundary information, and adaptive inhomogeneity correction enhances segmentation quality, particularly in challenging datasets.
    • This method holds promise for improving the reliability of quantitative analysis in neuroimaging research and clinical applications.