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 Video

Updated: May 14, 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

A deformable cosegmentation algorithm for brain MR images.

Tong Zhang1, Yong Xia, David Dagan Feng

  • 1Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia. tong@it.usyd.edu.au

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
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

Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction.

IEEE journal of biomedical and health informatics·2025
Same author

Modality-Aware Distillation Network for Microvascular Invasion Prediction of Hepatocellar Carcinoma From MRI Images.

IEEE transactions on bio-medical engineering·2025
Same author

MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics·2025
Same author

Enhancing Medical Vision-Language Contrastive Learning via Inter-Matching Relation Modeling.

IEEE transactions on medical imaging·2025
Same author

S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching.

IEEE transactions on bio-medical engineering·2024
Same author

Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging.

IEEE journal of biomedical and health informatics·2024
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

This study introduces a new deformable cosegmentation (D-C) algorithm for brain MR image segmentation. The D-C algorithm accurately differentiates brain structures and offers robust segmentation, even with image noise.

Area of Science:

  • Computer Vision
  • Medical Imaging Analysis

Background:

  • Image segmentation is crucial for analyzing medical images.
  • Cosegmentation, a recent computer vision technique, segments common objects in multiple images.
  • Brain MR image segmentation faces challenges due to noise and anatomical variability.

Purpose of the Study:

  • To develop a novel deformable cosegmentation (D-C) algorithm for segmenting brain MR images.
  • To leverage atlas information as constraints for improved segmentation accuracy.
  • To enhance the robustness of brain MR image segmentation.

Main Methods:

  • Proposed a deformable cosegmentation (D-C) algorithm integrating atlas information.
  • Implemented the D-C algorithm using multiphase Chan-Vese model and level set techniques.

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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

Related Experiment Videos

Last Updated: May 14, 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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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

  • Evaluated performance against standard and state-of-the-art algorithms on T1-weighted brain MR images with varying noise levels.

    • Main Results:

    • The D-C algorithm demonstrated superior accuracy in differentiating major brain structures.
    • Achieved more robust segmentation results compared to existing methods.
    • Effectively utilized atlas-derived constraints for controlling segmentation.

    Conclusions:

    • The proposed D-C algorithm offers an accurate and robust solution for brain MR image segmentation.
    • Integrating anatomical priors from co-registered atlases significantly improves segmentation.
    • This method holds promise for clinical applications requiring precise brain structure delineation.