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: Jun 26, 2026

3D Whole-heart Myocardial Tissue Analysis
06:53

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

Real-time myocardial segmentation using coupled active geometric functions.

Qi Duan1, Andrew F Laine, Vinay M Pai

  • 1Department of Biomedical Engineering, Columbia University, New York 10027, USA. qd2002@columbia.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
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

Inotodiol ameliorates oxidative stress and apoptosis by regulating PI3K/Akt/GSK-3β signaling pathways in diabetic nephropathy.

Renal failure·2026
Same author

Clinical feasibility of contrast-enhanced 7 T MPRAGE with universal pulses for intracranial tumor imaging: a preliminary study.

Neuroradiology·2026
Same author

Neurovascular Coupling Dysfunction: An Early Indicator of Brain Injury in Asymptomatic Moyamoya Disease.

Current neuropharmacology·2026
Same author

Reinforcement Learning-Based Predefined-Performance Control for Nonlinear Switched Interconnected Systems.

IEEE transactions on cybernetics·2026
Same author

Ensemble-learning-assisted exhaled gas disease analysis based on in-situ construction of MOF-derived MO<sub>x</sub>/GaN heterojunction sensor arrays.

Microsystems & nanoengineering·2026
Same author

Exploring the Fecal Microbiome Dysbiosis and Its Plasma Metabolome Determinants in Advanced Parkinson's Disease With Motor Complications.

CNS neuroscience & therapeutics·2026
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 novel automated real-time myocardial segmentation framework for cardiac MRI. The method achieves efficient and accurate segmentation, enabling faster clinical workflows for cardiac functional imaging.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Cardiovascular Research

Background:

  • Accurate myocardial segmentation is crucial for quantitative analysis of cardiac functional images.
  • Advancements in 3D and 4D cardiac imaging provide rich dynamic information but pose computational challenges for traditional algorithms.
  • Real-time analysis is needed to improve efficiency and clinical workflow.

Purpose of the Study:

  • To develop and validate an automated real-time myocardial segmentation framework.
  • To address the computational challenges of analyzing large 3D/4D cardiac image datasets.
  • To enable true real-time online segmentation for clinical applications.

Main Methods:

  • Proposed an automated real-time myocardial segmentation framework utilizing coupled Active Geometric Functions.

More Related Videos

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Related Experiment Videos

Last Updated: Jun 26, 2026

3D Whole-heart Myocardial Tissue Analysis
06:53

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

  • Tested the framework on 414 frames of real-time cardiac MR imaging (Phase Train Imaging) data.
  • Implemented the method in Matlab(c) for efficient processing.

    • Main Results:

    • The framework achieved myocardial segmentation with an average temporal resolution of 2 ms.
    • The method demonstrated high performance, validated both visually and quantitatively.
    • Processing time was less than 1.2 ms per cardiac phase, enabling real-time segmentation.

    Conclusions:

    • The developed framework provides efficient and accurate real-time myocardial segmentation.
    • This advancement can significantly enhance the clinical workflow for cardiac functional image analysis.
    • The method supports the clinical utility of advanced cardiac imaging techniques.