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 detection of left ventricular borders on electron beam CT sequential cardiac images using an adaptive

A Herment1, E Mousseaux, P Dumée

  • 1U. 494 INSERM CHU PITIE, Paris, France. herment@imed.jussieu.fr

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 5, 1998
PubMed
Summary

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

[Sinus node dysfunction associated with cardiac sarcoidosis: A case report].

La Revue de medecine interne·2021
Same author

4D flow MRI: impact of region of interest size, angulation and spatial resolution on aortic flow assessment.

Physiological measurement·2021
Same author

Acute and fatal cardiotoxicity following high-dose cyclophosphamide in a patient undergoing autologous stem cell transplantation for systemic sclerosis despite satisfactory cardiopulmonary screening.

Bone marrow transplantation·2017
Same author

Cardiopulmonary assessment of patients with systemic sclerosis for hematopoietic stem cell transplantation: recommendations from the European Society for Blood and Marrow Transplantation Autoimmune Diseases Working Party and collaborating partners.

Bone marrow transplantation·2017
Same author

Hypertrophic cardiomyopathy mimicking STEMI: The role of cardiac magnetic resonance imaging in the detection of microvascular coronary dysfunction.

Diagnostic and interventional imaging·2014
Same author

Descending aorta subject-specific one-dimensional model validated against in vivo data.

Journal of biomechanics·2013

An adaptive segmentation algorithm accurately detects myocardial borders in electron beam CT images, enhancing cardiac function analysis. Its performance closely matches human observer variability for improved dynamic assessments.

Area of Science:

  • Medical Imaging
  • Cardiology
  • Image Analysis

Background:

  • Accurate detection of myocardial borders is crucial for assessing cardiac function.
  • Electron beam CT (EBCT) provides dynamic imaging sequences for cardiac analysis.
  • Manual segmentation of myocardial borders can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and evaluate an adaptive segmentation algorithm for detecting myocardial borders in EBCT image sequences.
  • To enhance the dynamic analysis of cardiac function using automated image segmentation.
  • To compare the algorithm's performance against manual segmentation by multiple observers.

Main Methods:

  • An adaptive segmentation algorithm was developed utilizing grey level and gradient distributions.

Related Experiment Videos

  • The algorithm's adaptivity is based on the statistical properties of myocardial borders within image sequences.
  • Segmentation accuracy was assessed by comparing automated contours with manual tracings from five experimentators on 416 endocardial and epicardial contours.

    • Main Results:

    • The adaptive segmentation algorithm demonstrated effective detection of myocardial borders in EBCT images.
    • Automated segmentation results showed minimal differences compared to manual tracings.
    • The observed differences between automated and manual segmentation were comparable to inter-observer variability in manual tracing.

    Conclusions:

    • The developed adaptive segmentation algorithm provides a reliable method for myocardial border detection in EBCT.
    • This automated approach enhances the efficiency and reproducibility of dynamic cardiac function analysis.
    • The algorithm's accuracy suggests its potential to improve clinical assessments of cardiac performance.