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: Jul 20, 2026

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic

Elco Oost1, Gerhard Koning, Milan Sonka

  • 1Leiden University Medical Center, Department of Radiology, Division of Image Processing, 2300 RC Leiden, The Netherlands.

IEEE Transactions on Medical Imaging
|September 14, 2006
PubMed
Summary

Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep-learning analysis of computed tomography coronary angiography data enables more accurate computation of the shear stress distribution than conventional analysis by experts: A head-to-head comparison with near-infrared spectroscopy-intravascular ultrasound-based modelling.

Journal of cardiovascular computed tomography·2025
Same author

Fractional flow reserve from coronary CT angiography compared with quantitative flow ratio in complex CAD.

Journal of cardiovascular computed tomography·2025
Same author

Impact of elevated lipoprotein(a) on epicardial coronary flow conductance and endoluminal atherosclerotic disease distribution.

Cardiovascular revascularization medicine : including molecular interventions·2025
Same author

Diffuseness of coronary artery disease impacts on immediate hemodynamic and predicted clinical outcomes.

Scientific reports·2025
Same author

Coronary angiography: a review of the state of the art and the evolution of angiography in cardio therapeutics.

Frontiers in cardiovascular medicine·2024
Same author

Evaluation and clinical applicability of angiography-derived assessment of coronary microcirculatory resistance: a [<sup>15</sup>O]H<sub>2</sub>O PET study.

The international journal of cardiovascular imaging·2024

This study introduces an automated method for segmenting X-ray left ventricular (LV) angiograms using active appearance models and dynamic programming. The semi-automatic approach achieved high accuracy, showing clinical relevance for LV volume calculations.

Area of Science:

  • Medical Imaging
  • Cardiovascular Imaging
  • Image Analysis

Background:

  • Accurate segmentation of left ventricular (LV) angiograms is crucial for assessing cardiac function.
  • Traditional methods often require significant manual intervention, impacting efficiency and reproducibility.
  • Active Appearance Models (AAMs) offer a framework for incorporating shape and texture information.

Purpose of the Study:

  • To develop and evaluate a novel automated segmentation approach for X-ray left ventricular (LV) angiograms.
  • To couple shape and texture information between end-diastolic (ED) and end-systolic (ES) frames using a multiview AAM.
  • To compare the performance of a semi-automatic versus a fully automatic segmentation algorithm.

Main Methods:

  • Construction of a multiview Active Appearance Model (AAM) to link shape and texture across cardiac phases.

Related Experiment Videos

Last Updated: Jul 20, 2026

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

  • Application of dynamic programming to integrate intensity and motion features, compensating for model over-constraining.
  • Evaluation of two methods: semi-automatic with manual initialization and fully automatic segmentation.

    • Main Results:

    • The semi-automatic method demonstrated high robustness and accuracy (100% ED, 99% ES success rate; ~0.7-1.5 mm border error).
    • Calculated LV volumes were accurate and unbiased with the semi-automatic approach.
    • The fully automatic method showed high potential (91% ED, 83% ES success rate; ~0.8-1.6 mm border error) despite lower robustness.

    Conclusions:

    • The semi-automatic AAM-based segmentation is highly accurate and clinically relevant for LV angiograms.
    • The fully automatic approach shows promise for reducing user interaction while maintaining reasonable performance.
    • This dynamic programming-integrated AAM approach advances automated cardiac image analysis.