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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

515
Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
515

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Related Experiment Video

Updated: Oct 14, 2025

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

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Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography.

Shawn S Ahn1, Kevinminh Ta1, Stephanie Thorn2

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 3, 2021
PubMed
Summary

This study introduces a novel multi-frame attention network for segmenting the left ventricle in 3D echocardiography. The method significantly improves segmentation accuracy by utilizing spatiotemporal features from image sequences.

Keywords:
3D echocardiographyMulti-frame attentionSegmentation

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Last Updated: Oct 14, 2025

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Published on: October 28, 2020

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Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography
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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Deep Learning in Healthcare

Background:

  • Echocardiography is vital for cardiovascular health assessment.
  • Left ventricle segmentation is critical for quantifying ejection fraction.
  • Accurate segmentation in 3D echocardiography is challenging.

Purpose of the Study:

  • To develop an improved method for left ventricle segmentation in 3D echocardiography.
  • To leverage multi-frame attention mechanisms for enhanced segmentation performance.
  • To address the challenges of manual and semi-automated segmentation in cardiac imaging.

Main Methods:

  • Proposed a novel multi-frame attention network for 3D echocardiography segmentation.
  • The network utilizes spatiotemporal features from image sequences.
  • Evaluated on 51 in vivo porcine 3D+time echocardiography datasets.

Main Results:

  • The multi-frame attention network significantly improved left ventricle segmentation performance.
  • Utilizing correlated spatiotemporal features enhanced segmentation accuracy.
  • Outperformed standard deep learning-based medical image segmentation models.

Conclusions:

  • The proposed multi-frame attention network offers a robust solution for left ventricle segmentation in 3D echocardiography.
  • This approach enhances the accuracy of clinical measurements like ejection fraction.
  • The method shows promise for improving automated cardiac image analysis.