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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

543
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,...
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Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...
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Related Experiment Video

Updated: Nov 1, 2025

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

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SHAPE-REGULARIZED UNSUPERVISED LEFT VENTRICULAR MOTION NETWORK WITH SEGMENTATION CAPABILITY IN 3D+TIME

Kevinminh Ta1, Shawn S Ahn1, John C Stendahl2

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

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for analyzing cardiac ultrasound images. The shape-regularized network accurately estimates left ventricular motion and segmentation, improving cardiovascular health assessments.

Keywords:
3D+t echocardiographydeep learningmotion trackingsegmentation

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Area of Science:

  • Medical imaging
  • Cardiovascular research
  • Artificial intelligence in medicine

Background:

  • Accurate left ventricular motion estimation and segmentation are crucial for cardiovascular health evaluation.
  • Echocardiography is a cost-efficient, non-invasive imaging method but faces challenges due to low signal-to-noise ratios in ultrasound.
  • Automated analysis of echocardiograms is essential for efficient and reliable quantitative assessment.

Purpose of the Study:

  • To develop a shape-regularized convolutional neural network for motion estimation and segmentation of the left ventricle from 3D B-mode echocardiography.
  • To enable accurate dense displacement field estimation between sequential echocardiographic images.
  • To predict left ventricular segmentation masks concurrently with motion estimation.

Main Methods:

  • A shape-regularized convolutional neural network was proposed for unsupervised displacement estimation.
  • Manually traced segmentations were used to guide unsupervised learning and train segmentation prediction.
  • A flow incompressibility term was incorporated to enforce realistic cardiac motion by penalizing divergence.
  • The model was evaluated on an in vivo canine 3D+t B-mode echocardiographic dataset.

Main Results:

  • The shape regularizer demonstrably improved the motion estimation performance of the network.
  • The proposed model achieved favorable results when compared to competing methods.
  • The network successfully estimated dense displacement fields and predicted left ventricular segmentation masks.

Conclusions:

  • The developed shape-regularized convolutional neural network is effective for motion estimation and segmentation in 3D echocardiography.
  • The integration of shape regularization and flow incompressibility enhances the accuracy of cardiac motion analysis.
  • This approach shows promise for improving quantitative evaluation of cardiovascular health using ultrasound imaging.