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Mitral Stenosis II: Clinical features and Diagnostic Tests01:23

Mitral Stenosis II: Clinical features and Diagnostic Tests

Mitral stenosis is a heart condition in which the mitral valve, which allows blood to flow from the left atrium to the left ventricle, becomes narrowed or stenotic. This narrowing hinders blood flow and leads to clinical symptoms requiring specific medical evaluations and management strategies. The following overview outlines the clinical symptoms, assessments, diagnostic findings, prevention methods, and treatments for mitral stenosis.Clinical ManifestationsDyspnea (shortness of breath): This...

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Related Experiment Video

Updated: Jun 26, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Semi-automatic detection of the left ventricular border.

Maria do Carmo dos Reis1, Adson F da Rocha, Daniel F Vasconcelos

  • 1Electrical Engineering Department, University of Brasília, DF 70910-900 Brazil. carminhamcr@yahoo.com.br

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

Two semi-automatic methods accurately detect the left ventricular border in echocardiograms. These techniques evaluate cardiovascular dynamics and identify key clinical parameters for improved cardiac assessment.

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Last Updated: Jun 26, 2026

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

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Published on: October 28, 2020

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
09:05

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Area of Science:

  • * Cardiovascular imaging and analysis
  • * Medical image processing
  • * Echocardiography and cardiac mechanics

Background:

  • * Accurate segmentation of the left ventricle (LV) is crucial for assessing cardiac function.
  • * Traditional manual boundary detection is time-consuming and subjective.
  • * Semi-automatic methods offer a potential solution for efficient and reproducible LV analysis.

Purpose of the Study:

  • * To present and compare two novel semi-automatic methods for left ventricular border detection.
  • * To evaluate the capability of these methods in assessing cardiovascular dynamics.
  • * To validate the accuracy of the proposed algorithms against expert manual delineations.

Main Methods:

  • * Development of two semi-automatic algorithms for segmenting the left ventricular border in 2D short-axis echocardiograms.
  • * Calculation of the left ventricular area variation curve over a cardiac cycle using segmented frames.
  • * Modular design of algorithms for independent module evaluation and integration.

Main Results:

  • * Successful segmentation of left ventricular borders using both semi-automatic methods.
  • * Generation of left ventricular area variation curves reflecting cardiac cycle dynamics.
  • * Validation of semi-automatic results against manual boundaries drawn by a medical specialist, demonstrating comparable accuracy.

Conclusions:

  • * The presented semi-automatic methods provide a reliable approach for left ventricular border detection in echocardiography.
  • * These techniques facilitate the evaluation of cardiovascular dynamics and the extraction of critical clinical parameters.
  • * The validated algorithms offer a promising tool for enhancing the efficiency and objectivity of cardiac function assessment.