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Related Experiment Video

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Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography
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Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape

Mostafa Ghelich Oghli1, Vahab Dehlaghi1, Ali Mohammad Zadeh2

  • 1Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Journal of Medical Signals and Sensors
|October 10, 2014
PubMed
Summary

A new deformable model accurately segments the right ventricle in cardiac MRI scans, improving diagnosis of arrhythmogenic right ventricular dysplasia (ARVD) by providing reliable functional parameters.

Keywords:
Arrhythmogenic right ventricular dysplasiadeformable modelfunctional parameterssegmentationshape prior

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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Accurate assessment of cardiac right-ventricle function is crucial for diagnosing arrhythmogenic right ventricular dysplasia (ARVD).
  • Cardiac magnetic resonance imaging (MRI) is the leading imaging technique for ARVD diagnosis, enabling visualization of fatty infiltration.
  • Quantifying right-ventricle function from MRI requires precise segmentation of the ventricle in end-diastole and end-systole phases.

Purpose of the Study:

  • To develop and evaluate a robust, shape-aware deformable model for segmenting the right ventricle in short-axis cardiac MRI images.
  • To automatically calculate key right-ventricle functional parameters, including end-diastolic and end-systolic volumes and ejection fraction.
  • To quantitatively assess the accuracy and reliability of the automated segmentation method against manual delineations.

Main Methods:

  • A deformable model incorporating shape information was employed for right-ventricle segmentation in short-axis cardiac MRI slices.
  • Segmentation was performed for both end-diastole and end-systole phases, covering the ventricle from base to apex.
  • Quantitative evaluation involved linear regression analysis comparing automated parameters (RV EF, RV volume) with manual measurements, alongside sensitivity, specificity, similarity, and Jaccard indices for segmentation accuracy.

Main Results:

  • The automated method demonstrated high accuracy, with low root-mean-square error (RMSE) for Right Ventricle Ejection Fraction (RV EF ≤ 0.06) and Right Ventricle Volume (RV volume ≤ 10 mL).
  • Segmentation accuracy was further confirmed by high average similarity index (86.87%) and Jaccard index (83.85%).
  • Excellent average sensitivity (93.9%) and specificity (89.45%) indicate the method's reliability, especially where manual segmentation is challenging.

Conclusions:

  • The proposed shape prior-based deformable model offers a reliable and accurate solution for right-ventricle segmentation in cardiac MRI.
  • This automated approach provides clinically relevant functional parameters, supporting the diagnosis of ARVD and other conditions affecting right-ventricle function.
  • Future work could involve extending the method to four-dimensional processing for enhanced ventricular analysis.