Automatic quantitative left ventricular analysis of cine MR images by using three-dimensional information for contour detection
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Updated: Aug 7, 2026
Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
Published on: May 24, 2021
Robert Jan M van Geuns1, Timo Baks, Ed H B M Gronenschild
1Department of Cardiology, Erasmus Medical Center, Thoraxcenter Ba 585, PO Box 2040, 3000 CA Rotterdam, The Netherlands. r.vangeuns@erasmusmc.nl
Insights
This study presents an automatic algorithm for detecting left ventricular boundaries on cardiac MRI. The method achieves highly reproducible measurements of left ventricular volume, ejection fraction, and mass without manual corrections.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Computational Cardiology
Background:
- Accurate quantification of left ventricular (LV) parameters is crucial for diagnosing and managing cardiovascular diseases.
- Traditional manual segmentation of cardiac magnetic resonance (MR) images is time-consuming and prone to interobserver variability.
- Developing automated methods for LV analysis can improve efficiency and reproducibility in clinical practice.
Purpose of the Study:
- To evaluate an automatic boundary detection algorithm for the left ventricle (LV) on MR short-axis images.
- To assess the algorithm's performance without requiring any manual corrections.
- To determine the reproducibility of LV parameters derived from the automated method.
Main Methods:
- The study included 13 patients and 12 healthy volunteers.
- LV outlines were manually traced on long-axis MR images.
- Intersection points from long-axis views were used to initiate automatic contour detection on short-axis MR images.
Main Results:
- Automatically derived LV volumes showed a high correlation (R² = 0.98) with manually derived volumes.
- Ejection fraction (EF) and LV mass correlations were also high (0.95 and 0.93, respectively).
- The algorithm significantly reduced interobserver variability for endocardial and epicardial volumes, EF, and mass.
Conclusions:
- The developed automatic boundary detection algorithm enables accurate and highly reproducible quantification of left ventricular parameters from cardiac MR images.
- The algorithm's ability to perform without manual corrections enhances its clinical utility and efficiency.
- This automated approach holds promise for improving the consistency of LV assessment in clinical settings.
Abstract:
The purpose of this study was to evaluate an automatic boundary detection algorithm of the left ventricle on magnetic resonance (MR) short-axis images with the essential restriction of no manual corrections. The study comprised 13 patients (nine men, four women) and 12 healthy volunteers (11 men, one woman), and institutional review board approval and informed consent were obtained. The outline of the left ventricle was indicated manually on horizontal and vertical long-axis MR images. The calculated intersection points with the short-axis MR images were the basis of the automatic contour detection. Automatically derived volumes correlated highly with manually derived (short axis-based) volumes (R2 = 0.98); ejection fraction (EF) and mass showed a correlation of 0.95 and 0.93, respectively. Automatic contour detection reduced interobserver variability to 0.1 mL for endocardial end-diastolic and end-systolic volumes, 1.1 mL for epicardial end-diastolic and end-systolic volumes, 0.02% for EF, and 1.1 g for mass. Thus, the algorithm enabled highly reproducible left ventricular parameters to be obtained.
