Related Experiment Video
Updated: May 31, 2026
3D Whole-heart Myocardial Tissue Analysis
Published on: April 12, 2017
Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency
Maartje M Nillesen1, Richard G P Lopata, H J Huisman
1Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. m.m.nillesen@cukz.umcn.nl
Insights
This study introduces maximum temporal cross-correlation (MCC) from radio-frequency (RF) data to improve automated segmentation of 3-D echocardiographic images. MCC enhances accuracy in distinguishing blood from myocardium, especially in challenging low-contrast regions.
Area of Science:
- Medical imaging
- Biomedical engineering
- Cardiology
Background:
- Automated segmentation of 3-D echocardiographic images is crucial for diagnosing heart disease.
- Challenges include poor echogenicity contrast and speckle noise, limiting accuracy.
- Prior shape knowledge is unreliable, particularly in pediatric congenital heart disease.
Purpose of the Study:
- To evaluate the effectiveness of temporal cross-correlation of radio-frequency (RF) data for automated endocardial surface segmentation.
- To assess if Maximum Temporal Cross-Correlation (MCC) can improve segmentation in low-contrast areas.
Main Methods:
- Determined Maximum Temporal Cross-Correlation (MCC) values locally from RF data using an iterative 3-D technique.
- Integrated MCC values, alone or combined with adaptive filtered RF data, as external forces in a deformable model.
- Validated segmentation against manually segmented surfaces on 3-D full volume echocardiographic images from 10 healthy children.
Main Results:
- MCC values derived from RF signals effectively distinguish blood from myocardium in regions with poor echogenicity contrast.
- Incorporating MCC significantly improved the accuracy of endocardial surface segmentation.
- The method showed promising results on 3-D echocardiographic data from healthy children.
Conclusions:
- Maximum Temporal Cross-Correlation (MCC) is a valuable parameter for automated segmentation of 3-D echocardiographic images, particularly in challenging regions.
- The integration of MCC into deformable models enhances segmentation accuracy.
- Further research on MCC across the cardiac cycle is needed to maximize its potential.
Abstract:
Clinical diagnosis of heart disease might be substantially supported by automated segmentation of the endocardial surface in three-dimensional (3-D) echographic images. Because of the poor echogenicity contrast between blood and myocardial tissue in some regions and the inherent speckle noise, automated analysis of these images is challenging. A priori knowledge on the shape of the heart cannot always be relied on, e.g., in children with congenital heart disease, segmentation should be based on the echo features solely. The objective of this study was to investigate the merit of using temporal cross-correlation of radio-frequency (RF) data for automated segmentation of 3-D echocardiographic images. Maximum temporal cross-correlation (MCC) values were determined locally from the RF-data using an iterative 3-D technique. MCC values as well as a combination of MCC values and adaptive filtered, demodulated RF-data were used as an additional, external force in a deformable model approach to segment the endocardial surface and were tested against manually segmented surfaces. Results on 3-D full volume images (Philips, iE33) of 10 healthy children demonstrate that MCC values derived from the RF signal yield a useful parameter to distinguish between blood and myocardium in regions with low echogenicity contrast and incorporation of MCC improves the segmentation results significantly. Further investigation of the MCC over the whole cardiac cycle is required to exploit the full benefit of it for automated segmentation.
