Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach
Open accessAbstract
Purpose: This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning‐based method. Methods: The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). Results: An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end‐diastolic volume (0.98), end‐systolic volume (0.99), and ejection fraction (0.93) were observed. Conclusion: Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers. Magn Reson Med 78:2439–2448, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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- Authors
- Avendi, M. R., Kheradvar, A., & Jafarkhani, H.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Jafarkhani, Hamid
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2017-02-16
- Volume
- 78
- Issue
- 6
- Pages
- 2439-2448
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2588773529
- Cited by
- 135
- References
- 43
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 19 · 2022: 15 · 2021: 23 · 2020: 27 · 2019: 20 · 2018: 13 · 2017: 2
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- American Heart Association (14GRNT18800013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- right ventricle segmentation, cardiac MRI, automatic segmentation, convolutional neural networks, stacked autoencoder, deformable models, end-diastolic volume, end-systolic volume, ejection fraction
- MeSH terms
- Machine Learning, Algorithms, Automation, Heart, Heart Ventricles, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Reproducibility of Results, Models, Statistical, Neural Networks, Computer
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