Send to

Choose Destination
Magn Reson Med. 2017 Dec;78(6):2439-2448. doi: 10.1002/mrm.26631. Epub 2017 Feb 16.

Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach.

Author information

The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, California, USA.
Department Biomedical Engineering, University of California, Irvine, California, USA.
Center for Pervasive Communications and Computing, University of California, Irvine, California, USA.



This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method.


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).


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.


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.


cardiac MRI; deep learning; deformable models; right ventricle; segmentation

[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Wiley Icon for eScholarship, California Digital Library, University of California
Loading ...
Support Center