title: Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Avendi
authors.affiliation: The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, California, USA.|Department Biomedical Engineering, University of California, Irvine, California, USA.
authors.forename: Arash
authors.initials: A
authors.lastname: Kheradvar
authors.affiliation: Center for Pervasive Communications and Computing, University of California, Irvine, California, USA.
authors.forename: Hamid
authors.initials: H
authors.lastname: Jafarkhani
mesh_terms: D000465:Algorithms; D001331:Automation; D006321:Heart / Q000000981:diagnostic imaging*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results
keywords: cardiac MRI; deep learning; deformable models; right ventricle; segmentation