title: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.
abstract: Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are used to infer the LV shape. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81 mm and 0.86, versus those of 79.2-95.62%, 0.87-0.9, 1.76-2.97 mm and 0.67-0.78, obtained by other methods, respectively.
authors.affiliation: Center for Pervasive Communications and Computing, University of California, Irvine, USA; The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, USA. Electronic address: m.avendi@uci.edu.
authors.forename: M R
authors.initials: MR
authors.lastname: Avendi
authors.affiliation: The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, USA. Electronic address: arashkh@uci.edu.
authors.forename: Arash
authors.initials: A
authors.lastname: Kheradvar
authors.affiliation: Center for Pervasive Communications and Computing, University of California, Irvine, USA. Electronic address: hamidj@uci.edu.
authors.forename: Hamid
authors.initials: H
authors.lastname: Jafarkhani
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*
keywords: Caridac MRI; Deep learning; Deformable models; LV segmentation; Machine learning