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A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion.

, , , , , , and . Medical Image Anal., 26 (1): 133-145 (2015)

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Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network., , , , , , and . CoRR, (2019)Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images., , , , , , , , , and . ShapeMI@@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 258-267. Springer, (2018)Three-dimensional cardiovascular imaging-genetics: a mass univariate framework., , , , , , , , , and 3 other author(s). Bioinform., 34 (1): 97-103 (2018)MRIdb: Medical Image Management for Biobank Research., , and . J. Digital Imaging, 26 (5): 886-890 (2013)Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects., , , , , , and . FIMH, volume 9126 of Lecture Notes in Computer Science, page 3-11. Springer, (2015)Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension., , , , , , , , and . MICCAI (4), volume 11073 of Lecture Notes in Computer Science, page 595-603. Springer, (2018)3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders., , , , , , and . ISBI, page 1643-1646. IEEE, (2019)Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling., , , , , , , , , and 2 other author(s). MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 464-471. Springer, (2018)Learning-Based Quality Control for Cardiac MR Images., , , , , , , , , and 2 other author(s). IEEE Trans. Med. Imaging, 38 (5): 1127-1138 (2019)Gradient Projection Learning for Parametric Nonrigid Registration., , , and . MLMI, volume 7588 of Lecture Notes in Computer Science, page 226-233. Springer, (2012)