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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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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)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)Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network., , , , , , and . CoRR, (2019)Three-dimensional cardiovascular imaging-genetics: a mass univariate framework., , , , , , , , , and 3 other author(s). Bioinform., 34 (1): 97-103 (2018)Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach., , , , , , , , , and . CoRR, (2018)Deep learning cardiac motion analysis for human survival prediction., , , , , , , , , and 1 other author(s). CoRR, (2018)Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach., , , , , , , , , and . IEEE Trans. Med. Imaging, 38 (9): 2151-2164 (2019)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)