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

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Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018, methodology): “Given the high dimensionality of medical images, a popular approach in the literature is to analyze them by constructing statistical shape models of the heart using ﬁnite elements models or segmentation and co-registration algorithms to derive subject-speciﬁc meshes [8,9].”
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017, methodology): “Each image pair is acquired from a healthy subject using a standard imaging protocol [4], [15].”
Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases (2018, background): “In our field of interest —cardiac imaging, and especially motion and deformation analysis— current datasets with 3D sequences and coherent pathophysiological condition comprise around 1000 subjects [3]–[5].”