  Large prospective epidemiological studies acquire cardiovascular magnetic
resonance (CMR) images for pre-symptomatic populations and follow these over
time. To support this approach, fully automatic large-scale 3D analysis is
essential. In this work, we propose a novel deep neural network using both CMR
images and patient metadata to directly predict cardiac shape parameters. The
proposed method uses the promising ability of statistical shape models to
simplify shape complexity and variability together with the advantages of
convolutional neural networks for the extraction of solid visual features. To
the best of our knowledge, this is the first work that uses such an approach
for 3D cardiac shape prediction. We validated our proposed CMR analytics method
against a reference cohort containing 500 3D shapes of the cardiac ventricles.
Our results show broadly significant agreement with the reference shapes in
terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D
Dice, and mean and Hausdorff distance.
