  Cardiac left ventricle (LV) quantification provides a tool for diagnosing
cardiac diseases. Automatic calculation of all relevant LV indices from cardiac
MR images is an intricate task due to large variations among patients and
deformation during the cardiac cycle. Typical methods are based on segmentation
of the myocardium or direct regression from MR images. To consider cardiac
motion and deformation, recurrent neural networks and spatio-temporal
convolutional neural networks (CNNs) have been proposed. We study an approach
combining state-of-the-art models and emphasizing transfer learning to account
for the small dataset provided for the LVQuan19 challenge. We compare 2D
spatial and 3D spatio-temporal CNNs for LV indices regression and cardiac phase
classification. To incorporate segmentation information, we propose an
architecture-independent segmentation-based regularization. To improve the
robustness further, we employ a search scheme that identifies the optimal
ensemble from a set of architecture variants. Evaluating on the LVQuan19
Challenge training dataset with 5-fold cross-validation, we achieve mean
absolute errors of 111 +- 76mm^2, 1.84 +- 0.9mm and 1.22 +- 0.6mm for area,
dimension and regional wall thickness regression, respectively. The error rate
for cardiac phase classification is 6.7%.
