  Accurate segmentation of the heart is an important step towards evaluating
cardiac function. In this paper, we present a fully automated framework for
segmentation of the left (LV) and right (RV) ventricular cavities and the
myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and
3D convolutional neural network architectures for this task. We investigate the
suitability of various state-of-the art 2D and 3D convolutional neural network
architectures, as well as slight modifications thereof, for this task.
Experiments were performed on the ACDC 2017 challenge training dataset
comprising cardiac MR images of 100 patients, where manual reference
segmentations were made available for end-diastolic (ED) and end-systolic (ES)
frames. We find that processing the images in a slice-by-slice fashion using 2D
networks is beneficial due to a relatively large slice thickness. However, the
exact network architecture only plays a minor role. We report mean Dice
coefficients of $0.950$ (LV), $0.893$ (RV), and $0.899$ (Myo), respectively
with an average evaluation time of 1.1 seconds per volume on a modern GPU.
