  Cardiac image segmentation is a critical process for generating personalized
models of the heart and for quantifying cardiac performance parameters. Several
convolutional neural network (CNN) architectures have been proposed to segment
the heart chambers from cardiac cine MR images. Here we propose a multi-task
learning (MTL)-based regularization framework for cardiac MR image
segmentation. The network is trained to perform the main task of semantic
segmentation, along with a simultaneous, auxiliary task of pixel-wise distance
map regression. The proposed distance map regularizer is a decoder network
added to the bottleneck layer of an existing CNN architecture, facilitating the
network to learn robust global features. The regularizer block is removed after
training, so that the original number of network parameters does not change. We
show that the proposed regularization method improves both binary and
multi-class segmentation performance over the corresponding state-of-the-art
CNN architectures on two publicly available cardiac cine MRI datasets,
obtaining average dice coefficient of 0.84$\pm$0.03 and 0.91$\pm$0.04,
respectively. Furthermore, we also demonstrate improved generalization
performance of the distance map regularized network on cross-dataset
segmentation, showing as much as 42% improvement in myocardium Dice coefficient
from 0.56$\pm$0.28 to 0.80$\pm$0.14.
