  The success of deep convolutional neural networks on image classification and
recognition tasks has led to new applications in very diversified contexts,
including the field of medical imaging. In this paper we investigate and
propose neural network architectures for automated multi-class segmentation of
anatomical organs in chest radiographs, namely for lungs, clavicles and heart.
We address several open challenges including model overfitting, reducing number
of parameters and handling of severely imbalanced data in CXR by fusing recent
concepts in convolutional networks and adapting them to the segmentation
problem task in CXR. We demonstrate that our architecture combining delayed
subsampling, exponential linear units, highly restrictive regularization and a
large number of high resolution low level abstract features outperforms
state-of-the-art methods on all considered organs, as well as the human
observer on lungs and heart. The models use a multi-class configuration with
three target classes and are trained and tested on the publicly available JSRT
database, consisting of 247 X-ray images the ground-truth masks for which are
available in the SCR database. Our best performing model, trained with the loss
function based on the Dice coefficient, reached mean Jaccard overlap scores of
95.0\% for lungs, 86.8\% for clavicles and 88.2\% for heart. This architecture
outperformed the human observer results for lungs and heart.
