  Cardiac function is of paramount importance for both prognosis and treatment
of different pathologies such as mitral regurgitation, ischemia, dyssynchrony
and myocarditis. Cardiac behavior is determined by structural and functional
features. In both cases, the analysis of medical imaging studies requires to
detect and segment the myocardium. Nowadays, magnetic resonance imaging (MRI)
is one of the most relevant and accurate non-invasive diagnostic tools for
cardiac structure and function.
  In this work we propose to use a deep learning technique to assist the
automatization of myocardial segmentation in cardiac MRI. We present several
improvements to previous works in this paper: we propose to use the Jaccard
distance as optimization objective function, we integrate a residual learning
strategy into the code, and we introduce a batch normalization layer to train
the fully convolutional neural network. Our results demonstrate that this
architecture outperforms previous approaches based on a similar network
architecture, and that provides a suitable approach for myocardial
segmentation. Our benchmark shows that the automatic myocardial segmentation
takes less than 22 seg. for a volume of 128~x~128~x~13 pixels in a 3.1 GHz
intel core i7.
