  Heart is one of the vital organs of human body. A minor dysfunction of heart
even for a short time interval can be fatal, therefore, efficient monitoring of
its physiological state is essential for the patients with cardiovascular
diseases. In the recent past, various computer assisted medical imaging systems
have been proposed for the segmentation of the organ of interest. However, for
the segmentation of heart using MRI, only few methods have been proposed each
with its own merits and demerits. For further advancement in this area of
research, we analyze automated heart segmentation methods for magnetic
resonance images. The analysis are based on deep learning methods that
processes a full MR scan in a slice by slice fashion to predict desired mask
for heart region. We design two encoder decoder type fully convolutional neural
network models
