  Medical image analysis, especially segmenting a specific organ, has an
important role in developing clinical decision support systems. In cardiac
magnetic resonance (MR) imaging, segmenting the left and right ventricles helps
physicians diagnose different heart abnormalities. There are challenges for
this task, including the intensity and shape similarity between left ventricle
and other organs, inaccurate boundaries and presence of noise in most of the
images. In this paper we propose an automated method for segmenting the left
ventricle in cardiac MR images. We first automatically extract the region of
interest, and then employ it as an input of a fully convolutional network. We
train the network accurately despite the small number of left ventricle pixels
in comparison with the whole image. Thresholding on the output map of the fully
convolutional network and selection of regions based on their roundness are
performed in our proposed post-processing phase. The Dice score of our method
reaches 87.24% by applying this algorithm on the York dataset of heart images.
