  Automated cardiac segmentation from magnetic resonance imaging datasets is an
essential step in the timely diagnosis and management of cardiac pathologies.
We propose to tackle the problem of automated left and right ventricle
segmentation through the application of a deep fully convolutional neural
network architecture. Our model is efficiently trained end-to-end in a single
learning stage from whole-image inputs and ground truths to make inference at
every pixel. To our knowledge, this is the first application of a fully
convolutional neural network architecture for pixel-wise labeling in cardiac
magnetic resonance imaging. Numerical experiments demonstrate that our model is
robust to outperform previous fully automated methods across multiple
evaluation measures on a range of cardiac datasets. Moreover, our model is fast
and can leverage commodity compute resources such as the graphics processing
unit to enable state-of-the-art cardiac segmentation at massive scales. The
models and code are available at
https://github.com/vuptran/cardiac-segmentation
