  Segmentation of the left ventricle (LV) from cardiac magnetic resonance
imaging (MRI) datasets is an essential step for calculation of clinical indices
such as ventricular volume and ejection fraction. In this work, we employ deep
learning algorithms combined with deformable models to develop and evaluate a
fully automatic segmentation tool for the LV from short-axis cardiac MRI
datasets. The method employs deep learning algorithms to learn the segmentation
task from the ground true data. Convolutional networks are employed to
automatically detect the LV chamber in MRI dataset. Stacked autoencoders are
utilized to infer the shape of the LV. The inferred shape is incorporated into
deformable models to improve the accuracy and robustness of the segmentation.
We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009
LV segmentation challenge and showed that it outperforms the state-of-the art
methods. Excellent agreement with the ground truth was achieved. Validation
metrics, percentage of good contours, Dice metric, average perpendicular
distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus
those of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other
methods, respectively.
