  Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous
task that can vary depending on the patient, the Magnetic Resonance Images
(MRI) cuts and the experts. Still today, we consider manual delineation done by
experts as being the ground truth for cardiac diagnosticians. Thus, we are
reviewing the paper - written by Avendi and al. - who presents a combined
approach with Convolutional Neural Networks, Stacked Auto-Encoders and
Deformable Models, to try and automate the segmentation while performing more
accurately. Furthermore, we have implemented parts of the paper (around three
quarts) and experimented both the original method and slightly modified
versions when changing the architecture and the parameters.
