  Objective: This paper proposes a novel approach for automatic left ventricle
(LV) quantification using convolutional neural networks (CNN).
  Methods: The general framework consists of one CNN for detecting the LV, and
another for tissue classification. Also, three new deep learning architectures
were proposed for LV quantification. These new CNNs introduce the ideas of
sparsity and depthwise separable convolution into the U-net architecture, as
well as, a residual learning strategy level-to-level. To this end, we extend
the classical U-net architecture and use the generalized Jaccard distance as
optimization objective function.
  Results: The CNNs were trained and evaluated with 140 patients from two
public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas
Project) by using a 5-fold cross-validation strategy. Our results demonstrate a
suitable accuracy for myocardial segmentation ($\sim$0.9 Dice's coefficient),
and a strong correlation with the most relevant physiological measures: 0.99
for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass,
0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac
output.
  Conclusion: Our simulation and clinical evaluation results demonstrate the
capability and merits of the proposed CNN to estimate different structural and
functional features such as LV mass and EF which are commonly used for both
diagnosis and treatment of different pathologies.
  Significance: This paper suggests a new approach for automatic LV
quantification based on deep learning where errors are comparable to the inter-
and intra-operator ranges for manual contouring. Also, this approach may have
important applications on motion quantification.
