  Three-dimensional cine-MRI is of crucial importance for assessing the cardiac
function. Features that describe the anatomy and function of cardiac structures
(e.g. Left Ventricle (LV), Right Ventricle (RV), and Myocardium(MC)) are known
to have significant diagnostic value and can be computed from 3D cine-MR
images. However, these features require precise segmentation of cardiac
structures. Among the fully automated segmentation methods, Fully Convolutional
Networks (FCN) with Skip Connections have shown robustness in medical
segmentation problems. In this study, we develop a complete pipeline for
classification of subjects with cardiac conditions based on 3D cine-MRI. For
the segmentation task, we develop a 2D FCN and introduce Parallel Paths (PP) as
a way to exploit the 3D information of the cine-MR image. For the
classification task, 125 features were extracted from the segmented structures,
describing their anatomy and function. Next, a two-stage pipeline for feature
selection using the LASSO method is developed. A subset of 20 features is
selected for classification. Each subject is classified using an ensemble of
Logistic Regression, Multi-Layer Perceptron, and Support Vector Machine
classifiers through majority voting. The Dice Coefficient for segmentation was
0.95+-0.03, 0.89+-0.13, and 0.90+-0.03 for LV, RV, and MC respectively. The
8-fold cross validation accuracy for the classification task was 95.05% and
92.77% based on ground truth and the proposed methods segmentations
respectively. The results show that the PPs increase the segmentation accuracy,
by exploiting the spatial relations. Moreover, the classification algorithm and
the features showed discriminability while keeping the sensitivity to
segmentation error as low as possible.
