  Quantitative assessment of cardiac left ventricle (LV) morphology is
essential to assess cardiac function and improve the diagnosis of different
cardiovascular diseases. In current clinical practice, LV quantification
depends on the measurement of myocardial shape indices, which is usually
achieved by manual contouring of the endo- and epicardial. However, this
process subjected to inter and intra-observer variability, and it is a
time-consuming and tedious task. In this paper, we propose a spatio-temporal
multi-task learning approach to obtain a complete set of measurements
quantifying cardiac LV morphology, regional-wall thickness (RWT), and
additionally detecting the cardiac phase cycle (systole and diastole) for a
given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac
LVs using an encoder-decoder network and then introduce a multitask framework
to regress 11 LV indices and classify the cardiac phase, as parallel tasks
during model optimization. The proposed deep learning model is based on the 3D
spatio-temporal convolutions, which extract spatial and temporal features from
MR images. We demonstrate the efficacy of the proposed method using cine-MR
sequences of 145 subjects and comparing the performance with other
state-of-the-art quantification methods. The proposed method obtained high
prediction accuracy, with an average mean absolute error (MAE) of 129 $mm^2$,
1.23 $mm$, 1.76 $mm$, Pearson correlation coefficient (PCC) of 96.4%, 87.2%,
and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions,
and an error rate of 9.0\% for phase classification. The experimental results
highlight the robustness of the proposed method, despite varying degrees of
cardiac morphology, image appearance, and low contrast in the cardiac MR
sequences.
