  Segmentation of the left ventricle and quantification of various cardiac
contractile functions is crucial for the timely diagnosis and treatment of
cardiovascular diseases. Traditionally, the two tasks have been tackled
independently. Here we propose a convolutional neural network based multi-task
learning approach to perform both tasks simultaneously, such that, the network
learns better representation of the data with improved generalization
performance. Probabilistic formulation of the problem enables learning the task
uncertainties during the training, which are used to automatically compute the
weights for the tasks. We performed a five fold cross-validation of the
myocardium segmentation obtained from the proposed multi-task network on 97
patient 4-dimensional cardiac cine-MRI datasets available through the STACOM LV
segmentation challenge against the provided gold-standard myocardium
segmentation, obtaining a Dice overlap of $0.849 \pm 0.036$ and mean surface
distance of $0.274 \pm 0.083$ mm, while simultaneously estimating the
myocardial area with mean absolute difference error of $205\pm198$ mm$^2$.
