Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac disease. However, it is still a task of great challenge due to the high variability of cardiac structure across subjects and the complexity of temporal dynamics of cardiac sequences. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one phase (Diastole or Systole), is even more challenging since the ambiguous correlations existing among these indices may impinge upon the convergence and generalization of the learning procedure. In this paper, we propose a deep multitask relationship learning network (DMTRL) for full LV quantification. The proposed DMTRL first obtains expressive and robust cardiac representations with a deep convolution neural network (CNN); then models the temporal dynamics of cardiac sequences effectively with two parallel recurrent neural network (RNN) modules. After that, it estimates the three types of LV indices under a Bayesian framework that is capable of learning multitask relationships automatically, and estimates the cardiac phase with a softmax classifier. The CNN representation, RNN temporal modeling, Bayesian multitask relationship learning, and softmax classifier establish an effective and integrated network which can be learned in an end-to-end manner. The obtained task covariance matrix captures the correlations existing among these indices, therefore leads to accurate estimation of LV indices and cardiac phase.
Experiments on MR sequences of 145 subjects show that DMTRL achieves high accurate prediction, with average mean absolute error of 180mm2, 1.39mm, 2.51mm for areas, RWT, dimensions and error rate of 8.2% for the phase classification. This endows our method a great potential in comprehensive clinical assessment of global, regional and dynamic cardiac function.