Title: Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness,
Abstract: Cardiac left ventricle (LV) quantification is among the most clinically
important tasks for identification and diagnosis of cardiac diseases, yet still
a challenge due to the high variability of cardiac structure and the complexity
of temporal dynamics. 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 cardiac phase, is even more
challenging since the uncertain relatedness intra and inter each type of
indices may hinder the learning procedure from better convergence and
generalization. In this paper, we propose a newly-designed multitask learning
network (FullLVNet), which is constituted by a deep convolution neural network
(CNN) for expressive feature embedding of cardiac structure; two followed
parallel recurrent neural network (RNN) modules for temporal dynamic modeling;
and four linear models for the final estimation. During the final estimation,
both intra- and inter-task relatedness are modeled to enforce improvement of
generalization: 1) respecting intra-task relatedness, group lasso is applied to
each of the regression tasks for sparse and common feature selection and
consistent prediction; 2) respecting inter-task relatedness, three phase-guided
constraints are proposed to penalize violation of the temporal behavior of the
obtained LV indices. Experiments on MR sequences of 145 subjects show that
FullLVNet achieves high accurate prediction with our intra- and inter-task
relatedness, leading to MAE of 190mm$^2$, 1.41mm, 2.68mm for average areas,
RWT, dimensions and error rate of 10.4\% for the phase classification. This
endows our method a great potential in comprehensive clinical assessment of
global, regional and dynamic cardiac function.