Left Ventricle Full Quantification via Hierarchical Quantification Network

Automatic quantitative analysis of cardiac left ventricle (LV) function is one of challenging task for heart disease diagnosis. Four different parameters, i.e. regional wall thicknesses (RWT), area of myocardium and LV cavity, LV dimensions in different direction and cardiac phase, are used for evaluating the LV function. In this paper, we implemented a novel multi-task quantification network (HQNet) to simultaneously quantify the four different parameters. The network is mainly constituted by a customized convolutional neural network named Hierarchical convolutional neural network (HCNN) which includes different pyramid-like 3D convolution blocks with different kernel sizes for efficient feature embedding; and two long-short term memory (LSTM) networks for temporal modeling. Respecting inter-task correlations, our proposed network uses multi-task constraints for phase to improve the final estimation of phase. Selu activation function is selected instead of relu, which can bring better performance of model in experiments. Experiments on MR sequences of 145 patients show that HQNet achieves high accurate estimation by means of 7-fold cross validation. The mean absolute error (MAE) of average areas, RWT, dimensions are \( 197\,{\text{mm}}^{2} ,1.51\,{\text{mm}},2.57\,{\text{mm}} \) respectively. The error rate of phase classification is 9.8%. These results indicate that the approach we proposed has a promising performance to estimate all four parameters.