Direct full quantification of the left ventricle via multitask regression and classification

摘要

Left ventricle (LV) quantitative indices, such as the areas of the cavity and myocardium, dimensions of the cavity, regional wall thickness, and phase classification, play a vital role in assessing the overall and local cardiac function in clinical practice. However, due to variations in the cardiac structures of different subjects and the inconvenience of manual feature extraction, it is difficult to accurately estimate left ventricle indices. To solve the above problem, this paper presents a multitask regression and classification network (MTRC-net) based on image representation to improve the accuracy of the quantified results. The network employs an autoencoder network to perform image representation, and then the representative images are fed into the multitask regression and classification network to extract the temporal and spatial features of the cardiac images, which can better help the MTRC-net to learn the potential image features, reduce the experimental errors and speed up the convergence of the model. In the multitask regression and classification network, a pseudo-3D network is used to compress the network model, and the circular hypothesis is applied to fully obtain the temporal and spatial information of the cardiac images. The proposed algorithm was verified on a dataset that includes images of 145 patients from the left ventricle quantification competition of MICCAI2018, and the results are as follows. The average absolute error of MAE in the Areas (cavity area and myocardium area) is 106 ± 88 mm2, the average absolute error of MAE in the Dims (directional dimensions of the cavity) region is 1.54 ± 1.40 mm, the average absolute error of MAE in the RWTs(regional wall thicknesses region) is 0.96 ± 0.70 mm, and the error rate of phase classification is 1.2%. The errors of each index are almost always smaller than those of the indices of the existing methods, and the experimental results illustrate the feasibility and effectiveness of the proposed method.