Cardiac left ventricular volumes prediction method based on atlas location and deep learning

In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.