  Left ventricular (LV) volumes estimation is a critical procedure for cardiac
disease diagnosis. The objective of this paper is to address direct LV volumes
prediction task. Methods: In this paper, we propose a direct volumes prediction
method based on the end-to-end deep convolutional neural networks (CNN). We
study the end-to-end LV volumes prediction method in items of the data
preprocessing, networks structure, and multi-views fusion strategy. The main
contributions of this paper are the following aspects. First, we propose a new
data preprocessing method on cardiac magnetic resonance (CMR). Second, we
propose a new networks structure for end-to-end LV volumes estimation. Third,
we explore the representational capacity of different slices, and propose a
fusion strategy to improve the prediction accuracy. Results: The evaluation
results show that the proposed method outperforms other state-of-the-art LV
volumes estimation methods on the open accessible benchmark datasets. The
clinical indexes derived from the predicted volumes agree well with the ground
truth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE
=4.71%). Conclusion: Experimental results prove that the proposed method may be
useful for LV volumes prediction task. Significance: The proposed method not
only has application potential for cardiac diseases screening for large-scale
CMR data, but also can be extended to other medical image research fields
