Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images.

OBJECTIVE

Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task.

METHODS

In this paper, we propose a direct volume prediction method based on the end-to-end deep convolutional neural networks. We study the end-to-end LV volume prediction method in items of the data preprocessing, network structure, and multiview 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 network structure for end-to-end LV volume 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 volume estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth ( ${\rm{EDV:R}}^{{\rm 2}}={\text{0.974}}$, ${\rm{RMSE,}}= {\text{9.6}}{\rm{,ml}}$; ${\rm{ESV:R}}^{{\rm 2}}={\text{0.976}}$, ${\rm{RMSE}}= {\text{7.1}},{\text{ml}}$; ${\rm{EF:R}}^{{\rm 2}} ={\text{0.828}}$, ${\rm{RMSE}}= {\text{4.71}}% $).

CONCLUSION

Experimental results prove that the proposed method may be useful for the LV volume 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.