Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution

Abstract

Cardiac Magnetic Resonance Image (MRI) segmentation plays a helpful role in diagnosing cardiac disease. It is the preliminary step to estimate the functional indices such as ejection fraction (EF) and stroke volume. In this paper, we propose an automatic method for cardiac MRI segmentation based on deep learning. A nested U-shape network with Compressed Dense Blocks (CDBlocks) called BLU-Net is introduced. The Fully Convolutional Dense Network (FCD) is employed as the backbone. Compared with common dense blocks, the CDBlock reduces the connection between the input and the inner layers, and a 1 × 1 convolution is employed to compress the generated feature maps obtained by inner layers. Dilated convolution is employed in the CDBlock to obtain a larger receptive field without losing spatial resolution and reducing the loss of feature information. To learn more semantic information, an additional up-sampling path is adopted, and it makes our model more robust. Our method is evaluated on four cardiac MRI datasets, and the DSC and the HD metrics are employed in the experiment. The experimental results show that BLU-Net outperforms FCD and also outperforms some mainstream networks.