Automatic Segmentation Based on the Cardiac Magnetic Resonance Image Using a Modified Fully Convolutional Network

Segmentation of the cardiac magnetic resonance image (MRI) is an indispensable step for evaluating the cardiac function. For the cardiac MRI segmentation, the traditional methods need to manually segment the left ventricle (LV), right ventricle (RV) and myocardium (MYO), which is time-consuming and prone to mistakes. Therefore, it is still desirable to develop automatic MRI segmentation methods. Inspired by the power of deep neural networks, we propose an image-to-image modified Fully Convolutional Network (FCN) to perform the cardiac MRI segmentation. Firstly, the MRI data is preprocessed. Then, the preprocessed data is fed into modified FCN which is designed to learn the low-layer and high-layer representations from the cardiac MRI. The model of modified FCN is directly trained using cardiac MRI and a corresponding ground truth. Finally, a novel constraint scheme is introduced by combining the region loss (Loss(R)) with the multi-class cross-entropy loss (Loss(C)) to learn the more representative features. Experimental results show that the proposed method achieves a good achievement with the manual MRI segmentation results and outperforms the previous approaches in terms of the Dice Similarity Coefficient, Hausdorff distance and sensitivity.