Automatic Segmentation and Cardiopathy Classification in Cardiac Mri Images Based on Deep Neural Networks

Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium to obtain guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming and labor-intensive. In this paper, we propose automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks. First, we perform object detection based on a YOLO-based network to get region of interest (ROI) from the whole sequence of diastolic and systolic MRI. Then, we obtain a pixel-wise segmentation mask automatically by feeding ROI into fully convolutional neural networks (FCN). Finally, we construct a fully connected network for cardiopathy diagnosis to decide a heart disease from the given MRI. Experimental results show that the proposed method successfully segments LV, RV and myocardium as well as achieves 90% accuracy in heart disease classification.