Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study

Abstract

The heart’s mitral valve is the valve that separates the chambers of the heart between the left atrium and left ventricle. Heart valve disease is a fairly common heart disease, and one type of heart valve disease is mitral regurgitation, which is an abnormality of the mitral valve on the left side of the heart that causes an inability of the mitral valve to close properly. Convolutional Neural Network (CNN) is a type of deep learning that is suitable for use in image analysis. Segmentation is widely used in analyzing medical images because it can divide images into simpler ones to facilitate the analysis process by separating objects that are not analyzed into backgrounds and objects to be analyzed into foregrounds. This study builds a dataset from the data of patients with mitral regurgitation and patients who have normal hearts, and heart valve image analysis is done by segmenting the images of their mitral heart valves. Several types of CNN architecture were applied in this research, including U-Net, SegNet, V-Net, FractalNet, and ResNet architectures. The experimental results show that the best architecture is U-Net3 in terms of Pixel Accuracy (97.59%), Intersection over Union (86.98%), Mean Accuracy (93.46%), Precision (85.60%), Recall (88.39%), and Dice Coefficient (86.58%).

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