Identification of the left ventricle endocardial border on two-dimensional ultrasound images using the convolutional neural network Unet

Nowadays ultrasound studies of the heart, also called echocardiography (EchoCG), are widespread in modern cardiology. One of the most important steps in estimating the health of the heart is the tracking and segmentation of the left ventricular (LV) endocardial border from EchoCG, which is used for measuring the ejection fraction and assessing the regional wall motion [1]. The disadvantage of these methods is the necessity to apply image processing manually or in a semi-automatic mode, which requires special knowledge and skills. As a result, the issue of an automatic tracking and segmentation of the LV on EchoCG-images is an actual and practical problem. The capabilities of the fully trained model of the convolutional neural network Unet for automatic identification of the LV region are explored in this paper. The obtained accuracy of LV segmentation is up to 92.3%, which suggests the expediency of using Unet for automatic identification of the LV endocardial border on ultrasound images.