Abstract
Based on the VGG19-fully convolutional network (FCN) (VGG19-FCN) and U-Net model in the deep learning algorithms, the left ventricle in the ultrasonic cardiogram was segmented automatically. In addition, this study evaluated the value of ultrasonic cardiogram features after segmentation by the optimized algorithm in diagnosing patients with coronary heart disease (CHD) and angina pectorisody; patients with arrhythmia; and pa. In this study, 30 patients with confirmed CHD and 30 normal people without CHD from the same hospital in a certain area were selected as the research objects. Firstly, the VGG19-FCN and U-Net model algorithms were selected to automatically segment the left ventricular part of the apical four-chamber static image, which was realized through the weights of the fine-tune basic model algorithm. Subsequently, the experimental subjects were divided into a normal group and a CHD group, and the data were obtained through the ultrasonic cardiogram feature analysis of automatic segmentation by the algorithm. The differences in the ejection fraction (EF), left ventricular fractional shortening (FS), and E/A values (in early and late of the diastolic phase) of the left ventricle for patients in the two groups were compared. In addition, the ultrasonic cardiogram left ventricular segmentation results of normal people and patients with CHD were compared. A comprehensive analysis suggested that the U-Net model was more suitable for the practical application of automatic ultrasonic cardiogram segmentation. According to the analyzed data results, the global systolic function parameters (EF, FS, and E/A values) of the left ventricle for patients showed statistically obvious differences ( P < 0.05 ). In summary, deep learning algorithms can effectively improve the efficiency of ultrasonic cardiogram left ventricular segmentation, show a great role in the diagnosis of CHD patients, and provide a reliable theoretical basis and foundation research on the subsequent CHD imaging diagnosis. The comprehensive analysis showed that the U-Net model was more suitable for the practical application of echocardiographic automatic segmentation, and this study can effectively improve the efficiency of echocardiographic left ventricular segmentation, which played an important role in the diagnosis of coronary heart disease, providing a reliable theoretical basis and foundation for subsequent CHD imaging research.
Highlights
With the increasing development of the society and the gradual improvement of people’s living standards, cardiovascular disease has gradually become a high-risk disease, of which coronary heart disease (CHD) is a typical representative of cardiovascular disease
E results shown in Figure 9 suggested that the U-Net/B showed higher accuracy than U-Net/A for segmenting the left ventricle. e segmentation analysis data Dice similarity coefficient (DSC), mean pixel accuracy (MPA), and IOU of the left ventricle in the ultrasonic cardiogram had increased by 0.0294, 0.0123, and 0.0461, respectively
VGG19-fully convolutional network (FCN)/A showed a higher accuracy rate than U-Net/A, and the accuracy rate of VGG19-FCN/B was slightly higher than that of U-Net/ B. e amount of parameters required by the two network models was compared. e VGG19-FCN was modified from VGG19; so far, more parameter data were required than the U-Net model
Summary
With the increasing development of the society and the gradual improvement of people’s living standards, cardiovascular disease has gradually become a high-risk disease, of which CHD is a typical representative of cardiovascular disease. Relevant data show that the number of CHD patients in China in 2018 was approximately 11 million, and the morbidity and mortality rates have gradually increased [1]. CHD is a CVD mainly caused by the hardening of coronary arteries. It is clinically classified into five types: occult CHD, angina pectoris, myocardial infarction, ischemic heart disease (IHD), and sudden death [2, 3]. E patient’s general symptoms are typical chest pain and sudden death, and the chest pain is mostly paroxysmal colic or squeezing pain. One-third of the patients experienced sudden death with CHD for the first time. Because of the particularity of the left ventricle, it is generally selected to measure the left ventricle to reflect cardiac function in clinical practice
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