Abstract
This paper is based on an improved three-dimensional U-net convolutional neural network deep learning algorithm for heart coronary artery segmentation for disease risk prediction, and it is practical with multiple data sets under two backgrounds without centerline and with the centerline. By using a new local feature to extract the ventricular information, and using the deep belief network to extract the features to regress the contour coordinates of the biventricular. Combining features and deep belief networks and training regression networks can not only extract high-level information but also accurately divide the left and right ventricles at a small computational cost. The performance of segmentation based on the dice coefficient compared between the two datasets. The results show that the model training effect of the centerline preprocessing is superior to the original data. The experimental results show that the best effect reaches the dice coefficient of 0.8291. In the experiment, it found that simple data expansion may be detrimental to the test data. From the training curve, it is believed that with the improvement of the quality of training data, the performance of coronary artery segmentation can be further improved, and it is of great significance to provide doctors and patients with more accurate and efficient opinions and suggestions in clinical practice to improve the quality of diagnosis and treatment. The purpose of assisting experts in real-time diagnosis and analysis achieved.
Highlights
Coronary artery disease (CAD), known as ischemic heart disease, is a disease that can cause angina, myocardial infarction, and cardiac arrest
According to the evaluation criteria of the segmentation results in this experiment, the results compared with the results of other methods and the results are analyzed. It can provide doctors with three-dimensional visualization images of coronary vessels, which is convenient for doctors to diagnose and treat coronary artery diseases
Compared with some image segmentation methods, the author chose to use the fully convolutional neural network Full Convolutional Neural Network (FCN) for image segmentation, and the natural image dataset PASCAL VOC2012 for network training and testing
Summary
Coronary artery disease (CAD), known as ischemic heart disease, is a disease that can cause angina (chest pain), myocardial infarction (heart attack), and cardiac arrest. The research focus of this paper is to explore the application of deep learning in the automatic segmentation of coronary arteries To accomplish this task, the author used two different data sets to train and evaluate the network. B. PROCESS AND PREDICTIVE ANALYSIS Broadly speaking, the contribution of Bi-DBN segmentation method includes the following points: (1) Application, it is a kind of fully automatic biventricular segmentation method, which is based on fewer assumptions and helps to evaluate cardiac function indicators; (2) Way, the biventricular segmentation task is handled as a boundary regression problem, using a very flexible boundary representation strategy, which holistically uses deep learning to obtain optimal segmentation; (3) method, using local DAISY features and boundary regression to model The highly nonlinear mapping relationship between the ventricle with variable shape and the target boundary.
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