Heart Coronary Artery Segmentation and Disease Risk Warning Based on a Deep Learning Algorithm

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.