Abstract
The study focused on the intelligent algorithms-based segmentation of computed tomography (CT) images of patients with cardiovascular diseases (CVD) and the realization of visualization algorithms. The first step was to design a method for precise segmentation under the cylinder model based on the coronary body data of the coarse segmentation, and then the principles of different visualization algorithms were discussed. The results showed that the precise segmentation method can effectively eliminate most of the branches and calcified lesions; curved planar reformation (CPR) and straightened CPR can display the entire blood vessel on one image; and spherical CPR can display the complete coronary artery tree on an image, so that a problem with a certain blood vessel can be quickly found. In conclusion, the precise segmentation of CT images of CVD and visualization algorithm based on the cylinder model have clinical significance in the diagnosis of CVD.
Highlights
Cardiovascular and cerebrovascular diseases, such as stroke and ischemic heart disease, are the leading cause of death in China [1]
Coronary computed tomography (CT) angiography (CTA) is a rapid and low-cost diagnostic method widely used in clinic nowadays [8]
To realize curved planar reformation (CPR), the three main coronary arteries of left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) were selected for spherical fitting, and the spherical surface was obtained
Summary
Cardiovascular and cerebrovascular diseases, such as stroke and ischemic heart disease, are the leading cause of death in China [1]. It is inferred that an estimated 290 million population suffer from CVD, including 13 million with strokes, 11 million with coronary heart disease, and 245 million with hypertension. Olaf Ronneberger and others put forward a network structure called u-net [4], which is often used in image segmentation in medicine. Coronary CT angiography (CTA) is a rapid and low-cost diagnostic method widely used in clinic nowadays [8]. CTA has high sensitivity and negative predictive value, and it is one of the main noninvasive ways to check coronary artery disease [9]. The accuracy of CTA image diagnosis basically depends on Scientific Programming visualization technology. Accurate visualization can be realized according to the accurate segmentation of coronary artery. E so-called image segmentation refers to the separation of interested and relatively valuable objects when processing related images. is technical means is an extremely key technology applied to image processing [12]
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