Method for automatically detecting coronary artery calcified plaque of human heart

Abstract

The invention discloses a method for automatically detecting a coronary artery calcified plaque of a human heart. The method comprises the following steps of S1.adopting a deep learning neural networkto segment an original graph of a coronary artery CTA sequence in order obtain a coronary artery extraction graph of the human heart; S2.processing the coronary artery extraction graph of the human heart to generate a straightening picture of each branch vessel; S3.carrying out blood vessel segmentation on each straightening picture to obtain a straightening blood vessel graph of each branch vessel; S4.adjusting a window width and a window level, calculating a pixel value of the whole picture of each straightening blood vessel graph, if a pixel point whose pixel value is greater than 220 exists, determining that a calcified plaque exists, and screening out the straightening blood vessel graph with the calcified plaque; S5.converting the straightening blood vessel graph with the calcifiedplaque into a grey scale graph, filling the pixel point whose gray value is larger than 220 with the color, and obtaining a calcified plaque extraction result; and S6.calculating a rate of stenosis ofthe blood vessel and obtaining a quantization value. The method is effective for detection of most calcified plaques, automatic detection can be realized, and the efficiency is greatly improved.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a method for automatically detecting calcified plaques in human heart coronary arteries. Background technique [0002] Finding a safe and reliable method for detecting coronary artery disease is the main goal of clinical development in the future, so it is of great clinical value and practical significance to be able to accurately extract plaque from CTA image sequences to judge coronary artery disease. In the past ten years, the proportion of deaths caused by coronary artery disease has been increasing year by year. Therefore, it is necessary to accurately extract and quantify arterial vessels, especially the early detection and quantitative analysis of plaque. However, the early detection and quantification of plaque requires very experienced doctors to spend a long time on manual plaque segmentation and analysis. Therefore, it is necessary to propose an automatic and ra...

AI Technical Summary

Problems solved by technology

However, the early detection and quantification of plaque requires very experienced doctors to spend a long time on manual plaque segmentation and analysis. Therefore, it is necessary to propose an automatic and rapid detection method for cardiac coronary calcified plaque to improve the work efficiency of doctors.

[0003] In the direction of coronary artery calcified plaque detection, some methods have been proposed to improve the detection efficiency of calcified plaque, some of which require the participation of a large number of users; there are also some methods that mainly use threshold processing, which still have many limitations

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