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.