Coronary artery calcified plaque detection method based on model migration

Abstract

The present invention discloses a coronary artery calcified plaque detection method based on model migration. The method comprises: reading a coronary artery CT image in a training set, and extractinga candidate calcified plaque in the coronary artery CT image according to a medical imaging standard; carrying out a data enhancement operation on a candidate calcified plaque image; inputting the data enhanced candidate calcified plaque image into a full convolutional network model that has been trained by the natural image for training so as to obtain a detection model; reading the coronary artery CT image in a test set, and according to the medical imaging standard, extracting a candidate calcified plaque in the coronary CT image of the test set; and taking the candidate calcified plaque image as the input of the detection model, and obtaining a detection result whether each pixel belongs to the calcified plaque in an end to end manner. According to the method of the present invention,a small amount of training samples are used to train the model for detecting the coronary artery calcified plaques according to the characteristics that the convolutional neural network model can bemigrated in different areas.

Description

technical field

[0001] The invention relates to the fields of artificial intelligence and medical image processing, in particular to a method for detecting coronary artery calcified plaques based on model migration. Background technique

[0002] Coronary heart disease has become the first cause of death in many countries. Calcified plaques in coronary arteries can cause coronary artery stenosis, myocardial hypoxia, decreased cardiac diastolic and systolic function, and coronary heart disease. Therefore, the detection of coronary artery calcified plaque plays a vital role in the prevention of coronary heart disease.

[0003] In traditional medical image processing methods, the process of calcified plaque detection still requires manual intervention, such as the selection of seed points or initialization regions.

[0004] In recent years, with the development of deep learning, convolutional neural networks have achieved unprecedented results in the field of natural image pro...

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