A kind of human heart coronary artery extraction method

Abstract

The invention discloses a human heart coronary artery extraction method based on a deep learning neural network cascade model, which includes S1, converting the original CT sequence image into a picture format according to a certain window width and window level, and obtaining a CT sequence picture; S2, Segment the CT sequence pictures through the pre-trained full-image model to obtain the segmentation results of the main coronary artery and the main branch vessels; S3, based on the results of the S2 full-image segmentation, extract the foreground pixels of the blood vessels in the current layer, and calculate the current layer. The center of the root blood vessel, and then expand the patch image according to the center position of each blood vessel in the corresponding position of the adjacent layer picture, and segment the patch image through the pre-trained local patch model to obtain the segmentation result of small blood vessels; S4. Segmentation results of coronary arteries, branch vessels, and small blood vessels to obtain human heart coronary arteries. The invention has obvious advantages in the segmentation effect of small blood vessels, and the extracted small blood vessels are fused with main coronary arteries and main branch blood vessels to obtain a complete and clear heart coronary arteries extraction result.

Description

technical field [0001] The present invention relates to image segmentation, in particular to a human heart coronary artery extraction method based on a deep learning neural network cascade model. Background technique [0002] Extracting coronary arteries from CT image sequences has important clinical value and practical significance. Affected by image quality, case variability, few effective pixels in small blood vessels, and interference from other tissue structures, it is a great challenge to achieve accurate extraction of coronary arteries. Traditional extraction methods are mainly based on enhanced filtering and region growing methods. Due to the influence of complex threshold parameter adjustments, the traditional methods have poor adaptability and anti-interference ability to different cases, and there are obvious small blood vessel omissions, veins or other tissues. The problem of being mis-segmented into coronary arteries. [0003] With the increasingly extensive r...

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Problems solved by technology

[0004] However, in existing coronary artery segmentation methods, only one full-image segmentation model is used for coronary artery segmentation, such as figure 1 The proportion of small blood vessels in the CT image is shown as an example. Since the small blood vessels (the area in the rectangular box) are low-contrast tiny targets in the full-image field of view, in the existing segmentation method based on deep learning, after two Down After sampling, the small blood vessels are basically too small to be recognized, and the segmentation results often show the absence of small blood vessels.

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