Angiography image blood vessel segmentation method and system

Abstract

The invention provides an angiography image blood vessel segmentation method and an angiography image blood vessel segmentation system. The angiography image blood vessel segmentation method includesthe steps of taking each pixel coordinate of a live image as a reference coordinate, selecting one pixel coordinate matched with the reference coordinate from a mask image and taking the pixel coordinate as a contrast coordinate; extracting sub images in the same size from the live image and the mask image respectively to form an image pair; inputting all the images to a dual-passage convolutionalneural network to output a first blood vessel segmentation image; extracting multiple images in different size from each reference image according to preset rules, inputting all the images in different size to a multi-scale convolutional neural network to output a second blood vessel segmentation image, and finally displaying the second blood vessel segmentation image. As the dual-passage convolutional neural network and the multi-scale convolutional neural network are utilized for achieving twice classification, blood vessels of the images displayed finally have defined boundaries and cleardetails, and during the whole process, full-automatic interaction is achieved with high efficiency.

Description

technical field [0001] The present invention relates to the technical field of image processing, and more particularly, to a method and system for segmenting blood vessels in contrast images. Background technique [0002] Today, coronary artery disease has become a major threat to human health. In the diagnosis and treatment of coronary heart disease, cardiovascular angiography images have become the gold standard for diagnosis. The computer-aided diagnosis and treatment system based on angiography images can help doctors diagnose the disease and formulate treatment plans, which has very important research significance. In the computer-aided diagnosis system of contrast images, blood vessel segmentation technology is the key technology, and it is the basis of center line extraction, radius measurement, three-dimensional reconstruction and other technologies. [0003] Contrast images have disadvantages such as low contrast, uneven distribution of contrast agents, and severe ...

AI Technical Summary

Problems solved by technology

[0006] Model-based segmentation method, the advantage of this method is that the accuracy is relatively high, and the background noise can be avoided by initially selecting the seed point, but the disadvantage is that it requires interaction

Because there are a large number of artifact structures in the background area of ​​contrast images, the most widely used segmentation algorithms are almost all semi-automatic segmentation algorithms, because these algorithms can well avoid complex background noise, but the disadvantage of these algorithms is that they require more cumbersome The interactive operation increases the workload of doctors and limits the application of computer-aided diagnosis and treatment in contrast images

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