Arterial blood vessel image model train method, segmentation method, device and electronic device

Abstract

The invention discloses an artery blood vessel image model training method, a segmentation method, a device and an electronic device, belonging to the technical field of digital image processing. Theartery blood vessel image model training method comprises the following steps: 1, pre-processing the acquired DSA image to construct an artery blood vessel image database; 2, labeling part of the sample images in the arterial blood vessel image library to construct a labeled sample image set; 3, constructing a convolution depth network and setting parameter of that depth network to generate an initial artery blood vessel segmentation model; 4, training an initial artery blood vessel segmentation model by using a label sample image set to generate an artery blood vessel image segmentation model; 5, further labeling the blood vessel target image obtained by using the artery blood vessel image segmentation model to segment other images except part of the sample images in the artery blood vessel image library, so as to carry out iterative training on the artery blood vessel image segmentation model. The embodiment of the invention can extract target blood vessels from DSA images with highaccuracy.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an arterial blood vessel image model training method, segmentation method, device and electronic equipment. Background technique [0002] With the promulgation of the national "White Paper on Medical Artificial Intelligence Technology and Application" and more than 80 related national bonus policies, the application of the combination of artificial intelligence and medical treatment has good development opportunities. At present, there is a large imbalance between medical resources and demand in China, which is even more serious in second- and third-tier cities. The lack of high-quality doctor resources hinders timely diagnosis and treatment of patients. [0003] In terms of analyzing medical images, most doctors spend most of their time on a single and large number of image reading tasks, which brings obstacles to doctors' in-depth and effective diagnosis and tr...

AI Technical Summary

Problems solved by technology

Subtle object segmentation differences may lead to large gaps in diagnostic results

Moreover, the arterial vessels and their structures are complex, especially the brain vessels have many fine capillaries, and the traditional methods cannot extract blood vessel targets well.

[0005] In addition, there is a certain degree of difference in the equipment, data format, and picture quality of different medical imaging imaging, which brings a certain degree of interference to image segmentation.

A lot of DSA (Digital Subtraction Angiography, digital subtraction angiography) image data are poor in the shooting device, including a lot of non-vascular information, such as skull, teeth and other noise information, and the definition of blood vessels is low, which is very difficult for the later stage. Unfavorable analysis

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