Ultrasonic omics depth analysis method and system based on shear wave elastography

Abstract

The invention discloses an ultrasonic omics depth analysis method and system based on shear wave elastic imaging, and the method comprises the steps: obtaining a standardized shear wave elastic imagethrough the ultrasonic medical acoustic experience for different diseases; aiming at the corresponding disease model, utilizing the shear wave image to obtain corresponding elastic ultrasonic omics data of the organ; inputting the elastic ultrasonic omics data into a trained deep learning network, adjusting the connection weight, proportion convolution and pooling layer of neurons according to theelastic ultrasonic omics data to obtain adjusted elastic ultrasonic omics data, and obtaining the classification score of each lesion through deep learning; based on patient clinical information andexamination indexes, results are subjected to deep learning elastic classification scoring, and a deep analysis decision system is constructed through machine learning analysis. According to the invention, the repeatability of boundary data acquisition and the adaptability of image analysis can be improved, and a deep analysis decision system is constructed to improve the accuracy of an auxiliaryanalysis result.

Description

technical field [0001] The invention relates to the field of artificial intelligence-assisted decision-making, in particular to a method and system for in-depth analysis based on ultrasonic omics of shear wave elastography. Background technique [0002] Shear wave imaging technology generates transverse shear waves to the tissue particle tissue in the focused part by emitting continuously focused acoustic radiation force pulses, then collects shear waves through an ultra-high-speed imaging system, and obtains real-time elastic images and images with color coding technology. Young's modulus value. Color-coded images represent harder tissues in red and softer tissues in blue. The value of Young's modulus is given by the formula E=3ρc 2 (E is Young's modulus, c is the shear wave propagation velocity, ρ is the tissue density) calculated, the unit is kPa, reflecting the tissue hardness. Malignant tumor tissue is usually harder than benign lesions due to rapid cell proliferatio...

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Problems solved by technology

[0003] At present, the shear wave imaging technology can obtain the maximum, minimum, average and standard deviation Young's modulus values ​​of the region by selecting a region of interest (ROI), but there are the following disadvantages: (1) from a single It is one-sided to make a diagnosis of the hardness value, and the range of hardness values ​​between different lesions may overlap, which makes diagnosis difficult; (2) Since the minimum value, average value, and standard deviation all have a great influence on the ROI area, for To ensure the repeatability of ROI, a smaller ROI is usually selected, but this reduces the utilization rate of shear wave elastic images

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