Ultrasonic elastography liver fibrosis evaluation method based on artificial intelligence

Abstract

The invention relates to the technical field of elastography, in particular to an ultrasonic elastography liver fibrosis evaluation method based on artificial intelligence. The method comprises the following steps: analyzing the gray level and gradient distribution of pixel points in a gray level image of each image, and obtaining the fat deposition degree of the liver of a patient in each image; the fat display degree of the liver of the patient in each parameter combination is obtained, and a high-display parameter combination is screened out; according to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastic image, obtaining a corresponding correction parameter combination; according to gradient features of edge pixel points in different elastic modulus range areas, image resolution corresponding to the correction parameter combination is obtained; obtaining an optimal parameter combination, and obtaining the liver fibrosis risk degree of the patient according to the elastic modulus distribution in the shear wave elastic image under the optimal parameter combination. According to the method, the optimal parameter combination is obtained to carry out elastography, so that the accuracy of liver fibrosis risk assessment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of elastic imaging, in particular to an ultrasonic elastic imaging liver fibrosis evaluation method based on artificial intelligence. BACKGROUND

[0002] Ultrasonic elastic imaging can detect the elasticity and stiffness of the liver, and the hardness of the liver tissue increases with the increase of the degree of fibrosis, so by analyzing the ultrasonic elastic imaging, the degree of fibrosis of the liver can be reflected.

[0003] In the prior art, shear wave elastography based on fixed parameters is used for elastic imaging of the liver of different patients, but in clinical practice, fatty liver and liver fibrosis can both cause changes in the elasticity of the liver, and if there is a large amount of lipid droplet deposition on the surface of the liver, it will cause changes in the mechanical properties of the liver tissue, and the shear wave elastography result may be inaccurate due to the fact that the distribution of fat masks local or early fibrosis changes, resulting in inaccurate shear wave elastography and errors in liver fibrosis evaluation. SUMMARY

[0004] In order to solve the technical problem of poor liver fibrosis evaluation when fixed parameters are used for shear wave elastography due to changes in the elasticity of the liver caused by fatty liver and liver fibrosis in clinical practice, the purpose of the present application is to provide an ultrasonic elastic imaging liver fibrosis evaluation method based on artificial intelligence, and the technical solution adopted is as follows: The present application provides an ultrasonic elastic imaging liver fibrosis evaluation method based on artificial intelligence, which comprises: Obtaining B-mode images of normal liver and patient liver, and shear wave elastography images under different preset imaging depths and probe frequencies to form a parameter combination, wherein the shear wave elastography images contain the elastic modulus of each pixel point; For the B-mode images or shear wave elastography images, the fat deposition degree of the patient liver in each image is obtained according to the gray scale and gradient distribution of the pixel points in the gray scale images between the normal liver and the patient liver, and the fat display degree of the patient liver under each parameter combination is obtained according to the difference in fat deposition degree and the difference in gray scale distribution between the B-mode images of the patient liver and the shear wave elastography images under each parameter combination, and the high display parameter combination is screened out; According to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastography image, the corresponding modified parameter combination is obtained, a plurality of preset elastic modulus range regions of the shear wave elastography image under each modified parameter combination are obtained, the image resolution of the corresponding modified parameter combination is obtained according to the gradient characteristics of the edge pixel points in different elastic modulus range regions, and the optimal parameter combination is obtained according to the image resolution and the fat display degree of different modified parameter combinations. According to the elastic modulus distribution in the shear wave elastography image under the optimal parameter combination, the liver fibrosis risk degree of the patient is obtained.

[0005] Further, the method for obtaining the fat deposition degree comprises: For the gray-scale image of each image, a gray-scale histogram is constructed by the number of pixel points at different gray-scale values, and the average slope level between different gray-scale values and the previous gray-scale value in the gray-scale histogram is obtained as the average slope level of each image; The image edge of each gray-scale image is obtained, and the gradient average of all edge pixel points on all image edges is obtained as the average gradient feature; According to the difference between the average slope level of each image of the patient's liver and the normal liver, the difference of the average gradient feature, and the gray-scale average of all pixel points on the corresponding gray-scale image of the patient's liver, the fat deposition degree of the patient's liver in each image is obtained. The difference of the average gradient feature is negatively correlated with the fat deposition degree, and the difference of the average slope level and the gray-scale average of all pixel points are positively correlated with the fat deposition degree.

[0006] Further, the method for obtaining the fat display degree comprises: The corresponding gray-scale images between the B-mode ultrasound image of the patient's liver and the shear wave elastography image under each parameter combination are binarized, and the number of pixel points with consistent results after binarization is counted; According to the difference between the fat deposition degree of the ultrasound gray-scale image and the shear wave elastography gray-scale image, the fat display degree under each parameter combination is obtained. The number of pixel points with consistent results is positively correlated with the fat display degree, and the difference is negatively correlated with the fat display degree.

[0007] Further, the method for obtaining the high display parameter combination comprises: If the fat display degree of the patient's liver under any parameter combination is greater than or equal to the preset display threshold, the corresponding parameter combination is taken as the high display parameter combination.

[0008] Further, the method for obtaining the corresponding modified parameter combination comprises: The imaging depth is gain-adjusted according to the fat deposition degree of the shear wave elastography image under each high display parameter combination, and an imaging modified depth is obtained. The imaging modified depth replaces the imaging depth in the high display parameter combination to form the corresponding modified parameter combination.

[0009] Further, the method for obtaining the imaging modified depth comprises: The sum of the positive integer 1 and the fat deposition degree of the shear wave elastography image under each high display parameter combination is obtained as the fat deposition weight; The product between the fat deposition weight and the fat deposition degree is obtained as the imaging modified depth.

[0010] Further, the image resolution acquisition method comprises: For each correction parameter combination, the average of the gradient amplitude of all edge pixel points in each elastic modulus range region is obtained as the overall gradient amplitude; According to the maximum value of the overall gradient amplitude in all elastic modulus range regions and the amplitude difference between the maximum value of the overall gradient amplitude and the overall gradient amplitude in different elastic modulus range regions, the image resolution corresponding to the correction parameter combination is obtained, the maximum value of the overall gradient amplitude is positively correlated with the image resolution, and the amplitude difference is negatively correlated with the image resolution.

[0011] Further, the optimal parameter combination acquisition method comprises: An element in the coordinate system is formed by the fat display degree and the image resolution of each correction parameter combination, the element in the upper right corner of the coordinate system is selected, and the corresponding correction parameter combination is taken as the optimal parameter combination.

[0012] Further, the liver fibrosis degree acquisition method comprises: For the shear wave elastography image under the optimal parameter combination, target pixel points with an elastic modulus greater than a preset modulus threshold value are selected, and the ratio between the number of all target pixel points and the total number of pixel points is taken as the liver fibrosis risk degree.

[0013] Further, the preset display threshold value is 0.88.

[0014] The present application has the following beneficial effects: The present application obtains the fat deposition degree of the patient's liver in each image according to the gray scale and gradient distribution of the pixel points in the gray scale image between the normal liver and the patient's liver, more comprehensively quantifies the degree of fat deposition, obtains the fat display degree of the patient's liver under each parameter combination according to the difference in fat deposition degree and the difference in gray scale distribution between the B-ultrasound image of the patient's liver and the shear wave elastography image under each parameter combination, screens out high display parameter combinations, and helps to understand the display ability of the shear wave elastography image under the parameter combination to fat deposition, obtains the corresponding correction parameter combination according to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastography image, obtains the image resolution of the corresponding correction parameter combination according to the gradient characteristics of the edge pixel points in different elastic modulus range regions, reflects the imaging quality and the ability to distinguish the boundary between fat and fibrosis, obtains the optimal parameter combination, obtains the liver fibrosis risk degree of the patient according to the elastic modulus distribution in the shear wave elastography image under the optimal parameter combination, improves the imaging quality, and optimizes the evaluation of the fibrosis risk degree. The present application improves the accuracy of liver fibrosis risk evaluation by obtaining the optimal parameter combination for elastic imaging. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings required to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 A flow chart of a liver fibrosis evaluation method based on artificial intelligence provided by an embodiment of the present application; Figure 2 A flow chart of a fat deposition degree acquisition method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the specific embodiments, structures, features and effects of the liver fibrosis evaluation method based on artificial intelligence provided by the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0019] The specific scheme of the liver fibrosis evaluation method based on artificial intelligence provided by the present application is specifically described below with reference to the drawings.

[0020] Please refer to Figure 1 which shows a flow chart of a liver fibrosis evaluation method based on artificial intelligence provided by an embodiment of the present application, and the specific method comprises: Step S1: acquiring B-mode images of normal liver and patient liver, and shear wave elastography images under different preset imaging depth and probe frequency parameter combinations, the shear wave elastography images containing the elastic modulus of each pixel point.

[0021] In the embodiment of the present application, considering that ignoring clinically fatty liver and liver fibrosis will lead to the change of elasticity of the liver, the fixed parameters for elastic imaging will cause errors in the elastic evaluation, in order to reduce the influence of fat on the shear wave elasticity image, it is necessary to analyze the fat deposition by combining the liver B-ultrasound image; first, the conventional ultrasound imaging ultrasonic waves are emitted into the body through the probe, and the B-ultrasound image is formed according to the acoustic impedance difference of the tissue. The image can show the morphology, structure and tissue interface of the tissue.

[0022] The radiation force of the focused ultrasonic beam in the medical ultrasonic power range is used to generate shear waves in the local area of the biological viscous tissue; in the ultrasonic elastic imaging, the imaging depth determines the propagation distance of the acoustic wave, the deeper the imaging depth, the stronger the corresponding acoustic wave penetration, and the deeper the imaging of the organs and tissues; the probe frequency of the acoustic wave determines the probe frequency of the shear wave elastic imaging, the higher the frequency, the stronger the probe frequency, and the larger the image resolution; therefore, in order to improve the imaging quality of the shear wave elasticity, the imaging penetration and the probe frequency need to be considered.

[0023] In the embodiment of the present application, the probe frequency range is 1-15MHz, the probe frequency interval is 1MHz, the imaging depth range is 2-6cm, the imaging depth interval is 0.5cm, the parameters are combined from small to large, and the shear wave elasticity image under different preset imaging depth and probe frequency combination is obtained, wherein, in the ultrasonic elastic imaging, the elastic modulus of the pixel point on the elastic image can be displayed by ROI statistical value to reflect the hardness characteristics of the tissue, the larger the elastic modulus, the greater the hardness, and the specific means is the technical means familiar to those skilled in the art, which is not described here.

[0024] Step S2: for the B-ultrasound image or the shear wave elasticity image, the fat deposition degree of the patient's liver in each image is obtained according to the gray distribution of the pixel points in the gray image between the normal liver and the patient's liver; the fat display degree of the patient's liver in each parameter combination is obtained according to the difference of the fat deposition degree of the gray image between the B-ultrasound image of the patient's liver and the shear wave elasticity image under each parameter combination, and the gray distribution difference, and the high display parameter combination is screened out.

[0025] The greater the gray value of the pixel, the more extensive the fat distribution, and the more the number of pixels with greater gray value compared with the normal liver; the worse the morphological characteristics of the liver, the more blurred the outline, and the smaller the gradient, the more likely there is more fat distribution; compared with the normal liver and the patient's liver, the fat distribution of the liver in the image is quantified by analyzing the gray and gradient of the pixel points in the gray image of each image; for the B-ultrasound image or the shear wave elasticity image, the fat deposition degree of the patient's liver in each image is obtained according to the gray distribution of the pixel points in the gray image between the normal liver and the patient's liver.

[0026] Preferably, in an embodiment of the present application, the method for obtaining the fat deposition degree please refer to Figure 2 , which shows a flow chart of a method for obtaining the fat deposition degree, comprising: Step S201: For each gray image of the image, a gray histogram is constructed with the number of pixels at different gray values, and the average slope between different gray values and the previous gray value in the gray histogram is obtained as the average slope level of each image.

[0027] It should be noted that the slope can reflect the trend of the number of pixels, the greater the slope, the greater the difference in the number of pixels between each gray value and the previous gray value, the more pixels with greater gray values, indicating more fat distribution; the method for obtaining the slope is the ratio of the difference in the number of pixels between each gray value and the previous gray value and the corresponding gray value difference, as the slope between adjacent gray values. The specific means are well known to those skilled in the art and will not be described here.

[0028] Step S202: Obtain the image edge of each gray image, and obtain the gradient average of all edge pixels on all image edges as the average gradient feature.

[0029] The deposition of fat will cause changes in the tissue contour of the liver, resulting in a worse contour clarity, so by analyzing the gradient feature of the image edge, the edge clarity is reflected, the greater the gradient, the clearer the edge; in the embodiment of the present application, the image edge of each gray image can be obtained by the existing CANNY edge detection or Sobel algorithm, and the specific means are well known to those skilled in the art and will not be described here.

[0030] Step S203: According to the difference between the average slope level of each image of the patient's liver and the normal liver, the difference in the average gradient feature, and the gray average of all pixels on the corresponding gray image of the patient's liver, the fat deposition degree of the patient's liver at each image is obtained, the difference in the average gradient feature is negatively correlated with the fat deposition degree, and the difference in the average slope level and the gray average of all pixels are positively correlated with the fat deposition degree.

[0031] It should be noted that the difference of the average gradient feature reflects the gradient change deviation of the patient's liver relative to the normal liver, the greater the gradient change deviation, the greater the edge definition change, and therefore the greater the difference of the average gradient feature, the greater the edge definition of the patient's liver, and the smaller the degree of fat deposition; the greater the average slope level, the greater the number of pixel points gradually increases with the gray value, the greater the difference of the average slope level, the greater the number of pixel points with a larger gray value on the image corresponding to the patient's liver, the greater the density of fat, and the greater the degree of fat deposition; the greater the average gray value of the pixel points in the gray image corresponding to the patient's liver, the greater the fat that appears, the greater the gray value, and the greater the degree of fat deposition, so the difference of the average gradient feature is negatively correlated with the degree of fat deposition, and the difference of the average slope level and the average gray value of all pixel points are positively correlated with the degree of fat deposition.

[0032] In an embodiment of the present application, the product of the difference of the average slope level and the average gray value of all pixel points in the gray image corresponding to the patient's liver is calculated as the liver fat density of the patient's liver; the ratio of the liver fat density of the patient to the difference of the average gradient feature is obtained and normalized as the degree of fat deposition of the patient's liver in each image. Therefore, based on the above basic mathematical operation, the correlation between the difference of the average gradient feature, the difference of the average slope level, and the average gray value of all pixel points and the degree of fat deposition is established, that is, the greater the difference of the average gradient feature, the smaller the difference of the average slope level, and the smaller the average gray value of all pixel points, the less the fat distribution, and the smaller the degree of liver deposition.

[0033] It should be noted that in the embodiments of the present application, the ratio of the liver fat density of the patient to the difference of the average gradient feature is normalized to the range of [0, 1] by using existing linear normalization or normalization function, and the specific means is a technology known to those skilled in the art, which is not described here.

[0034] The presence of fat can cause the image color to deepen, and the greater the degree of fat deposition, the greater the difference in image color distribution, the greater the difference in fat deposition, the smaller the credibility of image texture similarity, and the smaller the fat display degree of the parameter combination, so the fat display degree of the patient's liver in each parameter combination is obtained according to the difference in fat deposition and the difference in gray distribution between the B-mode image of the patient's liver and the shear wave elastography image under each parameter combination, and the high display parameter combination is screened out.

[0035] Preferably, in an embodiment of the present application, the method for obtaining the fat display degree comprises: It should be noted that the B-ultrasound image reflects the echo intensity of the tissue, and the shear wave elasticity image reflects the hardness of the tissue, but in the case of liver cirrhosis, the change of the echo intensity and the hardness of the tissue is positively correlated, and the display proportion is consistent relative to the overall range; The corresponding gray images between the B-ultrasound image of the liver of the patient and the shear wave elasticity image under each parameter combination are binarized, and the number of pixel points with consistent results after binarization is counted; According to the number of pixel points with consistent results and the difference in fat deposition degree between the ultrasound gray image and the shear wave elasticity gray image, the fat display degree under each parameter combination is obtained, and the number of pixel points with consistent results is positively correlated with the fat display degree, and the difference is negatively correlated with the fat display degree.

[0036] In an embodiment of the present application, the difference between the fat deposition degree of the ultrasound gray image and the shear wave elasticity gray image or the ratio of the fat deposition degree of the ultrasound gray image and the shear wave elasticity gray image and the difference between the ratio and a positive integer 1 are calculated and normalized as the first display coefficient; the larger or smaller the ratio is, the greater the difference in fat deposition degree between the images is, the greater the difference between the ratio and the positive integer 1 is, the smaller the fat distribution similarity is, the more incomplete the shear wave elasticity image is in displaying fat, and the smaller the fat display degree is; the product between the superposition similarity and the first display coefficient is obtained as the fat display degree of each parameter combination; therefore, the correlation between the number of pixel points with consistent results, the difference and the fat display degree is constructed based on the above basic mathematical operation, that is, the smaller the number of pixel points with consistent results is, the greater the difference is, the more inconsistent the fat distribution is, and the smaller the fat display degree is.

[0037] Preferably, in an embodiment of the present application, the method for obtaining the high display parameter combination comprises: If the fat display degree of the liver of the patient under any parameter combination is greater than or equal to a preset display threshold, the corresponding parameter combination is taken as the high display parameter combination.

[0038] It should be noted that in an embodiment of the present application, the size of the preset display threshold is 0.88, and in other embodiments of the present application, the size of the preset display threshold can be set according to specific conditions, which is not limited or described here.

[0039] Step S3: According to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elasticity image, a corresponding modified parameter combination is obtained; a plurality of preset elastic modulus range regions of the shear wave elasticity image under each modified parameter combination are obtained, the image resolution of the corresponding modified parameter combination is obtained according to the gradient characteristics of the edge pixel points in different elastic modulus range regions; and the optimal parameter combination is obtained according to the image resolution and the fat display degree of different modified parameter combinations.

[0040] The deeper the imaging depth, the more imaging of deeper liver tissue can be provided, the fat deposition degree reflects the severity of the interference of fat on the ultrasonic signal, the greater the fat deposition degree, the more serious the fat interference, and the more deeper imaging depth is needed to penetrate the fat layer; therefore, according to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastography image, a corresponding modified parameter combination is obtained.

[0041] Preferably, in an embodiment of the present application, the method for obtaining the corresponding modified parameter combination comprises: According to the fat deposition degree of the shear wave elastography image under each high display parameter combination, the corresponding imaging depth is gain adjusted to obtain an imaging modified depth; In an embodiment of the present application, the sum of the positive integer 1 and the fat deposition degree of the shear wave elastography image under each high display parameter combination is obtained as a fat deposition weight; the product between the fat deposition weight and the fat deposition degree is obtained as the imaging modified depth; therefore, the correlation between the fat deposition degree, the imaging depth and the imaging modified depth is constructed based on the above basic mathematical operation, that is, the greater the fat deposition degree and the imaging depth, the more serious the influence of fat deposition, the more gain adjustment is needed for the imaging depth, and the greater the imaging modified depth, the deeper the penetration.

[0042] Based on this, the imaging modified depth is replaced with the imaging depth in the high display parameter combination to constitute the corresponding modified parameter combination, which can penetrate the fat layer and optimize the imaging quality.

[0043] In order to more specifically analyze different imaging areas, a plurality of preset elastic modulus range areas of the shear wave elastography image under each modified parameter combination are obtained; it should be noted that according to the elastic modulus performance corresponding to the imaging color in clinic, the elastic modulus range reflecting the same color is constituted as an area, such as the elastic modulus range of deep blue being 0-5, and the corresponding area in the range of 0-5 is taken as a preset elastic modulus range area; and then a plurality of preset elastic modulus range areas are obtained according to the existing clinical professional knowledge.

[0044] The higher the resolution of the image, the clearer the edge of different color areas is displayed, and the more intense the corresponding gradient change is; and the lower the resolution of the image, the more blurred the edge of different color areas is, and the slower the gradient change is, so that the probe frequency condition of the image is quantified by analyzing the edge pixel gradient characteristics of different elastic modulus range areas; the image resolution of the corresponding modified parameter combination is obtained according to the gradient characteristics of the edge pixel points in different elastic modulus range areas.

[0045] Preferably, in an embodiment of the present application, the method for obtaining the image resolution comprises: For each correction parameter combination, the average of the gradient amplitude of all edge pixel points in each elastic modulus range area is obtained as the overall gradient amplitude; According to the maximum value of the overall gradient amplitude in all elastic modulus range areas and the amplitude difference between the maximum value of the overall gradient amplitude and the overall gradient amplitude of different elastic modulus range areas, the image resolution corresponding to the correction parameter combination is obtained, the maximum value of the overall gradient amplitude is positively correlated with the image resolution, and the amplitude difference is negatively correlated with the image resolution.

[0046] It should be noted that the greater the maximum value of the overall gradient amplitude, the more intense the edge gradient change of the existing area, and the greater the edge display definition; the greater the amplitude difference between the maximum value of the overall gradient amplitude and the overall gradient amplitude of different elastic modulus range areas, the smaller the overall gradient amplitude of the elastic modulus range area relative to the maximum value, the smaller the image resolution of other ranges, the smaller the overall edge gradient change of the image, and the smaller the resolution, therefore, the maximum value of the overall gradient amplitude is positively correlated with the image resolution, and the amplitude difference is negatively correlated with the image resolution. In an embodiment of the present application, for each correction parameter combination, the average of the amplitude difference between the maximum value of the overall gradient amplitude and the overall gradient amplitude of different elastic modulus range areas is obtained, reflecting the change of the overall edge gradient amplitude, the greater the average of the amplitude difference, the smaller the gradient amplitude change, and the smaller the resolution; the ratio of the maximum value of the overall gradient amplitude and the average of the amplitude difference is obtained as the image resolution; therefore, the correlation between the maximum value of the overall gradient amplitude, the amplitude difference and the image resolution is constructed based on the above basic mathematical operation, that is, the greater the maximum value of the overall gradient amplitude, the smaller the amplitude difference, the greater the overall gradient amplitude, and the greater the image resolution.

[0047] The greater the fat display degree, the greater the penetration ability, and the more the imaging of deeper liver tissue is provided; the greater the image resolution, the clearer the detail display, and the higher the imaging quality; according to the image resolution and the fat display degree of different correction parameter combinations, the optimal parameter combination is obtained.

[0048] Preferably, the clearer the edge of the imaging, the greater the image resolution, the higher the probe frequency of the image, the greater the fat display degree, and the stronger the penetration of the parameter combination to fat, in an embodiment of the present application, the method for obtaining the optimal parameter combination comprises: An element in a coordinate system is formed by the fat display degree and the image resolution of each correction parameter combination, the element in the upper right corner of the coordinate system is selected, and the corresponding correction parameter combination is taken as the optimal parameter combination.

[0049] It should be noted that the method for obtaining the fat display degree of the parameter combination is to obtain the shear wave elastography image of the parameter combination for analysis, and the fat display degree of each parameter combination is obtained according to the method in step S2.

[0050] Step S4: According to the elastic modulus distribution in the shear wave elastography image under the optimal parameter combination, the liver fibrosis risk degree of the patient is obtained.

[0051] Since liver fibrosis will cause the hardness of liver cells to increase and the elasticity to decrease, the corresponding color area is darker, therefore, according to the color distribution characteristics in the shear wave elastography image of the optimal parameter combination, the liver fibrosis risk degree of the patient is obtained.

[0052] Preferably, in an embodiment of the present application, the method for obtaining the degree of liver fibrosis comprises: For the shear wave elastography image under the optimal parameter combination, target pixel points with an elastic modulus greater than a preset modulus threshold are selected, and a ratio between the number of all target pixel points and the total number of pixel points is obtained as the liver fibrosis risk degree.

[0053] It should be noted that in the embodiment of the present application, according to existing clinical knowledge, when the elastic modulus is greater than 30, the area will be considered as hard tissue, and the corresponding color is darker, therefore, the preset modulus threshold is 30.

[0054] Based on this, after the liver fibrosis risk degree is obtained, the liver fibrosis can be evaluated, the greater the liver fibrosis risk degree, the greater the hardness of liver cells, and the more need to prompt cirrhosis or severe liver damage; in the embodiment of the present application, if the liver fibrosis risk degree is in the range , the liver fibrosis is a low-risk, and if the liver fibrosis risk degree is greater than or equal to 0.4, the liver fibrosis is a high-risk, which is helpful to improve the accuracy of liver fibrosis evaluation of patients with fatty liver.

[0055] In summary, the application analyzes the gray scale and gradient distribution of the pixel points in the gray scale image of each image, and obtains the fat deposition degree of the liver of the patient in each image; according to the difference in the fat deposition degree of the gray scale image between the B-ultrasound image of the liver of the patient and the shear wave elastography image under each parameter combination, and the difference in the gray scale distribution, the fat display degree of the liver of the patient under each parameter combination is obtained, and the high display parameter combination is screened out; according to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastography image, the corresponding modified parameter combination is obtained; according to the gradient characteristics of the edge pixel points in different elastic modulus range regions, the image resolution of the corresponding modified parameter combination is obtained; the optimal parameter combination is obtained, and according to the elastic modulus distribution in the shear wave elastography image under the optimal parameter combination, the liver fibrosis risk degree of the patient is obtained. The application improves the accuracy of liver fibrosis risk assessment by obtaining the optimal parameter combination for elastic imaging.

[0056] It should be noted that the above-mentioned order of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0057] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. An artificial intelligence-based ultrasonic elastography liver fibrosis evaluation method, characterized by, The method comprises: acquiring B-ultrasound images of normal liver and patient liver, and shear wave elastography images under different preset imaging depth and probe frequency parameter combinations, wherein the shear wave elastography images contain elastic modulus of each pixel point; for the B-ultrasound images or the shear wave elastography images, obtaining fat deposition degree of the patient liver in each image according to the gray scale and gradient distribution of the pixel points in the gray scale image of each image between the normal liver and the patient liver, and obtaining fat display degree of the patient liver under each parameter combination according to the difference in fat deposition degree and the difference in gray scale distribution between the B-ultrasound images of the patient liver and the shear wave elastography images under each parameter combination, and screening out high display parameter combinations; obtaining corresponding correction parameter combinations according to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastography image, and obtaining a plurality of preset elastic modulus range regions of the shear wave elastography image under each correction parameter combination, and obtaining image resolution of the corresponding correction parameter combination according to the gradient characteristics of the edge pixel points in different elastic modulus range regions; and obtaining an optimal parameter combination according to the image resolution and the fat display degree of different correction parameter combinations; obtaining the liver fibrosis risk degree of the patient according to the elastic modulus distribution in the shear wave elastography image under the optimal parameter combination.

2. The method of claim 1, wherein the method is based on artificial intelligence. The method for obtaining the fat deposition degree comprises: for the gray scale image of each image, constructing a gray scale histogram with the number of pixel points at different gray scale values, and obtaining the average slope level between different gray scale values and the previous gray scale value in the gray scale histogram as the average slope level of each image; obtaining the image edge of each gray scale image, and obtaining the average gradient characteristics of all edge pixel points on all image edges; obtaining the fat deposition degree of the patient liver in each image according to the difference in the average slope level of each image between the patient liver and the normal liver, the difference in the average gradient characteristics, and the average gray scale of all pixel points on the corresponding gray scale image of the patient liver, wherein the difference in the average gradient characteristics is negatively correlated with the fat deposition degree, and the difference in the average slope level and the average gray scale of all pixel points are positively correlated with the fat deposition degree. 3.The liver fibrosis evaluation method based on artificial intelligence and ultrasonic elastography according to claim 1, characterized in that, The method for obtaining the fat display degree comprises: performing binaryzation processing on the corresponding gray scale images between the B-ultrasound images of the patient liver and the shear wave elastography images under each parameter combination, and counting the number of pixel points with consistent results after the binaryzation processing; obtaining the fat display degree under each parameter combination according to the difference in fat deposition degree between the ultrasound gray scale image and the shear wave elastography gray scale image, wherein the number of pixel points with consistent results is positively correlated with the fat display degree, and the difference is negatively correlated with the fat display degree. 4.The liver fibrosis evaluation method based on artificial intelligence and ultrasonic elastography according to claim 1, characterized in that, The method for obtaining the high display parameter combination comprises: if the fat display degree of the patient liver under any parameter combination is greater than or equal to a preset display threshold, the corresponding parameter combination is taken as a high display parameter combination. 5.The liver fibrosis evaluation method based on artificial intelligence and ultrasonic elastography according to claim 1, characterized in that, The method for obtaining the corresponding correction parameter combination comprises: gain adjusting the corresponding imaging depth according to the fat deposition degree of the shear wave elastography image under each high display parameter combination, to obtain an imaging correction depth; Replace the imaging depth in the high display parameter combination with the imaging correction depth to form a corresponding correction parameter combination.

6. The method of claim 5, wherein the method is based on artificial intelligence. The imaging correction depth is obtained by: Obtaining a positive integer 1 and the sum of the fat deposition degrees of the shear wave elastography images under each high display parameter combination as a fat deposition weight; Obtaining the product of the fat deposition weight and the fat deposition degree as the imaging correction depth.

7. The method of claim 1, wherein the method is based on artificial intelligence. The image resolution is obtained by: For each correction parameter combination, obtaining the average gradient amplitude of all edge pixels in each elastic modulus range region as the overall gradient amplitude; According to the maximum value of the overall gradient amplitude in all elastic modulus range regions and the amplitude difference between the maximum value of the overall gradient amplitude and the overall gradient amplitude of different elastic modulus range regions, the image resolution of the corresponding correction parameter combination is obtained, the maximum value of the overall gradient amplitude is positively correlated with the image resolution, and the amplitude difference is negatively correlated with the image resolution.

8. The method of claim 1, wherein the method is based on artificial intelligence. The optimal parameter combination is obtained by: Selecting an element in the coordinate system formed by the fat display degree and the image resolution of each correction parameter combination, selecting the element in the upper right corner of the coordinate system, and taking the corresponding correction parameter combination as the optimal parameter combination. 9.The liver fibrosis evaluation method based on artificial intelligence and ultrasonic elastography according to claim 1, characterized in that, The liver fibrosis degree is obtained by: For the shear wave elastography image under the optimal parameter combination, selecting a target pixel point with an elastic modulus greater than a preset modulus threshold, obtaining the ratio between the number of all target pixel points and the total number of pixel points as the liver fibrosis risk degree. 10.The method of claim 4, wherein, The preset display threshold is 0.88.

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