Method, device, equipment and medium for rapid extraction of carotid artery plaque image features

Abstract

The present invention discloses a method, device, equipment and medium for quickly extracting carotid plaque image features. The method comprises: extracting initial image features of each sample from a sample image of carotid plaque image information, grouping the initial image features according to the labels of each sample to obtain an initial image feature set and calculating the difference information corresponding to each initial image feature set on multiple index items, screening target feature items from multiple index items according to index item screening rules and difference information and assigning weight coefficients, configuring and updating the image feature extraction model according to the weight coefficients of the target feature items and extracting image features from the image to be extracted. Through the above method, the initial image features can be obtained and the model can be configured and updated based on the difference information of different labels on multiple index items, so that the image features can be quickly and accurately extracted through the updated model, which greatly improves the accuracy and efficiency of carotid plaque image feature extraction.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment and medium for quickly extracting carotid plaque image features. Background Art

[0002] Ischemic stroke, also known as cerebral infarction, refers to ischemic necrosis or softening of focal brain tissue caused by ischemia and hypoxia due to impaired blood supply to the brain. At present, a large number of clinical and basic medical studies have shown that unstable carotid plaque rupture and secondary thrombosis are the main causes of acute ischemic stroke. In the existing technology, ultrasound images of stable carotid plaques can usually obtain relatively accurate judgment results; however, the ultrasound imaging manifestations of unstable carotid plaques are diverse, and it is difficult for existing technical methods to accurately extract key image features from ultrasound images collected by ultrasound equipment by naked eyes, resulting in inaccurate subsequent recognition results of ultrasound images of unstable carotid plaques. Therefore, there is a problem in the existing technical methods that image features cannot be accurately obtained from carotid plaque images. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, equipment and medium for quickly extracting features of carotid plaque images, aiming to solve the problem that the existing methods cannot accurately obtain image features from carotid plaque images.

[0004] In a first aspect, an embodiment of the present invention provides a method for quickly extracting features of a carotid artery plaque image, the method comprising:

[0005] If the input carotid artery plaque image information is received, initial image features corresponding to each sample are extracted from sample images of the carotid artery plaque image information according to a preset image feature extraction model;

[0006] Grouping the initial image features according to the labels of each sample to obtain a set of initial image features corresponding to each label;

[0007] Performing difference calculation on each of the initial image feature sets according to a preset difference calculation rule to obtain difference information corresponding to different labels on multiple feature items;

[0008] Filtering the feature items included in the multiple indicator items to obtain the corresponding target feature items according to the preset indicator item screening rules and the difference information;

[0009] According to the preset weight allocation strategy and the difference information, a corresponding weight coefficient is allocated to each of the target feature items;

[0010] According to the weight coefficient of each target feature item, the contour feature extraction rule in the image feature extraction model is configured and updated to obtain an updated image feature extraction model;

[0011] The corresponding image features are extracted from the input image to be extracted according to the updated image feature extraction model.

[0012] In a second aspect, an embodiment of the present invention provides a device for quickly extracting features of a carotid plaque image, wherein the device is used to execute the method for quickly extracting features of a carotid plaque image as described in the first aspect above, and the device comprises:

[0013] An initial image feature acquisition unit, configured to extract initial image features corresponding to each sample from sample images of the carotid artery plaque image information according to a preset image feature extraction model upon receiving the input carotid artery plaque image information;

[0014] An initial image feature set acquisition unit, used to group the initial image features according to the labels of each sample to obtain initial image feature sets corresponding to each label;

[0015] A difference information acquisition unit, used to perform difference calculation on each of the initial image feature sets according to a preset difference calculation rule, so as to obtain difference information corresponding to different labels on multiple feature items;

[0016] A target feature item acquisition unit, configured to filter the feature items included in the multiple indicator items to obtain the corresponding target feature item according to the preset indicator item screening rule and the difference information;

[0017] A weight coefficient acquisition unit, used to assign corresponding weight coefficients to each of the target feature items according to a preset weight allocation strategy and the difference information;

[0018] An image feature extraction model updating unit, used for updating the configuration of the contour feature extraction rules in the image feature extraction model according to the weight coefficients of the target feature items, so as to obtain an updated image feature extraction model;

[0019] The image feature acquisition unit is used to extract corresponding image features from the input image to be extracted according to the updated image feature extraction model.

[0020] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer device executes the computer program, the method for rapid extraction of carotid plaque image features as described in the first aspect above is implemented.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for rapid extraction of carotid plaque image features as described in the first aspect above is implemented.

[0022] The embodiment of the present invention provides a method, device, equipment and medium for rapid extraction of carotid plaque image features. The method includes: extracting initial image features of each sample from a sample image of carotid plaque image information, grouping the initial image features according to the labels of each sample to obtain an initial image feature set and calculating the difference information corresponding to each initial image feature set on multiple index items, screening target feature items from multiple index items according to index item screening rules and difference information and assigning weight coefficients, configuring and updating the image feature extraction model according to the weight coefficients of the target feature items, and extracting image features from the image to be extracted according to the updated image feature extraction model. Through the above method, the initial image features can be extracted, and the difference information corresponding to different labels on multiple index items can be obtained based on the labels of the samples to configure and update the image feature extraction model, so that the image features can be quickly and accurately extracted through the updated image feature extraction model, which greatly improves the accuracy and efficiency of carotid plaque image feature extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0024] Figure 1 A schematic diagram of a process for rapidly extracting features of carotid plaque images provided by an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of an application scenario of the method for rapid extraction of carotid artery plaque image features provided by an embodiment of the present invention;

[0026] Figure 3 A diagram showing the application effect of the method for rapidly extracting features of carotid artery plaque images provided by an embodiment of the present invention;

[0027] Figure 4 A schematic block diagram of a device for rapidly extracting features of carotid artery plaque images provided by an embodiment of the present invention;

[0028] Figure 5 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0031] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0032] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0033] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of a process flow of a method for rapidly extracting features of a carotid artery plaque image provided by an embodiment of the present invention, Figure 2 Schematic diagram of the application scenario of the method for rapid extraction of carotid plaque image features provided in an embodiment of the present invention; the method for rapid extraction of carotid plaque image features is applied to a terminal device 10, and the terminal device 10 establishes a network connection with an ultrasound device 20 to achieve data information transmission; the method for rapid extraction of carotid plaque image features is executed by application software installed in the terminal device 10; the terminal device 10 is a terminal device for executing the method for rapid extraction of carotid plaque image features to rapidly extract image features of a static image of a carotid plaque, such as a desktop computer, a laptop computer, a tablet computer or a mobile phone; the ultrasound device 20 is a device for collecting ultrasound images of the human neck, thereby obtaining a two-dimensional static image of the longitudinal section of the maximum diameter of the carotid plaque. A wired connection or a wireless connection is established between the terminal device 10 and the ultrasound device 20 to achieve data information transmission, wherein the wireless connection may be a Bluetooth wireless connection or a WiFi wireless connection, etc. Figure 1 As shown, the method includes steps S110 to S170.

[0034] S110: If the input carotid artery plaque image information is received, initial image features corresponding to each sample are extracted from sample images of the carotid artery plaque image information according to a preset image feature extraction model.

[0035] The ultrasound device can collect static images of carotid plaques of different individuals. One or more static images of carotid plaques can be collected corresponding to one carotid cross-sectional position of one individual. Then, one individual can obtain static images of carotid plaques corresponding to each carotid cross-sectional position at different carotid cross-sectional positions. The carotid plaque image information includes sample images of multiple samples, each sample corresponds to one individual, and the sample image of each sample can be one or more static images of carotid plaques. The initial image features of each sample can be extracted from the sample reminder according to the image feature extraction model, and each sample corresponds to a set of initial image features; the image feature extraction model is also an intelligent model for feature extraction of sample images, and the obtained initial image features include feature values ​​corresponding to multiple index items, wherein the index items include plaque echo type, plaque boundary regularity, plaque morphology, sign type, total plaque volume, vascular lumen stenosis rate, etc.

[0036] In a specific embodiment, before extracting the initial image features corresponding to each sample from the sample image of the carotid plaque image information according to a preset image feature extraction model, it also includes: performing convolution processing on the basic image of each sample in the carotid plaque image information according to a preset convolutional neural network to obtain a corresponding convolution image; and combining the basic image of each sample with the corresponding convolution image into a sample image of the sample.

[0037] In a specific embodiment, in order to expand the richness of the sample images of each sample, the static image of the carotid plaque collected by the ultrasound device can be used as the basic image in the carotid plaque image information, and the basic image is convoluted by the convolutional neural network, wherein the convolution processing includes upsampling processing and downsampling processing, and the upsampling processing is to reduce the resolution of the basic image, and the downsampling is to increase the resolution of the basic image. Among them, the convolutional neural network is composed of a DenseNet network and a Unet. The feature extraction part of the first half of the Unet network continuously reduces the resolution in the process of continuous downsampling to obtain image information of different scales. The information of the image gradually transitions from the information of points, lines, gradients, etc. in the bottom layer information to the contours and more abstract information in the high-level information. The entire network completes the extraction and combination of "fine to coarse" features. The second half of the Unet network performs upsampling to obtain a probability map corresponding to the original image, and the structure is very simple. The encoder and decoder of the Unet network are also connected by skip connection. When an image is converted from a low-resolution image to a high-resolution image, it will be distorted (decoder part); while when a high-resolution image is converted to a low-resolution image, the distortion is relatively negligible and more detail information is retained (encoder part). Therefore, the sampling process in the decoder loses sensitivity to detail information. The skip connection directly cascades the more accurate gradient, point, line and other information of the encoder at the same height level to the decoder at the same level. This operation enables UNet to obtain more accurate segmentation results. DenseNet mainly defines the Dense Block structure. The input of each layer in the Dense Block is the cascade of the outputs of all previous layers.

[0038] By performing convolution processing on a base image through a convolutional neural network, a downsampled convolution image with a resolution greater than the initial base image can be obtained, and an upsampled convolution image with a resolution less than the initial base image can also be obtained; that is, the obtained convolution image includes an upsampled convolution image and a downsampled convolution image.

[0039] The basic image of the sample is combined with the convolution image of the sample and used as the sample image of the sample. This processing method of obtaining the convolution image can greatly expand the image richness of the sample image and improve the comprehensiveness and accuracy of subsequent feature extraction.

[0040] In a specific embodiment, the above-mentioned step S110 specifically includes the following sub-steps: performing contour extraction on each sample image according to the contour extraction rules in the image feature extraction model to obtain the image contour corresponding to each sample image; identifying the image contour of each sample image according to the contour feature extraction rules in the image feature extraction model to obtain corresponding contour type identification information; integrating the contour type identification information of each sample image of the sample to obtain the corresponding initial image feature.

[0041] Specifically, the contour extraction rules in the image feature extraction model can be used to extract the contour of each sample image, and the contrast information between the pixel point and the adjacent pixel points in each sample image can be calculated. The contrast information can be used to reflect the difference information between the pixel point and the adjacent pixel points. The calculation formula for obtaining the contrast information can be shown as formula (1):

[0042] (1);

[0043] Among them, S is the calculated pixel with the center pixel P 0 The corresponding contrast information, P 0 is the pixel value of the center pixel (the pixel value includes the pixel values ​​of the three RGB channels), n is the pixel point P 0 The number of pixels in the first outer circle, P i is the pixel point P 0 The pixel value of the i-th pixel in the first circle on the outside, e is the base of the natural logarithm, and m is the pixel point P 0 The number of pixels in the second outer circle.

[0044] According to the contrast information and the point and line information in the sample image, some pixel points with large contrast information and connected in a line are extracted from the sample image as the corresponding image contour.

[0045] Further, the image contour of each sample image is identified according to the contour feature extraction rules in the image feature extraction model to obtain contour type identification information. Then, each sample image can obtain a corresponding set of contour type identification information, and a set of contour type identification information also includes indicator feature information corresponding to multiple indicator items.

[0046] The contour recognition information of each sample image of the sample is integrated, so as to integrate multiple indicator feature information corresponding to the same indicator item of a sample, and obtain the indicator feature integration information uniquely corresponding to each indicator item as the initial image feature of the sample. The average value of multiple indicator feature information corresponding to the same indicator item of the sample can be taken to integrate the unique corresponding indicator feature integration information.

[0047] In a specific embodiment, the image contour of each of the sample images is identified according to the contour feature extraction rules in the image feature extraction model to obtain corresponding contour type identification information, including: determining the image contour matching each indicator item according to the resolution range corresponding to each indicator item in the contour feature extraction rule and the resolution of the sample image corresponding to the image contour; identifying the image contour corresponding to each indicator item according to the feature extraction strategy corresponding to each indicator item in the contour feature extraction rule, thereby obtaining contour type identification information corresponding to each image contour.

[0048] Specifically, the resolution interval corresponding to each indicator item can be matched with the resolution of the sample image corresponding to each image contour. If the resolution of the sample image is within the resolution interval of the indicator item, it is determined that the image contour of the sample image matches the indicator item. The image contours that match each indicator item can be determined through the above matching judgment. For example, for indicator items such as plaque echo type and total plaque volume, the resolution interval is small, that is, the indicator feature information of the corresponding indicator item can be accurately obtained without a very fine image; for indicator items such as plaque boundary regularity, plaque morphology, and sign type, the resolution interval is large, that is, it is necessary to obtain finer local detail features in the image to accurately obtain the indicator feature information of the corresponding indicator item.

[0049] According to the feature extraction strategy corresponding to each indicator item in the contour feature extraction rule, the image contour corresponding to each indicator item is identified. The pixel information corresponding to each contour area can be extracted from the sample image through the image contour, where the contour area contained in the image contour is as follows: Figure 3 As shown, 11 is the carotid contour area, 12 is the plaque contour area, 13 is the fibrous cap area, 14 is the lipid core area, and 15 is the cortical contour. The pixel values ​​of the pixels contained in different contour areas are different. For example, the carotid contour area is usually red, and a crescent-shaped lipid core area is formed outside the carotid contour area. The lipid core contained in the lipid core area is usually light yellow particles. There may be one or more plaque contour areas in the area adjacent to the carotid contour area. There may also be no plaque contour area in the carotid contour area or the lipid core area. The plaque contour area has dark yellow to brown plaques; the carotid contour area and the plaque contour area are separated by a light pink fibrous cap area.

[0050] For the indicator item of plaque echo type, the pixel information of the plaque contour area can be obtained, and the average value of the pixel value of the plaque contour area can be further calculated based on the pixel information of the plaque contour area, and the depth (i.e., brightness) corresponding to the average value of the pixel value of the plaque contour area on the red, yellow, and brown channels configured in the corresponding feature extraction strategy can be determined to obtain the indicator feature information corresponding to the plaque echo type. For the plaque boundary regularity, the gradient direction corresponding to each pixel point of the contour line in the plaque contour area can be obtained according to the corresponding feature extraction strategy, and the angle between the gradient directions of adjacent pixel points can be calculated and the average value of the obtained angle can be calculated as the indicator feature information corresponding to the plaque boundary regularity. For the indicator item of plaque morphology, the pixel information of the plaque contour area can be obtained, and the standard deviation of the pixel value of the plaque contour area can be calculated according to the corresponding feature extraction strategy as the indicator feature information corresponding to the plaque morphology. For the indicator item of sign type, the pixel average value of the plaque contour area can be calculated according to the pixel information of the plaque contour area; the pixel information of the carotid artery contour area is obtained and the pixel average value of the carotid artery contour area is calculated, and the difference between the pixel average value of the plaque contour area and the pixel average value of the carotid artery contour area is calculated according to the corresponding feature extraction strategy, as the indicator feature information corresponding to the sign type. For the indicator item of total plaque volume, the area value of the plaque contour area can be calculated according to the pixel information of the plaque contour area and the cumulative calculation is performed to obtain the indicator feature information corresponding to the total plaque volume; for the indicator item of vascular lumen stenosis rate, the difference between the area value of the carotid artery contour area and the total plaque volume can be calculated according to the corresponding feature extraction strategy and the pixel information of the carotid artery contour area, and the ratio between the difference and the cross-sectional area of ​​the peripheral cortical contour in the image contour is calculated to obtain the indicator feature information corresponding to the vascular lumen stenosis rate.

[0051] S120 , grouping the initial image features according to the labels of the samples to obtain initial image feature sets corresponding to the labels.

[0052] Furthermore, each sample corresponds to a sample label, and the sample labels include stroke-related and stroke-free, and all samples can be divided into relevant groups and non-relevant groups according to the sample labels. Furthermore, the initial image features of each sample are grouped according to the grouping of the samples, so as to obtain an initial image feature set corresponding to the samples of the relevant group and an initial image feature set corresponding to the samples of the non-relevant group.

[0053] S130 , performing difference calculation on each of the initial image feature sets according to a preset difference calculation rule to obtain difference information corresponding to different labels on a plurality of feature items.

[0054] According to the difference calculation rules, the difference calculation is performed on each initial image feature set, wherein the difference between two labels on multiple feature items can be calculated to obtain the corresponding difference information. Since the index item contains one or more feature items, in order to reflect the difference between different labels, it is necessary to perform the difference calculation for each feature item separately, so as to obtain the difference coefficient corresponding to each feature item, and the difference information can be obtained by obtaining the difference coefficient of each feature item. For example, the index item of plaque echo type includes the corresponding depths on the red, yellow, and brown channels, that is, the index item contains three feature items, and the corresponding difference coefficients need to be calculated for the three feature items.

[0055] Specifically, the difference calculation can be obtained by formula (2):

[0056] (2);

[0057] Where C is the calculated difference coefficient, f 1 is the mean of the eigenvalues ​​corresponding to the feature item and the first initial image feature set, f 2 is the mean of the eigenvalues ​​corresponding to the feature item and the second initial image feature set, σ 1 2 is the variance of the eigenvalues ​​corresponding to the feature item and the first initial image feature set, σ 2 2 is the variance of the eigenvalues ​​corresponding to the feature item and the second initial image feature set.

[0058] S140: Filter the feature items included in the multiple indicator items to obtain corresponding target feature items according to the preset indicator item screening rules and the difference information.

[0059] Furthermore, the characteristic items included in the multiple indicator items can be screened according to the indicator screening rule and the obtained difference information, so as to obtain the target characteristic item. Specifically, the indicator screening rule is configured with a difference coefficient threshold, and it can be determined whether the difference coefficient of each characteristic item in the difference information is greater than the difference coefficient threshold. If it is greater, the characteristic item is determined to be the target characteristic item, and if it is not greater, the characteristic item is determined not to be the target characteristic item.

[0060] S150, assigning corresponding weight coefficients to each of the target feature items according to a preset weight assignment strategy and the difference information.

[0061] Furthermore, a corresponding weight coefficient can be assigned to each target feature item according to the weight allocation strategy and difference information. The weight coefficient is used to quantify the importance of each feature value in the image features extracted later. Specifically, the weight coefficient can be calculated by the following formula (3):

[0062] (3);

[0063] Among them, C k is the difference coefficient of the kth target feature item, G is the total number of target feature items, r k is the weight coefficient corresponding to the kth target feature item.

[0064] S160. Update the configuration of the contour feature extraction rules in the image feature extraction model according to the weight coefficients of the target feature items to obtain an updated image feature extraction model.

[0065] The contour feature extraction rules in the image feature extraction model can be configured and updated according to the weight coefficient of the target feature item, and the updated image feature extraction model can be obtained after the configuration update.

[0066] In a specific embodiment, the above-mentioned step S160 specifically includes the following sub-steps: updating the feature extraction strategy included in the contour feature extraction rule according to the target feature item to obtain an updated contour feature extraction rule; configuring the extraction weight value of each feature extraction strategy in the updated contour feature extraction rule according to the weight coefficient of the target feature item to obtain an updated image feature extraction model.

[0067] Specifically, the feature extraction strategies included in the contour feature extraction rules may be updated according to the target feature items, that is, only the feature extraction strategies corresponding to the target feature items are retained, and the remaining feature extraction strategies not corresponding to the target feature items are deleted.

[0068] Furthermore, the extraction weight value of each feature extraction strategy in the updated contour feature extraction rule is configured according to the weight coefficient of the target feature item, thereby updating the contour feature extraction rule and obtaining an updated image feature extraction model.

[0069] S170. Extract corresponding image features from the input image to be extracted according to the updated image feature extraction model.

[0070] Furthermore, the terminal device can also receive the image to be extracted input by the ultrasound device, and after obtaining the image to be extracted, the corresponding image features can be extracted from the image to be extracted according to the image feature extraction model. At this time, the number of feature items contained in the image features obtained is less than the number of feature items contained in the initial image features.

[0071] In the method for rapid extraction of carotid plaque image features provided in an embodiment of the present invention, the method includes: extracting the initial image features of each sample from the sample image of the carotid plaque image information, grouping the initial image features according to the labels of each sample to obtain the initial image feature set and calculating the difference information corresponding to each initial image feature set on multiple index items, screening the target feature items from the multiple index items according to the index item screening rules and the difference information and assigning weight coefficients, configuring and updating the image feature extraction model according to the weight coefficients of the target feature items, and extracting the image features from the image to be extracted according to the updated image feature extraction model. Through the above method, the initial image features can be extracted, and the difference information corresponding to different labels on multiple index items can be obtained based on the labels of the samples to configure and update the image feature extraction model, so that the image features can be quickly and accurately extracted through the updated image feature extraction model, which greatly improves the accuracy and efficiency of the extraction of carotid plaque image features.

[0072] The embodiment of the present invention further provides a device for rapidly extracting features of carotid plaque images, which can be configured in the terminal device 10 and is used to execute any embodiment of the aforementioned method for rapidly extracting features of carotid plaque images. Figure 4 , Figure 4 A schematic block diagram of a device for rapidly extracting features of carotid plaque images provided in an embodiment of the present invention.

[0073] like Figure 4 As shown, the device 100 for rapidly extracting carotid plaque image features includes an initial image feature acquisition unit 110, an initial image feature set acquisition unit 120, a difference information acquisition unit 130, a target feature item acquisition unit 140, a weight coefficient acquisition unit 150, an image feature extraction model update unit 160 and an image feature acquisition unit 170.

[0074] The initial image feature acquisition unit 110 is used to extract initial image features corresponding to each sample from the sample images of the carotid artery plaque image information according to a preset image feature extraction model if the input carotid artery plaque image information is received.

[0075] In a more specific embodiment, the device for quickly extracting carotid plaque image features 100 also includes: a convolution image acquisition unit, used to perform convolution processing on the basic image of each sample in the carotid plaque image information according to a preset convolution neural network to obtain a corresponding convolution image; and a combination unit, used to combine the basic image of each sample with the corresponding convolution image into a sample image of the sample.

[0076] In a more specific embodiment, the initial image feature acquisition unit 110 includes: an image contour extraction unit, used to extract the contour of each sample image according to the contour extraction rule in the image feature extraction model, and obtain the image contour corresponding to each sample image; an identification unit, used to identify the image contour of each sample image according to the contour feature extraction rule in the image feature extraction model, and obtain the corresponding contour type identification information; an integration unit, used to integrate the contour type identification information of each sample image of the sample, and obtain the corresponding initial image feature.

[0077] The initial image feature set acquisition unit 120 is used to group the initial image features according to the labels of the samples to obtain initial image feature sets corresponding to the labels.

[0078] The difference information acquisition unit 130 is used to perform difference calculation on each of the initial image feature sets according to a preset difference calculation rule to obtain difference information corresponding to different labels on multiple feature items.

[0079] The target feature item acquisition unit 140 is used to filter the feature items included in the multiple indicator items to obtain the corresponding target feature item according to the preset indicator item filtering rule and the difference information.

[0080] The weight coefficient acquisition unit 150 is used to allocate corresponding weight coefficients to each of the target feature items according to a preset weight allocation strategy and the difference information.

[0081] The image feature extraction model updating unit 160 is used to configure and update the contour feature extraction rules in the image feature extraction model according to the weight coefficients of the target feature items to obtain an updated image feature extraction model.

[0082] The image feature acquisition unit 170 is used to extract corresponding image features from the input image to be extracted according to the updated image feature extraction model.

[0083] The device for rapid extraction of carotid plaque image features provided in the embodiment of the present invention applies the above-mentioned method for rapid extraction of carotid plaque image features, and the method includes: extracting the initial image features of each sample from the sample image of carotid plaque image information, grouping the initial image features according to the labels of each sample to obtain the initial image feature set and calculating the difference information corresponding to each initial image feature set on multiple index items, screening the target feature items from the multiple index items according to the index item screening rules and the difference information and assigning weight coefficients, configuring and updating the image feature extraction model according to the weight coefficients of the target feature items, and extracting the image features from the image to be extracted according to the updated image feature extraction model. Through the above-mentioned method, the initial image features can be extracted, and the difference information corresponding to different labels on multiple index items can be obtained based on the labels of the samples to configure and update the image feature extraction model, so that the image features can be quickly and accurately extracted through the updated image feature extraction model, which greatly improves the accuracy and efficiency of the extraction of carotid plaque image features.

[0084] The above-mentioned method for rapidly extracting features of carotid plaque images can be implemented in the form of a computer program, and the device for rapidly extracting features of carotid plaque images can be implemented as a computer device. The computer program can be used in Figure 5 The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the computer device executes the computer program, the method for rapidly extracting features of carotid artery plaque images as described in the above embodiment is implemented.

[0085] See also Figure 5 , Figure 5 is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device may be a terminal device for executing a method for rapidly extracting features of a carotid artery plaque image to rapidly extract image features of a carotid artery plaque image.

[0086] See also Figure 5 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a storage medium 503 and an internal memory 504 .

[0087] The storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can execute a method for rapidly extracting features of carotid plaque images, wherein the storage medium 503 can be a volatile storage medium or a non-volatile storage medium.

[0088] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0089] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for rapidly extracting features of carotid artery plaque images.

[0090] The network interface 505 is used for network communication, such as providing data information transmission, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 500 to which the solution of the present invention is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0091] The processor 502 is used to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned method for rapid extraction of carotid plaque image features.

[0092] Those skilled in the art will understand that Figure 5 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Figure 5 The embodiments shown are consistent and will not be described again here.

[0093] It should be understood that in the embodiment of the present invention, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0094] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps included in the above-mentioned method for rapid extraction of carotid plaque image features are implemented.

[0095] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment, devices and units can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0096] In the several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0098] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a computer-readable storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a magnetic disk or an optical disk.

[0100] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for rapidly extracting features of carotid artery plaque images, characterized in that: The method comprises: If the input carotid artery plaque image information is received, initial image features corresponding to each sample are extracted from sample images of the carotid artery plaque image information according to a preset image feature extraction model; Grouping the initial image features according to the labels of each sample to obtain a set of initial image features corresponding to each label; The difference calculation is performed on each of the initial image feature sets according to the preset difference calculation rules to obtain the difference information corresponding to different labels on multiple feature items; the index item includes one or more feature items, and the difference information includes the difference coefficient corresponding to each index item, and the index items include plaque echo type, plaque boundary regularity, plaque morphology, sign type, total plaque volume, and vascular lumen stenosis rate; the difference is calculated by the formula Calculated; where C is the calculated difference coefficient, f1 is the mean eigenvalue of the feature item corresponding to the first initial image feature set, f2 is the mean eigenvalue of the feature item corresponding to the second initial image feature set, σ1 2 is the variance of the eigenvalues ​​corresponding to the feature item and the first initial image feature set, σ2 2 is the variance of the eigenvalues ​​corresponding to the feature item and the second initial image feature set; Filtering the feature items included in the multiple indicator items to obtain the corresponding target feature items according to the preset indicator item screening rules and the difference information; According to the preset weight allocation strategy and the difference information, each target feature item is respectively allocated a corresponding weight coefficient; the weight coefficient is calculated by the formula Calculated; where C k is the difference coefficient of the kth target feature item, G is the total number of target feature items, r k is the weight coefficient corresponding to the kth target feature item; According to the weight coefficient of each target feature item, the contour feature extraction rule in the image feature extraction model is configured and updated to obtain an updated image feature extraction model; The corresponding image features are extracted from the input image to be extracted according to the updated image feature extraction model.

2. The method for rapid extraction of carotid plaque image features according to claim 1, characterized in that: Before extracting the initial image features corresponding to each sample from the sample image of the carotid artery plaque image information according to the preset image feature extraction model, the method further includes: Performing convolution processing on the basic image of each sample in the carotid artery plaque image information according to a preset convolutional neural network to obtain a corresponding convolution image; The base image of each sample is combined with the corresponding convolution image into a sample image of the sample.

3. The method for rapid extraction of carotid artery plaque image features according to claim 1 or 2, characterized in that: The extracting of initial image features corresponding to each sample from the sample images of the carotid artery plaque image information according to a preset image feature extraction model includes: Extract the contour of each sample image according to the contour extraction rule in the image feature extraction model to obtain the image contour corresponding to each sample image; Identify the image contour of each of the sample images according to the contour feature extraction rule in the image feature extraction model to obtain corresponding contour type identification information; The contour type identification information of each sample image of the sample is integrated to obtain the corresponding initial image features.

4. The method for rapid extraction of carotid artery plaque image features according to claim 3, characterized in that: The step of identifying the image contour of each sample image according to the contour feature extraction rule in the image feature extraction model to obtain corresponding contour type identification information includes: Determine an image contour that matches each indicator item according to the resolution interval corresponding to each indicator item in the contour feature extraction rule and the resolution of the sample image corresponding to the image contour; The image contours corresponding to the respective index items are identified according to the feature extraction strategies corresponding to the respective index items in the contour feature extraction rules, thereby obtaining contour type identification information corresponding to the respective image contours.

5. The method for rapid extraction of carotid artery plaque image features according to claim 1, characterized in that: The configuring and updating the contour feature extraction rules in the image feature extraction model according to the weight coefficients of the target feature items to obtain an updated image feature extraction model includes: updating the feature extraction strategy included in the contour feature extraction rule according to the target feature item to obtain an updated contour feature extraction rule; The extraction weight value of each feature extraction strategy in the updated contour feature extraction rule is configured according to the weight coefficient of the target feature item to obtain an updated image feature extraction model.

6. A device for quickly extracting features of carotid artery plaque images, characterized in that: The device is used to execute the method for quickly extracting features of carotid plaque images according to any one of claims 1 to 5, and the device comprises: An initial image feature acquisition unit, configured to extract initial image features corresponding to each sample from sample images of the carotid artery plaque image information according to a preset image feature extraction model upon receiving the input carotid artery plaque image information; An initial image feature set acquisition unit, used to group the initial image features according to the labels of each sample to obtain initial image feature sets corresponding to each label; A difference information acquisition unit, used to perform difference calculation on each of the initial image feature sets according to a preset difference calculation rule, so as to obtain difference information corresponding to different labels on multiple feature items; A target feature item acquisition unit, configured to filter the feature items included in the multiple indicator items to obtain the corresponding target feature item according to the preset indicator item screening rule and the difference information; A weight coefficient acquisition unit, used to assign corresponding weight coefficients to each of the target feature items according to a preset weight allocation strategy and the difference information; An image feature extraction model updating unit, used for updating the configuration of the contour feature extraction rules in the image feature extraction model according to the weight coefficients of the target feature items, so as to obtain an updated image feature extraction model; The image feature acquisition unit is used to extract corresponding image features from the input image to be extracted according to the updated image feature extraction model.

7. The device for rapidly extracting features of carotid artery plaque images according to claim 6, characterized in that: The device also includes: A convolution image acquisition unit, configured to perform convolution processing on the basic image of each sample in the carotid artery plaque image information according to a preset convolution neural network to obtain a corresponding convolution image; The combining unit is used to combine the basic image of each sample with the corresponding convolution image into a sample image of the sample.

8. The device for rapidly extracting features of carotid artery plaque images according to claim 6 or 7, characterized in that: The initial image feature set acquisition unit comprises: An image contour extraction unit, used to extract the contour of each sample image according to the contour extraction rule in the image feature extraction model to obtain the image contour corresponding to each sample image; An identification unit, used to identify the image contour of each of the sample images according to the contour feature extraction rule in the image feature extraction model to obtain corresponding contour type identification information; The integration unit is used to integrate the contour type identification information of each sample image of the sample to obtain the corresponding initial image features.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer device executes the computer program, the method for rapid extraction of carotid plaque image features according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for rapidly extracting features of carotid plaque images as described in any one of claims 1 to 5 is implemented.

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