Method and device for identifying plaque in blood vessel

Abstract

The present application provides a method and device for identifying plaques in blood vessels, including: when there are plaques in the target blood vessel to be identified, determining the original plaque medical image data corresponding to each plaque; for each branch blood vessel, dividing each plaque area according to the pixel value and area type division rule in the original plaque medical image data, and determining at least one plaque sub-area; for each target branch blood vessel with a target plaque sub-area of ​​any specified area type, identifying the target plaque area in the target branch blood vessel according to the high-risk plaque assessment rule and the corresponding image parameters, and determining whether the target branch blood vessel has a high-risk plaque; if so, outputting the high-risk plaque identification result of the target branch blood vessel; the plaque identification result includes the high-risk plaque type, plaque location and blood vessel name. In this way, through the technical solution provided by the present application, the high-risk plaques in the blood vessels can be quickly and accurately determined.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a method and device for identifying plaques in blood vessels. Background Art

[0002] With the rapid development of social economy, vascular disease has become a topic of great concern. Among them, high-risk / vulnerable plaques in coronary atherosclerotic plaques usually exist in the early stage of coronary plaque formation and are the main cause of acute cardiovascular events. Therefore, the treatment technology and prevention level of vascular diseases are of paramount importance. However, whether it is treatment or prevention, it is inseparable from the study of vascular morphology. Therefore, the automated detection of vascular plaques has important research value, clinical value and practical significance.

[0003] Most existing studies use traditional machine learning methods to detect high-risk plaques in coronary arteries, that is, firstly, experienced experts manually design features, and then send the extracted features to the machine learning classifier to predict the plaque type. However, this process is heavily dependent on the operator's experience and rich expertise, and is time-consuming and labor-intensive. However, given the diversity of coronary plaque sample features, it is difficult for machine learning methods to design specific features for high-risk plaques when designing features, and it is heavily dependent on the operator's experience and rich expertise, which is time-consuming and labor-intensive. In addition, due to the characteristics of high-risk coronary plaques, a plaque usually spans multiple CCTA scans, and the deep learning training method of a single image corresponding to a single label has errors and is not suitable for high-risk plaque detection tasks.

[0004] Therefore, there are still certain limitations in using traditional machine learning methods to detect high-risk coronary artery plaques. Summary of the invention

[0005] In view of this, the purpose of the present application is to provide a method and device for identifying plaques in blood vessels, which can effectively identify high-risk plaques in blood vessels.

[0006] The present application embodiment provides a method for identifying plaque in a blood vessel, the identification method comprising:

[0007] When there is a plaque in the target blood vessel to be identified, original plaque medical image data corresponding to each plaque is determined according to the original total medical image of the target blood vessel; the target blood vessel includes at least one branch blood vessel;

[0008] For each branch blood vessel, according to the pixel value and region type division rule in the original plaque medical image data, the plaque region corresponding to each plaque in the branch blood vessel is divided to determine at least one plaque sub-region and the region type of each plaque sub-region;

[0009] For each target branch vessel having a target plaque sub-region of any specified region type, identifying the plaque region of the target branch vessel having the target plaque sub-region according to the high-risk plaque assessment rule and the corresponding imaging parameters, and determining whether the target branch vessel has a high-risk plaque;

[0010] If so, the high-risk plaque identification result of the target branch vessel is output; the plaque identification result includes the high-risk plaque type, the plaque location and the vessel name of the target branch vessel.

[0011] Optionally, determining whether there is a plaque in the target blood vessel to be identified is performed by the following steps:

[0012] Acquiring a three-dimensional reconstructed blood vessel model of the target blood vessel;

[0013] identifying, according to a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel;

[0014] If present, it is determined that plaque exists in the target blood vessel.

[0015] Optionally, a three-dimensional reconstructed blood vessel model of the target blood vessel is constructed by the following steps:

[0016] Performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel;

[0017] Performing contour recognition according to the centerline of the target blood vessel and the original medical image to determine the intima contour and the adventitia contour of the target blood vessel;

[0018] Performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel;

[0019] According to the intima curved surface model and the outer diameter curved surface model of the target blood vessel, volume filling processing is performed to obtain a three-dimensional blood vessel model of the target blood vessel; the three-dimensional blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.

[0020] Optionally, the original plaque medical image data corresponding to each plaque is determined by the following steps:

[0021] Determine a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the inner membrane from a three-dimensional model of the outer membrane of the target blood vessel;

[0022] According to the three-dimensional reconstruction results of all plaques, the medical image at the corresponding position is extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

[0023] Optionally, the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0024] Optionally, for each target branch vessel having a target plaque sub-region of any specified region type, identifying the plaque region having the target plaque sub-region in the target branch vessel according to a high-risk plaque assessment rule and corresponding image parameters, and determining whether the target branch vessel has a high-risk plaque, includes:

[0025] For each plaque region in each target branch blood vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to a region type of the target plaque sub-region in the plaque region;

[0026] According to the determined recognition order of high-risk plaques, imaging parameters required for identifying each high-risk plaque are sequentially acquired;

[0027] Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area;

[0028] When the high-risk plaque identification result corresponding to any plaque region in the target branch blood vessel indicates the existence of a high-risk plaque, it is determined that the target branch blood vessel has a high-risk plaque.

[0029] Optionally, the plaque position includes the plaque position of each high-risk plaque, and the plaque position of each high-risk plaque is determined by the following steps:

[0030] When a high-risk plaque is identified in the target branch vessel, determining the plaque region to which the high-risk plaque belongs;

[0031] According to the determined plaque area and the original total medical image, the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.

[0032] The present application also provides a device for identifying plaque in a blood vessel, the device comprising:

[0033] A first determination module is used to determine original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel when there is a plaque in the target blood vessel to be identified; the target blood vessel includes at least one branch blood vessel;

[0034] A division module is used to divide the plaque area corresponding to each plaque in each branch blood vessel according to the pixel value and area type division rule in the original plaque medical image data, and determine at least one plaque sub-area and the area type of each plaque sub-area;

[0035] an identification module, for identifying, for each target branch vessel having a target plaque sub-region of any specified region type, a plaque region in the target branch vessel having a target plaque sub-region according to a high-risk plaque assessment rule and corresponding imaging parameters, and determining whether the target branch vessel has a high-risk plaque;

[0036] The output module is used to output the high-risk plaque identification result of the target branch blood vessel when it is determined that the target branch blood vessel has a high-risk plaque; the plaque identification result includes the high-risk plaque type, plaque location and blood vessel name of the target branch blood vessel.

[0037] Optionally, the identification device further includes a second determination module, and the second determination module is used to:

[0038] Acquiring a three-dimensional reconstructed blood vessel model of the target blood vessel;

[0039] identifying, according to a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel;

[0040] If present, it is determined that plaque exists in the target blood vessel.

[0041] Optionally, the identification device further includes a construction module, and the construction module is used to:

[0042] Performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel;

[0043] Performing contour recognition according to the centerline of the target blood vessel and the original medical image to determine the intima contour and the adventitia contour of the target blood vessel;

[0044] Performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel;

[0045] According to the intima curved surface model and the outer diameter curved surface model of the target blood vessel, a volume filling process is performed to obtain a three-dimensional blood vessel model of the target blood vessel; the three-dimensional blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.

[0046] Optionally, when the first determining module is used to determine the original plaque medical image data corresponding to each plaque through the following steps, the first determining module is used to:

[0047] Determine a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the inner membrane from a three-dimensional model of the outer membrane of the target blood vessel;

[0048] According to the three-dimensional reconstruction results of all plaques, the medical image at the corresponding position is extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

[0049] Optionally, the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0050] Optionally, when the identification module is used to identify, for each target branch vessel having a target plaque sub-region of any specified region type, according to a high-risk plaque assessment rule and corresponding image parameters, a plaque region having a target plaque sub-region in the target branch vessel, and determines whether the target branch vessel has a high-risk plaque, the identification module is used to:

[0051] For each plaque region in each target branch blood vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to a region type of the target plaque sub-region in the plaque region;

[0052] According to the determined recognition order of high-risk plaques, imaging parameters required for identifying each high-risk plaque are sequentially acquired;

[0053] Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area;

[0054] When the high-risk plaque identification result corresponding to any plaque region in the target branch blood vessel indicates the existence of a high-risk plaque, it is determined that the target branch blood vessel has a high-risk plaque.

[0055] Optionally, the identification module is further used for:

[0056] When a high-risk plaque is identified in the target branch vessel, determining the plaque region to which the high-risk plaque belongs;

[0057] According to the determined plaque area and the original total medical image, the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.

[0058] An embodiment of the present application also provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned identification method are performed.

[0059] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned identification method are executed.

[0060] An embodiment of the present application provides a method for identifying plaques in a blood vessel, the identification method comprising: when a plaque exists in a target blood vessel to be identified, determining original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel; the target blood vessel includes at least one branch blood vessel; for each branch blood vessel, dividing the plaque area corresponding to each plaque in the branch blood vessel according to the pixel values ​​and area type division rules in the original plaque medical image data, and determining at least one plaque sub-area and the area type of each plaque sub-area; for each target branch blood vessel having a target plaque sub-area of ​​any specified area type, identifying the plaque area of ​​the target branch blood vessel having the target plaque sub-area according to the high-risk plaque assessment rules and the corresponding image parameters, and determining whether the target branch blood vessel has a high-risk plaque; if so, outputting a high-risk plaque identification result of the target branch blood vessel; the plaque identification result includes the high-risk plaque type, the plaque location and the blood vessel name of the target branch blood vessel.

[0061] In this way, the present application further divides the plaque area according to the pixel value, determines the type of sub-areas included in each plaque area, and then determines whether to identify high-risk plaques and the identification order according to the type of sub-areas, and obtains the corresponding image parameters and identifies the high-risk plaques in sequence according to the identification order, thereby improving the identification speed and accuracy of high-risk plaques in blood vessels.

[0062] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flow chart of a method for identifying plaque in a blood vessel provided in an embodiment of the present application;

[0065] Figure 2 A schematic diagram of the structure of a blood vessel centerline provided in an embodiment of the present application;

[0066] Figure 3 A schematic diagram of the structure of a blood vessel adventitia contour provided in an embodiment of the present application;

[0067] Figure 4 A schematic diagram of the structure of a branch blood vessel with plaque provided in the present application;

[0068] Figure 5 A schematic diagram of the data results of the positive reconstruction high-risk plaque identification process provided in this application;

[0069] Figure 6 This is one of the structural schematic diagrams of a device for identifying plaques in a blood vessel provided in an embodiment of the present application;

[0070] Figure 7 The second structural schematic diagram of a device for identifying plaque in a blood vessel provided in an embodiment of the present application;

[0071] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0073] Most existing studies use traditional machine learning methods to detect high-risk plaques in coronary arteries, that is, firstly, experienced experts manually design features, and then send the extracted features to the machine learning classifier to predict the plaque type. However, this process is heavily dependent on the operator's experience and rich expertise, and is time-consuming and labor-intensive. However, given the diversity of coronary plaque sample features, it is difficult for machine learning methods to design specific features for high-risk plaques when designing features, and it is heavily dependent on the operator's experience and rich expertise, which is time-consuming and labor-intensive. In addition, due to the characteristics of high-risk coronary plaques, a plaque usually spans multiple CCTA scans, and the deep learning training method of a single image corresponding to a single label has errors and is not suitable for high-risk plaque detection tasks.

[0074] Therefore, there are still certain limitations in using traditional machine learning methods to detect high-risk coronary artery plaques.

[0075] Based on this, the embodiments of the present application provide a method and device for identifying plaques in blood vessels, which can effectively identify high-risk plaques in blood vessels and improve the speed and accuracy of high-risk plaque identification.

[0076] See also Figure 1 , Figure 1 This is a flow chart of a method for identifying plaque in a blood vessel provided in an embodiment of the present application. Figure 1 As shown in , the identification method provided by the embodiment of the present application includes:

[0077] S101. When plaques exist in a target blood vessel to be identified, original plaque medical image data corresponding to each plaque is determined according to an original total medical image of the target blood vessel.

[0078] In this step, the target blood vessel includes at least one branch blood vessel. For example, when the target blood vessel is a coronary artery, the branch blood vessels included in the target blood vessel include: left main trunk, left anterior descending branch, left circumflex branch and right coronary artery, or can be further divided into sharp marginal branch, sinoatrial node branch, atrioventricular node branch, posterior descending branch, left ventricular posterior branch and other branch blood vessels.

[0079] Here, the type of the branch blood vessels included in the target blood vessel may be set in advance by relevant personnel.

[0080] In one embodiment provided in the present application, whether there is a plaque in the target blood vessel to be identified is determined by the following steps:

[0081] S201: Acquire a three-dimensional reconstructed blood vessel model of the target blood vessel.

[0082] S202: Identify whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel according to a morphological processing method.

[0083] S203: If yes, determine whether there is a plaque in the target blood vessel.

[0084] With respect to step S201, in one embodiment provided in the present application, a three-dimensional reconstructed blood vessel model of the target blood vessel is constructed by the following steps:

[0085] S301 , performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel.

[0086] S302: Perform contour recognition based on the centerline of the target blood vessel and the original medical image to determine the intima contour and adventitia contour of the target blood vessel.

[0087] S303 , performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel.

[0088] S304, performing volume filling processing according to the intima curved surface model and the outer diameter curved surface model of the target blood vessel to obtain a three-dimensional blood vessel model of the target blood vessel; the three-dimensional blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.

[0089] For example, see step S301. Figure 2 , Figure 2 This is a schematic diagram of the structure of a blood vessel centerline provided in an embodiment of the present application. Figure 2 As shown, when performing centerline extraction, the center target points of multiple locations of the target blood vessel can be determined first to obtain multiple center target points, and then the multiple center target points can be connected in sequence according to the vascular direction and structural characteristics of the target blood vessel to determine the centerline of the target blood vessel.

[0090] Regarding step S302, it should be noted that the blood vessel wall tissue has an intima and an adventitia, and the inner edge of the intima and the outer edge of the adventitia are used as the intima contour and the adventitia contour, respectively.

[0091] The characteristics of the contour are: a closed curve formed by a series of spatial points on the same spatial plane, and the normal vector of the contour plane is parallel to the tangent direction of the center line of the point. For an example, see Figure 3 , Figure 3 This is a schematic diagram of the structure of a vascular adventitia contour provided in an embodiment of the present application. Figure 3 As shown, the outer membrane contour of the target blood vessel determined here is actually composed of contour lines of multiple positions of the target blood vessel arranged in sequence. Among them, each central target point on the center line of the target blood vessel corresponds to a contour (contour curve).

[0092] With respect to step S303, lofting is a method of generating a smooth and continuous shape by gradually transitioning or interpolating multiple curves or surfaces of different cross sections.

[0093] Among them, the lofting process includes contour preparation, interpolation, smoothing and adjustment, connection and supplementation, and shape output steps. When performing interpolation processing: adjacent contours are interpolated to generate an intermediate shape (contour), which can be achieved by interpolating contour points, adjusting control points, parameterizing curves, and other methods. When performing smoothing and adjustment processing: during the interpolation and fusion process, problems such as uneven shapes and unnatural corner transitions may occur. In this step, curve fitting, smoothing algorithms and other techniques can be used to adjust the shape to make it smoother and more continuous in the transition area. When performing connection and supplementation processing: during the Lofting process, some discontinuously connected parts may appear, or the shape needs to be supplemented in some places. This can be solved by curves, surface connection algorithms, or additional interpolation operations.

[0094] With respect to step S304, the performing of volume filling processing on the intima surface model and the outer diameter surface model of the target blood vessel to obtain a three-dimensional blood vessel model of the target blood vessel includes: performing volume filling processing on the intima surface model and the outer diameter surface model of the target blood vessel respectively to obtain a three-dimensional intima model and a three-dimensional adventitia model of the target blood vessel, and determining the three-dimensional blood vessel model of the target blood vessel based on the three-dimensional intima model and the three-dimensional adventitia model of the target blood vessel.

[0095] Thus, with respect to the determination of the original plaque medical image data corresponding to each plaque in step S101, in one embodiment provided in the present application, the original plaque medical image data corresponding to each plaque is determined by the following steps:

[0096] S1011. Determine a three-dimensional reconstruction result of all plaques in the target blood vessel by using a result obtained by subtracting the three-dimensional model of the inner membrane from the three-dimensional model of the outer membrane of the target blood vessel.

[0097] S1012: extracting medical images at corresponding positions from the original total medical image of the target blood vessel according to the three-dimensional reconstruction results of all plaques, and determining original plaque medical image data corresponding to each plaque.

[0098] Regarding step S1011, it should be noted that the inner membrane and the outer membrane of a normal plaque-free blood vessel are basically overlapped.

[0099] For this step, when the three-dimensional reconstruction results of all plaques in the target blood vessel are determined, the position of each plaque in the target blood vessel is also determined, so that step S1012 can be executed.

[0100] S102. For each branch blood vessel, divide the plaque area corresponding to each plaque in the branch blood vessel according to the pixel value and area type division rule in the original plaque medical image data, and determine at least one sub-plaque area / plaque sub-area and the area type of each sub-plaque area / plaque sub-area.

[0101] In this step, the plaque area in the blood vessel is further divided based on the blood vessel.

[0102] Here, the pixel value in the original plaque medical image data may specifically be a CT value.

[0103] The region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0104] For example, the regional type classification rule may specifically be: for the plaque area, the area with a CT value ≥ 350HU is determined as a calcified plaque area, and the area with a CT value < 350HU is determined as a non-calcified plaque area. The non-calcified plaque area also specifically includes: determining the area with a CT value in the range of -30 to 30HU as a non-calcified plaque area with a necrotic core, determining the area with a CT value in the range of 31 to 130HU as a non-calcified plaque area with fiber fat, and determining the area with a CT value in the range of 131 to 350HU as a non-calcified plaque area with fiber.

[0105] In this way, the corresponding area type can be determined for each divided patch sub-area.

[0106] For examples, see Figure 4 , Figure 4 This is a schematic diagram of the structure of a branch blood vessel with plaque provided by the present application. In this way, the plaque area can be further divided to determine at least one plaque sub-area.

[0107] S103. For each target branch vessel having a target plaque sub-region of any specified region type, identify each target plaque sub-region in the target branch vessel according to high-risk plaque assessment rules and corresponding imaging parameters to determine whether the target branch vessel has a high-risk plaque.

[0108] It should be noted that high-risk plaques are determined according to the Coronary CT Image Interpretation and Reporting Guidelines (SCCT Guidelines), which stipulate that high-risk plaques include positive remodeling, napkin ring sign, punctate calcification, and low-density plaques.

[0109] Among them, each high-risk plaque is defined as:

[0110] Positive remodeling is defined as a ratio of the maximum vessel diameter (including plaque and lumen) of the lesion segment to the normal average lumen diameter proximal and distal to the plaque of ≥1.1.

[0111] Low attenuation plaque: defined as an area >1 mm2 within the plaque with a CT value <30 HU, which is closely associated with severe macrophage infiltration and a large lipid necrotic core (>40% of the total plaque volume).

[0112] Spotty calcification is defined as a high-density lesion with a long diameter of <3 mm and an average density of >130 HU in any plane within a non-calcified plaque, and the long diameter of the calcification is less than 1.5 times the diameter of the vessel, and the short diameter of the calcification is less than 2 / 3 of the diameter of the vessel.

[0113] "Napkin ring" sign: It is defined as a ring-shaped slightly higher density sign at the edge of a low-density plaque.

[0114] In this step, the selection of designated area types is determined according to the high-risk plaque assessment rules. Here, the four types of high-risk plaques are all non-calcified areas, so the designated area types include: non-calcified plaque areas with necrotic cores, non-calcified plaque areas with fibrofat, and non-calcified plaque areas with fibers.

[0115] The corresponding image parameters are determined according to the assessment indexes required for identifying each high-risk plaque in the high-risk plaque assessment rules.

[0116] For example, quantitative analysis indicators for evaluating plaque characteristics also include luminal stenosis caused by plaques, plaque size, plaque length, total plaque volume, calcified plaque volume, non-calcified plaque volume, minimum luminal area, reconstruction index, plaque load, etc. Among them, the plaque size is determined based on parameters such as total plaque load and / or non-calcified plaque load (volume ratio) and / or calcified plaque load (volume ratio) and / or plaque length. When determining luminal stenosis caused by plaques, parameters such as plaque cross-sectional area and / or luminal cross-sectional area need to be obtained.

[0117] The step S103 of identifying each target plaque sub-region in the target branch vessel may specifically be identifying each plaque region in the target branch vessel where a target plaque sub-region exists.

[0118] When a high-risk plaque is identified in any plaque region, it is determined that the target branch vessel has a high-risk plaque.

[0119] In one embodiment provided in the present application, for each target branch vessel having a target plaque sub-region of any specified region type, identifying the plaque region having the target plaque sub-region in the target branch vessel according to a high-risk plaque assessment rule and corresponding imaging parameters, and determining whether the target branch vessel has a high-risk plaque, includes:

[0120] S1031. For each plaque region having a target plaque sub-region in each target branch blood vessel, determine an identification order of high-risk plaques according to the region type of the target plaque sub-region in the plaque region.

[0121] S1032. According to the determined recognition order of the high-risk plaques, image parameters required for identifying each high-risk plaque are acquired in sequence.

[0122] S1033, identifying the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtaining the high-risk plaque identification result corresponding to the plaque area.

[0123] S1034: When the high-risk plaque identification result corresponding to any plaque region in the target branch blood vessel indicates the existence of a high-risk plaque, determine that the target branch blood vessel has a high-risk plaque.

[0124] For step S1031, for example, assuming that the region type of the target plaque sub-region in a certain plaque region is a non-calcified plaque region with a necrotic core, the corresponding high-risk plaque identification order may be: low-density plaque → napkin ring sign → positive reconstruction. If the region type of the target plaque sub-region in a certain plaque region is a non-calcified plaque region with fibrofat, the corresponding high-risk plaque identification order may be: point calcification → positive reconstruction.

[0125] In this way, by determining the recognition order of high-risk plaques, the recognition of unnecessary types of high-risk plaques can be reduced, thereby increasing the recognition speed, and by performing recognition in sequence according to the determined recognition order, the accuracy of high-risk plaque recognition can be further improved.

[0126] Regarding step S1033, the high-risk plaque is identified according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque. Here, four ideas for identifying high-risk plaques are provided:

[0127] Idea for identifying low-density plaques: If the area type of the target plaque sub-area in the plaque area is a non-calcified plaque area with a necrotic core, calculate the CT mean of the target plaque sub-area; if the CT mean is less than 30HU, continue to measure the volume of the target plaque sub-area; if the CT mean is not less than 30HU, end the low-density plaque identification; where volume = number of pixels of the necrotic core × physical resolution of the pixel (x_spacing × y_spacing × interval (interval)); if volume>1mm 3 , it is identified as a low-density plaque, otherwise the output is the identification result that it is not a high-risk plaque.

[0128] Idea for identifying point calcification: If the area type of the target plaque sub-area within the plaque area is a non-calcified plaque area with fibers, calculate the CT mean of the fiber and fibrous fat areas; if the CT mean is greater than 130HU, continue to measure the volume of the fiber and fibrous fat; if the CT mean is not greater than 130HU, end the identification of point calcification; where volume = number of pixels of fiber and fibrous fat × physical resolution of pixel (x_spacing × y_spacing × interval (interval)); calculate the inner diameter of the location of the fiber and fibrous fat; calculate the farthest two pixels in the volume to see whether the long diameter of the point calcification is less than 1.5 times the inner diameter, and the short diameter of the point calcification is less than 2 / 3 of the inner diameter. If both are true, identify it as point calcification.

[0129] The identification idea of ​​the napkin ring sign is as follows: if the area type of the target plaque sub-area within the plaque area is a non-calcified plaque area with a necrotic core, the necrotic core is morphologically expanded; if the ratio of fiber and fibrous fat within the expansion range exceeds the threshold, it is identified as a napkin ring sign, otherwise, the output is an identification result that is not a high-risk plaque.

[0130] The identification idea of ​​positive reconstruction is as follows: arrange the contours of the target branch vessels in sequence; calculate the ratio of the current outer membrane contour diameter to the average of the proximal and distal outer diameter contour diameters. If the ratio is <1.1, the identification is terminated, otherwise, the next step of identification is performed; if the ratio is >1.1, and the diameter difference between the outer diameter and the inner diameter is >1mm, the target plaque sub-area is a non-calcified area, if so, it is identified as positive reconstruction.

[0131] For examples, see Figure 5 , Figure 5 This is a schematic diagram of the data results of the positive reconstruction high-risk plaque identification process provided in this application. Figure 5 As shown, based on the contour data of the blood vessel, it can be determined whether there is a positively remodeled high-risk plaque and its location.

[0132] S104. If so, output the high-risk plaque identification result of the target branch blood vessel; the plaque identification result includes the high-risk plaque type, plaque location and the blood vessel name of the target branch blood vessel.

[0133] In this step, after the high-risk plaque identification result of each target branch vessel is determined, the high-risk plaque identification result of the target vessel can be determined based on the high-risk plaque identification result of each target branch vessel.

[0134] In one embodiment provided in the present application, the plaque location includes the plaque location of each high-risk plaque, and the plaque location of each high-risk plaque is determined by the following steps: when a high-risk plaque is identified to exist in the target branch blood vessel, the plaque area to which the high-risk plaque belongs is determined; based on the determined plaque area and the original total medical image, the plaque location of the high-risk plaque is determined by the shortest distance mapping processing method.

[0135] An embodiment of the present application provides a method for identifying plaques in a blood vessel, the identification method comprising: when a plaque exists in a target blood vessel to be identified, determining original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel; the target blood vessel includes at least one branch blood vessel; for each branch blood vessel, dividing the plaque area corresponding to each plaque in the branch blood vessel according to the pixel values ​​and area type division rules in the original plaque medical image data, and determining at least one plaque sub-area and the area type of each plaque sub-area; for each target branch blood vessel having a target plaque sub-area of ​​any specified area type, identifying the plaque area of ​​the target branch blood vessel having the target plaque sub-area according to the high-risk plaque assessment rules and the corresponding image parameters, and determining whether the target branch blood vessel has a high-risk plaque; if so, outputting a high-risk plaque identification result of the target branch blood vessel; the plaque identification result includes the high-risk plaque type, the plaque location and the blood vessel name of the target branch blood vessel.

[0136] In this way, the present application further divides the plaque area according to the pixel value, determines the type of sub-areas included in each plaque area, and then determines whether to identify high-risk plaques and the identification order according to the type of sub-areas, and obtains the corresponding image parameters and identifies the high-risk plaques in sequence according to the identification order, thereby improving the identification speed and accuracy of high-risk plaques in blood vessels.

[0137] See also Figure 6 , Figure 7 , Figure 6 This is one of the structural schematic diagrams of a device for identifying plaque in a blood vessel provided in an embodiment of the present application. Figure 7 This is a second structural diagram of a device for identifying plaque in a blood vessel provided in an embodiment of the present application. Figure 6 As shown in , the identification device 600 includes:

[0138] A first determination module 610 is used to determine original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel when there is a plaque in the target blood vessel to be identified; the target blood vessel includes at least one branch blood vessel;

[0139] A division module 620 is used to divide the plaque area corresponding to each plaque in each branch blood vessel according to the pixel value and area type division rule in the original plaque medical image data, and determine at least one plaque sub-area and the area type of each plaque sub-area;

[0140] The identification module 630 is used to identify the plaque area of ​​the target branch vessel where the target plaque sub-area of ​​any specified area type exists according to the high-risk plaque assessment rule and the corresponding image parameters, and determine whether the target branch vessel has a high-risk plaque;

[0141] The output module 640 is used to output the high-risk plaque identification result of the target branch blood vessel when it is determined that the target branch blood vessel has a high-risk plaque; the plaque identification result includes the high-risk plaque type, plaque location and the blood vessel name of the target branch blood vessel.

[0142] Optional, such as Figure 7 As shown, the identification device 600 further includes a second determination module 650, and the second determination module 650 is used to:

[0143] Acquiring a three-dimensional reconstructed blood vessel model of the target blood vessel;

[0144] identifying, according to a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel;

[0145] If present, it is determined that plaque exists in the target blood vessel.

[0146] Optionally, the identification device 600 further includes a construction module 660, and the construction module 660 is used to:

[0147] Performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel;

[0148] Performing contour recognition according to the centerline of the target blood vessel and the original medical image to determine the intima contour and the adventitia contour of the target blood vessel;

[0149] Performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel;

[0150] According to the intima curved surface model and the outer diameter curved surface model of the target blood vessel, a volume filling process is performed to obtain a three-dimensional blood vessel model of the target blood vessel; the three-dimensional blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.

[0151] Optionally, when the first determining module 610 is used to determine the original plaque medical image data corresponding to each plaque through the following steps, the first determining module 610 is used to:

[0152] Determine a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the inner membrane from a three-dimensional model of the outer membrane of the target blood vessel;

[0153] According to the three-dimensional reconstruction results of all plaques, the medical image at the corresponding position is extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

[0154] Optionally, the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0155] Optionally, when the identification module 630 is used to identify the plaque area where the target plaque sub-area exists in each target branch vessel where a target plaque sub-area of ​​any specified area type exists according to the high-risk plaque assessment rule and the corresponding image parameters, and to determine whether the target branch vessel has a high-risk plaque, the identification module 630 is used to:

[0156] For each plaque region in each target branch blood vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to a region type of the target plaque sub-region in the plaque region;

[0157] According to the determined recognition order of high-risk plaques, imaging parameters required for identifying each high-risk plaque are sequentially acquired;

[0158] Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area;

[0159] When the high-risk plaque identification result corresponding to any plaque region in the target branch blood vessel indicates the existence of a high-risk plaque, it is determined that the target branch blood vessel has a high-risk plaque.

[0160] Optionally, the identification module 630 is further used for:

[0161] When a high-risk plaque is identified in the target branch vessel, determining the plaque region to which the high-risk plaque belongs;

[0162] According to the determined plaque area and the original total medical image, the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.

[0163] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown in , the electronic device 800 includes a processor 810 , a memory 820 and a bus 830 .

[0164] The memory 820 stores machine-readable instructions executable by the processor 810. When the electronic device 800 is running, the processor 810 communicates with the memory 820 via the bus 830. When the machine-readable instructions are executed by the processor 810, the above-mentioned Figure 1 The specific implementation of the steps in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0165] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The specific implementation of the steps in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0166] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0167] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0168] 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 solution of this embodiment.

[0169] In addition, each functional unit in each embodiment of the present application 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.

[0170] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several 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 application. The aforementioned 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), a random access memory (RAM), a magnetic disk or an optical disk.

[0171] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for identifying plaque in a blood vessel, characterized in that: The identification method comprises: When there is a plaque in the target blood vessel to be identified, original plaque medical image data corresponding to each plaque is determined according to the original total medical image of the target blood vessel; the target blood vessel includes at least one branch blood vessel; For each branch blood vessel, according to the pixel value and region type division rule in the original plaque medical image data, the plaque region corresponding to each plaque in the branch blood vessel is divided to determine at least one plaque sub-region and the region type of each plaque sub-region; For each target branch vessel having a target plaque sub-region of any specified region type, the plaque region of the target branch vessel having the target plaque sub-region is identified according to the high-risk plaque assessment rule and the corresponding image parameters to determine whether the target branch vessel has a high-risk plaque; wherein the corresponding image parameters are determined according to the assessment indicators required for identifying each type of high-risk plaque in the high-risk plaque assessment rule; If so, output the high-risk plaque identification result of the target branch vessel; the plaque identification result includes the high-risk plaque type, plaque location and the vessel name of the target branch vessel; The method of identifying, for each target branch vessel having a target plaque sub-region of any specified region type, a plaque region having a target plaque sub-region in the target branch vessel according to a high-risk plaque assessment rule and corresponding image parameters, and determining whether the target branch vessel has a high-risk plaque includes: For each plaque region in each target branch blood vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to a region type of the target plaque sub-region in the plaque region; According to the determined recognition order of high-risk plaques, imaging parameters required for identifying each high-risk plaque are sequentially acquired; Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area; When the high-risk plaque identification result corresponding to any plaque region in the target branch blood vessel indicates the existence of a high-risk plaque, it is determined that the target branch blood vessel has a high-risk plaque.

2. The identification method according to claim 1, characterized in that: Determine whether plaque is present in the target vessel to be identified by the following steps: Acquiring a three-dimensional reconstructed blood vessel model of the target blood vessel; identifying, according to a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel; If present, it is determined that plaque exists in the target blood vessel.

3. The identification method according to claim 2, characterized in that: The three-dimensional reconstructed vascular model of the target blood vessel is constructed by the following steps: Performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel; Performing contour recognition according to the centerline of the target blood vessel and the original medical image to determine the intima contour and the adventitia contour of the target blood vessel; Performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel; According to the intima curved surface model and the adventitia curved surface model of the target blood vessel, volume filling processing is performed to obtain a three-dimensional blood vessel model of the target blood vessel; the three-dimensional blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.

4. The identification method according to claim 3, characterized in that: The original plaque medical image data corresponding to each plaque is determined by the following steps: Determine a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the inner membrane from a three-dimensional model of the outer membrane of the target blood vessel; According to the three-dimensional reconstruction results of all plaques, the medical image at the corresponding position is extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

5. The identification method according to claim 1, characterized in that: The region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

6. The identification method according to claim 1, characterized in that: The plaque position includes the plaque position of each high-risk plaque, and the plaque position of each high-risk plaque is determined by the following steps: When a high-risk plaque is identified in the target branch vessel, determining the plaque region to which the high-risk plaque belongs; According to the determined plaque area and the original total medical image, the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.

7. A device for identifying plaque in a blood vessel, characterized in that: The identification device comprises: A first determination module is used to determine original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel when there is a plaque in the target blood vessel to be identified; the target blood vessel includes at least one branch blood vessel; A division module is used to divide the plaque area corresponding to each plaque in each branch blood vessel according to the pixel value and area type division rule in the original plaque medical image data, and determine at least one plaque sub-area and the area type of each plaque sub-area; an identification module, for identifying, for each target branch vessel having a target plaque sub-region of any specified region type, a plaque region in the target branch vessel having a target plaque sub-region according to a high-risk plaque assessment rule and corresponding image parameters, to determine whether the target branch vessel has a high-risk plaque; wherein the corresponding image parameters are determined according to the assessment index required for identifying each high-risk plaque in the high-risk plaque assessment rule; An output module, for outputting a high-risk plaque identification result of the target branch vessel when it is determined that the target branch vessel has a high-risk plaque; the plaque identification result includes the high-risk plaque type, the plaque location and the vessel name of the target branch vessel; When the identification module is used to identify the plaque area in each target branch vessel where the target plaque sub-area of ​​any specified area type exists according to the high-risk plaque assessment rule and the corresponding image parameters, and to determine whether the target branch vessel has a high-risk plaque, the identification module is used to: For each plaque region in each target branch blood vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to a region type of the target plaque sub-region in the plaque region; According to the determined recognition order of high-risk plaques, imaging parameters required for identifying each high-risk plaque are sequentially acquired; Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area; When the high-risk plaque identification result corresponding to any plaque region in the target branch blood vessel indicates the existence of a high-risk plaque, it is determined that the target branch blood vessel has a high-risk plaque.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to perform the steps of the identification method as described in any one of claims 1 to 6.

9. 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 steps of the identification method according to any one of claims 1 to 6 are executed.

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