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WO2025087110A1 - Procédé et dispositif d'identification de plaque dans un vaisseau sanguin - Google Patents

Procédé et dispositif d'identification de plaque dans un vaisseau sanguin Download PDF

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Publication number
WO2025087110A1
WO2025087110A1 PCT/CN2024/124929 CN2024124929W WO2025087110A1 WO 2025087110 A1 WO2025087110 A1 WO 2025087110A1 CN 2024124929 W CN2024124929 W CN 2024124929W WO 2025087110 A1 WO2025087110 A1 WO 2025087110A1
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Prior art keywords
plaque
blood vessel
target
risk
region
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English (en)
Chinese (zh)
Inventor
刘博清
黄星胜
余再新
马骏
郑凌霄
兰宏志
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Xiangya Hospital of Central South University
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Xiangya Hospital of Central South University
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Publication of WO2025087110A1 publication Critical patent/WO2025087110A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present application relates to the field of medical technology, and for example, to a method and device for identifying plaque in a blood vessel.
  • vascular disease has become a topic of great concern.
  • 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.
  • a plaque due to the characteristics of high-risk coronary plaques, a plaque usually spans multiple coronary CT angiography (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.
  • CCTA coronary CT angiography
  • the present application provides a method and device for identifying plaques in blood vessels, which can effectively identify high-risk plaques in blood vessels.
  • the present application embodiment provides a method for identifying plaque in a blood vessel, the identification method comprising:
  • original plaque medical image data corresponding to each plaque is determined according to an original total medical image of the target blood vessel;
  • the target blood vessel includes at least one branch blood vessel;
  • 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;
  • a 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.
  • the presence of plaque in the target blood vessel to be identified is determined by:
  • the three-dimensional reconstructed blood vessel model of the target blood vessel is constructed by:
  • volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel;
  • the three-dimensional reconstructed blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.
  • determining the original plaque medical image data corresponding to each plaque includes:
  • 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.
  • the region type division rule is to set different pixels for different region types. Threshold ranges to establish rules for plaque quantification analysis.
  • 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 comprises:
  • imaging parameters required for identifying each high-risk plaque are sequentially acquired
  • 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 plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.
  • the present application also provides a device for identifying plaque in a blood vessel, the device comprising:
  • a first determination module is configured to determine original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel in response to the presence of plaque in the target blood vessel to be identified; the target blood vessel includes at least one branch blood vessel;
  • a division module configured to 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 plaque sub-area and the area type of each plaque sub-area;
  • an identification module configured to identify, 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, to determine whether the target branch vessel has a high-risk plaque;
  • the output module is configured to output the target branch vessel in response to determining that the target branch vessel has a high-risk plaque.
  • the high-risk plaque identification result of the target branch blood vessel includes the high-risk plaque type, plaque location and the blood vessel name of the target branch blood vessel.
  • the identification device further includes a second determination module, and the second determination module is configured to:
  • the identification device further includes a building module, and the building module is configured to:
  • volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel;
  • the three-dimensional reconstructed blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.
  • the first determining module when the first determining module is configured to determine the original plaque medical image data corresponding to each plaque in the following manner, the first determining module is configured to:
  • 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.
  • the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.
  • the identification module when the identification module is configured to identify the plaque area in the target branch vessel where the target plaque sub-area exists according to the high-risk plaque assessment rule and the corresponding image parameters for each target branch vessel where the target plaque sub-area of any specified area type exists, and to determine whether the target branch vessel has a high-risk plaque, the identification module is configured to:
  • the area type of the target plaque sub-area in the plaque area determines the identification order of high-risk plaques
  • imaging parameters required for identifying each high-risk plaque are sequentially acquired
  • the identification module is further configured to:
  • the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.
  • An embodiment of the present application also provides an electronic device, including: 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 when the machine-readable instructions are executed by the processor, the identification method as described above is performed.
  • An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored.
  • the computer program is executed by a processor, the identification method as described above is executed.
  • FIG1 is a flow chart of a method for identifying plaque in a blood vessel provided in an embodiment of the present application
  • FIG2 is a schematic structural diagram of a blood vessel centerline provided in an embodiment of the present application.
  • FIG3 is a schematic structural diagram of a vascular adventitia profile provided in an embodiment of the present application.
  • FIG4 is a schematic diagram of the structure of a branch blood vessel with plaque provided by the present application.
  • FIG5 is a schematic diagram of the data results of the positive reconstruction high-risk plaque identification process provided by the present application.
  • FIG6 is a schematic diagram of a structure of a device for identifying plaque in a blood vessel provided in an embodiment of the present application.
  • FIG. 7 is a second schematic diagram of the structure of a device for identifying plaque in a blood vessel provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • 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.
  • Figure 1 is a flow chart of a method for identifying a plaque in a blood vessel provided by an embodiment of the present application.
  • the identification method provided by the embodiment of the present application includes:
  • the target blood vessel includes at least one branch blood vessel.
  • the target blood vessel when the target blood vessel is a coronary artery, the target blood vessel includes the branch blood vessels of the left main trunk, the left anterior descending branch, the left circumflex branch, and the right coronary artery, or can be further divided into the sharp edge branch, the sinoatrial node branch, the atrioventricular node branch, the posterior descending branch, the left posterior ventricular branch, and other branch blood vessels.
  • the type of branch blood vessels included in the target blood vessel may be set in advance by relevant personnel.
  • the presence of plaque in the target blood vessel to be identified is determined by the following steps:
  • S201 Acquire a three-dimensional reconstructed blood vessel model of the target blood vessel.
  • 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.
  • S203 In response to identifying that a mutation region exists in the three-dimensional reconstructed blood vessel model of the target blood vessel, determine that a plaque exists in the target blood vessel.
  • a three-dimensional reconstructed blood vessel model of the target blood vessel is constructed by the following steps:
  • S302 Perform contour recognition according to the center line of the target blood vessel and the original total medical image to determine the intima contour and adventitia contour of the target blood vessel.
  • the three-dimensional reconstructed blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.
  • step S301 please refer to FIG2 for example, which is a schematic diagram of the structure of a blood vessel centerline provided in an embodiment of the present application.
  • the center target points of multiple locations of the target blood vessel can be first determined to obtain multiple center target points, and then the multiple center target points can be connected in sequence according to the blood vessel direction and structural characteristics of the target blood vessel to determine the centerline of the target blood vessel.
  • 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.
  • 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 where the point is located.
  • Figure 3 is a structural schematic diagram of a vascular adventitia contour provided in an embodiment of the present application.
  • the adventitia 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).
  • the lofting process is a method of generating a smooth and continuous shape by gradually transitioning or interpolating a plurality of curves or surfaces of different cross sections.
  • the lofting process includes steps such as contour preparation, interpolation, smoothing and adjustment, connection and supplementation, and shape output.
  • 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.
  • smoothing and adjustment processing during the interpolation and fusion process, problems such as uneven shapes and unnatural corner transitions may occur.
  • 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.
  • 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 curve and surface connection algorithms, or additional interpolation operations.
  • performing volume filling processing on the intimal surface model and the adventitia surface model of the target blood vessel to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel includes: performing volume filling processing on the intimal surface model and the adventitia surface model of the target blood vessel to obtain a three-dimensional intimal model and a three-dimensional adventitia model of the target blood vessel, and reconstructing the three-dimensional intimal model and the adventitia surface model of the target blood vessel according to the three-dimensional intimal model and the adventitia surface model of the target blood vessel.
  • a three-dimensional reconstructed blood vessel model of the target blood vessel is determined based on the three-dimensional model.
  • the original plaque medical image data corresponding to each plaque is determined by the following steps:
  • 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.
  • 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.
  • step S1011 it should be noted that the inner membrane and the outer membrane of a normal plaque-free blood vessel are basically overlapped.
  • step S1012 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.
  • each branch blood vessel For each branch blood vessel, divide the plaque region corresponding to each plaque in the branch blood vessel according to the pixel values and region type division rules in the original plaque medical image data, and determine at least one plaque sub-region and the region type of each plaque sub-region.
  • step S102 the plaque area in the blood vessel is divided based on the blood vessel.
  • the pixel value in the original plaque medical imaging data may be, for example, a computed tomography (CT) value, the unit of which is Hounsfield unit (HU).
  • CT computed tomography
  • the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.
  • the regional type classification rule may 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 may also include: 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.
  • the corresponding area type can be determined for each divided patch sub-area.
  • Figure 4 is a schematic diagram of the structure of a branch blood vessel with plaque provided by the present application.
  • the plaque area can be divided to determine at least one plaque sub-area.
  • the plaque sub-area is identified to determine whether the target branch vessel has a high-risk plaque.
  • High-risk plaques are determined according to the coronary CT image interpretation and reporting guidelines (such as the Society of Cardiovascular Computed Tomography (SCCT) guidelines).
  • SCCT Society of Cardiovascular Computed Tomography
  • the SCCT guidelines stipulate that high-risk plaques include: positive remodeling, "napkin ring” sign, punctate calcification, and low-density plaques.
  • Positive remodelling refers to the ratio of the maximum vessel diameter (including plaque and lumen) of the diseased segment to the normal average lumen diameter proximal and distal to the plaque, which is ⁇ 1.1.
  • Low attenuation plaque refers to an area within the plaque with a CT value of ⁇ 30HU in an area of >1 square millimeter (mm 2 ). Low attenuation plaque is closely associated with severe macrophage infiltration and a large lipid necrotic core (>40% of the total plaque volume).
  • Spotty calcification refers to a high-density lesion with a long diameter of ⁇ 3 mm and an average density of >130HU 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 blood vessel, and the short diameter of the calcification is less than 2/3 of the diameter of the blood vessel.
  • Nepkin-ring refers to the ring-shaped slightly higher density sign at the edge of the low-density plaque.
  • the selection of designated area types is determined according to the high-risk plaque assessment rules.
  • the above four high-risk plaques i.e., positive remodeling, "napkin ring” sign, punctate calcification, and low-density plaques
  • 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.
  • 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.
  • 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.
  • parameters such as plaque cross-sectional area and/or luminal cross-sectional area need to be obtained.
  • the step S103 of identifying each target plaque sub-region in the target branch vessel may be, for example, identifying each plaque region in the target branch vessel where a target plaque sub-region exists.
  • Identifying a plaque region in the target branch vessel where a target plaque sub-region exists, and determining whether the target branch vessel has a high-risk plaque includes:
  • 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.
  • image parameters required for identifying each high-risk plaque are acquired in sequence.
  • 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 plaque region is a non-calcified plaque region with fibrofat, the corresponding high-risk plaque identification order may be: point calcification ⁇ positive reconstruction.
  • the recognition of unnecessary types of high-risk plaques can be reduced, thereby increasing the recognition speed.
  • the accuracy of high-risk plaque recognition can also be improved.
  • the high-risk plaque is identified according to the high-risk plaque assessment rule and the image parameters corresponding to the high-risk plaque.
  • Four high-risk plaque identification methods are provided below.
  • the inner diameter of the fat location is calculated; the farthest two pixels in the volume are calculated to see whether the long diameter of the point-like calcification is less than 1.5 times the inner diameter, and the short diameter of the point-like calcification is less than 2/3 of the inner diameter. If both are true, it is identified as point-like calcification.
  • the identification method 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 expanded 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.
  • Positive reconstruction was identified by arranging the contours of the target branch vessels in sequence; calculating the ratio of the current outer membrane contour diameter to the average of the proximal and distal outer diameter contour diameters; if the ratio was ⁇ 1.1, identification was terminated; if the ratio was >1.1, and the diameter difference between the outer diameter and the inner diameter of the target branch vessel was >1mm, and the target plaque sub-area was a non-calcified area, it was identified as positive reconstruction.
  • Figure 5 is a schematic diagram of the data results of the positively remodeled high-risk plaque identification process provided by the present application. As shown in Figure 5, whether there is a positively remodeled high-risk plaque and its location can be determined based on the contour data of the blood vessel.
  • S104 In response to determining that a high-risk plaque exists in the target branch vessel, output a high-risk plaque identification result of the target branch vessel; the plaque identification result includes the high-risk plaque type, the plaque location, and the vessel name of the target branch vessel.
  • 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.
  • 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.
  • 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 value and area type division rule 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 with a target plaque sub-area of any specified area type, identifying the plaque area in the target branch blood vessel with the target plaque sub-area 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; in response to determining that the target branch blood vessel has a high-risk plaque, outputting a high-risk plaque identification result of the target branch blood vessel; the plaque identification result includes the high
  • the present application divides the plaque area according to pixel values, 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 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.
  • Figure 6 is a schematic diagram of the structure of a device for identifying plaque in a blood vessel provided in an embodiment of the present application
  • Figure 7 is a schematic diagram of the structure of a device for identifying plaque in a blood vessel provided in an embodiment of the present application.
  • the identification device 600 includes:
  • the first determination module 610 is configured to determine original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel in response to the presence of plaque in the target blood vessel to be identified; the target blood vessel includes at least one branch blood vessel;
  • the division module 620 is configured to 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 plaque sub-area and the area type of each plaque sub-area;
  • the identification module 630 is configured to identify, 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;
  • the output module 640 is configured to output a high-risk plaque identification result of the target branch vessel in response to determining that the target branch vessel has a high-risk plaque; the plaque identification result includes the high-risk plaque type, plaque location and vessel name of the target branch vessel.
  • the identification device 600 further includes a second determination module 650, and the second determination module 650 is configured to:
  • the identification device 600 further includes a construction module 660, and the construction module 660 is configured to:
  • volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel;
  • the three-dimensional reconstructed blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.
  • the first determining module 610 is configured to determine the original plaque medical image data corresponding to each plaque in the following manner, the first determining module 610 is configured to:
  • 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.
  • the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.
  • the identification module 630 is configured to identify the plaque area in the target branch vessel where the target plaque sub-area exists according to the high-risk plaque assessment rule and the corresponding image parameters for each target branch vessel where the target plaque sub-area of any specified area type exists, and to determine whether the target branch vessel has a high-risk plaque, the identification module 630 is configured to:
  • imaging parameters required for identifying each high-risk plaque are sequentially acquired
  • the identification module 630 is further configured to:
  • the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.
  • FIG8 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • the electronic device 800 includes a processor 810 , a memory 820 , and a bus 830 .
  • the memory 820 stores machine-readable instructions executable by the processor 810.
  • the processor 810 communicates with the memory 820 via a bus 830.
  • the machine-readable instructions are executed by the processor 810, the steps in the method embodiment shown in FIG. 1 above can be executed.
  • the steps in the method embodiment shown in FIG. 1 above can be executed.
  • An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored.
  • a computer program is stored.
  • the steps in the method embodiment shown in FIG. 1 can be executed.
  • the steps in the method embodiment shown in FIG. 1 can be executed.
  • the steps in the method embodiment shown in FIG. 1 can be executed.
  • the disclosed systems, devices and methods can be implemented in other ways.
  • the device embodiments described above are schematic.
  • the division of the units is a logical function division. There may be other division methods in actual implementation.
  • 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, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
  • 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 implement the solution of this embodiment.
  • multiple functional units may be integrated into one processing unit, or multiple functional units may exist physically separately, or two or more functional units may be integrated into one processing unit.
  • 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.
  • the solution of the present application or the part that contributes to the relevant technology or the part of the solution, can be embodied in the form of a software product, which is stored in a storage medium and includes at least one instruction to enable a computer device (which can be a personal computer, server, or 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.

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  • Apparatus For Radiation Diagnosis (AREA)

Abstract

L'invention concerne un procédé et un dispositif d'identification de plaque dans un vaisseau sanguin. Le procédé comprend les étapes suivantes : lorsqu'il y a une plaque dans un vaisseau sanguin cible à identifier, déterminer des données d'image médicale brute de plaque correspondant à chaque plaque (S101); pour chaque vaisseau sanguin ramifié, sur la base de valeurs de pixels et d'une règle de division de type de région dans les données d'image médicale brute de plaque, diviser chaque région de plaque, et déterminer au moins une sous-région de plaque (S102); pour chaque vaisseau sanguin ramifié cible ayant des sous-régions de plaque cibles de n'importe quel type de région spécifié, sur la base d'une règle d'évaluation de plaque à haut risque et de paramètres d'image correspondants, identifier une sous-région de plaque cible dans le vaisseau sanguin ramifié cible, et déterminer si le vaisseau sanguin ramifié cible a une plaque à haut risque (S103); et si tel est le cas, délivrer en sortie un résultat d'identification de plaque à haut risque du vaisseau sanguin ramifié cible, le résultat d'identification de plaque comprenant le type de la plaque à haut risque, la position de la plaque et un nom de vaisseau sanguin (S104).
PCT/CN2024/124929 2023-10-24 2024-10-15 Procédé et dispositif d'identification de plaque dans un vaisseau sanguin Pending WO2025087110A1 (fr)

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