WO2021081771A1 - 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置 - Google Patents

基于vrds ai医学影像的心脏冠脉的分析方法和相关装置 Download PDF

Info

Publication number
WO2021081771A1
WO2021081771A1 PCT/CN2019/114082 CN2019114082W WO2021081771A1 WO 2021081771 A1 WO2021081771 A1 WO 2021081771A1 CN 2019114082 W CN2019114082 W CN 2019114082W WO 2021081771 A1 WO2021081771 A1 WO 2021081771A1
Authority
WO
WIPO (PCT)
Prior art keywords
target
coronary artery
heart
image data
degree
Prior art date
Application number
PCT/CN2019/114082
Other languages
English (en)
French (fr)
Inventor
李戴维伟
李斯图尔特平
Original Assignee
未艾医疗技术(深圳)有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 未艾医疗技术(深圳)有限公司 filed Critical 未艾医疗技术(深圳)有限公司
Priority to PCT/CN2019/114082 priority Critical patent/WO2021081771A1/zh
Priority to CN201980099742.5A priority patent/CN114340496A/zh
Publication of WO2021081771A1 publication Critical patent/WO2021081771A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to a method and related devices for analyzing the coronary arteries of the heart based on VRDS AI medical images.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • DTI Diffusion Tensor Imaging
  • Computed Tomography Positron Emission Computed Tomography
  • the embodiment of the application provides a method for analyzing the coronary artery of the heart and related devices based on VRDS AI medical images. Implementing the embodiment of the application can improve the accuracy and intelligence of selecting a coronary stent in a coronary artery disease.
  • the first aspect of the embodiments of the present application provides a method for analyzing the coronary artery of the heart based on VRDS AI medical images, including:
  • the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • a second aspect of the embodiments of the present application provides a medical imaging device, including:
  • An acquiring unit for acquiring a scanned image of a part of the coronary artery of the heart, wherein the scanned image further includes the part of the coronary artery of the heart and the blood vessels around the coronary artery of the heart;
  • a processing unit configured to perform image processing according to the scanned image to obtain a target image data set
  • a determining unit configured to determine the degree of coronary artery disease of the target user's heart according to the target image data set
  • the determining unit is also used to determine the type of coronary stent according to the degree of disease of the coronary artery of the heart.
  • a third aspect of the embodiments of the present application provides a medical imaging device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are generated It is executed by the processor to execute the instructions of the steps in any one of the methods of the first aspect of the above claims.
  • the fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, and the stored computer program is executed by the processor to implement the first aspect of the claims. Any of the methods.
  • the scan image of the coronary artery of the heart is obtained, wherein the scan image also includes the coronary artery of the heart and the blood vessels around the coronary artery of the heart.
  • the image is performed based on the scan image.
  • a target image data set is obtained, and then, according to the target image data set, the degree of coronary artery disease of the target user is determined, and finally the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • the quantitative selection of the coronary stent is realized, so that the coronary stent can be expanded to cover the plaque after the stent is expanded. It does not cause plaque to fall off, reduces the surgical risk of vascular stent implantation, and improves the accuracy and intelligence of selecting coronary stents in coronary heart diseases.
  • FIG. 1 is a schematic structural diagram of a cardiac coronary artery analysis system based on VRDS AI medical image provided by an embodiment of the application;
  • FIG. 2 is a schematic flow chart of a method for analyzing the coronary artery of the heart based on VRDS AI medical images according to an embodiment of the application;
  • FIG. 3 is a schematic flowchart of another method for analyzing the coronary artery of the heart based on VRDS AI medical image provided by an embodiment of the application;
  • FIG. 4 is a schematic flowchart of another method for analyzing the coronary artery of the heart based on VRDS AI medical images according to an embodiment of the application;
  • FIG. 5 is a schematic diagram of a medical imaging device provided by an embodiment of the application.
  • FIG. 6 is a schematic structural diagram of a medical imaging device in a hardware operating environment related to an embodiment of the application.
  • the medical imaging devices involved in the embodiments of this application refer to various instruments that use various media as information carriers to reproduce the internal structure of the human body as images.
  • the image information and the actual structure of the human body have spatial and temporal distributions.
  • DICOM data refers to the original image file data that reflects the internal structural characteristics of the human body collected by medical equipment, which can include electronic computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, and positron emission computed tomography PET-
  • image source refers to the Texture2D/3D image volume data generated by analyzing the original DICOM data.
  • VRDS refers to the Virtual Reality Doctor system (VRDS for short).
  • Fig. 1 is a schematic structural diagram of a cardiac coronary artery analysis system 100 based on VRDS AI medical imaging according to an embodiment of the present application.
  • the system 100 includes a medical imaging device 110 and a network database 120.
  • the medical imaging The device 110 may include a local medical imaging device 111 and/or a terminal medical imaging device 112.
  • the local medical imaging device 111 or the terminal medical imaging device 112 is used for the VRDS AI medical imaging based on the original DICOM data presented in the embodiment of this application.
  • the analysis algorithm of the coronary artery of the heart Based on the analysis algorithm of the coronary artery of the heart, it carries out the recognition, positioning, four-dimensional volume rendering, and abnormal analysis of the human heart coronary imaging area to realize the four-dimensional stereo imaging effect (the four-dimensional medical image specifically refers to the medical image including the inside of the displayed tissue Spatial structural features and external spatial structural features.
  • the internal spatial structural features refer to that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of the coronary artery, blood vessels and other tissues of the heart.
  • the external spatial structural characteristics refer to the relationship between the tissue and the tissue.
  • the environmental characteristics between tissues including the spatial location characteristics between tissues (including intersection, interval, fusion), etc., such as the edge structure characteristics of the intersection between the left coronary artery and the right coronary artery, etc.), local medical imaging
  • the device 111 can also be used to edit the scanned image to form the transfer function result of the four-dimensional human body image.
  • the transfer function result can include the transfer of the tissue structure in the surface of the human heart coronary artery and the human heart coronary artery.
  • the result of the function, as well as the result of the transfer function in the cube space such as the number of cube edit boxes and the array number, coordinates, color, transparency and other information of the arc edit required by the transfer function.
  • the network database 120 may be, for example, a cloud medical imaging device, etc.
  • the network database 120 is used to store the image source generated by analyzing the original DICOM data and the transfer function result of the four-dimensional human body image edited by the local medical imaging device 111.
  • the scanned image may be from Multiple local medical imaging devices 111 are used to realize interactive diagnosis of multiple doctors.
  • HMDS head-mounted Displays Set
  • the operating actions refer to the user’s actions through the medical imaging device.
  • External ingestion equipment such as mouse, keyboard, tablet (portable android device, Pad), iPad (internet portable apple device), etc., operate and control the four-dimensional human image to achieve human-computer interaction.
  • the operation actions include at least the following One: (1) Change the color and/or transparency of a specific organ/tissue, (2) Position the zoom view, (3) Rotate the view, realize the multi-view 360-degree observation of the four-dimensional human body image, (4) "Enter” Observe the internal structure of human organs, render real-time clipping effects, and (5) move the view up and down.
  • FIG. 2 is a schematic flowchart of a method for analyzing the coronary artery of the heart based on VRDS AI medical images according to an embodiment of the application.
  • a method for analyzing the coronary artery of the heart based on VRDS AI medical image provided by an embodiment of the present application may include:
  • a medical imaging device acquires a scanned image of a coronary artery of the heart, wherein the scanned image further includes the coronary artery of the heart and blood vessels around the coronary artery of the heart.
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the coronary arteries of the heart are divided into 22 branches, each branch corresponding to its own blood vessel.
  • the medical imaging device performs image processing according to the scanned image to obtain a target image data set.
  • the image data includes three-dimensional spatial image data of the coronary arteries and blood vessels of the heart.
  • the performing image processing based on the scanned image to obtain a target image data set includes: performing image preprocessing based on the scanned image to obtain first image data;
  • the first image data generates an original image data set of the coronary artery part of the heart; boundary optimization processing is performed on the original image data set to obtain a target image data set.
  • the coronary arteries of the heart include coronary arteries and coronary veins.
  • the coronary arteries may include, for example, left coronary arteries, right coronary arteries, and the like.
  • Coronary veins may include, for example, the great cardiac vein, the middle cardiac vein, the cardiac small vein, the posterior left ventricular vein, the oblique left atrium vein, and the like.
  • the boundary optimization processing includes at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization processing, and data enhancement processing.
  • the 2D boundary optimization processing includes: multiple sampling to obtain low-resolution information and high-resolution information, where the low-resolution information can provide contextual semantic information of the segmentation target in the entire image, that is, reflecting the segmentation target and the environment The features of the relationship between these features are used to determine the object category, and the high-resolution information is used to provide more refined features, such as gradients, for the segmentation target.
  • the segmentation targets include the coronary arteries of the heart, coronary arteries and coronary veins.
  • the 3D boundary optimization processing includes: 3D convolution, 3D max pooling, and 3D upward convolution layer.
  • the input data size is a1, a2, a3, the number of channels is c, and the filter size is f, that is, the filter
  • the dimension is f*f*f*c, the number of filters is n, and the final output of the 3-dimensional convolution is:
  • each layer contains two 3*3*3 convolution kernels, each of which is followed by an activation function (Relu), and then there is a maximum pooling of 2*2*2 in each dimension to merge the two Steps.
  • each layer is composed of 2*2*2 upward convolutions, with a step size of 2 in each dimension, and then two 3*3*3 convolutions, and then Relu. Then in the analysis path, the shortcut connections of equal resolution layers provide the basic high-resolution features of the synthesized path. In the last layer, 1*1*1 convolution reduces the number of output channels.
  • the 3D boundary optimization processing includes the following operations: inputting the original image data into a 3D convolution layer to perform a 3D convolution operation to obtain a feature map; inputting the feature map to a 3D pooling layer for pooling and Non-linear activation to obtain a first feature map; and a cascade operation is performed on the first feature map to obtain a prediction result.
  • the data enhancement processing includes any one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random cut And data enhancement based on simulating different lighting changes.
  • the image data includes a data collection of the coronary arteries of the heart, a data collection of the coronary arteries of the heart, and a data collection of the coronary arteries of the heart.
  • the method further includes: obtaining an image quality score corresponding to the image data According to the image quality score, filter out the enhanced data whose image quality score is greater than the preset image quality score from the image data; set the enhanced data to VRDS 4D imaging data; display the VRDS 4D imaging on the output device data.
  • the enhanced data with the image quality score greater than the preset image quality score is filtered from the image data according to the image quality score, and the enhanced data is set to VRDS 4D imaging The data, and finally, VRDS 4D imaging data is displayed on the output device to assist doctors in making a quick diagnosis.
  • the medical imaging device determines the degree of disease of the coronary artery of the target user's heart according to the target image data set.
  • the symptoms that may appear in the degree of disease include: angina pectoris, myocardial infarction, heart failure, and sudden cardiac death.
  • the determining the degree of coronary artery disease of the target user's heart according to the target image data set includes: determining the target user's heart according to the target image data set The lesion area of the coronary artery; identifying the degree of vascular stenosis of the plaque in the lesion area and the degree of calcification of the plaque; according to the degree of stenosis and the degree of calcification of the plaque, determine the expansion of the blood vessels around the coronary artery of the heart Target size; determine the blood vessel parameters around the coronary artery of the heart according to the target size, the blood vessel parameters including the type, diameter and length of the blood vessel; determine the degree of disease of the coronary artery of the target user's heart according to the blood vessel parameters.
  • the target size can be obtained by querying a preset medical database to obtain blood vessel parameters around the coronary artery of the heart, querying a preset medical database, and obtaining blood vessel parameters matching the target size in the preset medical database.
  • the preset medical database includes the mapping relationship between size and blood vessel parameters.
  • mapping relationship may be one-to-one, one-to-many, and many-to-many, which is not uniquely limited here.
  • the lesion characteristics in the lesion area are obtained by analyzing the lesion area, and the target size of the blood vessel after expansion is further obtained according to the lesion characteristics, and finally, the degree of the lesion is determined from the target size after the expansion of the blood vessel , So as to achieve more accurate acquisition of the information of coronary artery and vascular diseases of the heart.
  • the determining the degree of disease of the coronary artery of the target user's heart according to the blood vessel parameter includes: obtaining a preset recognition model of the coronary artery part of the heart; and inputting the blood vessel parameter
  • the preset heart coronary artery part recognition model obtains the pathological value of each blood vessel parameter in the blood vessel parameters; and the pathological degree corresponding to the blood vessel parameter is determined according to the mapping relationship between the preset pathological value and the degree of pathological change.
  • mapping relationship may be one-to-one, one-to-many, and many-to-many, which is not uniquely limited here.
  • the degree of disease can be quickly determined by pre-setting the recognition model of the coronary artery position of the heart, so as to achieve more accurate acquisition of the information of the coronary artery and vascular disease of the heart, and also improve the determination efficiency.
  • the identifying the vascular stenosis degree and the calcification degree of the plaque in the lesion area includes: acquiring a lesion image of the lesion area in the target image data set Extract the shadow part of the lesion image; detect the size of the shadow area of the shadow part; determine the degree of calcification of the plaque according to the size of the shadow area; detect the size of the blood vessel around the shadow part; determine according to the size of the blood vessel The degree of vascular stenosis.
  • determining the degree of calcification of the patch according to the size of the shadow area can be obtained through a preset model or big data analysis, which is not uniquely limited here.
  • the degree of calcification can be determined by the shadow area and the degree of stenosis can be determined by the size of the blood vessel, which improves the accuracy and convenience of obtaining the information of the coronary vascular disease of the heart.
  • the determining the degree of coronary artery disease of the target user's heart according to the target image data set includes: each target image in the target image data set A coordinate system is established in the data, and the origin of the coordinate system is the center position of the centerline of the coronary artery in the coronary artery of the heart; the regional division of the coronary artery of the heart is performed according to the coordinate system to obtain The regional image data in the target image data; through the coordinate system, the regional image data is detected to obtain the spatial positions of a plurality of target pixels, and the spatial positions of the plurality of target pixels are based on the detected first pixel
  • the gray value corresponding to the point belongs to the gray value corresponding to the vascular cell data of the coronary artery of the heart
  • the spatial position corresponding to the first pixel point is recorded; the spatial positions of the multiple target pixel points are divided according to the regions to obtain A regional image data set of each area under the same area in the target image data set; according to the regional image data set of each area
  • the method further includes: for each outermost vascular cell data set, performing the following steps: obtaining the currently processed outermost vascular cell data set and projecting it on any Plane characteristic curve; select any point on the characteristic curve as the starting point; starting from the starting point, continuously mark pixels along the positive and negative directions of the characteristic curve, and stop when the target pixel is marked Mark, the positive direction of the characteristic curve is the lateral positive direction of the image data, the reverse direction of the characteristic curve is the lateral reverse direction of the image data, and the target pixel point is the curvature change of the target coronary artery segment of the heart
  • the largest pixel the target blood vessel segment is the blood vessel between the starting point and the target space position of the target blood vessel, the target blood vessel corresponds to the currently processed outermost blood vessel cell data set, and the target space position is The position corresponding to the target pixel point; the curvature corresponding to the target blood vessel segment is acquired; the curvature corresponding to the target blood vessel segment is set as the corresponding curvature of
  • the medical imaging device determines the type of coronary stent according to the degree of disease of the coronary artery of the heart.
  • the determining the type of coronary stent according to the degree of the disease of the coronary artery of the heart includes: querying a first database, and obtaining data related to the disease in the first database.
  • the first database includes the mapping relationship between the disease degree and the coronary stent type.
  • mapping relationship may be one-to-one, one-to-many, and many-to-many, which is not uniquely limited here.
  • the determining the type of coronary stent according to the degree of the disease of the coronary artery of the heart includes: obtaining the survival time of the target user corresponding to the degree of the disease; The degree is brought into the preset lesion model, and the lesion process within the survival time is simulated; the lesion process is analyzed to obtain the overlapping area of the plaque and the blood vessel during the lesion process; according to the overlapping area , Obtain the parameter range of the coronary stent; determine the type of the coronary stent according to the parameter range of the coronary stent.
  • the appropriate coronary stent is further selected to achieve the quantitative selection of the coronary stent, so that the coronary stent can be expanded to cover the plaque after the stent is expanded. It will cause the plaque to fall off and reduce the surgical risk of vascular stent placement.
  • the scan image of the coronary artery of the heart is obtained, wherein the scan image also includes the coronary artery of the heart and the blood vessels around the coronary artery of the heart.
  • the image is performed based on the scan image.
  • a target image data set is obtained, and then, according to the target image data set, the degree of coronary artery disease of the target user is determined, and finally the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • the quantitative selection of the coronary stent is realized, so that the coronary stent can be expanded to cover the plaque after the stent is expanded. It does not cause plaque to fall off, reduces the surgical risk of vascular stent implantation, and improves the accuracy and intelligence of selecting coronary stents in coronary heart diseases.
  • the medical imaging device performs image processing according to the scanned image to obtain a target image data set, including: performing a first preset processing on the scanned image to obtain a bitmap BMP Data source; import the BMP data source into a preset VRDS medical network model to obtain first medical image data, the first medical image data including the data set of the coronary arteries of the heart and the data of the coronary arteries of the heart
  • the data set of the coronary arteries of the heart includes the fusion data of the intersection position of the left coronary artery and the right coronary arteries, and the data set of the coronary arteries of the heart is a cube of the surface of the coronary arteries of the heart and the tissue structure in the coronary arteries of the heart
  • a spatial transfer function result the data set of the coronary vessels of the heart is the result of the cubic space transfer function of the surface of the coronary vessels of the heart and the tissue structure inside the coronary vessels of the heart; and the first medical image data Import a preset cross blood vessel
  • the first preset processing includes at least one of the following operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, and VRDS Ai elastic deformation processing.
  • the VRDS limited contrast adaptive histogram equalization includes the following steps: performing regional noise ratio limiting and global contrast limiting on the image source; dividing the local histogram of the image source into multiple partitions; The slope of the cumulative histogram of the neighborhood of each of the multiple partitions determines multiple slopes of the multiple transformation functions; the pixels of each of the multiple partitions are determined according to the multiple slopes.
  • the degree of contrast magnification around the value; according to the degree of contrast magnification around the pixel value of each of the multiple partitions, the multiple partitions are subject to limited cropping processing to obtain the distribution of the effective histogram and the effectively usable neighborhood
  • the value of the size; the histogram cut by the limit is evenly distributed to other areas of the local histogram of the image source.
  • the hybrid partial differential denoising includes the following steps: the image source is processed through VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, so that the curvature of the image edge is less than the preset curvature, which can protect the edge of the image, and The mixed partial differential denoising model that can avoid the step effect in the smoothing process;
  • the VRDS Ai elastic deformation processing includes the following steps: acquiring the image dot matrix of the image source, superimposing the positive and negative random distances on the image dot matrix to form a difference position matrix, and for each of the difference position matrix Perform grayscale processing on each difference position to obtain a new difference position matrix, so as to realize the distortion inside the image, and then perform rotation, distortion, and translation operations on the image.
  • the hybrid partial differential denoising is processed by the medical imaging device using a CDD and a high-order denoising model to process the image source.
  • the CDD model (Curvature Driven Diffusions) model is formed by introducing a curvature drive on the basis of the TV (Total Variation) model, which solves the problem that the TV model cannot repair the visual connectivity of the image.
  • the high-order denoising refers to denoising the image based on a partial differential equation (PDE) method.
  • the image source is subjected to a noise filtering effect according to the specified differential equation function change to obtain the BMP data source.
  • the solution of the partial differential equation is the BMP data source obtained after high-order denoising.
  • the PDE-based image denoising method has the characteristics of anisotropic diffusion, so it can perform different degrees in different regions of the image source. The effect of diffusion, so as to achieve the effect of suppressing noise while protecting the edge texture information of the image.
  • the medical imaging device uses at least one of the following image processing operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing, which improves the execution efficiency of image processing, and Improve image quality and protect the edge texture of the image.
  • the performing the first preset processing on the scanned image to obtain the bitmap BMP data source includes: setting the scanned image as the user's medical digital imaging and communication DICOM Data; parse the DICOM data to generate the image source of the user, the image source includes Texture 2D/3D image volume data; perform the first preset processing on the image source to obtain the BMP data source.
  • the DICOM Digital Imaging and Communications in Medicine
  • the medical imaging device first acquires multiple scanned images that reflect the structural characteristics of the user's heart and coronary arteries, and can screen out suitable at least one scanned image that contains the coronary arteries of the heart based on sharpness, accuracy, etc. , And then perform further processing on the scanned image to obtain a bitmap BMP data source.
  • the medical imaging device can obtain a bitmap BMP data source after filtering, parsing, and first preset processing based on the acquired scanned image, which improves the accuracy and clarity of medical image imaging.
  • the medical imaging device processes the scanned image into image data that can reflect the spatial structure characteristics of the coronary artery of the heart through a series of data processing, and the left coronary artery image data and the right coronary artery image data at the crossing position They are independent of each other, support accurate presentation of three-dimensional space, and improve the accuracy and comprehensiveness of data processing.
  • the importing the BMP data source into the preset VRDS medical network model to obtain the first medical image data includes: importing the BMP data source into the preset VRDS medical network model , Call each transfer function in the set of pre-stored transfer functions through the VRDS medical network model, and process the BMP data source through multiple transfer functions in the transfer function set to obtain the first medical image data.
  • the function set includes the transfer function of the coronary arteries of the heart and the transfer function of the coronary arteries of the heart set in advance by a reverse editor.
  • BMP full name Bitmap
  • DDB device-dependent bitmap
  • DIB device-independent bitmap
  • the VRDS medical network model is a preset network model, and its training method includes the following three steps: image sampling and scale scaling; 3D convolutional neural network feature extraction and scoring; medical imaging device evaluation and network training.
  • first sampling will be required to obtain N BMP data sources, and then M BMP data sources will be extracted from the N BMP data sources at a preset interval. It needs to be explained that the preset interval can be flexibly set according to the usage scenario.
  • Sample M from N then scale the sampled M BMP data sources to a fixed size (for example, the length is S pixels, the width is S pixels), and the resulting processing result is used as the input of the 3D convolutional neural network .
  • M BMP data sources are used as the input of the 3D convolutional neural network.
  • a 3D convolutional neural network is used to perform 3D convolution processing on the BMP data source to obtain a feature map.
  • FIG. 3 is a schematic flowchart of another method for analyzing the coronary artery of the heart based on VRDS AI medical image according to an embodiment of the application.
  • a medical imaging device acquires a scanned image of a coronary artery of the heart.
  • the medical imaging device performs image preprocessing according to the scanned image to obtain first image data.
  • the medical imaging device generates an original image data set of the coronary artery part of the heart according to the first image data.
  • the medical imaging device performs boundary optimization processing on the original image data set to obtain a target image data set.
  • the medical imaging device determines the lesion area of the coronary artery of the target user's heart according to the target image data set.
  • the medical imaging device recognizes the degree of vascular stenosis of the plaque and the degree of calcification of the plaque in the diseased area.
  • the medical imaging device determines the target size after expansion of the blood vessel around the coronary artery of the heart according to the degree of stenosis and the degree of calcification of the plaque.
  • the medical imaging device determines the blood vessel parameters around the coronary artery of the heart according to the target size, where the blood vessel parameters include blood vessel type, diameter, and length.
  • the medical imaging device determines the degree of disease of the coronary artery of the target user's heart according to the blood vessel parameter.
  • the medical imaging device determines the type of coronary stent according to the degree of disease of the coronary artery of the heart.
  • the scan image of the coronary artery of the heart is obtained, wherein the scan image also includes the coronary artery of the heart and the blood vessels around the coronary artery of the heart.
  • the image is performed based on the scan image.
  • a target image data set is obtained, and then, according to the target image data set, the degree of coronary artery disease of the target user is determined, and finally the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • the quantitative selection of the coronary stent is realized, so that the coronary stent can be expanded to cover the plaque after the stent is expanded. It does not cause plaque to fall off, reduces the surgical risk of vascular stent implantation, and improves the accuracy and intelligence of selecting coronary stents in coronary heart diseases.
  • the lesion characteristics in the lesion area are obtained, and the target size of the blood vessel after expansion is further obtained according to the lesion characteristics, and finally, the degree of the lesion is determined according to the target size after the expansion of the blood vessel, so as to achieve a more accurate acquisition Go to the heart coronary vascular disease information.
  • FIG. 4 is a schematic flowchart of another method for analyzing the coronary artery of the heart based on VRDS AI medical image provided by an embodiment of the application.
  • the medical imaging device acquires a scanned image of the coronary artery of the heart.
  • the medical imaging device performs image processing according to the scanned image to obtain a target image data set.
  • the medical imaging device determines the degree of disease of the coronary artery of the target user's heart according to the target image data set.
  • the medical imaging device separately establishes a coordinate system in each target image data in the target image data set.
  • the medical imaging device performs regional segmentation on the coronary artery of the heart according to the coordinate system to obtain regional image data in each of the target image data.
  • the medical imaging device detects the regional image data through the coordinate system to obtain the spatial positions of a plurality of target pixels, and the spatial positions of the plurality of target pixels are determined according to the gray scale corresponding to the first pixel.
  • the degree value belongs to the gray value corresponding to the vascular cell data of the coronary artery of the heart, the spatial position corresponding to the first pixel point is recorded.
  • the medical imaging device divides the spatial positions of the multiple target pixel points according to regions to obtain a regional image data set of each region under the same region in the target image data set.
  • the medical imaging device obtains a plurality of outermost vascular cell data sets corresponding to the coronary artery of the heart according to the regional image data set of each region, and each outermost vascular cell data set includes a plurality of outermost layers. Blood vessel cell data.
  • the medical imaging device searches for a lesion degree corresponding to the plurality of outermost vascular cell data sets, and sets the target lesion degree.
  • the scan image of the coronary artery of the heart is obtained, wherein the scan image also includes the coronary artery of the heart and the blood vessels around the coronary artery of the heart.
  • the image is performed based on the scan image.
  • a target image data set is obtained, and then, according to the target image data set, the degree of coronary artery disease of the target user is determined, and finally the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • the quantitative selection of the coronary stent is realized, so that the coronary stent can be expanded to cover the plaque after the stent is expanded. It does not cause plaque to fall off, reduces the surgical risk of vascular stent implantation, and improves the accuracy and intelligence of selecting coronary stents in coronary heart diseases.
  • At least one cardiac coronary vascular locating disease with abnormal image data a more precise locating disease is realized, and the recognition accuracy of a cardiac coronary disease is improved.
  • the medical imaging apparatus 500 may include:
  • the acquiring unit 501 is configured to acquire a scanned image of a part of the coronary artery of the heart, wherein the scanned image further includes the part of the coronary artery of the heart and the blood vessels around the coronary artery of the heart;
  • the processing unit 502 is configured to perform image processing according to the scanned image to obtain a target image data set
  • the determining unit 503 is configured to determine the degree of disease of the coronary artery of the target user's heart according to the target image data set;
  • the determining unit 503 is further configured to determine the type of coronary stent according to the degree of disease of the coronary artery of the heart.
  • the scan image of the coronary artery of the heart is obtained, wherein the scan image also includes the coronary artery of the heart and the blood vessels around the coronary artery of the heart.
  • the image is performed based on the scan image.
  • a target image data set is obtained, and then, according to the target image data set, the degree of coronary artery disease of the target user is determined, and finally the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • the quantitative selection of the coronary stent is realized, so that the coronary stent can be expanded and supported to cover the plaque It does not cause plaque to fall off, reduces the surgical risk of vascular stent implantation, and improves the accuracy and intelligence of selecting coronary stents in coronary heart diseases.
  • the processing module 502 is specifically configured to perform image preprocessing according to the scanned image to obtain first image data; generate an original image data set of the coronary artery part of the heart according to the first image data; The original image data set is subjected to boundary optimization processing to obtain a target image data set.
  • the determining module 503 is specifically configured to determine the lesion area of the coronary artery of the target user's heart according to the target image data set; identify the vascular stenosis and plaque calcification of the plaque in the lesion area Degree; according to the degree of stenosis and the degree of plaque calcification, determine the target size of the blood vessels around the coronary artery of the heart after expansion; determine the blood vessel parameters around the coronary artery of the heart according to the target size, the blood vessel parameters including the type of blood vessel , Diameter and length; determining the degree of lesions of the coronary artery of the target user's heart according to the blood vessel parameters.
  • the determining module 503 is specifically configured to obtain a preset heart coronary artery part recognition model; input the blood vessel parameters into the preset heart coronary artery part recognition model to obtain each blood vessel parameter in the blood vessel parameters According to the preset mapping relationship between the lesion value and the extent of the lesion, the extent of the lesion corresponding to the vascular parameter is determined.
  • the determining module 503 is specifically configured to obtain a lesion image of a lesion area in the target image data set; extract a shadow part in the lesion image; detect the size of the shadow area of the shadow part; The size of the shadow area determines the degree of calcification of the plaque; the size of the blood vessel around the shadow part is detected; and the degree of vascular stenosis is determined according to the size of the blood vessel.
  • the determining module 503 is specifically configured to query a first database to obtain a target coronary stent type that matches the degree of the lesion in the first database, and the first database includes the degree of the lesion and the coronary stent.
  • the mapping relationship of the type of stent is specifically configured to query a first database to obtain a target coronary stent type that matches the degree of the lesion in the first database, and the first database includes the degree of the lesion and the coronary stent.
  • the mapping relationship of the type of stent is specifically configured to query a first database to obtain a target coronary stent type that matches the degree of the lesion in the first database, and the first database includes the degree of the lesion and the coronary stent. The mapping relationship of the type of stent.
  • the determining module 503 is specifically configured to obtain the survival time of the target user corresponding to the degree of the disease; bring the degree of the disease into a preset disease model, and simulate the pathological process within the survival time Analyze the pathological process to obtain the overlapping area of the plaque and the blood vessel during the pathological process; obtain the parameter range of the coronary stent according to the overlapping area; determine the coronary stent according to the parameter range of the coronary stent Type of vein stent.
  • the determining module 503 is specifically configured to separately establish a coordinate system in each target image data in the target image data set, and the origin of the coordinate system is the center of the coronary artery in the coronary artery of the heart The center position of the line; perform regional segmentation of the coronary arteries of the heart according to the coordinate system to obtain regional image data in each target image data; detect the regional image data through the coordinate system, Obtain the spatial positions of a plurality of target pixels, and record the gray value corresponding to the vascular cell data of the coronary artery according to the gray value corresponding to the first pixel.
  • the spatial position corresponding to the first pixel divide the spatial positions of the multiple target pixels according to regions to obtain a regional image data set of each region under the same region in the target image data set; according to the The regional image data set of each region is used to obtain multiple outermost vascular cell data sets corresponding to the coronary artery of the heart, and each outermost vascular cell data set includes multiple outermost vascular cell data;
  • the lesion degree corresponding to the multiple outermost vascular cell data sets is the target lesion degree.
  • the following steps are performed: acquiring the characteristic curve of the currently processed outermost vascular cell data set projected on any plane; selecting any point on the characteristic curve as Starting point; starting from the starting point, continuously mark pixels along the positive and negative directions of the characteristic curve, and stop marking when the target pixel is marked.
  • the positive direction of the characteristic curve is the direction of the image data
  • the reverse direction of the characteristic curve is the reverse lateral direction of the image data
  • the target pixel is the pixel with the largest change in the curvature of the target coronary artery segment of the target heart
  • the target blood vessel segment is the target blood vessel in the The blood vessel between the starting point and the target spatial position
  • the target blood vessel corresponds to the currently processed outermost blood vessel cell data set
  • the target spatial position is the position corresponding to the target pixel;
  • FIG. 6 is a schematic structural diagram of a medical imaging device in a hardware operating environment involved in an embodiment of the application.
  • the medical imaging device in the hardware operating environment involved in the embodiment of the present application may include:
  • the processor 601 is, for example, a CPU.
  • the memory 602 optionally, the memory may be a high-speed RAM memory, or a stable memory, such as a disk memory.
  • the communication interface 603 is used to implement connection and communication between the processor 601 and the memory 602.
  • FIG. 6 does not constitute a limitation to it, and may include more or less components than those shown in the figure, or a combination of certain components, or different component arrangements. .
  • the memory 602 may include an operating system, a network communication module, and an information processing program.
  • the operating system is a program that manages and controls the hardware and software resources of the medical imaging device, and supports the operation of personnel management programs and other software or programs.
  • the network communication module is used to implement communication between various components in the memory 602 and communication with other hardware and software in the medical imaging device.
  • the processor 601 is used to execute the information migration program stored in the memory 602 to implement the following steps: obtain a scanned image of the coronary artery of the heart, wherein the scanned image also includes the coronary heart.
  • the part of the vein and the blood vessels around the coronary artery of the heart image processing is performed according to the scanned image to obtain a target image data set; according to the target image data set, the degree of disease in the coronary artery of the target user’s heart is determined;
  • the degree of coronary heart disease determines the type of coronary stent.
  • the present application also provides a computer-readable storage medium for storing a computer program, and the stored computer program is executed by the processor to implement the following steps: Obtain a scan of the coronary artery of the heart Image, wherein the scanned image further includes the part of the coronary artery of the heart and the blood vessels around the coronary artery of the heart; image processing is performed according to the scanned image to obtain a target image data set; and the target image data set is determined according to the target image data set.
  • the degree of disease of the coronary artery of the target user's heart; the type of coronary stent is determined according to the degree of disease of the coronary artery of the heart.
  • the disclosed device can be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the modules is only a logical function division, and there may be other divisions in actual implementation, for example, multiple modules or components may be combined or may be Integrate into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or modules, and may be in electrical or other forms.
  • modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or software function modules.
  • the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this application essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage
  • the medium includes a number of instructions to enable a computer device (which may be a personal computer, a medical imaging device, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes. .

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Engineering & Computer Science (AREA)
  • Radiology & Medical Imaging (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Optics & Photonics (AREA)
  • Pathology (AREA)
  • Physics & Mathematics (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

基于VRDS AI医学影像的心脏冠脉的分析方法和相关装置，包括：获取心脏冠脉部位的扫描图像，其中，扫描图像还包括心脏冠脉部位和心脏冠脉周围的血管（201）；根据扫描图像进行图像处理，得到目标影像数据集合（202）；根据目标影像数据集合，确定目标用户心脏冠脉的病变程度（203）；根据心脏冠脉的病变程度确定冠脉支架的类型（204）。能够提高在心脏冠脉疾病中选择冠脉支架的准确性和智能性。

Description

基于VRDS AI医学影像的心脏冠脉的分析方法和相关装置 技术领域
本申请涉及医学成像装置技术领域，尤其涉及基于VRDS AI医学影像的心脏冠脉的分析方法和相关装置。
背景技术
目前，医生通过电子计算机断层扫描(Computed Tomography，CT)、磁共振成像(Magnetic Resonance Imaging，MRI)、弥散张量成像(Diffusion Tensor Imaging，DTI)、正电子发射型计算机断层显像(Positron Emission Computed Tomography，PET)等技术获取心脏冠脉血管的形态、位置、拓扑结构等信息。医生仍然采用观看阅读连续的二维切片扫描图像，以此来诊断病情。然而，二维切片扫描图像无法呈现出心脏冠脉的空间结构特性，影响到医生对疾病的诊断。随着医学成像技术的飞速发展，人们对医学成像提出了新的需求。
发明内容
本申请实施例提供了基于VRDS AI医学影像的心脏冠脉的分析方法和相关装置，实施本申请实施例，提高在心脏冠脉疾病中选择冠脉支架的准确性和智能性。
本申请实施例第一方面提供了基于VRDS AI医学影像的心脏冠脉的分析方法，包括：
获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；
根据所述扫描图像进行图像处理，得到目标影像数据集合；
根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；
根据所述心脏冠脉的病变程度确定冠脉支架的类型。
本申请实施例第二方面提供了一种医学成像装置，包括：
获取单元，用于获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；
处理单元，用于根据所述扫描图像进行图像处理，得到目标影像数据集合；
确定单元，用于根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；
所述确定单元，还用于根据所述心脏冠脉的病变程度确定冠脉支架的类型。
本申请实施例第三方面提供了一种医学成像装置，包括处理器、存储器、通信接口以及一个或多个程序，其中，所述一个或多个程序被存储在所述存储器中，并且被生成由所述处理器执行，以执行权利要求上述第一方面任一项方法中的步骤的指令。
本申请实施例第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质用于存储计算机程序，所述存储计算机程序被所述处理器执行，以实现权利要求上述第一方面任一项所述的方法。
可以看出，上述技术方案中，通过获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管，其次，根据所述扫描图像进行图像处理，得到目标影像数据集合，然后，根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，最后根据所述心脏冠脉的病变程度确定冠脉支架的类型。通过采用与心脏冠脉的影像数据进行分析来定位病症，并根据病症选择得到合适的冠脉支架，实现了冠脉支架的量化选型，使得冠脉支架膨胀支撑血管支架撑开后能够覆盖斑块，不会导致斑块脱落，降低了血管支架置入术的手术风险，提高在心脏冠脉疾病中选择冠脉支架的准确性和智能性。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的一种基于VRDS AI医学影像的心脏冠脉的分析***的结构示意图；
图2为本申请实施例提供的一种基于VRDS AI医学影像的心脏冠脉的分析方法的流程示意图；
图3为本申请实施例提供的又一种基于VRDS AI医学影像的心脏冠脉的分析方法的流程示意图；
图4为本申请的一个实施例提供的又一种基于VRDS AI医学影像的心脏冠脉的分析方法的流程示意图；
图5为本申请实施例提供的一种医学成像装置的示意图；
图6为本申请的实施例涉及的硬件运行环境的医学成像装置结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
以下分别进行详细说明。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。
本申请实施例所涉及到的医学成像装置是指利用各种不同媒介作为信息载体，将人体内部的结构重现为影像的各种仪器，其影像信息与人体实际结构有着空间和时间分布上的对应关系。“DICOM数据”是指通过医疗设备采集的反映人体内部结构特征的原始图像文件数据，可以包括电子计算机断层扫描CT、核磁共振MRI、弥散张量成像DTI、正电子发射型计算机断层显像PET-CT等信息，“图源”是指解析原始DICOM数据生成的Texture2D/3D图像体数据。“VRDS”是指虚拟现实医用***(Virtual Reality Doctor system，简称为VRDS)。
首先，参见图1，图1是本申请实施例提供了一种基于VRDS AI医学影像的心脏冠脉的分析***100的结构示意图，该***100包括医学成像装置110和网络数据库120，其中医学成像装置110可以包括本地医学成像装置111和/或终端医学成像装置112，本地医学成像装置111或终端医学成像装置112用于基于原始DICOM数据，以本申请实施例所呈现的基于VRDS AI医学影像的心脏冠脉的分析算法为基础，进行人体心脏冠脉影像区域的识别、定位、四维体绘制、异常分析，实现四维立体成像效果(该4维医学影像具体是指医学影像包括所显示组织的内部空间结构特征及外部空间结构特征，所述内部空间结构特征是指组织内部的切片数据未丢失，即医学成像装置可以呈现心脏冠脉、血管等组织的内部构造，外部空间结构特性是指组织与组织之间的环境特征，包括组织与组织之间的空间位置特性(包括交叉、间隔、融合)等，如左冠状动脉与右冠状动脉之间的交叉位置的边缘结构特性等)，本地医学成像装置111相对于终端医学成像装置112还可以用于对扫描图像进行编辑，形成四维人体图像的传递函数结果，该传递函数结果可以包括人体心脏冠脉表面和人体心脏冠脉内的组织结构的传递函数结果，以及立方体空间的传递函数结果，如传递函数所需的立方编辑框与弧线编辑的数组数量、坐标、颜色、透明度等信息。网络数据库120例如可以是云医学成像装置等，该网络数据库120用于存储解析原始DICOM数据生成的图源，以及本地医学成像装置111编辑得到的四维人体图像的传递函数结果，扫描图像可以是来自于多个本地医学成像装置111以实现多个医生的交互诊断。
用户通过上述医学成像装置110进行具体的图像显示时，可以选择显示器或者虚拟现实VR的头戴式显示器(Head mounted Displays Set，HMDS)结合操作动作进行显示，操作动作是指用户通过医学成像装置的外部摄入设备，如鼠标、键盘、平板电脑(portable android device，Pad)、iPad(internet portable apple device)等，对四维人体图像进行的操作控制，以实现人机交互，该操作动作包括以下至少一种：(1)改变某个具体器官/组织的颜色和/或透明度，(2)定位缩放视图，(3)旋转视图，实现四维人体图像的多视角360度观察，(4)“进入”人体器官内部观察内部构造，实时剪切效果渲染，(5)上下移动视图。
参见图2，图2为本申请的一个实施例提供的基于VRDS AI医学影像的心脏冠脉的分析方法的流程示意图。其中，如图2所示，本申请的一个实施例提供的一种基于VRDS AI 医学影像的心脏冠脉的分析方法可以包括：
201、医学成像装置获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管。
其中，所述扫描图像包括以下任意一种：CT图像、MRI图像、DTI图像、PET-CT图像。
其中，血液经由两条主要冠状动脉进入心脏，并经由心脏肌肉表面上的一个血管网络，且心脏冠脉动脉分为22个分支，每个分支对应各自的血管。
202、医学成像装置根据所述扫描图像进行图像处理，得到目标影像数据集合。
其中，影像数据包括所述心脏冠脉血管的三维空间影像数据。
可选的，在一种可能的实施方式中，所述根据所述扫描图像进行图像处理，得到目标影像数据集合，包括：根据所述扫描图像进行图像预处理，得到第一图像数据；根据所述第一图像数据生成所述心脏冠脉部位的原始影像数据集合；对所述原始影像数据集合进行边界优化处理，得到目标影像数据集合。
其中，心脏冠脉血管包括冠状动脉和冠状静脉。进一步的，冠状动脉例如可以包括：左冠动脉、右冠动脉等。冠状静脉例如可以包括心大静脉、心中静脉、心小静脉、左室后静脉、左房斜静脉等。
其中，所述边界优化处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理。
其中，所述2D边界优化处理包括：多次采样获取低分辨率信息和高分辨率信息，其中，低分辨率信息能够提供分割目标在整个图像中上下文语义信息，即反映所述分割目标与环境之间关系的特征，这些特征用于物体类别判断，高分辨率信息用于为分割目标提供更加精细的特征，如梯度等。
其中，分割目标包括心脏冠脉、冠脉动脉和冠脉静脉。
其中，所述3D边界优化处理包括：3D卷积、3D最大池化和3D向上卷积层，输入数据的大小为a1、a2、a3，通道数为c，过滤器大小为f，即过滤器维度为f*f*f*c，过滤器数量为n，则3维卷积最终输出为：
(a1-f+1)*(a2-f+1)*(a3-f+1)*n
具有分析路径和合成路径。在分析路径中，每一层包含两个3*3*3的卷积核，每一个都跟随一个激活函数(Relu)，然后在每个维度上有2*2*2的最大池化合并两个步长。在合成路径中，每个层由2*2*2的向上卷积组成，每个维度上步长都为2，接着，两个3*3*3的卷积，然后Relu。然后在分析路径中从相等分辨率层的shortcut连接提供了合成路径的基本高分辨特征。在最后一层中，1*1*1卷积减少了输出通道的数量。
进一步的，所述3D边界优化处理包括以下操作：将所述原始影像数据输入3D卷积层中进行3D卷积操作，以得到特征图；将所述特征图输入3D池化层进行池化和非线性激活， 以得到第一特征图；对所述第一特征图进行级联操作以得到预测结果。
其中，所述数据增强处理包括以下任意一种：基于任意角度旋转的数据增强、基于直方图均衡的数据增强、基于白平衡的数据增强、基于镜像操作的数据增强、基于随机剪切的数据增强和基于模拟不同光照变化的数据增强。
其中，所述影像数据包括所述心脏冠脉的数据集合、所述心脏冠脉动脉的数据集合以及所述心脏冠脉静脉的数据集合。
可以看出，上述技术方案中，通过对扫描图像的图像处理，得到边界清楚的目标影像数据集合，从而辅助医生进行快速确诊。
可选的，在一种可能的实施方式中，在对对所述原始影像数据集合进行边界优化处理，得到目标影像数据集合之后，所述方法还包括：获取所述影像数据对应的影像质量评分；根据所述影像质量评分从所述影像数据中筛选出影像质量评分大于预设影像质量评分的增强数据；将所述增强数据设置为VRDS 4D成像数据；在输出设备上显示所述VRDS 4D成像数据。
可以看出，上述技术方案中，通过获取影像数据对应的影像质量评分，根据影像质量评分从影像数据中筛选出影像质量评分大于预设影像质量评分的增强数据，将增强数据设置为VRDS 4D成像数据，最后，在输出设备上显示VRDS 4D成像数据，从而辅助医生进行快速确诊。
203、医学成像装置根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度。
其中，病变程度中可能会出现的病症表现信息包括：心绞痛、心肌梗塞、心力衰竭和心源性猝死等。
可选的，在一种可能的实施方式中，所述根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，包括：根据所述目标影像数据集合确定所述目标用户心脏冠脉的病变区域；识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度；根据所述狭窄度和所述斑块钙化程度确定所述心脏冠脉周围的血管膨胀后的目标尺寸；根据目标尺寸确定所述心脏冠脉周围的血管参数，所述血管参数包括血管类型、直径和长度；根据所述血管参数确定所述目标用户心脏冠脉的病变程度。
其中，所述目标尺寸可通过查询预设医疗数据库，得到心脏冠脉周围的血管参数，查询预设医疗数据库，获取在所述预设医疗数据库中与所述目标尺寸匹配的血管参数，所述预设医疗数据库中包括尺寸与血管参数的映射关系。
其中，所述映射关系可以为一对一、一对多和多对多，此处不做唯一限定。
可以看出，上述技术方案中，通过解析病变区域，从而获取病变区域中的病变特征，进一步的根据病变特征得到血管膨胀后的目标尺寸，最后，从根据血管膨胀后的目标尺寸确定定病变程度，从而实现更加精准的获取到心脏冠脉血管疾病信息。
进一步的，在一种可能的的实施方式中，所述根据所述血管参数确定所述目标用户心脏冠脉的病变程度，包括：获取预设心脏冠脉部位识别模型；将所述血管参数输入所述预设心脏冠脉部位识别模型，得到所述血管参数中每个血管参数的病变值；按照预设的病变值与病变程度之间的映射关系，确定所述血管参数对应的病变程度。
其中，所述映射关系可以为一对一、一对多和多对多，此处不做唯一限定。
可以看出，上述技术方案中，通过预设心脏冠脉部位识别模型，快速的确定病变程度，实现更加精准的获取到心脏冠脉血管疾病信息，也提高了确定效率。
进一步的，在一种可能的的实施方式中，所述识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度，包括：获取所述目标影像数据集合中病变区域的病变图像；提取所述病变图像中的阴影部分；检测所述阴影部分的阴影面积大小；根据所述阴影面积大小确定斑块的钙化程度；检测所述阴影部分周围的血管大小；根据所述血管大小确定血管狭窄度。
其中，根据所述阴影面积大小确定斑块的钙化程度可通过预设模型或是大数据分析得到，此处不做唯一限定。
可以看出，上述技术方案中，能够通过阴影面积确定钙化程度及通过血管大小确定狭窄度，提高了获取心脏冠脉血管病变信息的准确度和便捷度。
可选的，在一种可能的实施方式中，所述根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，包括：在所述目标影像数据集合中的每个目标影像数据中分别建立坐标系，所述坐标系的原点均为所述心脏冠脉中冠脉中心线的中心位置；根据所述坐标系对所述心脏冠脉进行区域分割，得到在所述每个目标影像数据中的区域影像数据；通过所述坐标系，对所述区域影像数据进行检测，得到多个目标像素点的空间位置，所述多个目标像素点的空间位置根据检测到第一像素点对应的灰度值属于心脏冠脉的血管细胞数据对应的灰度值时，记录所述第一像素点对应的空间位置；将所述多个目标像素点的空间位置按照区域进行划分，得到在所述目标影像数据集合中相同区域下的每个区域的区域影像数据集合；根据所述每个区域的区域影像数据集合，得到所述心脏冠脉对应的多个最外层血管细胞数据集，每个最外层血管细胞数据集包括多个最外层血管细胞数据；查找与所述多个最外层血管细胞数据集对应的病变程度，为目标病变程度。
进一步的，在一种可能的的实施方式中，所述方法还包括：针对所述每个最外层血管细胞数据集，执行以下步骤：获取当前处理的最外层血管细胞数据集投影在任意平面的特征曲线；选取在所述特征曲线的任意一点作为起始点；从所述起始点出发，沿着所述特征曲线的正方向和反方向不断标记像素点，当标记到目标像素点时停止标记，所述特征曲线的正方向为所述影像数据的横向正方向，所述特征曲线的反方向为所述影像数据的横向反方向，所述目标像素点为目标心脏冠脉血管段曲率变化最大的像素点，所述目标血管段为目标血管在所述起始点至目标空间位置之间的血管，所述目标血管与当前处理的最外层血 管细胞数据集对应，所述目标空间位置是所述目标像素点对应的位置；获取所述目标血管段对应的曲率；将所述目标血管段对应的曲率设置为所述目标血管对应弯曲度。
可以看出，上述技术方案中，根据影像数据异常的至少一个心脏冠脉血管定位病症，实现更加精准的定位病症，提高心脏冠脉疾病的识别准确度。
204、医学成像装置根据所述心脏冠脉的病变程度确定冠脉支架的类型。
可选的，在一种可能的实施方式中，所述根据所述心脏冠脉的病变程度确定冠脉支架的类型，包括:查询第一数据库，获取在所述第一数据库中与所述病变程度匹配的目标冠脉支架类型，所述第一数据库中包括病变程度与冠脉支架类型的映射关系。
其中，所述映射关系可以为一对一、一对多和多对多，此处不做唯一限定。
可选的，在一种可能的实施方式中，所述根据所述心脏冠脉的病变程度确定冠脉支架的类型，包括：获取所述病变程度对应的目标用户的存活时间；将所述病变程度带入预设病变模型中，模拟得到在所述存活时间内的病变过程；解析所述病变过程，得到所述病变过程中所述斑块与所述血管的重合区域；根据所述重合区域，得到冠脉支架的参数范围；根据所述冠脉支架的参数范围确定冠脉支架的类型。
可以看出，上述技术方案中，根据查询数据库，进一步的选择得到合适的冠脉支架，实现了冠脉支架的量化选型，使得冠脉支架膨胀支撑血管支架撑开后能够覆盖斑块，不会导致斑块脱落，降低了血管支架置入术的手术风险。
可以看出，上述技术方案中，通过获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管，其次，根据所述扫描图像进行图像处理，得到目标影像数据集合，然后，根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，最后根据所述心脏冠脉的病变程度确定冠脉支架的类型。通过采用与心脏冠脉的影像数据进行分析来定位病症，并根据病症选择得到合适的冠脉支架，实现了冠脉支架的量化选型，使得冠脉支架膨胀支撑血管支架撑开后能够覆盖斑块，不会导致斑块脱落，降低了血管支架置入术的手术风险，提高在心脏冠脉疾病中选择冠脉支架的准确性和智能性。
可选的，在一种可能的实施方式中，所述医学成像装置根据所述扫描图像进行图像处理，得到目标影像数据集合，包括：对所述扫描图像执行第一预设处理得到位图BMP数据源；将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述心脏冠脉血管的数据集合和所述心脏冠脉的数据集合，所述心脏冠脉血管的数据集合中包括左冠状动脉与右冠状动脉的交叉位置的融合数据，所述心脏冠脉的数据集合为心脏冠脉表面和心脏冠脉内的组织结构的立方体空间的传递函数结果，所述心脏冠脉血管的数据集合为所述心脏冠脉血管表面和所述心脏冠脉血管内部的组织结构的立方体空间的传递函数结果；将所述第一医学影像数据导入预设的交叉血管网络模型，得到第二医学影像数据，所述第二医学影像数据包括所述心脏冠脉的数据集合、所述左冠 动脉的数据集合以及所述右冠动脉的数据集合，且所述左冠动脉的数据集合中的第一数据和所述右冠动脉的数据集合中的第二数据相互独立，所述第一数据为与所述交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据；对所述第二医学影像数据执行第二预设处理得到所述影像数据。
其中，所述第一预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
其中，所述VRDS限制对比度自适应直方图均衡包括以下步骤：对所述图源执行区域噪音比度限幅和全局对比度限幅；将所述图源的局部直方图划分多个分区；根据所述多个分区中的每个分区的邻域的累积直方图的斜度确定多个变换函数的多个斜度；根据所述多个斜度确定所述多个分区中的每个分区的像素值周边的对比度放大程度；根据所述多个分区中的每个分区的像素值周边的对比度放大程度对所述多个分区进行限度裁剪处理，以得到有效直方图的分布和有效可用的邻域大小的取值；将限度裁剪掉的直方图均匀的分布到所述图源的局部直方图的其他区域。
所述混合偏微分去噪包括以下步骤：通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪对所述图源进行处理，使得图像边缘的曲率小于预设曲率，实现即可保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型；
所述VRDS Ai弹性变形处理包括以下步骤：获取所述图源的图像点阵，对所述图像点阵叠加正负向随机距离以形成差值位置矩阵，对所述差值位置矩阵中的每个差值位置上进行灰度处理，以得到新的差值位置矩阵，从而实现图像内部的扭曲变形，再对图像进行旋转、扭曲、平移操作。
其中，所述混合偏微分去噪由所述医学成像装置采用CDD和高阶去噪模型对所述图源进行处理。
其中，所述CDD模型(Curvature Driven Diffusions)模型是在TV(Total Variation)模型的基础上引进了曲率驱动而形成的，解决了TV模型不能修复图像视觉连通性的问题。
其中，所述高阶去噪是指基于偏微分方程(PDE)方法对图像进行去噪处理。具体实现中，按照指定的微分方程函数变化对所述图源进行滤噪作用，以得到所述BMP数据源。其中，偏微分方程的解就是高阶去噪后的得到的所述BMP数据源，基于PDE的图像去噪方法具有各向异性扩散的特点，因此能够在所述图源的不同区域进行不同程度的扩散作用，从而取得抑制噪声的同时保护图像边缘纹理信息的效果。
可见，本示例中，所述医学成像装置通过以下至少一种图像处理操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理，提高了图像处理的执行效率，还提高了图像质量，保护图像边缘纹理。
其中，在一种可能的实施方式中，所述对所述扫描图像执行第一预设处理得到位图BMP数据源，包括：将所述扫描图像设置为所述用户的医学数字成像和通信DICOM数据； 解析所述DICOM数据生成所述用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；对所述图源执行所述第一预设处理得到所述BMP数据源。
其中，所述DICOM(Digital Imaging and Communications in Medicine)即医学数字成像和通信，是医学图像和相关信息的国际标准。具体实现中，所述医学成像装置先获取已经采集的反映用户的心脏冠脉结构特征的多张扫描图像，可以通过清晰度、准确度等筛选出合适的包含心脏冠脉的至少一张扫描图像，再对所述扫描图像执行进一步处理，得到位图BMP数据源。
可见，本示例中，所述医学成像装置可以基于获取的扫描图像，进行筛选、解析和第一预设处理处理后得到位图BMP数据源，提高了医学影像成像的准确度和清晰度。
可以看出，本示例中，医学成像装置通过一些列数据处理，将扫描图像处理为能够反映心脏冠脉的空间结构特性的影像数据，且交叉位置的左冠动脉影像数据、右冠动脉影像数据相互独立，支持三维空间准确呈现，提高数据处理准确度和全面性。
在本申请一种可能的示例中，所述将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，包括：将所述BMP数据源导入预设的VRDS医学网络模型，通过所述VRDS医学网络模型调用预存的传递函数集合中的每个传递函数，通过所述传递函数集合中的多个传递函数处理所述BMP数据源，得到第一医学影像数据，所述传递函数集合包括通过反向编辑器预先设置的所述心脏冠脉的传递函数和所述心脏冠脉血管的传递函数。
其中，BMP(全称Bitmap)是Windows操作***中的标准图像文件格式，可以分成两类：设备相关位图(DDB)和设备无关位图(DIB)。所述扫描图像包括以下任意一种：CT图像、MRI图像、DTI图像、PET-CT图像。
其中，所述VRDS医学网络模型为预设网络模型，其训练方法包含如下三个步骤：图像采样及尺度缩放；3D卷积神经网络特征提取及打分；医学成像装置评价与网络训练。在实施过程中，先将需要进行采样，获取N个BMP数据源，再按照预设的间隔从N个BMP数据源中提取出M个BMP数据源。需要进行说明的是，预设的间隔可根据使用场景进行灵活设定。从N个中采样出M个，然后，将采样出来的M个BMP数据源缩放到固定尺寸(例如，长为S像素，宽为S像素)，得到的处理结果作为3D卷积神经网络的输入。这样将M个BMP数据源作为3D卷积神经网络的输入。具体的，利用3D卷积神经网络对所述BMP数据源进行3D卷积处理，获得特征图。
参见图3，图3为本申请的一个实施例提供的又一种基于VRDS AI医学影像的心脏冠脉的分析方法的流程示意图。包括：
301、医学成像装置获取心脏冠脉部位的扫描图像。
302、医学成像装置根据所述扫描图像进行图像预处理，得到第一图像数据。
303、医学成像装置根据所述第一图像数据生成所述心脏冠脉部位的原始影像数据集合。
304、医学成像装置对所述原始影像数据集合进行边界优化处理，得到目标影像数据集合。
305、医学成像装置根据所述目标影像数据集合确定所述目标用户心脏冠脉的病变区域。
306、医学成像装置识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度。
307、医学成像装置根据所述狭窄度和所述斑块钙化程度确定所述心脏冠脉周围的血管膨胀后的目标尺寸。
308、医学成像装置根据目标尺寸确定所述心脏冠脉周围的血管参数，所述血管参数包括血管类型、直径和长度。
309、医学成像装置根据所述血管参数确定所述目标用户心脏冠脉的病变程度。
310、医学成像装置根据所述心脏冠脉的病变程度确定冠脉支架的类型。
可以看出，上述技术方案中，通过获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管，其次，根据所述扫描图像进行图像处理，得到目标影像数据集合，然后，根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，最后根据所述心脏冠脉的病变程度确定冠脉支架的类型。通过采用与心脏冠脉的影像数据进行分析来定位病症，并根据病症选择得到合适的冠脉支架，实现了冠脉支架的量化选型，使得冠脉支架膨胀支撑血管支架撑开后能够覆盖斑块，不会导致斑块脱落，降低了血管支架置入术的手术风险，提高在心脏冠脉疾病中选择冠脉支架的准确性和智能性。
此外，通过解析病变区域，从而获取病变区域中的病变特征，进一步的根据病变特征得到血管膨胀后的目标尺寸，最后，从根据血管膨胀后的目标尺寸确定定病变程度，从而实现更加精准的获取到心脏冠脉血管疾病信息。
参见图4，图4为本申请的一个实施例提供的又一种基于VRDS AI医学影像的心脏冠脉的分析方法的流程示意图。
401、医学成像装置获取心脏冠脉部位的扫描图像。
402、医学成像装置根据所述扫描图像进行图像处理，得到目标影像数据集合。
403、医学成像装置根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度。
404、医学成像装置在所述目标影像数据集合中的每个目标影像数据中分别建立坐标系。
405、医学成像装置根据所述坐标系对所述心脏冠脉进行区域分割，得到在所述每个目标影像数据中的区域影像数据。
406、医学成像装置通过所述坐标系，对所述区域影像数据进行检测，得到多个目标像素点的空间位置，所述多个目标像素点的空间位置根据检测到第一像素点对应的灰度值属于心脏冠脉的血管细胞数据对应的灰度值时，记录所述第一像素点对应的空间位置。
407、医学成像装置将所述多个目标像素点的空间位置按照区域进行划分，得到在所述目标影像数据集合中相同区域下的每个区域的区域影像数据集合。
408、医学成像装置根据所述每个区域的区域影像数据集合，得到所述心脏冠脉对应的多个最外层血管细胞数据集，每个最外层血管细胞数据集包括多个最外层血管细胞数据。
409、医学成像装置查找与所述多个最外层血管细胞数据集对应的病变程度，为目标病变程度。
可以看出，上述技术方案中，通过获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管，其次，根据所述扫描图像进行图像处理，得到目标影像数据集合，然后，根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，最后根据所述心脏冠脉的病变程度确定冠脉支架的类型。通过采用与心脏冠脉的影像数据进行分析来定位病症，并根据病症选择得到合适的冠脉支架，实现了冠脉支架的量化选型，使得冠脉支架膨胀支撑血管支架撑开后能够覆盖斑块，不会导致斑块脱落，降低了血管支架置入术的手术风险，提高在心脏冠脉疾病中选择冠脉支架的准确性和智能性。
此外，根据影像数据异常的至少一个心脏冠脉血管定位病症，实现更加精准的定位病症，提高心脏冠脉疾病的识别准确度。
参见图5，本申请的一个实施例提供的一种医学成像装置500的示意图，医学成像装置500可以包括：
获取单元501，用于获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；
处理单元502，用于根据所述扫描图像进行图像处理，得到目标影像数据集合；
确定单元503，用于根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；
所述确定单元503，还用于根据所述心脏冠脉的病变程度确定冠脉支架的类型。
可以看出，上述技术方案中，通过获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管，其次，根据所述扫描图像进行图像处理，得到目标影像数据集合，然后，根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，最后根据所述心脏冠脉的病变程度确定冠脉支架的类型。通过采用与心脏冠脉的影像数据进行分析来定位病症，并根据病症选择得到合适的冠脉支架，实现了冠脉支架的量化选型，使得冠脉支架膨胀支撑血管支架撑开后能够覆盖斑块，不会导致斑块脱落，降低了血管支架置入术的手术风险，提高在心脏冠脉疾病中选择冠脉支架的准确性 和智能性。
可选的，所述处理模块502，具体用于根据所述扫描图像进行图像预处理，得到第一图像数据；根据所述第一图像数据生成所述心脏冠脉部位的原始影像数据集合；对所述原始影像数据集合进行边界优化处理，得到目标影像数据集合。
可选的，所述确定模块503，具体用于根据所述目标影像数据集合确定所述目标用户心脏冠脉的病变区域；识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度；根据所述狭窄度和所述斑块钙化程度确定所述心脏冠脉周围的血管膨胀后的目标尺寸；根据目标尺寸确定所述心脏冠脉周围的血管参数，所述血管参数包括血管类型、直径和长度；根据所述血管参数确定所述目标用户心脏冠脉的病变程度。
可选的，所述确定模块503，具体用于获取预设心脏冠脉部位识别模型；将所述血管参数输入所述预设心脏冠脉部位识别模型，得到所述血管参数中每个血管参数的病变值；按照预设的病变值与病变程度之间的映射关系，确定所述血管参数对应的病变程度。
可选的，所述确定模块503，具体用于获取所述目标影像数据集合中病变区域的病变图像；提取所述病变图像中的阴影部分；检测所述阴影部分的阴影面积大小；根据所述阴影面积大小确定斑块的钙化程度；检测所述阴影部分周围的血管大小；根据所述血管大小确定血管狭窄度。
可选的，所述确定模块503，具体用于查询第一数据库，获取在所述第一数据库中与所述病变程度匹配的目标冠脉支架类型，所述第一数据库中包括病变程度与冠脉支架类型的映射关系。
可选的，所述确定模块503，具体用于获取所述病变程度对应的目标用户的存活时间；将所述病变程度带入预设病变模型中，模拟得到在所述存活时间内的病变过程；解析所述病变过程，得到所述病变过程中所述斑块与所述血管的重合区域；根据所述重合区域，得到冠脉支架的参数范围；根据所述冠脉支架的参数范围确定冠脉支架的类型。
可选的，所述确定模块503，具体用于在所述目标影像数据集合中的每个目标影像数据中分别建立坐标系，所述坐标系的原点均为所述心脏冠脉中冠脉中心线的中心位置；根据所述坐标系对所述心脏冠脉进行区域分割，得到在所述每个目标影像数据中的区域影像数据；通过所述坐标系，对所述区域影像数据进行检测，得到多个目标像素点的空间位置，所述多个目标像素点的空间位置根据检测到第一像素点对应的灰度值属于心脏冠脉的血管细胞数据对应的灰度值时，记录所述第一像素点对应的空间位置；将所述多个目标像素点的空间位置按照区域进行划分，得到在所述目标影像数据集合中相同区域下的每个区域的区域影像数据集合；根据所述每个区域的区域影像数据集合，得到所述心脏冠脉对应的多个最外层血管细胞数据集，每个最外层血管细胞数据集包括多个最外层血管细胞数据；查找与所述多个最外层血管细胞数据集对应的病变程度，为目标病变程度。
可选的，针对所述每个最外层血管细胞数据集，执行以下步骤：获取当前处理的最外 层血管细胞数据集投影在任意平面的特征曲线；选取在所述特征曲线的任意一点作为起始点；从所述起始点出发，沿着所述特征曲线的正方向和反方向不断标记像素点，当标记到目标像素点时停止标记，所述特征曲线的正方向为所述影像数据的横向正方向，所述特征曲线的反方向为所述影像数据的横向反方向，所述目标像素点为目标心脏冠脉血管段曲率变化最大的像素点，所述目标血管段为目标血管在所述起始点至目标空间位置之间的血管，所述目标血管与当前处理的最外层血管细胞数据集对应，所述目标空间位置是所述目标像素点对应的位置；获取所述目标血管段对应的曲率；将所述目标血管段对应的曲率设置为所述目标血管对应弯曲度。
参见图6，图6为本申请的实施例涉及的硬件运行环境的医学成像装置结构示意图。其中，如图6所示，本申请的实施例涉及的硬件运行环境的医学成像装置可以包括：
处理器601，例如CPU。
存储器602，可选的，存储器可以为高速RAM存储器，也可以是稳定的存储器，例如磁盘存储器。
通信接口603，用于实现处理器601和存储器602之间的连接通信。
本领域技术人员可以理解，图6中示出的医学成像装置的结构并不构成对其的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图6所示，存储器602中可以包括操作***、网络通信模块以及信息处理的程序。操作***是管理和控制医学成像装置硬件和软件资源的程序，支持人员管理的程序以及其他软件或程序的运行。网络通信模块用于实现存储器602内部各组件之间的通信，以及与医学成像装置内部其他硬件和软件之间通信。
在图6所示的医学成像装置中，处理器601用于执行存储器602中存储的信息迁移的程序，实现以下步骤：获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；根据所述扫描图像进行图像处理，得到目标影像数据集合；根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；根据所述心脏冠脉的病变程度确定冠脉支架的类型。
本申请涉及的医学成像装置的具体实施可参见上述基于VRDS AI医学影像的心脏冠脉的分析方法的各实施例，在此不做赘述。
本申请还提供了一种计算机可读存储介质，所述计算机可读存储介质用于存储计算机程序，所述存储计算机程序被所述处理器执行，以实现以下步骤：获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；根据所述扫描图像进行图像处理，得到目标影像数据集合；根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；根据所述心脏冠脉的病变程度确定冠脉支架的类型。
本申请涉及的计算机可读存储介质的具体实施可参见上述基于VRDS AI医学影像的心脏冠脉的分析方法的各实施例，在此不做赘述。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应所述知悉，本申请并不受所描述的动作顺序的限制，因为依据本申请，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应所述知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本申请所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应所述理解到，所揭露的装置，可通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或模块的间接耦合或通信连接，可以是电性或者其它的形式。
所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者所述技术方案的全部或部分可以以软件产品的形式体现出来，所述计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可为个人计算机、医学成像装置或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。

Claims (20)

  1. 基于VRDS AI医学影像的心脏冠脉的分析方法，其特征在于，包括：
    获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；
    根据所述扫描图像进行图像处理，得到目标影像数据集合；
    根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；
    根据所述心脏冠脉的病变程度确定冠脉支架的类型。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述扫描图像进行图像处理，得到目标影像数据集合，包括：
    根据所述扫描图像进行图像预处理，得到第一图像数据；
    根据所述第一图像数据生成所述心脏冠脉部位的原始影像数据集合；
    对所述原始影像数据集合进行边界优化处理，得到目标影像数据集合。
  3. 根据权利要求1或2所述的方法，其特征在于，所述根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，包括：
    根据所述目标影像数据集合确定所述目标用户心脏冠脉的病变区域；
    识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度；
    根据所述狭窄度和所述斑块钙化程度确定所述心脏冠脉周围的血管膨胀后的目标尺寸；
    根据目标尺寸确定所述心脏冠脉周围的血管参数，所述血管参数包括血管类型、直径和长度；
    根据所述血管参数确定所述目标用户心脏冠脉的病变程度。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述血管参数确定所述目标用户心脏冠脉的病变程度，包括：
    获取预设心脏冠脉部位识别模型；
    将所述血管参数输入所述预设心脏冠脉部位识别模型，得到所述血管参数中每个血管参数的病变值；
    按照预设的病变值与病变程度之间的映射关系，确定所述血管参数对应的病变程度。
  5. 根据权利要求3所述的方法，其特征在于，所述识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度，包括：
    获取所述目标影像数据集合中病变区域的病变图像；
    提取所述病变图像中的阴影部分；
    检测所述阴影部分的阴影面积大小；
    根据所述阴影面积大小确定斑块的钙化程度；
    检测所述阴影部分周围的血管大小；
    根据所述血管大小确定血管狭窄度。
  6. 根据权利要求1所述的方法，其特征在于，所述根据所述心脏冠脉的病变程度确定冠脉支架的类型，包括:
    查询第一数据库，获取在所述第一数据库中与所述病变程度匹配的目标冠脉支架类型，所述第一数据库中包括病变程度与冠脉支架类型的映射关系。
  7. 根据权利要求1所述的方法，其特征在于，所述根据所述心脏冠脉的病变程度确定冠脉支架的类型，包括:
    获取所述病变程度对应的目标用户的存活时间；
    将所述病变程度带入预设病变模型中，模拟得到在所述存活时间内的病变过程；
    解析所述病变过程，得到所述病变过程中所述斑块与所述血管的重合区域；
    根据所述重合区域，得到冠脉支架的参数范围；
    根据所述冠脉支架的参数范围确定冠脉支架的类型。
  8. 根据权利要求1或2所述的方法，其特征在于，所述根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度，包括：
    在所述目标影像数据集合中的每个目标影像数据中分别建立坐标系，所述坐标系的原点均为所述心脏冠脉中冠脉中心线的中心位置；
    根据所述坐标系对所述心脏冠脉进行区域分割，得到在所述每个目标影像数据中的区域影像数据；
    通过所述坐标系，对所述区域影像数据进行检测，得到多个目标像素点的空间位置，所述多个目标像素点的空间位置根据检测到第一像素点对应的灰度值属于心脏冠脉的血管细胞数据对应的灰度值时，记录所述第一像素点对应的空间位置；
    将所述多个目标像素点的空间位置按照区域进行划分，得到在所述目标影像数据集合中相同区域下的每个区域的区域影像数据集合；
    根据所述每个区域的区域影像数据集合，得到所述心脏冠脉对应的多个最外层血管细胞数据集，每个最外层血管细胞数据集包括多个最外层血管细胞数据；
    查找与所述多个最外层血管细胞数据集对应的病变程度，为目标病变程度。
  9. 根据权利要求8所述的方法，其特征在于，所述方法还包括：
    针对所述每个最外层血管细胞数据集，执行以下步骤：
    获取当前处理的最外层血管细胞数据集投影在任意平面的特征曲线；
    选取在所述特征曲线的任意一点作为起始点；
    从所述起始点出发，沿着所述特征曲线的正方向和反方向不断标记像素点，当标记到目标像素点时停止标记，所述特征曲线的正方向为所述影像数据的横向正方向，所述特征曲线的反方向为所述影像数据的横向反方向，所述目标像素点为目标心脏冠脉血管段曲率变化最大的像素点，所述目标血管段为目标血管在所述起始点至目标空间位置之间的血管， 所述目标血管与当前处理的最外层血管细胞数据集对应，所述目标空间位置是所述目标像素点对应的位置；获取所述目标血管段对应的曲率；
    将所述目标血管段对应的曲率设置为所述目标血管对应弯曲度。
  10. 一种医学成像装置，其特征在于，包括：
    获取单元，用于获取心脏冠脉部位的扫描图像，其中，所述扫描图像还包括心脏冠脉部位和所述心脏冠脉周围的血管；
    处理单元，用于根据所述扫描图像进行图像处理，得到目标影像数据集合；
    确定单元，用于根据所述目标影像数据集合，确定所述目标用户心脏冠脉的病变程度；
    所述确定单元，还用于根据所述心脏冠脉的病变程度确定冠脉支架的类型。
  11. 根据权利要求10所述的装置，其特征在于，所述处理模块，具体用于根据所述扫描图像进行图像预处理，得到第一图像数据；根据所述第一图像数据生成所述心脏冠脉部位的原始影像数据集合；对所述原始影像数据集合进行边界优化处理，得到目标影像数据集合。
  12. 根据权利要求10或11所述的装置，其特征在于，所述确定模块，具体用于根据所述目标影像数据集合确定所述目标用户心脏冠脉的病变区域；识别所述病变区域的中斑块的血管狭窄度和斑块的钙化程度；根据所述狭窄度和所述斑块钙化程度确定所述心脏冠脉周围的血管膨胀后的目标尺寸；根据目标尺寸确定所述心脏冠脉周围的血管参数，所述血管参数包括血管类型、直径和长度；根据所述血管参数确定所述目标用户心脏冠脉的病变程度。
  13. 根据权利要求12所述的装置，其特征在于，所述确定模块，具体用于获取预设心脏冠脉部位识别模型；将所述血管参数输入所述预设心脏冠脉部位识别模型，得到所述血管参数中每个血管参数的病变值；按照预设的病变值与病变程度之间的映射关系，确定所述血管参数对应的病变程度。
  14. 根据权利要求12所述的装置，其特征在于，所述确定模块，具体用于获取所述目标影像数据集合中病变区域的病变图像；提取所述病变图像中的阴影部分；检测所述阴影部分的阴影面积大小；根据所述阴影面积大小确定斑块的钙化程度；检测所述阴影部分周围的血管大小；根据所述血管大小确定血管狭窄度。
  15. 根据权利要求10所述的装置，其特征在于，所述确定模块，具体用于查询第一数据库，获取在所述第一数据库中与所述病变程度匹配的目标冠脉支架类型，所述第一数据库中包括病变程度与冠脉支架类型的映射关系。
  16. 根据权利要求10所述的装置，其特征在于，所述确定模块，具体用于获取所述病变程度对应的目标用户的存活时间；将所述病变程度带入预设病变模型中，模拟得到在所述存活时间内的病变过程；解析所述病变过程，得到所述病变过程中所述斑块与所述血管的重合区域；根据所述重合区域，得到冠脉支架的参数范围；根据所述冠脉支架的参数 范围确定冠脉支架的类型。
  17. 根据权利要求10或11所述的装置，其特征在于，所述确定模块，具体用于在所述目标影像数据集合中的每个目标影像数据中分别建立坐标系，所述坐标系的原点均为所述心脏冠脉中冠脉中心线的中心位置；根据所述坐标系对所述心脏冠脉进行区域分割，得到在所述每个目标影像数据中的区域影像数据；通过所述坐标系，对所述区域影像数据进行检测，得到多个目标像素点的空间位置，所述多个目标像素点的空间位置根据检测到第一像素点对应的灰度值属于心脏冠脉的血管细胞数据对应的灰度值时，记录所述第一像素点对应的空间位置；将所述多个目标像素点的空间位置按照区域进行划分，得到在所述目标影像数据集合中相同区域下的每个区域的区域影像数据集合；根据所述每个区域的区域影像数据集合，得到所述心脏冠脉对应的多个最外层血管细胞数据集，每个最外层血管细胞数据集包括多个最外层血管细胞数据；查找与所述多个最外层血管细胞数据集对应的病变程度，为目标病变程度。
  18. 根据权利要求17所述的装置，其特征在于，针对所述每个最外层血管细胞数据集，执行以下步骤：获取当前处理的最外层血管细胞数据集投影在任意平面的特征曲线；选取在所述特征曲线的任意一点作为起始点；从所述起始点出发，沿着所述特征曲线的正方向和反方向不断标记像素点，当标记到目标像素点时停止标记，所述特征曲线的正方向为所述影像数据的横向正方向，所述特征曲线的反方向为所述影像数据的横向反方向，所述目标像素点为目标心脏冠脉血管段曲率变化最大的像素点，所述目标血管段为目标血管在所述起始点至目标空间位置之间的血管，所述目标血管与当前处理的最外层血管细胞数据集对应，所述目标空间位置是所述目标像素点对应的位置；获取所述目标血管段对应的曲率；将所述目标血管段对应的曲率设置为所述目标血管对应弯曲度。
  19. 一种医学成像装置，其特征在于，包括处理器、存储器、通信接口以及一个或多个程序，其中，所述一个或多个程序被存储在所述存储器中，并且被生成由所述处理器执行，以执行权利要求1-9任一项方法中的步骤的指令。
  20. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质用于存储计算机程序，所述存储计算机程序被所述处理器执行，以实现权利要求1-9任一项所述的方法。
PCT/CN2019/114082 2019-10-29 2019-10-29 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置 WO2021081771A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/CN2019/114082 WO2021081771A1 (zh) 2019-10-29 2019-10-29 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置
CN201980099742.5A CN114340496A (zh) 2019-10-29 2019-10-29 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2019/114082 WO2021081771A1 (zh) 2019-10-29 2019-10-29 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置

Publications (1)

Publication Number Publication Date
WO2021081771A1 true WO2021081771A1 (zh) 2021-05-06

Family

ID=75714726

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/114082 WO2021081771A1 (zh) 2019-10-29 2019-10-29 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置

Country Status (2)

Country Link
CN (1) CN114340496A (zh)
WO (1) WO2021081771A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113408603A (zh) * 2021-06-15 2021-09-17 西安理工大学 一种基于多分类器融合的冠状动脉狭窄病变程度识别方法
CN113723406A (zh) * 2021-09-03 2021-11-30 乐普（北京）医疗器械股份有限公司 一种对冠脉造影图像进行支架定位的处理方法和装置
CN114947916A (zh) * 2022-05-24 2022-08-30 深圳睿心智能医疗科技有限公司 一种冠状动脉病变syntax评分的计算方法及装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1695568A (zh) * 2005-04-08 2005-11-16 北京思创贯宇科技开发有限公司 一种血管支架定位及术前选取的方法及***
CN1871998A (zh) * 2006-04-20 2006-12-06 北京思创贯宇科技开发有限公司 一种血管三维重建及虚拟支架置入的方法和***
US7340083B2 (en) * 2005-06-29 2008-03-04 University Of Washington Method and system for atherosclerosis risk scoring
JP2018509971A (ja) * 2015-02-27 2018-04-12 パイ メディカル イメージング ビー ヴイＰｉｅ Ｍｅｄｉｃａｌ Ｉｍａｇｉｎｇ Ｂ．Ｖ． 定量的フロー分析のための方法および装置
CN108242055A (zh) * 2018-01-25 2018-07-03 北京雅森科技发展有限公司 一种心肌融合图像处理方法及***
CN109949899A (zh) * 2019-02-28 2019-06-28 未艾医疗技术(深圳)有限公司 图像三维测量方法、电子设备、存储介质及程序产品

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4503265B2 (ja) * 2003-11-12 2010-07-14 株式会社日立メディコ Ｘ線ｃｔ装置
US9295447B2 (en) * 2011-08-17 2016-03-29 Volcano Corporation Systems and methods for identifying vascular borders
CN103479381A (zh) * 2013-10-22 2014-01-01 霍云龙 精确诊断冠状动脉粥样硬化的方法和设备
SG11201707951WA (en) * 2015-03-31 2017-10-30 Agency Science Tech & Res Method and apparatus for assessing blood vessel stenosis

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1695568A (zh) * 2005-04-08 2005-11-16 北京思创贯宇科技开发有限公司 一种血管支架定位及术前选取的方法及***
US7340083B2 (en) * 2005-06-29 2008-03-04 University Of Washington Method and system for atherosclerosis risk scoring
CN1871998A (zh) * 2006-04-20 2006-12-06 北京思创贯宇科技开发有限公司 一种血管三维重建及虚拟支架置入的方法和***
JP2018509971A (ja) * 2015-02-27 2018-04-12 パイ メディカル イメージング ビー ヴイＰｉｅ Ｍｅｄｉｃａｌ Ｉｍａｇｉｎｇ Ｂ．Ｖ． 定量的フロー分析のための方法および装置
CN108242055A (zh) * 2018-01-25 2018-07-03 北京雅森科技发展有限公司 一种心肌融合图像处理方法及***
CN109949899A (zh) * 2019-02-28 2019-06-28 未艾医疗技术(深圳)有限公司 图像三维测量方法、电子设备、存储介质及程序产品

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113408603A (zh) * 2021-06-15 2021-09-17 西安理工大学 一种基于多分类器融合的冠状动脉狭窄病变程度识别方法
CN113408603B (zh) * 2021-06-15 2023-10-31 西安华企众信科技发展有限公司 一种基于多分类器融合的冠状动脉狭窄病变程度识别方法
CN113723406A (zh) * 2021-09-03 2021-11-30 乐普（北京）医疗器械股份有限公司 一种对冠脉造影图像进行支架定位的处理方法和装置
CN113723406B (zh) * 2021-09-03 2023-07-18 乐普（北京）医疗器械股份有限公司 一种对冠脉造影图像进行支架定位的处理方法和装置
CN114947916A (zh) * 2022-05-24 2022-08-30 深圳睿心智能医疗科技有限公司 一种冠状动脉病变syntax评分的计算方法及装置

Also Published As

Publication number Publication date
CN114340496A (zh) 2022-04-12

Similar Documents

Publication Publication Date Title
US10713856B2 (en) Medical imaging system based on HMDS
WO2021081771A1 (zh) 基于vrds ai医学影像的心脏冠脉的分析方法和相关装置
WO2020168698A1 (zh) 基于VRDS 4D医学影像的静脉的Ai内镜分析方法及产品
WO2021081841A1 (zh) 基于vrds 4d医学影像的脾脏肿瘤识别方法及相关装置
US9530238B2 (en) Image processing apparatus, method and program utilizing an opacity curve for endoscopic images
WO2021030995A1 (zh) 基于vrds ai下腔静脉影像的分析方法及产品
AU2019431568B2 (en) Method and product for processing of vrds 4d medical images
WO2020173055A1 (zh) VRDS 4D医学影像多设备Ai联动显示方法及产品
WO2020168695A1 (zh) 基于VRDS 4D医学影像的肿瘤与血管Ai处理方法及产品
WO2021081850A1 (zh) 基于vrds 4d医学影像的脊椎疾病识别方法及相关装置
WO2021081839A1 (zh) 基于vrds 4d的病情分析方法及相关产品
WO2020168697A1 (zh) 基于VRDS 4D医学影像的栓塞的Ai识别方法及产品
WO2021081842A1 (zh) 基于vrds ai医学影像的肠肿瘤与血管分析方法和相关装置
WO2021081846A1 (zh) 静脉血管肿瘤影像处理方法及相关产品
WO2021081772A1 (zh) 基于vrds ai脑部影像的分析方法和相关装置
WO2020168694A1 (zh) 基于VRDS 4D医学影像的肿瘤Ai处理方法及产品
WO2020168696A1 (zh) 基于VRDS 4D医学影像的动脉与静脉Ai处理方法及产品
WO2021081836A1 (zh) 基于vrds 4d医学影像的胃肿瘤识别方法及相关产品
WO2021081845A1 (zh) 一种基于vrds ai的肝脏肿瘤和血管分析方法及相关产品
WO2021030994A1 (zh) 基于vrds ai静脉影像的识别方法及产品

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19950257

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19950257

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 19.10.2022)

122 Ep: pct application non-entry in european phase

Ref document number: 19950257

Country of ref document: EP

Kind code of ref document: A1