WO2021081850A1 - 基于vrds 4d医学影像的脊椎疾病识别方法及相关装置 - Google Patents

基于vrds 4d医学影像的脊椎疾病识别方法及相关装置 Download PDF

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WO2021081850A1
WO2021081850A1 PCT/CN2019/114499 CN2019114499W WO2021081850A1 WO 2021081850 A1 WO2021081850 A1 WO 2021081850A1 CN 2019114499 W CN2019114499 W CN 2019114499W WO 2021081850 A1 WO2021081850 A1 WO 2021081850A1
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image data
spine
artery
blood vessel
vein
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PCT/CN2019/114499
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English (en)
French (fr)
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李斯图尔特平
李戴维伟
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未艾医疗技术(深圳)有限公司
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Priority to PCT/CN2019/114499 priority Critical patent/WO2021081850A1/zh
Priority to CN201980099984.4A priority patent/CN114402395A/zh
Publication of WO2021081850A1 publication Critical patent/WO2021081850A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to a method and related devices for identifying spine diseases based on VRDS 4D medical images.
  • Spinal cord ischemia refers to the lack of blood supply to the spinal cord, which damages neurons in the spinal cord, causing irreversible spinal cord function damage. Spinal cord ischemia is mostly spinal vascular malformations.
  • CT electronic computer tomography
  • MRI magnetic resonance imaging
  • DTI diffusion tensor imaging
  • PET positron emission computed tomography
  • the embodiment of the present application provides a method and related device for identifying spine diseases based on VRDS 4D medical images, which is beneficial to improve the accuracy and efficiency of spine disease identification by the medical imaging device.
  • the first aspect of the embodiments of this application provides a method for identifying spine diseases based on VRDS 4D medical images, including:
  • Target medical image data includes image data of the spine and image data of blood vessels
  • a second aspect of the embodiments of the present application provides a medical imaging device, including:
  • the acquiring unit is used to acquire a scanned image of the spine of the target user
  • a processing unit configured to process the scanned image of the spine to obtain target medical image data, wherein the target medical image data includes image data of the spine and image data of blood vessels;
  • a determining unit configured to determine an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;
  • An identification unit configured to identify the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine;
  • the output unit is configured to perform 4D medical imaging according to the target medical image data and output the disease type of the spine.
  • 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 spine of the target user is obtained, and secondly, the scan image of the spine is processed to obtain target medical image data, where the target medical image data includes the image data of the spine and the image of the blood vessel.
  • the target medical image data includes the image data of the spine and the image of the blood vessel.
  • Data secondly, identify abnormal blood vessels based on the image data of the spine and blood vessels; secondly, identify the type of disease of the spine based on the relationship between the abnormal blood vessels and the spine; finally, perform 4D medical imaging based on the target medical image data and output the spine Type of disease.
  • the medical imaging device in the present application can identify the type of disease of the spine by processing the scanned image of the spine, and output the type of disease of the spine, avoiding the situation that the observation based on the human eye is not accurate enough, and is beneficial to improve the medical imaging device to perform the spine The accuracy and efficiency of disease recognition.
  • Fig. 1 is a schematic structural diagram of a VRDS 4D medical image intelligent analysis and processing system provided by an embodiment of the application;
  • FIG. 2 is a schematic flowchart of a method for identifying spine diseases based on VRDS 4D medical images according to an embodiment of the application;
  • FIG. 3 is a schematic diagram of a medical imaging device provided by an embodiment of the application.
  • FIG. 4 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 VRDS-based 4D medical image intelligent analysis and processing system 100 provided by an embodiment of the present application.
  • the system 100 includes a medical imaging device 110 and a network database 120.
  • the medical imaging device 110 can Including the local medical imaging device 111 and/or the terminal medical imaging device 112, the local medical imaging device 111 or the terminal medical imaging device 112 is used to identify spine diseases based on the VRDS 4D medical image presented in the embodiment of the application based on the original DICOM data
  • the identification and positioning of human spine diseases, four-dimensional volume rendering, and abnormal analysis are carried out to achieve four-dimensional three-dimensional imaging effects (the four-dimensional medical image specifically refers to the medical image including the internal spatial structure characteristics and external spatial structure characteristics of the displayed tissue
  • the internal spatial structure feature refers to that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of target organs, blood vessels and other tissues
  • the external spatial structure feature refers to the environmental features between tissues, including tissues The characteristics
  • the transfer function result can include the transfer function result of the surface of the internal organs and the tissue structure in the internal organs of the human body, and the transfer function result of the cube space, as shown in 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 recognizing spine diseases based on VRDS 4D medical imaging according to an embodiment of the present application, which is applied to the medical imaging device described in FIG. 1, as shown in FIG. 2, this embodiment
  • the provided methods for identifying spine diseases based on VRDS 4D medical images include:
  • the scanned image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • Target medical image data includes image data of the spine and image data of blood vessels.
  • processing the scanned image of the spine to obtain target medical image data includes: generating an image source of the spine according to the scanned image of the spine; and executing a first image source for the image source.
  • Preset processing 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, where the first medical image data includes the image data of the spine and all The first image data of the blood vessel; the second image data of the blood vessel is filtered from the first image data of the blood vessel according to the image data of the spine to obtain second medical image data, wherein the second The medical image data includes the image data of the spine and the second image data of the blood vessel; the second medical image data is imported into a preset cross-vessel network model to obtain the third medical image data, wherein the third The medical image data includes the image data of the spine, the image data of the artery, and the image data of the vein; the target medical image data is obtained by performing a second preset processing on the third medical image data.
  • generating the image source of the spine according to the scanned image of the spine includes: the medical imaging device acquires multiple scanned images that reflect the internal structural features of the target user's human body collected by medical equipment; and from the multiple scanned images Filter out at least one scanned image containing the spine, and use at least one scanned image as the target user's medical digital imaging and communication DICOM data; analyze the DICOM data to generate the target user's image source, the image source includes Texture 2D/3D image volume data .
  • 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.
  • VRDS limited contrast adaptive histogram equalization includes: regional noise ratio limiting, global contrast limiting; the local histogram of the image source is divided into multiple partitions, for each partition, according to the accumulation of the neighborhood of the partition
  • the slope of the histogram determines the slope of the transformation function, and the degree of contrast magnification around the pixel value of the partition is determined according to the gradient of the transformation function, and then the limit cropping process is performed according to the degree of contrast magnification to generate the distribution of the effective histogram. It also generates effective and available neighborhood consignment values, and evenly distributes these cropped parts of the histogram to other areas of the histogram.
  • hybrid partial differential denoising includes: different from Gaussian low-pass filtering (indiscriminately weakening the high-frequency components of the image, denoising will also produce image edge blurring), the isoilluminance formed by objects in natural images
  • the line (including the edge) should be a smooth and smooth curve, that is, the absolute value of the curvature of these isoilluminance lines should be small enough.
  • the design uses VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising to protect the edges of the image and avoid the step effect in the smoothing process.
  • the hybrid partial differential denoising model is used.
  • the VRDS Ai elastic deformation processing includes: superimposing positive and negative random distances on the original lattice to form a difference position matrix, and then the grayscale at each difference position forms a new lattice, which can realize the distortion of the image. Deform, and then rotate, distort, and translate the image.
  • the medical imaging device obtains the BMP data source by processing the original scanned image data, which increases the amount of information of the original data, and increases the depth information, and finally obtains data that meets the requirements of 4D medical image display.
  • the VRDS medical network model is provided with a transfer function of the structural characteristics of the spine and a transfer function of the structural characteristics of the blood vessel.
  • the BMP data source obtains first medical image data through the processing of the transfer function, and the first medical image data includes the spine
  • the image data of and the first image data of the blood vessel, and the first image data of the blood vessel includes the fusion data of the intersection position of the artery and the vein.
  • filtering the second image data of the blood vessel from the first image data of the blood vessel according to the image data of the spine includes: determining the abnormal position of the spine according to the image data of the spine; The abnormal position of the spine determines the blood vessel screening range; according to the blood vessel screening range, the second image data of the blood vessel is selected from the first image data of the blood vessel.
  • the first image data of blood vessels includes the image data of all blood vessels around the spine.
  • the abnormal position of the spine can be determined according to the image data of the spine.
  • the blood vessel screening range can be determined according to the abnormal position of the spine, and the blood vessel screening range is the abnormal position of the spine.
  • the second image data of the blood vessel is filtered from the first image data of the blood vessel according to the blood vessel screening range, that is, the image data of the blood vessel in the range around the abnormal position of the spine is filtered from the image data of all blood vessels around the spine. This can reduce the amount of subsequent data calculations and improve the accuracy of the identification of spine diseases.
  • determining the blood vessel screening range according to the abnormal position of the spine includes: taking the abnormal position of the spine as the center of the circle, and intercepting a circular image according to a first radius to obtain the first circular range; and determining the first circular range; The number of blood vessels in the circular range; determine whether the number of blood vessels in the first circular range exceeds a preset blood vessel number threshold; if the number of blood vessels in the first circular range exceeds the preset blood vessel number threshold, then It is determined that the first circular range is the blood vessel screening range; if the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold, the abnormal position of the spine is taken as the center of the circle, and the A circular image is intercepted with two radii to obtain a second circular range, wherein the second radius is greater than the first radius, and the number of blood vessels in the second circular range exceeds the preset blood vessel number threshold.
  • the abnormal position of the spine is taken as the center, and the circular image is intercepted according to the radius of 3cm to obtain the first circular range.
  • the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold.
  • the second radius can be 5cm, the abnormal position of the spine is taken as the center, and the circular image is intercepted according to the radius of 5cm, and the second circular range is obtained.
  • the number of blood vessels in the second circular range exceeds the expected Set a threshold for the number of blood vessels.
  • importing the second medical image data into a preset cross-vessel network model to obtain third medical image data includes: importing the second medical image data into the cross-vessel network model, and the cross-vessel network model passes the following Operation to realize the data separation of arteries and veins: (1) Extract the fusion data of the intersection position; (2) Separate the fusion data based on the preset data separation algorithm for each fusion data to obtain independent arterial boundary point data and venous boundary points Data; (3) integrating multiple arterial boundary point data obtained after processing into the first data, and integrating multiple venous boundary point data obtained after processing into the second data.
  • the third medical image data is obtained, and the third medical image data includes the image data of the spine, the image data of the arteries, and the image data of the veins.
  • the second preset 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.
  • the low-resolution information can provide the contextual semantic information of the segmentation target in the entire image, that is, the characteristics that reflect the relationship between the target and the environment. 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 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, the filter size is f, that is, the filter dimension is f*f*f*c, the number of filters is n, 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 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 Data enhancement to simulate different lighting changes.
  • the medical imaging device can process the BMP data source through the VRDS medical network model and the cross blood vessel network model, and combine boundary optimization and data enhancement processing to obtain target image data, which solves the problem that traditional medical imaging cannot achieve segmentation of arteries and arteries.
  • the overall separation of veins improves the authenticity, comprehensiveness and refinement of medical image display.
  • determining the abnormal blood vessel according to the imaging data of the spine and the imaging data of the blood vessel includes: determining the position of the spinal cord according to the imaging data of the spine; determining the abnormal position of the spine and the spinal cord The positional relationship of the spine, wherein the positional relationship includes that the abnormal position of the spine is located in the dura of the spinal cord, the abnormal position of the spine is located around the spinal cord, and the abnormal position of the spine is located at the position of the spinal cord. Any one of internal or surface relationships; acquiring image data of a normal blood vessel corresponding to the positional relationship; comparing the image data of the blood vessel with the image data of the normal blood vessel to determine the abnormal blood vessel.
  • the imaging data of the normal spinal dural artery and the imaging data of the normal root vein are acquired; if the positional relationship is If the abnormal position of the spine is located around the spinal cord, at least one of the image data of the normal anterior spinal artery and the image data of the normal posterior spinal artery is obtained, and the image data of the normal anterior spinal vein and the normal posterior spinal vein are obtained.
  • At least one item of image data if the positional relationship is that the abnormal position of the spine is located inside or on the surface of the spinal cord, obtain image data of normal root medullary arteries, and obtain image data of normal intramedullary veins or normal Image data of the perimedullary vein.
  • spinal vascular malformations include spinal dural arteriovenous atrophy, perimedullary arteriovenous malformation, and spinal arteriovenous malformations.
  • spinal dural arteriovenous malformation is acquired, and perimedullary arteriovenous malformations and spinal arteriovenous malformations continue to be congenital.
  • the supplying artery for spinal dural arteriovenous atrophy is the spinal dural artery
  • the draining vein is the root vein
  • the pathophysiology is characterized by chronic venous congestion
  • the supplying artery for the perimedullary arteriovenous atrophy is the anterior and/or posterior spinal artery
  • draining Veins are anterior and/or posterior spinal cord veins, and their pathophysiological manifestations are spinal cord parenchymal or subarachnoid hemorrhage, venous congestion, and space-occupying effect
  • the feeding artery of spinal arteriovenous malformation is the root medullary artery
  • the draining vein is intramedullary or perimedullary Veins
  • pathophysiological manifestations are spinal cord parenchyma or subarachnoid hemorrhage, venous congestion, space-occupying effect.
  • the image data of a blood vessel includes image data of an artery
  • the image data of a normal blood vessel includes image data of a normal artery.
  • the image data of the blood vessel is compared with the image data of the normal blood vessel to determine the abnormal blood vessel.
  • the image data of the blood vessel further includes the image data of the vein
  • the image data of the normal blood vessel also includes the image data of the normal vein.
  • the image data of the blood vessel is compared with the image data of the normal blood vessel to determine the
  • the abnormal blood vessel further includes: obtaining the diameter of the vein according to the image data of the vein; comparing the image data of the vein with the image data of the normal vein to determine the degree of surface expansion of the vein;
  • the physical parameters of the target user determine the third weight corresponding to the diameter of the vein and the fourth weight corresponding to the degree of surface expansion of the vein;
  • the diameter of the tube and the surface expansion degree of the vein are weighted to obtain the comprehensive expansion degree of the vein; when the comprehensive expansion degree of the vein exceeds a preset vein expansion threshold, the vein is determined to be an abnormal vein.
  • the diameter of the artery/vein can be obtained in the following way: segment the artery/vein according to the image data of the artery/vein to obtain the M segment of the artery/vein, where M is a positive integer; determine the M segment of the artery/vein The average diameter of each segment of artery/vein, where the difference between the average diameter of the i-th artery/vein and the average diameter of the (i+1)-th artery/vein is not less than the preset segment threshold, i It is a positive integer less than M; determine the weight corresponding to each segment of the artery/vein in the M segment artery/vein; according to the weight corresponding to each segment of the artery/vein in the M segment artery/vene, calculate each segment of the artery/vein in the M segment artery/vein The average diameter of the artery is weighted to obtain the diameter of the artery/venous.
  • comparing the image data of the artery/vein with the image data of the normal artery/vein to determine the degree of surface expansion of the artery/vein includes: obtaining the surface characteristics of the artery/vein according to the image data of the artery/vein Determine the target surface area of the artery/vein according to the surface characteristics of the artery/vein, and obtain the first surface feature of the target surface area of the artery/vein; obtain the normal artery according to the image data of the normal artery/vein / The second surface feature of the target surface area of the vein; the first surface feature is compared with the second surface feature to determine the degree of surface expansion of the artery/vein.
  • the method for determining the target surface area of the artery/vein according to the surface characteristics of the artery/vein may be: analyzing the feature point distribution of the surface area of the artery/vein according to the surface characteristics of the artery/vein; The surface area of the artery/vein is intercepted according to N different circle centers to obtain N circular surface partitions, where N is an integer greater than 3; determine each circular surface in the N circular surface partitions The number of feature points included in the partition; a target circular surface partition is selected from the N circular surface partitions, wherein the number of feature points included in the target circular surface partition is greater than the N circular surface partitions The number of feature points included in other circular surface divisions in the surface division; the target circular surface division is determined as the target surface area.
  • the surface characteristics of the target surface area are compared with the surface characteristics of the normal blood vessel.
  • the complexity of feature comparison can be reduced, the comparison time can be shortened, and the comparison efficiency can be improved.
  • the image data of the blood vessel also includes the curvature of the blood vessel
  • the method for determining the curvature of the blood vessel may be:
  • the origin of the coordinate system is any position of the blood vessel, and the X axis, Y axis and Z axis of the coordinate system are perpendicular to each other and follow the right-hand spiral rule;
  • the spatial position corresponding to the first pixel is recorded, and whenever the first pixel is detected.
  • the gray value corresponding to the two pixel points does not belong to the gray value corresponding to the blood vessel cell data of the outermost layer of the blood vessel, and the gray value corresponding to the adjacent pixels of the second pixel point belongs to the blood vessel of the outermost layer of the blood vessel
  • the spatial position corresponding to the second pixel point is recorded;
  • the third image data of the blood vessel is segmented according to the spatial positions corresponding to all the first pixels and the spatial positions corresponding to all the second pixels, so as to obtain a plurality of outermost parts corresponding to a plurality of blood vessels.
  • Layer vascular cell data set each outermost vascular cell data set includes multiple outermost vascular cell data;
  • the positive direction of the characteristic curve is the transverse positive direction of the third image data of the blood vessel, and the reverse direction of the characteristic curve is the first horizontal direction of the blood vessel.
  • the target pixel is the pixel with the largest change in the curvature of the target blood vessel segment
  • the target blood vessel segment is the blood vessel between the starting point and the target spatial position of the target blood vessel
  • the target blood vessel Corresponding to the currently processed outermost blood vessel cell data set, the target spatial position is the position corresponding to the target pixel; acquiring the curvature corresponding to the target blood vessel segment; setting the curvature corresponding to the target blood vessel segment to be The target blood vessel corresponds to the degree of curvature.
  • comparing the image data of the blood vessel with the image data of the normal blood vessel to determine the abnormal blood vessel includes: acquiring the image data of the blood vessel The diameter of the blood vessel; compare the image data of the blood vessel with the image data of the normal blood vessel to determine the degree of surface expansion of the blood vessel; obtain the body parameters of the target user, wherein the body of the target user The parameters include at least one of the height, weight, blood pressure, blood sugar, and heart rate of the target user; the fifth weight corresponding to the diameter of the blood vessel and the degree of surface expansion of the blood vessel are determined according to the physical parameters of the target user The corresponding sixth weight, the seventh weight corresponding to the curvature of the blood vessel; according to the fifth weight, the sixth weight, and the seventh weight, the diameter of the blood vessel and the surface of the blood vessel are respectively The degree of expansion and the degree of curvature of the blood vessel are weighted to obtain the overall degree of expansion of the blood vessel.
  • identifying the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine includes: if the abnormal artery is a spinal dural artery and the abnormal vein is a root vein, determining Whether the abnormal artery supplies blood to the spinal cord, if the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as spinal arteriovenous atrophy; if the abnormal artery is the anterior spinal artery and the posterior spinal cord At least one of the arteries, and the abnormal vein is at least one of the anterior spinal cord vein and the posterior spinal vein, determining whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, then Identify the type of disease of the spine as perimedullary arteriovenous atrophy; if the abnormal artery is a root medullary artery, and the abnormal vein is an intramedullary vein or a perimedullary vein, determine whether the abnormal artery supplies blood to the spinal cord, If the abnormal artery supplies
  • 4D medical imaging refers to the presentation of 4-dimensional medical images.
  • performing 4D medical imaging according to the target medical image data includes: the medical imaging device selects enhancement data with a quality score greater than a preset score from the target medical image data as VRDS 4D imaging data; according to VRDS 4D imaging The data undergoes 4D medical imaging.
  • the quality score can be comprehensively evaluated from the following dimensions: average gradient, information entropy, visual information fidelity, peak signal-to-noise ratio PSNR, structural similarity SSIM, mean square error MSE, etc.
  • average gradient information entropy
  • visual information fidelity visual information fidelity
  • peak signal-to-noise ratio PSNR peak signal-to-noise ratio
  • structural similarity SSIM structural similarity
  • mean square error MSE mean square error
  • the scan image of the spine of the target user is acquired, and secondly, the scan image of the spine is processed to obtain the target medical image data.
  • the target medical image data includes the image data of the spine and the blood vessel.
  • Image data secondly, identify abnormal blood vessels based on the image data of the spine and blood vessels; secondly, identify the disease type of the spine based on the relationship between the abnormal blood vessels and the spine; finally, perform 4D medical imaging based on the target medical image data and output the spine Type of disease.
  • the medical imaging device in the present application can identify the type of disease of the spine by processing the scanned image of the spine, and output the type of disease of the spine, avoiding the situation that the observation based on the human eye is not accurate enough, and is beneficial to improve the medical imaging device to perform the spine The accuracy and efficiency of disease recognition.
  • FIG. 3 is a schematic diagram of a medical imaging apparatus 300 provided by an embodiment of the present application.
  • the medical imaging apparatus 300 may include:
  • the obtaining unit 301 is used to obtain a scanned image of the spine of the target user
  • the processing unit 302 is configured to process the scanned image of the spine to obtain target medical image data, where the target medical image data includes image data of the spine and image data of blood vessels;
  • the determining unit 303 is configured to determine an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;
  • the identification unit 304 is configured to identify the disease type of the spine according to the association relationship between the abnormal blood vessel and the spine;
  • the output unit 305 is configured to perform 4D medical imaging according to the target medical image data and output the disease type of the spine.
  • the processing unit 302 is specifically configured to: generate an image source of the spine according to the scanned image of the spine; perform first preset processing on the image source to obtain a bitmap BMP data source;
  • the BMP data source imports a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes image data of the spine and first image data of the blood vessel;
  • the image data of the spine filters the second image data of the blood vessel from the first image data of the blood vessel to obtain the second medical image data, wherein the second medical image data includes the image data of the spine and The second image data of the blood vessel;
  • the second medical image data is imported into a preset cross-vessel network model to obtain third medical image data, wherein the third medical image data includes the image data of the spine, The image data of the arteries and the image data of the veins; performing a second preset processing on the third medical image data to obtain the target medical image data.
  • the processing unit 302 is specifically configured to: determine the abnormal position of the spine according to the image data of the spine; determine the blood vessel screening range according to the abnormal position of the spine; The second image data of the blood vessel is filtered out of the first image data of the blood vessel.
  • the processing unit 302 is specifically configured to: take the abnormal position of the spine as the center of the circle and perform a circular image interception according to a first radius to obtain a first circle range; and determine the first circle The number of blood vessels in the first circular range; determine whether the number of blood vessels in the first circular range exceeds a preset blood vessel number threshold; if the number of blood vessels in the first circular range exceeds the preset blood vessel number threshold, determine The first circular range is the blood vessel screening range; if the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold, the abnormal position of the spine is taken as the center of the circle, and the second The radius is a circular image interception to obtain a second circular range, wherein the second radius is greater than the first radius, and the number of blood vessels in the second circular range exceeds the preset blood vessel number threshold.
  • the determining unit 303 is specifically configured to: determine the position of the spinal cord according to the image data of the spine; determine the positional relationship between the abnormal position of the spine and the spinal cord, wherein the positional relationship includes The abnormal position of the spine is located in the dura mater of the spinal cord, the abnormal position of the spine is located around the spinal cord, and the abnormal position of the spine is located inside or on the surface of the spinal cord; Image data of a normal blood vessel corresponding to the position relationship; comparing the image data of the blood vessel with the image data of the normal blood vessel to determine the abnormal blood vessel.
  • the determining unit 303 is specifically configured to: if the positional relationship is that the abnormal position of the spine is located in the dura of the spinal cord, obtain the image data of the normal spinal dural artery and the normal root Image data of the vein; if the positional relationship is that the abnormal position of the spine is located around the spinal cord, obtain at least one of the image data of the normal anterior spinal artery and the image data of the normal posterior spinal artery, and obtain the normal At least one of the image data of the anterior spinal cord vein and the image data of the normal posterior spinal vein; if the positional relationship is that the abnormal position of the spine is located inside or on the surface of the spinal cord, the image data of the normal root medullary artery is acquired , And obtain the imaging data of normal intramedullary veins or normal perimedullary veins.
  • the image data of the blood vessel includes the image data of the artery
  • the image data of the normal blood vessel includes the image data of the normal artery
  • the determining unit 303 is specifically configured to: Data to obtain the diameter of the artery; compare the image data of the artery with the image data of the normal artery to determine the degree of surface expansion of the artery; obtain the body parameters of the target user, wherein the The body parameters of the target user include at least one of the height, weight, blood pressure, blood sugar, and heart rate of the target user; the first weight corresponding to the diameter of the artery and the artery are determined according to the body parameters of the target user The second weight corresponding to the degree of surface expansion of the artery; the diameter of the artery and the degree of surface expansion of the artery are respectively weighted according to the first weight and the second weight to obtain the comprehensive degree of expansion of the artery When the comprehensive expansion degree of the artery exceeds the preset arterial expansion threshold, it is determined that the artery is an abnormal artery.
  • the image data of the blood vessel further includes the image data of the vein
  • the image data of the normal blood vessel further includes the image data of the normal vein
  • the determining unit 303 is specifically configured to: Obtain the diameter of the vein from the image data; compare the image data of the vein with the image data of the normal vein to determine the degree of surface expansion of the vein; determine the diameter of the vein according to the physical parameters of the target user
  • the third weight corresponding to the diameter of the vein and the fourth weight corresponding to the degree of surface expansion of the vein; according to the third weight and the fourth weight, the diameter of the vein and the surface of the vein are respectively expanded
  • the degree is weighted to obtain the comprehensive expansion degree of the vein; when the comprehensive expansion degree of the vein exceeds a preset vein expansion threshold, it is determined that the vein is an abnormal vein.
  • the identification unit 304 is specifically configured to: if the abnormal artery is a spinal dural artery and the abnormal vein is a root vein, determine whether the abnormal artery supplies blood to the spinal cord, if If the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as spinal arteriovenous atrophy; if the abnormal artery is at least one of the anterior spinal artery and the posterior spinal artery, and the abnormal vein is At least one of the anterior spinal vein and the posterior spinal vein to determine whether the abnormal artery supplies blood to the spinal cord.
  • the disease type of the spine is identified as a perimedullary arteriovenous atrophy If the abnormal artery is a root medullary artery, and the abnormal vein is an intramedullary vein or a perimedullary vein, determine whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, identify The disease type of the spine is spinal arteriovenous malformation.
  • FIG. 4 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 401 such as a CPU.
  • the memory 402 optionally, the memory may be a high-speed RAM memory, or a stable memory, such as a disk memory.
  • the communication interface 403 is used to implement connection and communication between the processor 401 and the memory 402.
  • FIG. 4 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 402 may include an operating system, a network communication module, and a program for identifying spinal diseases.
  • 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 the spine disease recognition program and other software or programs.
  • the network communication module is used to implement communication between various components in the memory 402 and communication with other hardware and software in the medical imaging device.
  • the processor 401 is configured to execute the spine disease recognition program stored in the memory 402, and implement the following steps:
  • Target medical image data includes image data of the spine and image data of blood vessels
  • the present application also provides a computer-readable storage medium for storing a computer program, and the computer program is executed by the processor to implement the following steps:
  • Target medical image data includes image data of the spine and image data of blood vessels
  • 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. .

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Abstract

基于VRDS 4D医学影像的脊椎疾病识别方法包括：获取目标用户的脊椎的扫描图像；对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。

Description

基于VRDS 4D医学影像的脊椎疾病识别方法及相关装置 技术领域
本申请涉及医学成像装置技术领域，具体涉及一种基于VRDS 4D医学影像的脊椎疾病识别方法及相关装置。
背景技术
脊髓缺血是指脊髓由于血液供应缺乏，从而损伤脊髓内神经元，造成不可逆的脊髓功能损害，脊髓缺血大多数为脊髓血管畸形。
目前，医生仍然采用观看阅读连续的二维切片扫描图像，例如，CT(电子计算机断层扫描)、MRI(磁共振成像)、DTI(弥散张量成像)、PET(正电子发射型计算机断层显像)等，以此对患者的脊椎疾病进行判断分析。然而，脊椎内血管分布丰富，仅仅通过直接观看两维切片数据有时无法识别出脊髓血管的异常类型，严重影响到医生对脊椎疾病的诊断，从而延误了脊椎疾病的治疗。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一种基于VRDS 4D医学影像的脊椎疾病识别方法及相关装置，有利于提高医学成像装置进行脊椎疾病识别的准确度和效率。
本申请实施例第一方面提供了基于VRDS 4D医学影像的脊椎疾病识别方法，包括：
获取目标用户的脊椎的扫描图像；
对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
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，用于获取目标用户的脊椎的扫描图像；
处理单元，用于对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
确定单元，用于根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
识别单元，用于根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
输出单元，用于根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
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，获取目标用户的脊椎的扫描图像，其次，对脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，目标医学影像数据包括脊椎的影像数据和血管的影像数据，其次，根据脊椎的影像数据和血管的影像数据确定异常血管，其次，根据异常血管与脊椎的关联关系识别脊椎的疾病类型，最后，根据目标医学影像数据进行4D医学成像，并输出脊椎的疾病类型。可见，本申请中的医学成像装置能够通过处理脊椎的扫描图像，识别脊椎的疾病类型，并输出该脊椎的疾病类型，避免了基于人眼观察不够精准的情况，有利于提高医学成像装置进行脊椎疾病识别的准确度和效率。
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。
其中：
图1为本申请实施例提供的一种基于VRDS 4D医学影像智能分析处理***的结构示意图；
图2为本申请实施例提供的一种基于VRDS 4D医学影像的脊椎疾病识别方法的流程示意图；
图3为本申请实施例提供的一种医学成像装置的示意图；
图4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的一种基于VRDS 4D医学影像智能分析处理***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 4D医学影像的脊椎疾病识别方法为基础，进行人体脊椎疾病的识别、定位和四维体绘制、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目标器官、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，如脊椎与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内脏器官表面和人体内脏器官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。
下面对本申请实施例涉及到的基于VRDS 4D医学影像的脊椎疾病识别方法进行详细 介绍。
参见图2，图2是本申请实施例提供的一种基于VRDS 4D医学影像的脊椎疾病识别方法的流程示意图，应用于如图1所述的医学成像装置，如图2所示，本实施例提供的基于VRDS 4D医学影像的脊椎疾病识别方法包括：
201、获取目标用户的脊椎的扫描图像。
其中，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。
202、对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据。
在一种可能的示例中，对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，包括：根据所述脊椎的扫描图像生成所述脊椎的图源；针对所述图源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，其中，所述第一医学影像数据包括所述脊椎的影像数据和所述血管的第一影像数据；根据所述脊椎的影像数据从所述血管的第一影像数据中筛选出所述血管的第二影像数据，从而得到第二医学影像数据，其中，所述第二医学影像数据包括所述脊椎的影像数据和所述血管的第二影像数据；将所述第二医学影像数据导入预设的交叉血管网络模型，得到第三医学影像数据，其中，所述第三医学影像数据包括所述脊椎的影像数据、动脉的影像数据和静脉的影像数据；针对所述第三医学影像数据执行第二预设处理得到所述目标医学影像数据。
可选的，根据所述脊椎的扫描图像生成所述脊椎的图源，包括：医学成像装置获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从多张扫描图像中筛选出包含脊椎的至少一张扫描图像，将至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；解析DICOM数据生成目标用户的图源，图源包括纹理Texture 2D/3D图像体数据。
可选的，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，VRDS限制对比度自适应直方图均衡包括：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图爱划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域代销的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域。
其中，混合偏微分去噪包括：不同于高斯低通滤波(对图像的高频成分不加区别的减弱，去噪的同时会产生图像边缘模糊化)，自然图像中的物体所形成的等照度线(包括边缘)应该是足够光滑顺畅的曲线，即这些等照度线的曲率的绝对值应该足够小，当图像受到噪 声污染后，图像的局部灰度值会发生随机起伏，导致等照度线的不规则震荡，形成局部曲率很大的等照度线，根据这一原理，设计通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，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。
其中，VRDS Ai弹性变形处理包括：在原有点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，另外再对图像进行旋转、扭曲、平移等操作。
可见，本示例中，医学成像装置通过对原始扫描图像数据的处理，得到BMP数据源，提高了原始数据的信息量，且增加了深度信息，最终得到符合4D医学影像显示需求的数据。
其中，VRDS医学网络模型设置有脊椎的结构特性的传递函数和血管的结构特性的传递函数，BMP数据源通过传递函数的处理得到第一医学影像数据，所述第一医学影像数据包括所述脊椎的影像数据和所述血管的第一影像数据，所述血管的第一影像数据包括动脉和静脉的交叉位置的融合数据。
可选的，根据所述脊椎的影像数据从所述血管的第一影像数据中筛选出所述血管的第二影像数据包括：根据所述脊椎的影像数据确定所述脊椎的异常位置；根据所述脊椎的异常位置确定血管筛选范围；根据所述血管筛选范围从所述血管的第一影像数据中筛选出所述血管的第二影像数据。
具体的，血管的第一影像数据包括脊椎周围所有血管的影像数据，根据脊椎的影像数据可以确定脊椎的异常位置，这样可以根据脊椎的异常位置确定血管筛选范围，血管筛选范围为脊椎的异常位置周围的范围，根据血管筛选范围从血管的第一影像数据中筛选出血管的第二影像数据，即从脊椎周围所有血管的影像数据中筛选出脊椎的异常位置周围范围内的血管的影像数据。这样可以减少后续数据计算量，并且可以提高脊椎疾病识别的准确率。
可选的，根据所述脊椎的异常位置确定血管筛选范围包括：以所述脊椎的异常位置为圆心，按照第一半径进行圆形图像截取，以得到第一圆形范围；确定所述第一圆形范围内的血管数量；判断所述第一圆形范围内的血管数量是否超过预设血管数量阈值；若所述第一圆形范围内的血管数量超过所述预设血管数量阈值，则确定所述第一圆形范围为所述血管筛选范围；若所述第一圆形范围内的血管数量不超过所述预设血管数量阈值，则以所述脊椎的异常位置为圆心，按照第二半径进行圆形图像截取，以得到第二圆形范围，其中，所述第二半径大于所述第一半径，所述第二圆形范围内的血管数量超过所述预设血管数量阈值。
例如，第一半径为3cm，以脊椎的异常位置为圆心，按照3cm的半径进行圆形图像截取，得到第一圆形范围，第一圆形范围内的血管数量不超过预设血管数量阈值，则重新确定第二半径，第二半径可以是5cm，以脊椎的异常位置为圆心，按照5cm的半径进行圆形 图像截取，得到第二圆形范围，第二圆形范围内的血管数量超过预设血管数量阈值。
可选的，将所述第二医学影像数据导入预设的交叉血管网络模型，得到第三医学影像数据，包括：将第二医学影像数据导入交叉血管网络模型，所述交叉血管网络模型通过以下操作实现动脉和静脉的数据分离：(1)提取交叉位置的融合数据；(2)针对每个融合数据基于预设数据分离算法分离该融合数据，得到相互独立的动脉边界点数据和静脉边界点数据；(3)将处理后得到的多个动脉边界点数据整合为第一数据，将处理后得到的多个静脉边界点数据整合为第二数据。最终得到第三医学影像数据，所述第三医学影像数据包括脊椎的影像数据、动脉的影像数据和静脉的影像数据。
可选的，所述第二预设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，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。
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。
其中，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。
可见，本示例中，医学成像装置能够通过VRDS医学网络模型、交叉血管网络模型对BMP数据源进行处理，结合边界优化和数据增强处理得到目标影像数据，解决了传统的医学影像无法实现分割动脉和静脉的整体分离的问题，提高医学影像显示的真实性、全面性和精细化程度。
203、根据所述脊椎的影像数据和所述血管的影像数据确定异常血管。
在一种可能的示例中，根据所述脊椎的影像数据和所述血管的影像数据确定异常血管包括：根据所述脊椎的影像数据确定脊髓的位置；确定所述脊椎的异常位置与所述脊髓的位置关系，其中，所述位置关系包括所述脊椎的异常位置位于所述脊髓的硬膜内、所述脊 椎的异常位置位于所述脊髓的周围以及所述脊椎的异常位置位于所述脊髓的内部或表面中的任意一种关系；获取与所述位置关系对应的正常血管的影像数据；将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管。
可选的，若所述位置关系为所述脊椎的异常位置位于所述脊髓的硬膜内，则获取正常硬脊膜动脉的影像数据和正常根静脉的影像数据；若所述位置关系为所述脊椎的异常位置位于所述脊髓的周围，则获取正常脊髓前动脉的影像数据和正常脊髓后动脉的影像数据中的至少一项，以及获取正常脊髓前静脉的影像数据和正常脊髓后静脉的影像数据中的至少一项；若所述位置关系为所述脊椎的异常位置位于所述脊髓的内部或表面，则获取正常根髓动脉的影像数据，以及获取正常髓内静脉的影像数据或者正常髓周静脉的影像数据。
具体的，脊髓血管畸形包括硬脊膜动静脉痿、髓周动静脉痿和脊髓动静脉畸形，其中，硬脊膜动静脉痿是获得性的，髓周动静脉痿和脊髓动静脉继续是先天性的，硬脊膜动静脉痿的供养动脉为硬脊膜动脉，引流静脉为根静脉，病理生理学表现为慢性静脉淤血；髓周动静脉痿的供养动脉为脊髓前和/或后动脉，引流静脉为脊髓前和/或后静脉，病理生理学表现为脊髓实质或蛛网膜下腔出血、静脉淤血、占位效应；脊髓动静脉畸形的供养动脉为根髓动脉，引流静脉为髓内或髓周静脉，病理生理学表现为脊髓实质或蛛网膜下腔出血、静脉淤血、占位效应。
可选的，血管的影像数据包括动脉的影像数据，正常血管的影像数据包括正常动脉的影像数据，将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管，包括：根据所述动脉的影像数据获取所述动脉的管径；将所述动脉的影像数据与所述正常动脉的影像数据进行对比，以确定所述动脉的表面扩张程度；获取所述目标用户的身体参数，其中，所述目标用户的身体参数包括所述目标用户的身高、体重、血压、血糖、心率中的至少一项；根据所述目标用户的身体参数确定所述动脉的管径对应的第一权重和所述动脉的表面扩张程度对应的第二权重；根据所述第一权重和所述第二权重分别对所述动脉的管径和所述动脉的表面扩张程度进行加权运算，得到所述动脉的综合扩张程度。当所述动脉的综合扩张程度超过预设动脉扩张阈值时，确定所述动脉为异常动脉。
可选的，血管的影像数据还包括静脉的影像数据，正常血管的影像数据还包括正常静脉的影像数据，将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管，还包括：根据所述静脉的影像数据获取所述静脉的管径；将所述静脉的影像数据与所述正常静脉的影像数据进行对比，以确定所述静脉的表面扩张程度；根据所述目标用户的身体参数确定所述静脉的管径对应的第三权重和所述静脉的表面扩张程度对应的第四权重；根据所述第三权重和所述第四权重分别对所述静脉的管径和所述静脉的表面扩张程度进行加权运算，得到所述静脉的综合扩张程度；当所述静脉的综合扩张程度超过预设静脉扩张阈值时，确定所述静脉为异常静脉。
其中，动脉/静脉的管径可以通过以下方式得到：根据动脉/静脉的影像数据对动脉/静 脉进行分段，得到M段动脉/静脉，其中，M为正整数；确定M段动脉/静脉中每一段动脉/静脉的平均管径，其中，第i段动脉/静脉的平均管径与第(i+1)段动脉/静脉的平均管径的管径差不小于预设分段阈值，i为小于M的正整数；确定M段动脉/静脉中每一段动脉/静脉对应的权重；根据M段动脉/静脉中每一段动脉/静脉对应的权重对M段动脉/静脉中每一段动脉/静脉的平均管径进行加权运算，得到动脉/静脉的管径。
其中，将动脉/静脉的影像数据与正常动脉/静脉的影像数据进行对比，以确定动脉/静脉的表面扩张程度，包括：根据所述动脉/静脉的影像数据得到所述动脉/静脉的表面特征；根据所述动脉/静脉的表面特征确定所述动脉/静脉的目标表面区域，获取所述动脉/静脉的目标表面区域的第一表面特征；根据所述正常动脉/静脉的影像数据获取正常动脉/静脉的目标表面区域的第二表面特征；将第一表面特征与第二表面特征进行对比，以确定所述动脉/静脉的表面扩张程度。
其中，根据所述动脉/静脉的表面特征确定所述动脉/静脉的目标表面区域的方法可以是：根据所述动脉/静脉的表面特征分析所述动脉/静脉的表面区域的特征点分布；将所述动脉/静脉的表面区域按照N个不同圆心进行圆形图像截取，以得到N个圆形表面分区，N为大于3的整数；确定所述N个圆形表面分区中每个圆形表面分区所包含的特征点的数量；从所述N个圆形表面分区中选出目标圆形表面分区，其中，所述目标圆形表面分区所包含的特征点的数量大于所述N个圆形表面分区中的其他圆形表面分区所包含的特征点的数量；确定所述目标圆形表面分区为所述目标表面区域。
可见，本示例中，通过筛选动脉/静脉的目标表面区域，将目标表面区域的表面特征与正常血管的表面特征进行对比，这样，可以减少特征对比的复杂度，缩短对比时间，提高对比效率。
在一种可能的示例中，血管的影像数据还包括血管的弯曲度，确定血管的弯曲度的方法可以是：
根据血管的第三影像数据建立坐标系，所述坐标系的原点为所述血管的任意位置，所述坐标系的X轴、Y轴和Z轴相互垂直并遵循右手螺旋法则；
从所述坐标系的原点出发，分别按照预设距离沿着所述坐标系的X轴的正方向和反方向、Y轴的正方向和反方向以及Z轴的正方向和反方向进行检测，每当检测到第一像素点对应的灰度值属于所述血管最外层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，每当检测到所述第二像素点对应的灰度值不属于所述血管最外层的血管细胞数据对应的灰度值且所述第二像素点相邻像素点对应的灰度值属于所述血管最外层的血管细胞数据对应的灰度值时，记录所述第二像素点对应的空间位置；
根据所有的所述第一像素点对应的空间位置以及所有的所述第二像素点对应的空间位置将所述血管的第三影像数据进行切分，以得到多个血管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；
针对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血管的第三影像数据的横向正方向，所述特征曲线的反方向为所述血管的第三影像数据的横向反方向，所述目标像素点为目标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。
基于上述示例，无论所述血管是动脉还是静脉，将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管，包括：根据所述血管的影像数据获取所述血管的管径；将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述血管的表面扩张程度；获取所述目标用户的身体参数，其中，所述目标用户的身体参数包括所述目标用户的身高、体重、血压、血糖、心率中的至少一项；根据所述目标用户的身体参数确定所述血管的管径对应的第五权重、所述血管的表面扩张程度对应的第六权重、所述血管的弯曲度对应的第七权重；根据所述第五权重、所述第六权重、所述第七权重分别对所述血管的管径、所述血管的表面扩张程度、所述血管的弯曲度进行加权运算，得到所述血管的综合扩张程度。当所述血管的综合扩张程度超过预设血管扩张阈值时，确定所述血管为异常血管。
204、根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型。
在一种可能的示例中，根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型包括：若所述异常动脉为硬脊膜动脉，且所述异常静脉为根静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为硬脊膜动静脉痿；若所述异常动脉为脊髓前动脉和脊髓后动脉中的至少一条动脉，且所述异常静脉为脊髓前静脉和脊髓后静脉中的至少一条静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为髓周动静脉痿；若所述异常动脉为根髓动脉，且所述异常静脉为髓内静脉或者髓周静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为脊髓动静脉畸形。
205、根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
其中，4D医学成像是指呈现4维医学影像。
在一个可能的示例中，根据所述目标医学影像数据进行4D医学成像，包括：医学成像装置从目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据；根据VRDS 4D成像数据进行4D医学成像。
其中，质量评分可以从以下维度进行综合评价，平均梯度、信息熵、视觉信息保真度、峰值信噪比PSNR、结构相似性SSIM、均方误差MSE等，具体可以参考图像领域的常见图像质量评分算法，此处不再赘述。
可以看出，本申请实施例中，获取目标用户的脊椎的扫描图像，其次，对脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，目标医学影像数据包括脊椎的影像数据和血管的影像数据，其次，根据脊椎的影像数据和血管的影像数据确定异常血管，其次，根据异常血管与脊椎的关联关系识别脊椎的疾病类型，最后，根据目标医学影像数据进行4D医学成像，并输出脊椎的疾病类型。可见，本申请中的医学成像装置能够通过处理脊椎的扫描图像，识别脊椎的疾病类型，并输出该脊椎的疾病类型，避免了基于人眼观察不够精准的情况，有利于提高医学成像装置进行脊椎疾病识别的准确度和效率。
参见图3，图3是本申请的一个实施例提供的一种医学成像装置300的示意图，医学成像装置300可以包括：
获取单元301，用于获取目标用户的脊椎的扫描图像；
处理单元302，用于对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
确定单元303，用于根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
识别单元304，用于根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
输出单元305，用于根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
在一个可能的示例中，所述处理单元302具体用于：根据所述脊椎的扫描图像生成所述脊椎的图源；针对所述图源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，其中，所述第一医学影像数据包括所述脊椎的影像数据和所述血管的第一影像数据；根据所述脊椎的影像数据从所述血管的第一影像数据中筛选出所述血管的第二影像数据，从而得到第二医学影像数据，其中，所述第二医学影像数据包括所述脊椎的影像数据和所述血管的第二影像数据；将所述第二医学影像数据导入预设的交叉血管网络模型，得到第三医学影像数据，其中，所述第三医学影像数据包括所述脊椎的影像数据、动脉的影像数据和静脉的影像数据；针对所述第三医学影像数据执行第二预设处理得到所述目标医学影像数据。
在一个可能的示例中，所述处理单元302具体用于：根据所述脊椎的影像数据确定所述脊椎的异常位置；根据所述脊椎的异常位置确定血管筛选范围；根据所述血管筛选范围从所述血管的第一影像数据中筛选出所述血管的第二影像数据。
在一个可能的示例中，所述处理单元302具体用于：以所述脊椎的异常位置为圆心，按照第一半径进行圆形图像截取，以得到第一圆形范围；确定所述第一圆形范围内的血管 数量；判断所述第一圆形范围内的血管数量是否超过预设血管数量阈值；若所述第一圆形范围内的血管数量超过所述预设血管数量阈值，则确定所述第一圆形范围为所述血管筛选范围；若所述第一圆形范围内的血管数量不超过所述预设血管数量阈值，则以所述脊椎的异常位置为圆心，按照第二半径进行圆形图像截取，以得到第二圆形范围，其中，所述第二半径大于所述第一半径，所述第二圆形范围内的血管数量超过所述预设血管数量阈值。
在一个可能的示例中，所述确定单元303具体用于：根据所述脊椎的影像数据确定脊髓的位置；确定所述脊椎的异常位置与所述脊髓的位置关系，其中，所述位置关系包括所述脊椎的异常位置位于所述脊髓的硬膜内、所述脊椎的异常位置位于所述脊髓的周围以及所述脊椎的异常位置位于所述脊髓的内部或表面中的任意一种关系；获取与所述位置关系对应的正常血管的影像数据；将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管。
在一个可能的示例中，所述确定单元303具体用于：若所述位置关系为所述脊椎的异常位置位于所述脊髓的硬膜内，则获取正常硬脊膜动脉的影像数据和正常根静脉的影像数据；若所述位置关系为所述脊椎的异常位置位于所述脊髓的周围，则获取正常脊髓前动脉的影像数据和正常脊髓后动脉的影像数据中的至少一项，以及获取正常脊髓前静脉的影像数据和正常脊髓后静脉的影像数据中的至少一项；若所述位置关系为所述脊椎的异常位置位于所述脊髓的内部或表面，则获取正常根髓动脉的影像数据，以及获取正常髓内静脉的影像数据或者正常髓周静脉的影像数据。
在一个可能的示例中，所述血管的影像数据包括所述动脉的影像数据，所述正常血管的影像数据包括正常动脉的影像数据，所述确定单元303具体用于：根据所述动脉的影像数据获取所述动脉的管径；将所述动脉的影像数据与所述正常动脉的影像数据进行对比，以确定所述动脉的表面扩张程度；获取所述目标用户的身体参数，其中，所述目标用户的身体参数包括所述目标用户的身高、体重、血压、血糖、心率中的至少一项；根据所述目标用户的身体参数确定所述动脉的管径对应的第一权重和所述动脉的表面扩张程度对应的第二权重；根据所述第一权重和所述第二权重分别对所述动脉的管径和所述动脉的表面扩张程度进行加权运算，得到所述动脉的综合扩张程度；当所述动脉的综合扩张程度超过预设动脉扩张阈值时，确定所述动脉为异常动脉。
在一个可能的示例中，所述血管的影像数据还包括所述静脉的影像数据，所述正常血管的影像数据还包括正常静脉的影像数据，所述确定单元303具体用于：根据所述静脉的影像数据获取所述静脉的管径；将所述静脉的影像数据与所述正常静脉的影像数据进行对比，以确定所述静脉的表面扩张程度；根据所述目标用户的身体参数确定所述静脉的管径对应的第三权重和所述静脉的表面扩张程度对应的第四权重；根据所述第三权重和所述第四权重分别对所述静脉的管径和所述静脉的表面扩张程度进行加权运算，得到所述静脉的综合扩张程度；当所述静脉的综合扩张程度超过预设静脉扩张阈值时，确定所述静脉为异 常静脉。
在一个可能的示例中，所述识别单元304具体用于：若所述异常动脉为硬脊膜动脉，且所述异常静脉为根静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为硬脊膜动静脉痿；若所述异常动脉为脊髓前动脉和脊髓后动脉中的至少一条动脉，且所述异常静脉为脊髓前静脉和脊髓后静脉中的至少一条静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为髓周动静脉痿；若所述异常动脉为根髓动脉，且所述异常静脉为髓内静脉或者髓周静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为脊髓动静脉畸形。
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 4D医学影像的脊椎疾病识别方法的各实施例，在此不做赘述。
参见图4，图4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4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401，例如CPU。
存储器402，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403，用于实现处理器401和存储器402之间的连接通信。
本领域技术人员可以理解，图4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4所示，存储器402中可以包括操作***、网络通信模块以及脊椎疾病识别的程序。操作***是管理和控制医学成像装置硬件和软件资源的程序，支持脊椎疾病识别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402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4所示的医学成像装置中，处理器401用于执行存储器402中存储的脊椎疾病识别的程序，实现以下步骤：
获取目标用户的脊椎的扫描图像；
对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
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 4D医学影像的脊椎疾病识别方法的各实施例，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，所述计算机程序被所述处理器执行，以实现以下步骤：
获取目标用户的脊椎的扫描图像；
对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
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 4D医学影像的脊椎疾病识别方法的各实施例，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. 一种基于VRDS 4D医学影像的脊椎疾病识别方法，其特征在于，应用于医学成像装置，所述方法包括：
    获取目标用户的脊椎的扫描图像；
    对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
    根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
    根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
    根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
  2. 根据权利要求1所述的方法，其特征在于，所述对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，包括：
    根据所述脊椎的扫描图像生成所述脊椎的图源；
    针对所述图源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，其中，所述第一医学影像数据包括所述脊椎的影像数据和所述血管的第一影像数据；
    根据所述脊椎的影像数据从所述血管的第一影像数据中筛选出所述血管的第二影像数据，从而得到第二医学影像数据，其中，所述第二医学影像数据包括所述脊椎的影像数据和所述血管的第二影像数据；
    将所述第二医学影像数据导入预设的交叉血管网络模型，得到第三医学影像数据，其中，所述第三医学影像数据包括所述脊椎的影像数据、动脉的影像数据和静脉的影像数据；
    针对所述第三医学影像数据执行第二预设处理得到所述目标医学影像数据。
  3. 根据权利要求2所述的方法，其特征在于，所述根据所述脊椎的影像数据从所述血管的第一影像数据中筛选出所述血管的第二影像数据包括：
    根据所述脊椎的影像数据确定所述脊椎的异常位置；
    根据所述脊椎的异常位置确定血管筛选范围；
    根据所述血管筛选范围从所述血管的第一影像数据中筛选出所述血管的第二影像数据。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述脊椎的异常位置确定血管筛选范围包括：
    以所述脊椎的异常位置为圆心，按照第一半径进行圆形图像截取，以得到第一圆形范围；
    确定所述第一圆形范围内的血管数量；
    判断所述第一圆形范围内的血管数量是否超过预设血管数量阈值；
    若所述第一圆形范围内的血管数量超过所述预设血管数量阈值，则确定所述第一圆形 范围为所述血管筛选范围；
    若所述第一圆形范围内的血管数量不超过所述预设血管数量阈值，则以所述脊椎的异常位置为圆心，按照第二半径进行圆形图像截取，以得到第二圆形范围，其中，所述第二半径大于所述第一半径，所述第二圆形范围内的血管数量超过所述预设血管数量阈值。
  5. 根据权利要求3或4所述的方法，其特征在于，所述根据所述脊椎的影像数据和所述血管的影像数据确定异常血管包括：
    根据所述脊椎的影像数据确定脊髓的位置；
    确定所述脊椎的异常位置与所述脊髓的位置关系，其中，所述位置关系包括所述脊椎的异常位置位于所述脊髓的硬膜内、所述脊椎的异常位置位于所述脊髓的周围以及所述脊椎的异常位置位于所述脊髓的内部或表面中的任意一种关系；
    获取与所述位置关系对应的正常血管的影像数据；
    将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管。
  6. 根据权利要求5所述的方法，其特征在于，所述获取与所述位置关系对应的正常血管的影像数据包括：
    若所述位置关系为所述脊椎的异常位置位于所述脊髓的硬膜内，则获取正常硬脊膜动脉的影像数据和正常根静脉的影像数据；
    若所述位置关系为所述脊椎的异常位置位于所述脊髓的周围，则获取正常脊髓前动脉的影像数据和正常脊髓后动脉的影像数据中的至少一项，以及获取正常脊髓前静脉的影像数据和正常脊髓后静脉的影像数据中的至少一项；
    若所述位置关系为所述脊椎的异常位置位于所述脊髓的内部或表面，则获取正常根髓动脉的影像数据，以及获取正常髓内静脉的影像数据或者正常髓周静脉的影像数据。
  7. 根据权利要求5或6所述的方法，其特征在于，所述血管的影像数据包括所述动脉的影像数据，所述正常血管的影像数据包括正常动脉的影像数据，所述将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管，包括：
    根据所述动脉的影像数据获取所述动脉的管径；
    将所述动脉的影像数据与所述正常动脉的影像数据进行对比，以确定所述动脉的表面扩张程度；
    获取所述目标用户的身体参数，其中，所述目标用户的身体参数包括所述目标用户的身高、体重、血压、血糖、心率中的至少一项；
    根据所述目标用户的身体参数确定所述动脉的管径对应的第一权重和所述动脉的表面扩张程度对应的第二权重；
    根据所述第一权重和所述第二权重分别对所述动脉的管径和所述动脉的表面扩张程度进行加权运算，得到所述动脉的综合扩张程度；
    当所述动脉的综合扩张程度超过预设动脉扩张阈值时，确定所述动脉为异常动脉。
  8. 根据权利要求7所述的方法，其特征在于，所述血管的影像数据还包括所述静脉的影像数据，所述正常血管的影像数据还包括正常静脉的影像数据，所述将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管，还包括：
    根据所述静脉的影像数据获取所述静脉的管径；
    将所述静脉的影像数据与所述正常静脉的影像数据进行对比，以确定所述静脉的表面扩张程度；
    根据所述目标用户的身体参数确定所述静脉的管径对应的第三权重和所述静脉的表面扩张程度对应的第四权重；
    根据所述第三权重和所述第四权重分别对所述静脉的管径和所述静脉的表面扩张程度进行加权运算，得到所述静脉的综合扩张程度；
    当所述静脉的综合扩张程度超过预设静脉扩张阈值时，确定所述静脉为异常静脉。
  9. 根据权利要求8所述的方法，其特征在于，所述根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型包括：
    若所述异常动脉为硬脊膜动脉，且所述异常静脉为根静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为硬脊膜动静脉痿；
    若所述异常动脉为脊髓前动脉和脊髓后动脉中的至少一条动脉，且所述异常静脉为脊髓前静脉和脊髓后静脉中的至少一条静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为髓周动静脉痿；
    若所述异常动脉为根髓动脉，且所述异常静脉为髓内静脉或者髓周静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为脊髓动静脉畸形。
  10. 一种医学成像装置，其特征在于，所述装置包括：
    获取单元，用于获取目标用户的脊椎的扫描图像；
    处理单元，用于对所述脊椎的扫描图像进行处理，以得到目标医学影像数据，其中，所述目标医学影像数据包括所述脊椎的影像数据和血管的影像数据；
    确定单元，用于根据所述脊椎的影像数据和所述血管的影像数据确定异常血管；
    识别单元，用于根据所述异常血管与所述脊椎的关联关系识别所述脊椎的疾病类型；
    输出单元，用于根据所述目标医学影像数据进行4D医学成像，并输出所述脊椎的疾病类型。
  11. 根据权利要求10所述的装置，其特征在于，所述处理单元具体用于：
    根据所述脊椎的扫描图像生成所述脊椎的图源；
    针对所述图源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，其中， 所述第一医学影像数据包括所述脊椎的影像数据和所述血管的第一影像数据；
    根据所述脊椎的影像数据从所述血管的第一影像数据中筛选出所述血管的第二影像数据，从而得到第二医学影像数据，其中，所述第二医学影像数据包括所述脊椎的影像数据和所述血管的第二影像数据；
    将所述第二医学影像数据导入预设的交叉血管网络模型，得到第三医学影像数据，其中，所述第三医学影像数据包括所述脊椎的影像数据、动脉的影像数据和静脉的影像数据；
    针对所述第三医学影像数据执行第二预设处理得到所述目标医学影像数据。
  12. 根据权利要求11所述的装置，其特征在于，所述处理单元具体用于：
    根据所述脊椎的影像数据确定所述脊椎的异常位置；
    根据所述脊椎的异常位置确定血管筛选范围；
    根据所述血管筛选范围从所述血管的第一影像数据中筛选出所述血管的第二影像数据。
  13. 根据权利要求12所述的装置，其特征在于，所述处理单元具体用于：
    以所述脊椎的异常位置为圆心，按照第一半径进行圆形图像截取，以得到第一圆形范围；
    确定所述第一圆形范围内的血管数量；
    判断所述第一圆形范围内的血管数量是否超过预设血管数量阈值；
    若所述第一圆形范围内的血管数量超过所述预设血管数量阈值，则确定所述第一圆形范围为所述血管筛选范围；
    若所述第一圆形范围内的血管数量不超过所述预设血管数量阈值，则以所述脊椎的异常位置为圆心，按照第二半径进行圆形图像截取，以得到第二圆形范围，其中，所述第二半径大于所述第一半径，所述第二圆形范围内的血管数量超过所述预设血管数量阈值。
  14. 根据权利要求12或13所述的装置，其特征在于，所述确定单元具体用于：
    根据所述脊椎的影像数据确定脊髓的位置；
    确定所述脊椎的异常位置与所述脊髓的位置关系，其中，所述位置关系包括所述脊椎的异常位置位于所述脊髓的硬膜内、所述脊椎的异常位置位于所述脊髓的周围以及所述脊椎的异常位置位于所述脊髓的内部或表面中的任意一种关系；
    获取与所述位置关系对应的正常血管的影像数据；
    将所述血管的影像数据与所述正常血管的影像数据进行对比，以确定所述异常血管。
  15. 根据权利要求14所述的装置，其特征在于，所述确定单元具体用于：
    若所述位置关系为所述脊椎的异常位置位于所述脊髓的硬膜内，则获取正常硬脊膜动脉的影像数据和正常根静脉的影像数据；
    若所述位置关系为所述脊椎的异常位置位于所述脊髓的周围，则获取正常脊髓前动脉的影像数据和正常脊髓后动脉的影像数据中的至少一项，以及获取正常脊髓前静脉的影像 数据和正常脊髓后静脉的影像数据中的至少一项；
    若所述位置关系为所述脊椎的异常位置位于所述脊髓的内部或表面，则获取正常根髓动脉的影像数据，以及获取正常髓内静脉的影像数据或者正常髓周静脉的影像数据。
  16. 根据权利要求14或15所述的装置，其特征在于，所述血管的影像数据包括所述动脉的影像数据，所述正常血管的影像数据包括正常动脉的影像数据，所述确定单元具体用于：
    根据所述动脉的影像数据获取所述动脉的管径；
    将所述动脉的影像数据与所述正常动脉的影像数据进行对比，以确定所述动脉的表面扩张程度；
    获取所述目标用户的身体参数，其中，所述目标用户的身体参数包括所述目标用户的身高、体重、血压、血糖、心率中的至少一项；
    根据所述目标用户的身体参数确定所述动脉的管径对应的第一权重和所述动脉的表面扩张程度对应的第二权重；
    根据所述第一权重和所述第二权重分别对所述动脉的管径和所述动脉的表面扩张程度进行加权运算，得到所述动脉的综合扩张程度；
    当所述动脉的综合扩张程度超过预设动脉扩张阈值时，确定所述动脉为异常动脉。
  17. 根据权利要求16所述的装置，其特征在于，所述血管的影像数据还包括所述静脉的影像数据，所述正常血管的影像数据还包括正常静脉的影像数据，所述确定单元具体用于：
    根据所述静脉的影像数据获取所述静脉的管径；
    将所述静脉的影像数据与所述正常静脉的影像数据进行对比，以确定所述静脉的表面扩张程度；
    根据所述目标用户的身体参数确定所述静脉的管径对应的第三权重和所述静脉的表面扩张程度对应的第四权重；
    根据所述第三权重和所述第四权重分别对所述静脉的管径和所述静脉的表面扩张程度进行加权运算，得到所述静脉的综合扩张程度；
    当所述静脉的综合扩张程度超过预设静脉扩张阈值时，确定所述静脉为异常静脉。
  18. 根据权利要求17所述的装置，其特征在于，所述识别单元具体用于：
    若所述异常动脉为硬脊膜动脉，且所述异常静脉为根静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为硬脊膜动静脉痿；
    若所述异常动脉为脊髓前动脉和脊髓后动脉中的至少一条动脉，且所述异常静脉为脊髓前静脉和脊髓后静脉中的至少一条静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为髓周动静脉痿；
    若所述异常动脉为根髓动脉，且所述异常静脉为髓内静脉或者髓周静脉，判断所述异常动脉是否给所述脊髓供血，若所述异常动脉给所述脊髓供血，则识别所述脊椎的疾病类型为脊髓动静脉畸形。
  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。
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