WO2021081772A1 - 基于vrds ai脑部影像的分析方法和相关装置 - Google Patents

基于vrds ai脑部影像的分析方法和相关装置 Download PDF

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Publication number
WO2021081772A1
WO2021081772A1 PCT/CN2019/114095 CN2019114095W WO2021081772A1 WO 2021081772 A1 WO2021081772 A1 WO 2021081772A1 CN 2019114095 W CN2019114095 W CN 2019114095W WO 2021081772 A1 WO2021081772 A1 WO 2021081772A1
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cerebrovascular
data
brain
thickness
disease
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PCT/CN2019/114095
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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/CN2019/114095 priority Critical patent/WO2021081772A1/zh
Priority to CN201980099745.9A priority patent/CN114287042A/zh
Publication of WO2021081772A1 publication Critical patent/WO2021081772A1/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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to an analysis method and related devices based on VRDS AI brain images.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • DTI Diffusion Tensor Imaging
  • Positron Emission Computed Tomography Computed Tomography
  • PET Magnetic Resonance Imaging
  • PET Positron Emission Computed Tomography
  • the embodiments of the application provide an analysis method and related devices based on VRDS AI brain images, and implement the embodiments of the application to improve the recognition efficiency and accuracy of cerebrovascular diseases.
  • the first aspect of the embodiments of this application provides an analysis method based on VRDS AI brain images, including:
  • the cerebrovascular disease analysis strategy is used to analyze the image data to locate the disease.
  • a second aspect of the embodiments of the present application provides a medical imaging device, including:
  • the first acquisition module is used to acquire the scanned image of the user's brain and the symptoms performance information of the user;
  • the determining module is used to determine the cerebrovascular disease information corresponding to the symptom performance information
  • the second obtaining module is used to obtain the cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from the cerebrovascular disease analysis strategy database;
  • the analysis module is used to analyze the image data using the cerebrovascular disease analysis strategy to locate the disease.
  • 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.
  • FIG. 1 is a schematic structural diagram of an analysis system based on VRDS AI brain image provided by an embodiment of the application;
  • FIG. 2 is a schematic flowchart of a method for analyzing brain images based on VRDS AI according to an embodiment of the application;
  • FIG. 3 is a schematic flowchart of another method for analyzing brain images based on VRDS AI according to an embodiment of the application;
  • FIG. 4 is a schematic flowchart of another method for analyzing brain images based on VRDS AI 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 an analysis system 100 based on VRDS AI brain image 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 analyze the brain image based on VRDS AI based on the original DICOM data.
  • the four-dimensional medical image specifically refers to the medical image including the internal spatial structure characteristics of the displayed tissue and the external spatial structure
  • the internal spatial structure characteristic means that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of the brain, blood vessels and other tissues.
  • the external spatial structural characteristic refers to the environmental characteristics between tissues, including The spatial location characteristics between tissues (including crossing, spacing, fusion), etc., such as the edge structure characteristics of the crossing position between the brainstem and cerebral arteries, etc.), the local medical imaging device 111 is still more effective than the terminal medical imaging device 112.
  • the transfer function result can include the transfer function result of the human brain surface and the tissue structure in the human brain, and the transfer function result of the cube space, such as The cube edit box and arc edit array quantity, coordinates, color, transparency and other information 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 VRDS AI brain image analysis method provided by an embodiment of the application.
  • a VRDS AI brain image-based analysis method provided by an embodiment of the present application may include:
  • the medical imaging device acquires a scanned image of a user's brain and information about the symptoms of the user.
  • the symptom performance information may include, for example, contralateral hemiplegia, partial numbness, repeated speech, indifference, lack of initiative, and the like.
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the medical imaging device generates image data including cerebral blood vessels according to the scanned image.
  • cerebral blood vessels include cerebral arteries and cerebral veins.
  • cerebral arteries may include, for example, vertebral arteries, internal carotid arteries, and the like.
  • Cerebral veins may include, for example, large cerebral veins, diencephalic veins, brainstem veins, cerebellar veins, and the like.
  • the image data includes three-dimensional spatial image data of the cerebral blood vessel.
  • the medical imaging device can also generate image data including the brain skull according to the scanned image, and the method further includes: acquiring characteristic data corresponding to the brain skull according to the image data of the brain skull; and according to the brain skull Corresponding feature data is determined from the brain and skull disease analysis strategy database.
  • the brain and skull disease analysis strategy database stores a variety of characteristic data corresponding to the brain and skull in a variety of brain and skull disorders, and the variety of characteristic data includes at least one of the following: structural data, frontal bone included in the brain and skull Connection data between, parietal bone, occipital bone, sphenoid bone, temporal bone and ethmoid bone. Furthermore, the corresponding characteristic data of the skull in different brain diseases are also different. 203.
  • the medical imaging device determines cerebrovascular disease information corresponding to the disease manifestation information.
  • the disease manifestation information may be repeated speech, indifference, and lack of initiative.
  • the cerebrovascular disease information may be, for example, the posterior communicating artery (PComA) whose larger branch is the distribution area infarction of the anterior mammary artery.
  • PComA posterior communicating artery
  • the determining the cerebrovascular disease information corresponding to the symptom manifestation information includes: parsing the symptom manifestation information to obtain multiple symptom manifestations corresponding to the symptom manifestation information Field; obtain synonyms or synonyms of each disease performance field in the multiple disease performance fields to obtain multiple disease performance field sets, where the i-th disease performance field set in the multiple disease performance field sets includes The synonym or synonym of the i-th symptom manifestation field in the multiple symptom manifestation fields, i is a positive integer; search for the cerebrovascular disease information matching the multiple symptom manifestation field sets from the cerebrovascular disease database.
  • i can be, for example, 1, 2, 3, 4, 6, 8, 11, 20 and other numerical values.
  • multiple symptom manifestations fields corresponding to the symptom manifestation information are obtained by parsing the symptom manifestation information, so as to obtain synonyms or synonyms of each symptom manifestation field in the multiple symptom manifestation fields to obtain multiple symptom manifestations.
  • Symptom manifestation field set search for cerebrovascular disease information matching multiple symptom manifestation field sets from the cerebrovascular disease database, so as to achieve more accurate acquisition of cerebrovascular disease information.
  • the searching for the cerebrovascular disease information that matches the multiple disease manifestation field sets from the cerebrovascular disease database includes: performing the search on the multiple disease manifestation fields Set the search priority according to the search priority; search for the cerebrovascular disease information matching the multiple disease manifestation field sets from the cerebrovascular disease database according to the search priority.
  • the search priority is obtained by the medical imaging device randomly setting a search order for the multiple disease manifestation field sets.
  • the determining the cerebrovascular disease information corresponding to the symptom manifestation information includes: generating a symptom manifestation identifier corresponding to the symptom manifestation information; from a cerebrovascular disease database Find the cerebrovascular disease information matching the symptom manifestation identifier in the cerebrovascular disease database, wherein a plurality of symptom manifestation markers and a plurality of cerebrovascular disease information are associated and stored in the cerebrovascular disease database, and the plurality of symptom manifestation markers are associated with The multiple pieces of cerebrovascular disease information correspond one to one.
  • the medical imaging device obtains the cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from the cerebrovascular disease analysis strategy database.
  • the cerebrovascular disease analysis strategy database includes multiple cerebrovascular disease analysis strategies corresponding to various cerebrovascular disease information, and each cerebrovascular disease analysis strategy is different from each other.
  • the medical imaging device uses the cerebrovascular disease analysis strategy to analyze the image data to locate the disease, and the disease includes a brain tumor.
  • brain tumors include one of the following: benign intracranial tumors, malignant intracranial tumors, neuroepithelial tumors, meningeal tumors, lymphomas and hematopoietic tissue tumors, germ cell tumors, or sella tumors.
  • the generating image data including cerebral blood vessels according to the scanned image includes: performing a first preset processing on the scanned image to obtain a bitmap BMP data source;
  • the BMP data source imports a preset VRDS medical network model to obtain first medical image data.
  • the first medical image data includes a data set of the cerebrovascular and a data set of the brain, and data of the cerebrovascular
  • the set includes the fusion data of the intersection position of the cerebral artery and the cerebral vein.
  • the data set of the brain is the result of the transfer function of the cube space of the brain surface and the tissue structure inside the brain.
  • the data set is the result of the transfer function of the cube space of the cerebrovascular surface and the tissue structure inside the cerebrovascular; the first medical image data is imported into the preset cross-vascular network model to obtain the second medical image data, so
  • the second medical image data includes the data set of the brain, the data set of the cerebral artery, and the data set of the cerebral vein, and the first data in the data set of the cerebral artery and the data set of the cerebral vein
  • the second data in the data set are independent of each other.
  • the first data is data associated with the intersection position
  • the second data is data associated with the intersection position; the second medical image data is executed Second, the image data is obtained by preset processing.
  • 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; parsing the DICOM data to generate the image source of the user, the image source including texture 2D/3D image volume data; performing 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 internal structural features of the user's brain, and can screen out at least one suitable scan image containing the brain based on clarity, accuracy, etc., and then correct The scanned image is further processed 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 brain through a series of data processing, and the cerebral venous image data and cerebral artery image data at the crossing position are independent of each other, supporting Accurate presentation in three-dimensional space improves 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 brain and the transfer function of the cerebrovascular 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.
  • the first data is extracted by the medical imaging device from the cerebrovascular data set including fusion data of the intersection of cerebral arteries and cerebral veins, and preset data is used
  • the separation algorithm separates the fusion data to obtain cerebral artery boundary point data.
  • the second data is extracted by the medical imaging device from the cerebrovascular data set including the fusion data of the intersection of the cerebral artery and the cerebral vein, and the fusion data is separated by a preset data separation algorithm to obtain the cerebral venous boundary point data .
  • 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, 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.
  • segmentation targets include the brain, cerebral arteries and cerebral 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, 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 3D boundary optimization processing includes the following operations: inputting the second medical 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; cascading 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 set of the brain, a data set of the cerebral artery, and a data set of the cerebral vein.
  • the method further includes: obtaining the image quality corresponding to the image data Score; 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 as VRDS 4D imaging data; display the VRDS 4D on the output device Imaging 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 using the cerebrovascular disease analysis strategy to analyze the image data to locate the disease includes: using the cerebrovascular disease analysis strategy to analyze the image data in the image data Analyze the thickness of the cerebrovascular wall of the multiple cerebral blood vessels to obtain the thickness of the multiple cerebrovascular walls corresponding to the multiple cerebral blood vessels, wherein the multiple cerebral blood vessels correspond to multiple parts of the brain The cerebrovascular thickness; compare each of the multiple cerebrovascular wall thicknesses with a preset set of cerebrovascular wall thicknesses to determine each of the multiple cerebrovascular wall thicknesses Each cerebrovascular wall thickness falls within a plurality of preset cerebrovascular wall thickness subsets in the preset cerebrovascular wall thickness set; and the disease is located according to the plurality of preset cerebrovascular wall thickness subsets.
  • the plurality of cerebral blood vessels includes at least one cerebral artery or at least one cerebral vein.
  • the at least one cerebral artery may include, for example, at least one of the following: vertebral artery, internal carotid artery, and the like.
  • the at least one cerebral vein may include, for example, at least one of the following: a large cerebral vein, a diencephalon vein, a brain stem vein, a cerebellar vein, and the like.
  • each preset cerebrovascular wall thickness subset in the plurality of preset cerebrovascular wall thickness subsets includes multiple preset cerebrovascular wall thicknesses corresponding to the same cerebrovascular.
  • the disease is located according to a plurality of preset subsets of the thickness of the cerebrovascular wall, so that a more precise location of the disease is achieved and the recognition accuracy of cerebrovascular diseases is improved.
  • the analyzing the image data to locate the disease using the cerebrovascular disease analysis strategy includes: performing preset processing on the image data to obtain multiple A plurality of feature data corresponding to a cerebrovascular, each feature data of the multiple feature data includes the color, shape, cross-sectional area, and curvature corresponding to each cerebrovascular; the cerebrovascular is used according to the multiple feature data
  • the disease analysis strategy analyzes each of the multiple cerebrovascular vessels to obtain at least one cerebrovascular vessel whose first characteristic data is abnormal.
  • the first characteristic data includes at least one of the following colors, shapes, cross-sectional areas, and Curvature; at least one cerebrovascular localization disorder that is abnormal according to the first characteristic data.
  • the preset processing includes the following steps: establishing a coordinate system according to the image data, the origin of the coordinate system is an arbitrary position of the brain, 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; starting from the origin of the coordinate system, follow the preset distance along the positive and negative directions of the X axis, the positive and negative directions of the Y axis, and the positive direction of the Z axis, respectively, according to the preset distance Detect in the opposite direction.
  • the spatial position corresponding to the first pixel is recorded, and whenever the second pixel is detected
  • the gray value corresponding to the point does not belong to the gray value corresponding to the cerebrovascular and the gray value corresponding to the adjacent pixel of the second pixel is the gray value corresponding to the cerebrovascular
  • record the gray value corresponding to the second pixel Spatial position segment the image data according to the spatial positions corresponding to all the first pixels and the spatial positions corresponding to all the second pixels to obtain the plurality of cerebral blood vessels; to obtain the The color, shape, cross-sectional area, and curvature of each cerebral blood vessel in the multiple cerebral blood vessels.
  • the curvature corresponding to each cerebrovascular among the plurality of cerebrovascular is obtained by the medical imaging device by performing the following steps, including: acquiring the outermost cerebrovascular cell data set of each cerebrovascular among the plurality of cerebrovascular; Each outermost cerebrovascular cell data set includes multiple outermost cerebrovascular cell data; for each outermost cerebrovascular cell data set, perform the following steps: Obtain the currently processed outermost cerebrovascular cell data set projection A characteristic curve on an arbitrary plane; 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, when the target pixel is marked Stop mark, the positive direction of the characteristic curve is the horizontal positive direction of the image data, the reverse direction of the characteristic curve is the horizontal reverse direction of the image data, and the target pixel point is the curvature change of the target cerebrovascular segment The largest pixel point, the target cerebrovascular segment is the cerebrovascular of the target cerebrovascular from the starting point to the target spatial position, and the target cerebrovascular correspond
  • FIG. 3 is a schematic flowchart of another VRDS AI brain image analysis method provided by an embodiment of the application.
  • an embodiment of the present application provides the use of the cerebrovascular disease analysis strategy to analyze the image data to locate the disease, including:
  • the medical imaging device uses the cerebrovascular disease analysis strategy to analyze the thickness of multiple cerebrovascular sets located in multiple parts of the brain in the image data to obtain the corresponding cerebrovascular sets.
  • a plurality of cerebral blood vessel thickness sets wherein each of the plurality of parts of the brain in the image data includes a plurality of cerebral blood vessels, and each cerebral blood vessel set in the plurality of cerebral blood vessels includes all
  • each cerebral blood vessel thickness set in the multiple cerebral blood vessel thickness sets includes multiple cerebral blood vessel thicknesses corresponding to the multiple cerebral blood vessels.
  • the plurality of cerebral blood vessels includes at least one cerebral artery or at least one cerebral vein.
  • the at least one cerebral artery may include, for example, at least one of the following: vertebral artery, internal carotid artery, and the like.
  • the at least one cerebral vein may include, for example, at least one of the following: a large cerebral vein, a diencephalon vein, a brain stem vein, a cerebellar vein, and the like.
  • the cerebrovascular disease analysis strategy is used to analyze the thickness of multiple cerebrovascular sets located in multiple parts of the brain in the image data to Obtaining multiple cerebrovascular thickness sets corresponding to the multiple cerebrovascular sets includes:
  • the cerebrovascular thickness analysis priority set includes a plurality of cerebrovascular thickness analysis priority subsets, and the multiple cerebrovascular thickness analysis priority subsets and the multiple brains One-to-one correspondence between blood vessel sets;
  • the cerebrovascular disease analysis strategy is used to analyze the thicknesses of multiple cerebrovascular sets located in multiple parts of the brain in the image data to obtain the multiple Multiple brain blood vessel thickness sets corresponding to the cerebrovascular set.
  • the cerebrovascular thickness analysis priority set is obtained by the medical imaging device randomly setting and analyzing the sequence of the multiple cerebrovascular sets.
  • the cerebrovascular disease analysis strategy is used to analyze multiple cerebrovascular sets located in multiple parts of the brain in the image data according to the cerebrovascular thickness analysis priority set. Analyze the thickness of the cerebrovascular system to obtain multiple cerebrovascular thickness sets corresponding to multiple cerebrovascular sets, which improves the efficiency of analysis.
  • the medical imaging device compares each cerebrovascular thickness set in the multiple cerebrovascular thickness sets, including multiple cerebrovascular thicknesses corresponding to the multiple cerebrovascular thicknesses, with a preset cerebrovascular thickness set, respectively. It is determined that each cerebrovascular thickness set in the multiple cerebrovascular thickness sets includes multiple cerebrovascular thicknesses corresponding to the multiple cerebrovascular thicknesses that fall into the preset cerebrovascular thickness sets. Preset a subset of the thickness of cerebral blood vessels.
  • each preset cerebrovascular thickness subset in the plurality of preset cerebrovascular thickness subsets includes a plurality of preset cerebrovascular thicknesses corresponding to the same cerebrovascular.
  • the medical imaging device locates the disease according to the plurality of preset subsets of cerebrovascular thickness.
  • the thickness of multiple brain blood vessel sets located in multiple parts of the brain in the image data is analyzed by using the cerebrovascular disease analysis strategy to obtain multiple brain blood vessel sets corresponding to the multiple brain blood vessel sets.
  • Vascular thickness set and then compare each cerebrovascular thickness set in multiple cerebrovascular thickness sets including multiple cerebrovascular thicknesses corresponding to multiple cerebrovascular thicknesses with the preset cerebrovascular thickness sets to determine respectively
  • Each cerebrovascular thickness set in the multiple cerebrovascular thickness sets includes multiple cerebrovascular thicknesses corresponding to multiple cerebrovascular thicknesses that fall into multiple preset cerebrovascular thickness subsets in the preset cerebrovascular thickness set,
  • the disease is located according to a plurality of preset subsets of cerebrovascular thickness, so as to realize more precise locating of the disease and improve the recognition accuracy of cerebrovascular diseases.
  • FIG. 4 is a schematic flowchart of another VRDS AI brain image analysis method provided by an embodiment of the application.
  • an embodiment of the present application provides the method for analyzing the thickness of multiple cerebrovascular sets located in multiple parts of the brain in the image data using the cerebrovascular disease analysis strategy.
  • the method further includes:
  • the medical imaging device acquires a plurality of pixels, the gray value and texture corresponding to each pixel in the plurality of pixel points are different from each other, and the gray value corresponding to each pixel in the plurality of pixel points And texture are the gray value and texture corresponding to any cerebrovascular cell data in each cerebrovascular cell layer, and each cerebrovascular cell layer constitutes the cerebrovascular;
  • the medical imaging device establishes a coordinate system according to the image data, the origin of the coordinate system is any position of the brain, 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 medical imaging device starts from the origin of the coordinate system and runs along the positive and negative directions of the X-axis, the positive and negative directions of the Y-axis, and the positive and negative directions of the Z-axis according to the preset distances of the coordinate system.
  • the image data is divided according to the gray value and texture corresponding to each pixel of the plurality of pixels, so as to obtain a plurality of cerebral blood vessel sets located in a plurality of parts of the brain in the image data.
  • the preset distance is determined according to the thickness of the cerebrovascular cell layer.
  • the cerebrovascular is segmented from the image data with high efficiency, and the positive and negative directions of the X axis and the positive and negative directions of the Y axis of the coordinate system are followed according to the preset distance.
  • the direction and the positive and negative directions of the Z-axis are segmented to avoid missing data and improve the accuracy of segmented cerebrovascular.
  • the medical imaging apparatus 500 may include:
  • the first acquiring module 501 is configured to acquire a scanned image of the user's brain and information about the symptoms of the user;
  • the symptom performance information may include, for example, contralateral hemiplegia, partial numbness, repeated speech, indifference, lack of initiative, and the like.
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the generating module 502 is configured to generate image data including cerebral blood vessels according to the scanned image;
  • cerebral blood vessels include cerebral arteries and cerebral veins.
  • cerebral arteries may include, for example, vertebral arteries, internal carotid arteries, and the like.
  • Cerebral veins may include, for example, large cerebral veins, diencephalon veins, brainstem veins, cerebellar veins and the like.
  • the image data includes three-dimensional spatial image data of the cerebral blood vessel.
  • the generating module is specifically configured to perform 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 the first medical image data, the
  • the first medical image data includes a data set of the cerebrovascular and a data set of the brain, the data set of the cerebrovascular includes fusion data of the crossing position of a cerebral artery and a cerebral vein, and the data set of the brain Is the transfer function result of the cube space of the brain surface and the tissue structure inside the brain, and the data set of the cerebrovascular is the transmission of the cube space of the tissue structure on the cerebrovascular surface and the internal tissue structure of the cerebrovascular Function result; import the first medical image data into a preset cross-vascular network model to obtain second medical image data, the second medical image data including the data set of the brain and the data set of the cerebral artery And the data set of the cerebral vein, and the first data in the data set of the cerebral artery and the second data in the data set of the
  • the determining module 503 is used to determine the cerebrovascular disease information corresponding to the disease manifestation information
  • the disease manifestation information may be repeated speech, indifference, and lack of initiative.
  • the cerebrovascular disease information may be, for example, the posterior communicating artery (PComA) whose larger branch is the distribution area infarction of the anterior mammary artery.
  • PComA posterior communicating artery
  • the determining module is specifically configured to parse the symptom manifestation information to obtain multiple symptom manifestations fields corresponding to the symptom manifestation information; obtain synonyms for each symptom manifestation field of the multiple symptom manifestations field Or a synonym to obtain a plurality of disease manifestation field sets, wherein the i-th symptom manifestation field set in the plurality of disorder manifestation field sets includes synonyms or synonyms of the i-th disorder manifestation field in the plurality of disorder manifestations field , I is a positive integer; searching for the cerebrovascular disease information matching the multiple disease manifestation field sets from the cerebrovascular disease database.
  • the determining module is specifically configured to generate a symptom manifestation identifier corresponding to the symptom manifestation information; search for the cerebrovascular disease information matching the symptom manifestation marker from a cerebrovascular disease database, wherein, in In the cerebrovascular disease database, a plurality of symptom manifestations and a plurality of cerebrovascular disease information are associated and stored, and the plurality of symptom manifestations are in one-to-one correspondence with the plurality of cerebrovascular disease information.
  • the second obtaining module 504 is configured to obtain the cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from the cerebrovascular disease analysis strategy database;
  • the cerebrovascular disease analysis strategy database includes multiple cerebrovascular disease analysis strategies corresponding to various cerebrovascular disease information, and each cerebrovascular disease analysis strategy is different from each other.
  • the analysis module 505 is configured to analyze the image data using the cerebrovascular disease analysis strategy to locate a disease, and the disease includes a brain tumor.
  • the analysis module is specifically configured to use the cerebrovascular disease analysis strategy to analyze the thickness of multiple cerebrovascular sets located in multiple parts of the brain in the image data to obtain the Multiple brain blood vessel thickness sets corresponding to multiple brain blood vessel sets, wherein each of the multiple parts of the brain in the image data includes multiple brain blood vessels, and the multiple brain blood vessels are concentrated
  • Each cerebrovascular set includes the multiple cerebrovascular thicknesses, and each cerebrovascular thickness set in the multiple cerebrovascular thickness sets includes multiple cerebrovascular thicknesses corresponding to the multiple cerebrovascular thicknesses;
  • Each cerebrovascular thickness set in a set of cerebrovascular thicknesses includes multiple cerebrovascular thicknesses corresponding to the multiple cerebrovessels and is respectively compared with a preset cerebrovascular thickness set to determine the multiple cerebrovascular thicknesses respectively
  • Each cerebrovascular thickness set in the thickness set includes multiple cerebrovascular thicknesses corresponding to the multiple cerebrovascular thicknesses respectively falling into multiple preset cerebrovascular thickness subsets in the preset cerebrovascular thickness set;
  • the device further includes a processing module configured to obtain a plurality of pixels, and the gray value and texture corresponding to each pixel of the plurality of pixels are different from each other.
  • the gray value and texture corresponding to each pixel in the multiple pixels are the gray value and texture corresponding to any cerebrovascular cell data in each cerebrovascular cell layer, and each cerebrovascular cell layer constitutes the cerebrovascular;
  • the image data establishes a coordinate system, the origin of the coordinate system is an arbitrary position of the brain, 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; Starting from the origin, the positive and negative directions of the X-axis, the positive and negative directions of the Y-axis, and the positive and negative directions of the Z-axis of the coordinate system are carried out according to the preset distances according to each of the plurality of pixels.
  • the image data is segmented by the gray value and texture corresponding to each pixel to obtain
  • the analysis module is specifically configured to obtain a cerebrovascular thickness analysis priority set, wherein the cerebrovascular thickness analysis priority set includes multiple cerebrovascular thickness analysis priority subsets, and the multiple cerebrovascular thickness analysis priority sets
  • the coarse and fine analysis priority subsets correspond to the multiple cerebrovascular sets; according to the cerebrovascular thickness analysis priority set, the cerebrovascular disease analysis strategy is used to analyze the multiple brain vascular disease analysis strategies in the image data.
  • the thickness of the multiple brain blood vessel sets of the part is analyzed to obtain multiple brain blood vessel thickness sets corresponding to the multiple brain blood vessel sets.
  • the analysis module is configured to perform preset processing on the image data to obtain multiple feature data corresponding to multiple cerebral blood vessels, and each feature data of the multiple feature data includes each brain The color, shape, structure, and curvature of the blood vessel; according to the multiple feature data, the cerebrovascular disease analysis strategy is used to analyze each of the multiple cerebrovascular vessels to obtain the first feature data abnormality
  • the first characteristic data includes at least one of the following colors, shapes, structures and curvatures; at least one cerebrovascular abnormality in the first characteristic data is used to locate the disease.
  • the preset processing includes the following steps: establishing a coordinate system based on the image data, the origin of the coordinate system is any position of the brain, and the X axis, Y axis, and Z axis of the coordinate system They are perpendicular to each other and follow the right-hand spiral law; starting from the origin of the coordinate system, along the positive and negative directions of the X-axis, the positive and negative directions of the Y-axis, and the Z-axis of the coordinate system at a preset distance.
  • the detection is performed in the forward and reverse directions.
  • the spatial position corresponding to the first pixel is recorded, and whenever the first pixel is detected
  • the second pixel is recorded
  • Corresponding spatial positions segment the image data according to the spatial positions corresponding to all the first pixels and the spatial positions corresponding to all the second pixels to obtain the multiple cerebral blood vessels; obtain The color, shape, structure and curvature of each cerebral blood vessel in the plurality of cerebral blood vessels.
  • 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 user's brain and information about the symptoms of the user;
  • the scan image generation includes the image data of the cerebrovascular; determining the cerebrovascular disease information corresponding to the disease manifestation information; obtaining the cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from the cerebrovascular disease analysis strategy database; adopting
  • the cerebrovascular disease analysis strategy analyzes the image data to locate diseases, and the diseases include brain tumors.
  • This 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 user’s brain Image and the user’s symptom performance information; generate image data including cerebrovascular according to the scanned image; determine the cerebrovascular disease information corresponding to the symptom performance information; obtain the brain vascular disease information from the cerebrovascular disease analysis strategy database; A cerebrovascular disease analysis strategy corresponding to the vascular disease information; using the cerebrovascular disease analysis strategy to analyze the image data to locate a disease, and the disease includes a brain tumor.
  • 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 AI脑部影像的分析方法和相关装置，包括：获取用户的脑部的扫描图像和所述用户的病症表现信息（201）；根据所述扫描图像生成包括脑血管的影像数据（202）；确定与所述病症表现信息对应的脑血管疾病信息（203）；从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略（204）；采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，所述病症包括脑部肿瘤（205）。提高脑血管疾病的识别效率和准确度。

Description

基于VRDS AI脑部影像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脑部影像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)等技术获取脑部血管的形态、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脑部血管的空间结构特性，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脑部影像的分析方法和相关装置，实施本申请实施例，提高脑血管疾病的识别效率和准确度。
本申请实施例第一方面提供了基于VRDS AI脑部影像的分析方法，包括：
获取用户的脑部的扫描图像和所述用户的病症表现信息；
根据所述扫描图像生成包括脑血管的影像数据；
确定与所述病症表现信息对应的脑血管疾病信息；
从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；
采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症。
本申请实施例第二方面提供了一种医学成像装置，包括：
第一获取模块，用于获取用户的脑部的扫描图像和所述用户的病症表现信息；
生成模块，用于根据所述扫描图像生成包括脑血管的影像数据；
确定模块，用于确定与所述病症表现信息对应的脑血管疾病信息；
第二获取模块，用于从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；
分析模块，用于采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症。
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，通过获取用户的脑部的扫描图像和用户的病症表现信息，实现根据扫描图像生成包括脑血管的影像数据，接着，确定与病症表现信息对应的脑血管 疾病信息，从脑血管疾病分析策略库中获取与脑血管疾病信息对应的脑血管疾病分析策略，最后，采用脑血管疾病分析策略对影像数据进行分析以定位病症。通过采用与脑血管疾病信息对应的脑血管疾病分析策略对包括脑血管的影像数据进行分析来定位病症，提高脑血管疾病的识别准确度，避免了由于二维切片扫描图像无法呈现出脑部血管的空间结构特性导致的脑血管疾病识别效率低的问题。
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 AI脑部影像的分析系统的结构示意图；
图2为本申请实施例提供的一种基于VRDS AI脑部影像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脑部影像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脑部影像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”是指虚拟现实医用系统(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脑部影像的分析系统100的结构示意图，该系统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脑部影像的分析算法为基础，进行人体脑部影像区域的识别、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脑部、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，如脑干与脑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脑部表面和人体脑部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脑部影像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脑部影像的分析方法可以包括：
201、医学成像装置获取用户的脑部的扫描图像和所述用户的病症表现信息。
其中，病症表现信息例如可以包括：对侧偏瘫、偏身麻木、反复言语、冷漠、缺乏主动性等。
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。
202、医学成像装置根据所述扫描图像生成包括脑血管的影像数据。
其中，脑血管包括脑动脉和脑静脉。进一步的，脑动脉例如可以包括：椎动脉、颈内动脉等。脑静脉例如可以包括大脑大静脉、间脑静脉、脑干静脉、小脑静脉等。
其中，影像数据包括所述脑血管的三维空间影像数据。
进一步的，医学成像装置还可以根据所述扫描图像生成包括脑颅骨的影像数据，所述方法还包括：根据所述脑颅骨的影像数据获取所述脑颅骨对应的特征数据；根据所述脑颅骨对应的特征数据从脑颅骨疾病分析策略库中确定脑颅骨病症。
可以理解的，在脑颅骨疾病分析策略库中存储有脑颅骨处于多种脑颅骨病症时对应的多种特征数据，该多种特征数据至少包括以下一种：结构数据、脑颅骨包括的额骨、顶骨、枕骨、蝶骨、颞骨和筛骨之间的连接数据。进一步的，脑颅骨处于不同脑颅骨病症时对应的特征数据也不同。203、医学成像装置确定与所述病症表现信息对应的脑血管疾病信息。
举例来说，病症表现信息例如为反复言语、冷漠、缺乏主动性，那么，脑血管疾病信息例如可以为后交通动脉(PComA)其较大分支为前乳动脉的分布区梗死。
可选的，在一种可能的实施方式中，所述确定与所述病症表现信息对应的脑血管疾病信息，包括：解析所述病症表现信息以得到所述病症表现信息对应的多个病症表现字段；获取所述多个病症表现字段中的每个病症表现字段的同义词或者近义词，以得到多个病症表现字段集，其中，所述多个病症表现字段集中的第i个病症表现字段集包括所述多个病症表现字段中的第i个病症表现字段的同义词或者近义词，i为正整数；从脑血管疾病数据库中查找与所述多个病症表现字段集匹配的所述脑血管疾病信息。
其中，i例如可以为1、2、3、4、6、8、11、20等数值。
可以看出，上述技术方案中，通过解析病症表现信息以得到病症表现信息对应的多个病症表现字段，从而获取多个病症表现字段中的每个病症表现字段的同义词或者近义词，以得到多个病症表现字段集，最后，从脑血管疾病数据库中查找与多个病症表现字段集匹配的脑血管疾病信息，从而实现更加精准的获取到脑血管疾病信息。
进一步的，在一种可能的的实施方式中，所述从脑血管疾病数据库中查找与所述多个病症表现字段集匹配的所述脑血管疾病信息，包括：对所述多个病症表现字段集设置查找优先级；按照所述查找优先级从脑血管疾病数据库中查找与所述多个病症表现字段集匹配的所述脑血管疾病信息。
可选的，所述查找优先级由医学成像装置对所述多个病症表现字段集随机设置查找先后顺序得到。
可以看出，上述技术方案中，通过对多个病症表现字段集设置查找优先级，实现按照查找优先级从脑血管疾病数据库中查找与多个病症表现字段集匹配的脑血管疾病信息，实现更加精准的获取到脑血管疾病信息，也提高了查找效率。
可选的，在一种可能的的实施方式中，所述确定与所述病症表现信息对应的脑血管疾病信息，包括：生成与所述病症表现信息对应的病症表现标识；从脑血管疾病数据库中查 找与所述病症表现标识匹配的所述脑血管疾病信息，其中，在所述脑血管疾病数据库中关联存储多个病症表现标识与多个脑血管疾病信息，所述多个病症表现标识与所述多个脑血管疾病信息一一对应。
可以看出，上述技术方案中，通过生成与病症表现信息对应的病症表现标识，实现以病症表现标识为索引条件从脑血管疾病数据库中查找脑血管疾病信息，提高了获取脑血管疾病信息的效率。
204、医学成像装置从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略。
其中，脑血管疾病分析策略库包括多种脑血管疾病信息对应的多种脑血管疾病分析策略，每种脑血管疾病分析策略互不相同。
205、医学成像装置采用所述脑血管疾病分析策略对所述影像数据进行分析以定位所述病症，所述病症包括脑部肿瘤。
其中，脑部肿瘤包括以下一种：良性颅内肿瘤、恶性颅内肿瘤、神经上皮组织肿瘤、脑膜肿瘤、淋巴瘤及造血组织肿瘤、生殖细胞肿瘤或碟鞍区肿瘤。
可以看出，上述技术方案中，通过获取用户的脑部的扫描图像和用户的病症表现信息，实现根据扫描图像生成包括脑血管的影像数据，接着，确定与病症表现信息对应的脑血管疾病信息，从脑血管疾病分析策略库中获取与脑血管疾病信息对应的脑血管疾病分析策略，最后，采用脑血管疾病分析策略对影像数据进行分析以定位病症。通过采用与脑血管疾病信息对应的脑血管疾病分析策略对包括脑血管的影像数据进行分析来定位病症，提高脑血管疾病的识别准确度，避免了由于二维切片扫描图像无法呈现出脑部血管的空间结构特性导致的脑血管疾病识别效率低的问题。
可选的，在一种可能的实施方式中，所述根据所述扫描图像生成包括脑血管的影像数据，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脑血管的数据集合和所述脑部的数据集合，所述脑血管的数据集合中包括脑动脉和脑静脉的交叉位置的融合数据，所述脑部的数据集合为所述脑部表面和所述脑部内部的组织结构的立方体空间的传递函数结果，所述脑血管的数据集合为所述脑血管表面和所述脑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脑部的数据集合、所述脑动脉的数据集合以及所述脑静脉的数据集合，且所述脑动脉的数据集合中的第一数据和所述脑静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大脑内部结构特征的多张扫描图像，可以通过清晰度、准确度等筛选 出合适的包含大脑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大脑的空间结构特性的影像数据，且交叉位置的脑静脉影像数据、脑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脑部的传递函数和所述脑血管的传递函数。
其中，BMP(全称Bitmap)是Windows操作系统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。
可选的，在一种可能的实施方式中，所述第一数据由所述医学成像装置提取所述脑血管的数据集合中包括脑动脉和脑静脉的交叉位置的融合数据，采用预设数据分离算法将所述融合数据进行分离得到脑动脉边界点数据。
第二数据由所述医学成像装置提取所述脑血管的数据集合中包括脑动脉和脑静脉的交叉位置的融合数据，采用预设数据分离算法将所述融合数据进行分离得到脑静脉边界点数据。
其中，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，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，分割目标包括脑部、脑动脉和脑静脉。
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：将所述第二医学影像数据输入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脑部的数据集合、所述脑动脉的数据集合以及所述脑静脉的数据集合。
可选的，在一种可能的实施方式中，在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。
可选的，在一种可能的实施方式中，所述采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，包括：采用所述脑血管疾病分析策略对所述影像数据中的多个脑血管的脑血管壁厚薄进行分析，以得到所述多个脑血管对应的多个脑血管壁厚薄度，其中，所述多个脑血管为位于所述脑部的多个部位对应的脑血管；将所述多个脑血管壁厚薄度中的每个脑血管壁厚薄度与预设脑血管壁厚薄度集进行对比，以分别确定所述多个脑血管壁厚薄度中的每个脑血管壁厚薄度分别落入所述预设脑血管壁厚薄度集中的多个预设脑血管壁厚薄度子集；根据所述多个预设脑血管壁厚薄度子集定位病症。
其中，多个脑血管包括至少一个脑动脉或至少一个脑静脉。进一步的，至少一个脑动脉例如至少可以包括以下一种：椎动脉、颈内动脉等。至少一个脑静脉例如至少可以包括以下一种：大脑大静脉、间脑静脉、脑干静脉、小脑静脉等。
其中，所述多个预设脑血管壁厚薄度子集中的每个预设脑血管壁厚薄度子集包括同一脑血管对应的多个预设脑血管壁厚薄度。
可以看出，上述技术方案中，根据多个预设脑血管壁厚薄度子集定位病症，实现更加精准的定位病症，提高脑血管疾病的识别准确度。
可选的，在一种可能的实施方式中，所述采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，包括：对所述影像数据执行预设处理，以得到多个脑血管对应的多个特征数据，所述多个特征数据中的每个特征数据包括每个脑血管对应的颜色、形状、截面积和弯曲度；根据所述多个特征数据采用所述脑血管疾病分析策略对所述多个脑血管中的每个脑血管进行分析，以得到第一特征数据异常的至少一个脑血管，所述第一特征数据至少包括以下一种颜色、形状、截面积和弯曲度；根据所述第一特征数据异常的至少一个脑血管定位病症。
其中，所述预设处理包括以下步骤：根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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脑血管对应的灰度值时，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脑血管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脑血管对应的灰度值时，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影像数据进行切分，以得到所述多个脑血管；获取所述多个脑血管中每个脑血管对应的颜色、形状、截面积和弯曲度。
其中，所述多个脑血管中每个脑血管对应的弯曲度由医学成像装置执行以下步骤得到，包括：获取所述多个脑血管中每个脑血管的最外层脑血管细胞数据集，每个最外层脑血管细胞数据集包括多个最外层脑血管细胞数据；针对每个最外层脑血管细胞数据集，执行以下步骤：获取当前处理的最外层脑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脑血管段曲率变化最大的像素点，所述目标脑血管段为目标脑血管在所述起始点至目标空间位置之间的脑血管，所述目标脑血管与当前处理的最外层脑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；获取所述目标脑血管段对应的曲 率；将所述目标脑血管段对应的曲率设置为所述目标脑血管对应弯曲度。
可以看出，上述技术方案中，根据第一特征数据异常的至少一个脑血管定位病症，实现更加精准的定位病症，提高脑血管疾病的识别准确度。
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脑部影像的分析方法的流程示意图。其中，如图3所示，本申请的一个实施例提供的采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，包括：
301、医学成像装置采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集，其中，所述影像数据中位于所述脑部的多个部位中的每个部位包括多个脑血管，所述多个脑血管集中的每个脑血管集包括所述多个脑血管，所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度。
其中，多个脑血管包括至少一个脑动脉或至少一个脑静脉。进一步的，至少一个脑动脉例如至少可以包括以下一种：椎动脉、颈内动脉等。至少一个脑静脉例如至少可以包括以下一种：大脑大静脉、间脑静脉、脑干静脉、小脑静脉等。
可选的，在一种可能的实施方式中，所述采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集，包括：
获取脑血管粗细分析优先级集，其中，所述脑血管粗细分析优先级集包括多个脑血管粗细分析优先级子集，所述多个脑血管粗细分析优先级子集与所述多个脑血管集一一对应；
按照所述脑血管粗细分析优先级集采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集。
其中，脑血管粗细分析优先级集由医学成像装置对所述多个脑血管集随机设置分析先后顺序得到。
可以看出，上述技术方案中，通过获取脑血管粗细分析优先级集，按照脑血管粗细分析优先级集采用脑血管疾病分析策略对影像数据中位于脑部的多个部位的多个脑血管集的粗细进行分析，以得到多个脑血管集对应的多个脑血管粗细度集，提高了分析效率。
302、医学成像装置将所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别与预设脑血管粗细度集进行对比，以分别确定所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别落入所述预设脑血管粗细度集中的多个预设脑血管粗细度子集。
其中，所述多个预设脑血管粗细度子集中的每个预设脑血管粗细度子集包括同一脑血管对应的多个预设脑血管粗细度。
303、医学成像装置根据所述多个预设脑血管粗细度子集定位所述病症。
可以看出，上述技术方案中，通过采用脑血管疾病分析策略对影像数据中位于脑部的多个部位的多个脑血管集的粗细进行分析，以得到多个脑血管集对应的多个脑血管粗细度集，再将多个脑血管粗细度集中的每个脑血管粗细度集包括多个脑血管对应的多个脑血管粗细度分别与预设脑血管粗细度集进行对比，以分别确定多个脑血管粗细度集中的每个脑血管粗细度集包括多个脑血管对应的多个脑血管粗细度分别落入预设脑血管粗细度集中的多个预设脑血管粗细度子集，最后，根据多个预设脑血管粗细度子集定位病症，实现更加精准的定位病症，提高脑血管疾病的识别准确度。
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脑部影像的分析方法的流程示意图。其中，如图4所示，本申请的一个实施例提供的在所述采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集之前，所述方法还包括：
401、医学成像装置获取多个像素点，所述多个像素点中每个像素点对应的灰度值和纹理均互不相同，所述多个像素点中每个像素点对应的灰度值和纹理分别是每层脑血管细胞层中任意一个脑血管细胞数据对应的灰度值和纹理，每层脑血管细胞层构成脑血管；
402、医学成像装置根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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；
403、医学成像装置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按照所述多个像素点中每个像素点对应的灰度值和纹理分割所述影像数据，以得到所述影像数据中位于所述脑部的多个部位的多个脑血管集。
其中，预设距离是根据脑血管细胞层的厚度确定的。
可以看出，上述技术方案中，实现高效率的从影像数据切分出脑血管，并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切分，避免了数据缺失，提高切分出的脑血管的精准性。
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：
第一获取模块501，用于获取用户的脑部的扫描图像和所述用户的病症表现信息；
其中，病症表现信息例如可以包括：对侧偏瘫、偏身麻木、反复言语、冷漠、缺乏主动性等。
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。
生成模块502，用于根据所述扫描图像生成包括脑血管的影像数据；
其中，脑血管包括脑动脉和脑静脉。进一步的，脑动脉例如可以包括：椎动脉、颈内动脉等。脑静脉例如可以包括大脑大静脉、间脑静脉、脑干静脉、小脑静脉等。
其中，影像数据包括所述脑血管的三维空间影像数据。
所述生成模块，具体用于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脑血管的数据集合和所述脑部的数据集合，所述脑血管的数据集合中包括脑动脉和脑静脉的交叉位置的融合数据，所述脑部的数据集合为所述脑部表面和所述脑部内部的组织结构的立方体空间的传递函数结果，所述脑血管的数据集合为所述脑血管表面和所述脑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脑部的数据集合、所述脑动脉的数据集合以及所述脑静脉的数据集合，且所述脑动脉的数据集合中的第一数据和所述脑静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。
确定模块503，用于确定与所述病症表现信息对应的脑血管疾病信息；
举例来说，病症表现信息例如为反复言语、冷漠、缺乏主动性，那么，脑血管疾病信息例如可以为后交通动脉(PComA)其较大分支为前乳动脉的分布区梗死。
可选的，所述确定模块，具体用于解析所述病症表现信息以得到所述病症表现信息对应的多个病症表现字段；获取所述多个病症表现字段中的每个病症表现字段的同义词或者近义词，以得到多个病症表现字段集，其中，所述多个病症表现字段集中的第i个病症表现字段集包括所述多个病症表现字段中的第i个病症表现字段的同义词或者近义词，i为正整数；从脑血管疾病数据库中查找与所述多个病症表现字段集匹配的所述脑血管疾病信息。
可选的，所述确定模块，具体用于生成与所述病症表现信息对应的病症表现标识；从脑血管疾病数据库中查找与所述病症表现标识匹配的所述脑血管疾病信息，其中，在所述脑血管疾病数据库中关联存储多个病症表现标识与多个脑血管疾病信息，所述多个病症表现标识与所述多个脑血管疾病信息一一对应。
第二获取模块504，用于从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；
其中，脑血管疾病分析策略库包括多种脑血管疾病信息对应的多种脑血管疾病分析策略，每种脑血管疾病分析策略互不相同。
分析模块505，用于采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，所述病症包括脑部肿瘤。
可选的，所述分析模块，具体用于采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集，其中，所述影像数据中位于所述脑部的多个部位中的每个部位包括多个脑血管，所述多个脑血管集中的每个脑血管集包括所述多个脑血管，所述多个脑血管 粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度；将所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别与预设脑血管粗细度集进行对比，以分别确定所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别落入所述预设脑血管粗细度集中的多个预设脑血管粗细度子集；根据所述多个预设脑血管粗细度子集定位所述病症。
可选的，所述装置还包括处理模块，所述处理模块，用于获取多个像素点，所述多个像素点中每个像素点对应的灰度值和纹理均互不相同，所述多个像素点中每个像素点对应的灰度值和纹理分别是每层脑血管细胞层中任意一个脑血管细胞数据对应的灰度值和纹理，每层脑血管细胞层构成脑血管；根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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按照所述多个像素点中每个像素点对应的灰度值和纹理分割所述影像数据，以得到所述影像数据中位于所述脑部的多个部位的多个脑血管集。
可选的，所述分析模块，具体用于获取脑血管粗细分析优先级集，其中，所述脑血管粗细分析优先级集包括多个脑血管粗细分析优先级子集，所述多个脑血管粗细分析优先级子集与所述多个脑血管集一一对应；按照所述脑血管粗细分析优先级集采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集。
可选的，所述分析模块，用于对所述影像数据执行预设处理，以得到多个脑血管对应的多个特征数据，所述多个特征数据中的每个特征数据包括每个脑血管对应的颜色、形状、结构和弯曲度；根据所述多个特征数据采用所述脑血管疾病分析策略对所述多个脑血管中的每个脑血管进行分析，以得到第一特征数据异常的至少一个脑血管，所述第一特征数据至少包括以下一种颜色、形状、结构和弯曲度；根据所述第一特征数据异常的至少一个脑血管定位所述病症。
可选的，所述预设处理包括以下步骤：根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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脑血管对应的灰度值时，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脑血管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脑血管对应的灰度值时，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影像数据进行切分，以得到所述多个脑血管；获取所述多个脑血管中每个脑血管对应的颜 色、形状、结构和弯曲度。
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中可以包括操作系统、网络通信模块以及信息处理的程序。操作系统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：获取用户的脑部的扫描图像和所述用户的病症表现信息；根据所述扫描图像生成包括脑血管的影像数据；确定与所述病症表现信息对应的脑血管疾病信息；从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，所述病症包括脑部肿瘤。
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脑部影像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：获取用户的脑部的扫描图像和所述用户的病症表现信息；根据所述扫描图像生成包括脑血管的影像数据；确定与所述病症表现信息对应的脑血管疾病信息；从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，所述病症包括脑部肿瘤。
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脑部影像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系统，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脑部影像的分析方法，其特征在于，包括：
    获取用户的脑部的扫描图像和所述用户的病症表现信息；
    根据所述扫描图像生成包括脑血管的影像数据；
    确定与所述病症表现信息对应的脑血管疾病信息；
    从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；
    采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，所述病症包括脑部肿瘤。
  2. 根据权利要求1所述的方法，其特征在于，所述确定与所述病症表现信息对应的脑血管疾病信息，包括：
    解析所述病症表现信息以得到所述病症表现信息对应的多个病症表现字段；
    获取所述多个病症表现字段中的每个病症表现字段的同义词或者近义词，以得到多个病症表现字段集，其中，所述多个病症表现字段集中的第i个病症表现字段集包括所述多个病症表现字段中的第i个病症表现字段的同义词或者近义词，i为正整数；
    从脑血管疾病数据库中查找与所述多个病症表现字段集匹配的所述脑血管疾病信息。
  3. 根据权利要求1所述的方法，其特征在于，所述确定与所述病症表现信息对应的脑血管疾病信息，包括：
    生成与所述病症表现信息对应的病症表现标识；
    从脑血管疾病数据库中查找与所述病症表现标识匹配的所述脑血管疾病信息，其中，在所述脑血管疾病数据库中关联存储多个病症表现标识与多个脑血管疾病信息，所述多个病症表现标识与所述多个脑血管疾病信息一一对应。
  4. 根据权利要求1所述的方法，其特征在于，采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，包括：
    采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集，其中，所述影像数据中位于所述脑部的多个部位中的每个部位包括多个脑血管，所述多个脑血管集中的每个脑血管集包括所述多个脑血管，所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度；
    将所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别与预设脑血管粗细度集进行对比，以分别确定所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别落入所述预设脑血管粗细度集中的多个预设脑血管粗细度子集；
    根据所述多个预设脑血管粗细度子集定位所述病症。
  5. 根据权利要求4所述的方法，其特征在于，在所述采用所述脑血管疾病分析策略 对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集之前，所述方法还包括：
    获取多个像素点，所述多个像素点中每个像素点对应的灰度值和纹理均互不相同，所述多个像素点中每个像素点对应的灰度值和纹理分别是每层脑血管细胞层中任意一个脑血管细胞数据对应的灰度值和纹理，每层脑血管细胞层构成脑血管；
    根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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按照所述多个像素点中每个像素点对应的灰度值和纹理分割所述影像数据，以得到所述影像数据中位于所述脑部的多个部位的多个脑血管集。
  6. 根据权利要求4或5所述的方法，其特征在于，所述采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集，包括：
    获取脑血管粗细分析优先级集，其中，所述脑血管粗细分析优先级集包括多个脑血管粗细分析优先级子集，所述多个脑血管粗细分析优先级子集与所述多个脑血管集一一对应；
    按照所述脑血管粗细分析优先级集采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集。
  7. 根据权利要求1所述的方法，其特征在于，所述采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，包括：
    对所述影像数据执行预设处理，以得到多个脑血管对应的多个特征数据，所述多个特征数据中的每个特征数据包括每个脑血管对应的颜色、形状、截面积和弯曲度；
    根据所述多个特征数据采用所述脑血管疾病分析策略对所述多个脑血管中的每个脑血管进行分析，以得到第一特征数据异常的至少一个脑血管，所述第一特征数据至少包括以下一种颜色、形状、截面积和弯曲度；
    根据所述第一特征数据异常的至少一个脑血管定位所述病症。
  8. 根据权利要求7所述的方法，其特征在于，所述预设处理包括以下步骤：
    根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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脑血管对应的灰度值时，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脑血管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脑血管对应的灰度值时，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影像数据进行切分，以得到所述多个脑血管；
    获取所述多个脑血管中每个脑血管对应的颜色、形状、截面积和弯曲度。
  9. 根据权利要求1-8任意一项所述的方法，其特征在于，所述根据所述扫描图像生成包括脑血管的影像数据，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脑血管的数据集合和所述脑部的数据集合，所述脑血管的数据集合中包括脑动脉和脑静脉的交叉位置的融合数据，所述脑部的数据集合为所述脑部表面和所述脑部内部的组织结构的立方体空间的传递函数结果，所述脑血管的数据集合为所述脑血管表面和所述脑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脑部的数据集合、所述脑动脉的数据集合以及所述脑静脉的数据集合，且所述脑动脉的数据集合中的第一数据和所述脑静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。
  10. 一种医学成像装置，其特征在于，包括：
    第一获取模块，用于获取用户的脑部的扫描图像和所述用户的病症表现信息；
    生成模块，用于根据所述扫描图像生成包括脑血管的影像数据；
    确定模块，用于确定与所述病症表现信息对应的脑血管疾病信息；
    第二获取模块，用于从脑血管疾病分析策略库中获取与所述脑血管疾病信息对应的脑血管疾病分析策略；
    分析模块，用于采用所述脑血管疾病分析策略对所述影像数据进行分析以定位病症，所述病症包括脑部肿瘤。
  11. 根据权利要求10所述的装置，其特征在于，所述确定模块，具体用于解析所述病症表现信息以得到所述病症表现信息对应的多个病症表现字段；获取所述多个病症表现字段中的每个病症表现字段的同义词或者近义词，以得到多个病症表现字段集，其中，所述多个病症表现字段集中的第i个病症表现字段集包括所述多个病症表现字段中的第i个病症表现字段的同义词或者近义词，i为正整数；从脑血管疾病数据库中查找与所述多个病症表现字段集匹配的所述脑血管疾病信息。
  12. 根据权利要求10所述的装置，其特征在于，所述确定模块，具体用于生成与所述病症表现信息对应的病症表现标识；从脑血管疾病数据库中查找与所述病症表现标识匹 配的所述脑血管疾病信息，其中，在所述脑血管疾病数据库中关联存储多个病症表现标识与多个脑血管疾病信息，所述多个病症表现标识与所述多个脑血管疾病信息一一对应。
  13. 根据权利要求10所述的装置，其特征在于，所述分析模块，具体用于采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集，其中，所述影像数据中位于所述脑部的多个部位中的每个部位包括多个脑血管，所述多个脑血管集中的每个脑血管集包括所述多个脑血管，所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度；将所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别与预设脑血管粗细度集进行对比，以分别确定所述多个脑血管粗细度集中的每个脑血管粗细度集包括所述多个脑血管对应的多个脑血管粗细度分别落入所述预设脑血管粗细度集中的多个预设脑血管粗细度子集；根据所述多个预设脑血管粗细度子集定位所述病症。
  14. 根据权利要求10所述的装置，其特征在于，所述装置还包括处理模块，所述处理模块，用于获取多个像素点，所述多个像素点中每个像素点对应的灰度值和纹理均互不相同，所述多个像素点中每个像素点对应的灰度值和纹理分别是每层脑血管细胞层中任意一个脑血管细胞数据对应的灰度值和纹理，每层脑血管细胞层构成脑血管；根据所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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按照所述多个像素点中每个像素点对应的灰度值和纹理分割所述影像数据，以得到所述影像数据中位于所述脑部的多个部位的多个脑血管集。
  15. 根据权利要求13或14所述的装置，其特征在于，所述分析模块，具体用于获取脑血管粗细分析优先级集，其中，所述脑血管粗细分析优先级集包括多个脑血管粗细分析优先级子集，所述多个脑血管粗细分析优先级子集与所述多个脑血管集一一对应；按照所述脑血管粗细分析优先级集采用所述脑血管疾病分析策略对所述影像数据中位于所述脑部的多个部位的多个脑血管集的粗细进行分析，以得到所述多个脑血管集对应的多个脑血管粗细度集。
  16. 根据权利要求10所述的装置，其特征在于，所述分析模块，用于对所述影像数据执行预设处理，以得到多个脑血管对应的多个特征数据，所述多个特征数据中的每个特征数据包括每个脑血管对应的颜色、形状、结构和弯曲度；根据所述多个特征数据采用所述脑血管疾病分析策略对所述多个脑血管中的每个脑血管进行分析，以得到第一特征数据异常的至少一个脑血管，所述第一特征数据至少包括以下一种颜色、形状、结构和弯曲度；根据所述第一特征数据异常的至少一个脑血管定位所述病症。
  17. 根据权利要求16所述的装置，其特征在于，所述预设处理包括以下步骤：根据 所述影像数据建立坐标系，所述坐标系的原点为所述脑部的任意位置，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脑血管对应的灰度值时，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脑血管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脑血管对应的灰度值时，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影像数据进行切分，以得到所述多个脑血管；获取所述多个脑血管中每个脑血管对应的颜色、形状、结构和弯曲度。
  18. 根据权利要求10-17任意一项所述的装置，其特征在于，所述生成模块，具体用于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脑血管的数据集合和所述脑部的数据集合，所述脑血管的数据集合中包括脑动脉和脑静脉的交叉位置的融合数据，所述脑部的数据集合为所述脑部表面和所述脑部内部的组织结构的立方体空间的传递函数结果，所述脑血管的数据集合为所述脑血管表面和所述脑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脑部的数据集合、所述脑动脉的数据集合以及所述脑静脉的数据集合，且所述脑动脉的数据集合中的第一数据和所述脑静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。
  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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