CN114287042A

CN114287042A - Analysis method and related device based on VRDS AI brain image

Info

Publication number: CN114287042A
Application number: CN201980099745.9A
Authority: CN
Inventors: 斯图尔特平·李; 戴维伟·李
Original assignee: Weiai Medical Technology Shenzhen Co ltd
Current assignee: Cao Sheng
Priority date: 2019-10-29
Filing date: 2019-10-29
Publication date: 2022-04-05
Also published as: WO2021081772A1

Abstract

A method and a related device for analyzing brain images based on VRDS AI comprise the following steps: acquiring a scan image of a user's brain and a user's medical condition performance information (201); generating image data (202) including a cerebral blood vessel from the scan image; determining cerebrovascular disease information (203) corresponding to the condition manifestation information; acquiring a cerebrovascular disease analysis strategy (204) corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis strategy library; the image data is analyzed using the cerebrovascular disease analysis strategy to locate a condition, including a brain tumor (205). The recognition efficiency and accuracy of the cerebrovascular diseases are improved.

Description

Analysis method and related device based on VRDS AI brain image

Technical Field

The present application relates to the field of medical imaging devices, and in particular, to a VRDS AI brain image-based analysis method and related devices.

Background

Currently, doctors acquire information such as the shape, position, and topology of cerebral vessels by using techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), and Positron Emission Tomography (PET). Doctors still diagnose the disease condition by watching and reading the continuous two-dimensional slice scanning images. However, the two-dimensional slice scan image cannot show the spatial structural characteristics of the cerebral vessels, which affects the diagnosis of diseases by doctors. With the rapid development of medical imaging technology, people put new demands on medical imaging.

Disclosure of Invention

The embodiment of the application provides an analysis method and a related device based on VRDS AI brain images, and the embodiment of the application is implemented to improve the identification efficiency and accuracy of cerebrovascular diseases.

The embodiment of the present application provides, in a first aspect, a VRDS AI-based brain image analysis method, including:

acquiring a scan image of a user's brain and disease manifestation information of the user;

generating image data including a cerebral blood vessel from the scan image;

determining cerebrovascular disease information corresponding to the disease manifestation information;

acquiring a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis strategy library;

analyzing the image data using the cerebrovascular disease analysis strategy to locate a condition.

A second aspect of embodiments of the present application provides a medical imaging apparatus, including:

a first acquisition module for acquiring a scan image of a user's brain and a user's disease manifestation information;

the generating module is used for generating image data comprising cerebral vessels according to the scanning image;

a determination module for determining cerebrovascular disease information corresponding to the disease manifestation information;

the second acquisition module is used for acquiring a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from the cerebrovascular disease analysis strategy library;

and the analysis module is used for analyzing the image data by adopting the cerebrovascular disease analysis strategy so as to locate the disease symptoms.

A third aspect of embodiments of the present application provides a medical imaging apparatus comprising 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 as instructions to be executed by the processor to perform the steps of the method of any of the first aspects of the claims above.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium for storing a computer program, the stored computer program being executable by the processor to implement the method of any one of the above first aspects.

It can be seen that, in the above technical solution, by obtaining the scanned image of the brain of the user and the disease manifestation information of the user, generating image data including cerebrovascular diseases according to the scanned image is achieved, then, cerebrovascular disease information corresponding to the disease manifestation information is determined, a cerebrovascular disease analysis policy corresponding to the cerebrovascular disease information is obtained from the cerebrovascular disease analysis policy library, and finally, the image data is analyzed by using the cerebrovascular disease analysis policy to locate the disease. Cerebrovascular disease analysis strategies corresponding to cerebrovascular disease information are adopted to analyze image data including cerebral vessels so as to locate symptoms, the identification accuracy of cerebrovascular diseases is improved, and the problem of low cerebrovascular disease identification efficiency caused by the fact that two-dimensional slice scanning images cannot show the spatial structure characteristics of cerebral vessels is solved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Wherein:

fig. 1 is a schematic structural diagram of an analysis system based on VRDS AI brain images according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a VRDS AI-based brain image analysis method according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of another method for analyzing a brain image based on a VRDS AI according to an embodiment of the present disclosure;

fig. 4 is a schematic flowchart of another method for analyzing a brain image based on a VRDS AI according to an embodiment of the present application;

FIG. 5 is a schematic view of a medical imaging apparatus provided by an embodiment of the present application;

fig. 6 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The following are detailed below.

The terms "first," "second," and "third" in the description and claims of this application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions.

The medical imaging apparatus according to the embodiments of the present application refers to various apparatuses that reproduce the internal structure of a human body as an image using various media as information carriers, and the image information corresponds to the actual structure of the human body in terms of spatial and temporal distribution. The "DICOM data" refers to original image file data which reflects internal structural features of a human body and is acquired by medical equipment, and may include information such as computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, positron emission tomography PET-CT, and the "map source" refers to Texture2D/3D image volume data generated by analyzing the original DICOM data. "VRDS" refers to a Virtual Reality medical system (VRDS).

First, referring to fig. 1, fig. 1 is a schematic structural diagram of an analysis system 100 based on VRDS AI brain images, the system 100 includes a medical imaging apparatus 110 and a network database 120, wherein the medical imaging apparatus 110 may include a local medical imaging apparatus 111 and/or a terminal medical imaging apparatus 112, the local medical imaging apparatus 111 or the terminal medical imaging apparatus 112 is used for performing identification, positioning, four-dimensional volume rendering, and anomaly analysis of a human brain image region based on original DICOM data based on an analysis algorithm based on VRDS AI brain images presented in this application embodiment, so as to achieve a four-dimensional stereoscopic imaging effect (the 4-dimensional medical image specifically means that the medical image includes internal spatial structure features and external spatial structure features of a displayed tissue, the internal spatial structure features mean that slice data inside the tissue is not lost, that is, the medical imaging apparatus may present the internal structure of the brain, blood vessels, etc., the external spatial structure characteristics refer to the environmental characteristics between tissues, including the spatial position characteristics (including intersection, separation, fusion) between tissues, such as the edge structure characteristics of the intersection position between the brain stem and the cerebral artery, etc.), the local medical imaging apparatus 111 may also be used to edit the scanned image with respect to the terminal medical imaging apparatus 112, to form the transfer function result of the four-dimensional human body image, which may include the transfer function result of the tissue structure on the surface of the human body brain and in the human body brain, and the transfer function result of the cubic space, such as the number of sets of the cubic edit box and arc edit required by the transfer function, coordinates, colors, transparency, etc. The network database 120 may be, for example, a cloud medical imaging apparatus, and the like, and the network database 120 is used to store the image sources generated by parsing the raw DICOM data and the transfer function results of the four-dimensional human body images edited by the local medical imaging apparatus 111, and the scanned images may be from a plurality of local medical imaging apparatuses 111 to realize interactive diagnosis of a plurality of doctors.

When the user performs specific image display by using the medical imaging apparatus 110, the user may select a display or a Head Mounted Display (HMDS) of the virtual reality VR to display in combination with an operation action, where the operation action refers to operation control performed on a four-dimensional human body image by the user through an external shooting device of the medical imaging apparatus, such as a mouse, a keyboard, a tablet computer (Pad), an ipad (internet portable device), and the like, so as to implement human-computer interaction, and the operation action includes at least one of the following: (1) changing the color and/or transparency of a specific organ/tissue, (2) positioning a zoom view, (3) rotating the view to realize multi-view 360-degree observation of a four-dimensional human body image, (4) entering 'internal observation of a human body organ, real-time shearing effect rendering', and (5) moving the view up and down.

Referring to fig. 2, fig. 2 is a flowchart illustrating a method for analyzing a brain image based on a VRDS AI according to an embodiment of the present disclosure. As shown in fig. 2, a method for analyzing a brain image based on a VRDS AI according to an embodiment of the present application may include:

201. a medical imaging device acquires a scan image of a user's brain and condition performance information of the user.

The disease manifestation information may include, for example: contralateral hemiplegia, hemianesthesia, repeated speech, apathy, lack of activeness, etc.

Wherein the scanned image comprises any one of: CT images, MRI images, DTI images, PET-CT images.

202. A medical imaging device generates image data including a cerebral blood vessel from the scan image.

Among them, the cerebral vessels include cerebral arteries and cerebral veins. Further, the cerebral artery may include, for example: vertebral artery, internal carotid artery, etc. The cerebral veins may include, for example, the cerebral great veins, the mesencephalic veins, the brainstem veins, the cerebellar veins, and the like.

Wherein the image data comprises three-dimensional spatial image data of the cerebral blood vessel.

Further, the medical imaging apparatus may further generate image data including a skull bone from the scan image, and the method further includes: acquiring characteristic data corresponding to the skull according to the image data of the skull; and determining the cranial bone diseases from a cranial bone disease analysis strategy library according to the characteristic data corresponding to the cranial bones.

It is understood that a plurality of feature data corresponding to the cranial bone under a plurality of cranial bone diseases are stored in the cranial bone disease analysis strategy library, and the plurality of feature data at least comprise one of the following data: structural data, and data on connections between frontal, parietal, occipital, sphenoid, temporal, and ethmoid bones included in the skull. Further, the corresponding characteristic data of the skull under different cranial disorders are different. 203. The medical imaging device determines cerebrovascular disease information corresponding to the condition manifestation information.

For example, the condition manifestation information is, for example, repeated speech, apathy, lack of activeness, and then the cerebrovascular disease information may, for example, be a distributed infarct with the posterior communicating artery (PComA) whose larger branch is the anterior mammary artery.

Optionally, in a possible embodiment, the determining cerebrovascular disease information corresponding to the disease manifestation information includes: analyzing the disease manifestation information to obtain a plurality of disease manifestation fields corresponding to the disease manifestation information; obtaining synonyms or synonyms of each disease manifestation field in the plurality of disease manifestation fields to obtain a plurality of disease manifestation field sets, wherein the ith disease manifestation field set in the plurality of disease manifestation field sets comprises synonyms or synonyms of the ith disease manifestation field in the plurality of disease manifestation fields, and i is a positive integer; looking up the cerebrovascular disease information matching the plurality of condition expression field sets from a cerebrovascular disease database.

In addition, i may be, for example, 1, 2, 3, 4, 6, 8, 11, 20, or the like.

It can be seen that, in the above technical solution, a plurality of disease manifestation fields corresponding to disease manifestation information are obtained by analyzing the disease manifestation information, so as to obtain a synonym or a synonym of each disease manifestation field of the plurality of disease manifestation fields to obtain a plurality of disease manifestation field sets, and finally, cerebrovascular disease information matched with the plurality of disease manifestation field sets is searched from a cerebrovascular disease database, thereby achieving more accurate acquisition of cerebrovascular disease information.

Further, in a possible embodiment, the searching the cerebrovascular disease information matched with the plurality of disease expression field sets from the cerebrovascular disease database includes: setting a lookup priority for the plurality of sets of condition performance fields; and searching the cerebrovascular disease information matched with the plurality of disease expression field sets from the cerebrovascular disease database according to the searching priority.

Optionally, the search priority is obtained by randomly setting a search sequence of the medical imaging device for the plurality of disease manifestation field sets.

According to the technical scheme, the cerebrovascular disease information matched with the disease expression field sets is searched from the cerebrovascular disease database according to the searching priority by setting the searching priority for the disease expression field sets, the cerebrovascular disease information is acquired more accurately, and the searching efficiency is improved.

Optionally, in a possible embodiment, the determining cerebrovascular disease information corresponding to the disease manifestation information includes: generating a disease manifestation identifier corresponding to the disease manifestation information; and searching the cerebrovascular disease information matched with the disease expression identification from a cerebrovascular disease database, wherein a plurality of disease expression identifications and a plurality of cerebrovascular disease information are stored in the cerebrovascular disease database in an associated manner, and the plurality of disease expression identifications and the plurality of cerebrovascular disease information are in one-to-one correspondence.

It can be seen that, in the above technical scheme, by generating the disease manifestation identifier corresponding to the disease manifestation information, the cerebrovascular disease information is searched from the cerebrovascular disease database under the condition that the disease manifestation identifier is used as an index, and the efficiency of acquiring the cerebrovascular disease information is improved.

204. The medical imaging device acquires a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis strategy library.

The cerebrovascular disease analysis strategy library comprises a plurality of cerebrovascular disease analysis strategies corresponding to a plurality of cerebrovascular disease information, and each cerebrovascular disease analysis strategy is different from each other.

205. The medical imaging device analyzes the image data using the cerebrovascular disease analysis strategy to locate the condition, including a brain tumor.

Wherein the brain tumor comprises one of: benign intracranial tumors, malignant intracranial tumors, neuroepithelial tumors, meningeal tumors, lymphomas, hematopoietic tumors, germ cell tumors, or tumors of the saddled area.

Optionally, in a possible implementation, the generating image data including a cerebral blood vessel according to the scan image includes: performing first preset processing on the scanned image to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a data set of the cerebral vessels and a data set of the brain, the data set of the cerebral vessels comprises fusion data of the intersection positions of the cerebral arteries and the cerebral veins, the data set of the brain is a transfer function result of a cubic space of the brain surface and a tissue structure inside the brain, and the data set of the cerebral vessels is a transfer function result of a cubic space of the brain surface and the tissue structure inside the cerebral vessels; importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises a data set of the brain, a data set of the cerebral artery and a data set of the cerebral vein, and first data in the data set of the cerebral artery and second data in the data set of the cerebral vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position; and executing second preset processing on the second medical image data to obtain the image data.

Wherein the first preset processing comprises at least one of the following operations: VRDS limits contrast self-adaptive histogram equalization, mixed partial differential de-noising and VRDS Ai elastic deformation processing.

Wherein the VRDS limited contrast adaptive histogram equalization comprises the steps of: performing regional noise contrast clipping and global contrast clipping on the map source; dividing the local histogram of the graph source into a plurality of partitions; determining a plurality of slopes of a plurality of transform functions from slopes of the cumulative histogram of the neighborhood of each of the plurality of partitions; determining a degree of contrast magnification around a pixel value of each of the plurality of partitions from the plurality of slopes; performing limit clipping processing on the plurality of partitions according to the contrast amplification degree of the periphery of the pixel value of each partition in the plurality of partitions to obtain the distribution of an effective histogram and the value of the effective available neighborhood size; and uniformly distributing the histogram with the cut-off limit to other areas of the local histogram of the graph source.

The hybrid partial differential denoising comprises the following steps: processing the image source through VRDS Ai curvature drive and VRDS Ai high-order mixed denoising, so that the curvature of the image edge is smaller than the preset curvature, and a mixed partial differential denoising model which can protect the image edge and can avoid the step effect in the smoothing process is realized;

the VRDS Ai elastic deformation processing comprises the following steps: and acquiring an image lattice of the image source, superposing positive and negative random distances on the image lattice to form a difference position matrix, performing gray level processing on each difference position in the difference position matrix to obtain a new difference position matrix, thereby realizing distortion deformation in the image, and then performing rotation, distortion and translation operations on the image.

Wherein the hybrid partial differential denoising is performed by the medical imaging device on the image source by using CDD and a higher-order denoising model.

The CDD model (Curvature drive diffusion) is formed by introducing Curvature drive on the basis of a TV (Total variation) model, and the problem that the TV model cannot repair image visual connectivity is solved.

The high-order denoising refers to denoising the image based on a Partial Differential Equation (PDE) method. In specific implementation, the graph source is subjected to noise filtering according to the change of a specified differential equation function, so as to obtain the BMP data source. The partial differential equation is the BMP data source obtained after high-order denoising, and the image denoising method based on the PDE has the characteristic of anisotropic diffusion, so that the diffusion effect of different degrees can be performed in different areas of the image source, and the effect of inhibiting noise and protecting image edge texture information is achieved.

It can be seen that in the present example, the medical imaging apparatus operates by at least one of the following image processing operations: the VRDS limits contrast self-adaptive histogram equalization, mixed partial differential denoising and VRDS Ai elastic deformation processing, improves the execution efficiency of image processing, also improves the image quality and protects the edge texture of the image.

In a possible implementation manner, the performing a first preset process on the scan image to obtain a bitmap BMP data source includes: setting the scan image as digital imaging and communications in medicine (DICOM) data of the user; parsing the DICOM data to generate a map source of the user, the map source comprising Texture2D/3D image volume data; and executing the first preset treatment on the graph source to obtain the BMP data source.

The dicom (digital Imaging and Communications in medicine), i.e., digital Imaging and Communications in medicine, is an international standard for medical images and related information. In specific implementation, the medical imaging device firstly acquires a plurality of acquired scanning images reflecting internal structural features of a brain of a user, screens out at least one scanning image suitable for containing the brain through definition, accuracy and the like, and then performs further processing on the scanning images to obtain a bitmap BMP data source.

In this example, the medical imaging apparatus may perform screening, analysis and first preset processing based on the acquired scan image to obtain a bitmap BMP data source, so as to improve the accuracy and definition of medical image imaging.

It can be seen that, in this example, the medical imaging apparatus processes the scan image into image data capable of reflecting the spatial structure characteristics of the brain through a plurality of lines of data processing, and the image data of the cerebral veins and the image data of the cerebral arteries at the intersection positions are independent from each other, so that accurate presentation of a three-dimensional space is supported, and the accuracy and comprehensiveness of data processing are improved.

In a possible example of the present application, the importing the BMP data source into a preset VRDS medical network model to obtain first medical image data includes: importing the BMP data source into a preset VRDS medical network model, calling each transfer function in a pre-stored transfer function set through the VRDS medical network model, and processing the BMP data source through a plurality of transfer functions in the transfer function set to obtain first medical image data, wherein the transfer function set comprises the brain transfer function and the cerebrovascular transfer function which are preset through a reverse editor.

The BMP (full Bitmap) is a standard image file format in the Windows operating system, and can be divided into two types: a Device Dependent Bitmap (DDB) and a Device Independent Bitmap (DIB). The scan image includes any one of: CT images, MRI images, DTI images, PET-CT images.

The VRDS medical network model is a preset network model, and the training method comprises the following three steps: sampling an image and scaling; extracting and scoring the 3D convolutional neural network characteristics; and evaluating the medical imaging device and training the network. In the implementation process, sampling is firstly carried out to obtain N BMP data sources, and then M BMP data sources are extracted from the N BMP data sources according to a preset interval. It should be noted that the preset interval can be flexibly set according to the usage scenario. M BMP data sources are sampled from the N BMP, and then the sampled M BMP data sources are scaled to a fixed size (for example, S pixels long and S pixels wide), and the resulting processing result is used as an input to the 3D convolutional neural network. This takes the M BMP data sources as inputs to the 3D convolutional neural network. Specifically, 3D convolution processing is performed on the BMP data source by using a 3D convolution neural network, and a feature map is obtained.

Optionally, in a possible implementation manner, the medical imaging device extracts the first data from the data set of the cerebral vessels, where the data set of the cerebral vessels includes fusion data of intersection positions of cerebral arteries and cerebral veins, and the fusion data is separated by using a preset data separation algorithm to obtain cerebral artery boundary point data.

And extracting the second data from the medical imaging device to obtain fusion data of the data set of the cerebral vessels, wherein the fusion data comprises the intersection positions of the cerebral arteries and the cerebral veins, and separating the fusion data by adopting a preset data separation algorithm to obtain cerebral vein boundary point data.

Wherein the second preset processing comprises at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization processing and data enhancement processing.

Wherein the 2D boundary optimization process comprises: and acquiring low-resolution information and high-resolution information through multiple sampling, wherein the low-resolution information can provide context semantic information of the segmented target in the whole image, namely characteristics reflecting the relation between the segmented target and the environment, the characteristics are used for judging the object type, and the high-resolution information is used for providing more fine characteristics such as gradient and the like for the segmented target.

Wherein, the segmentation target comprises a brain, a brain artery and a brain vein.

The 3D boundary optimization process includes: 3D convolution, 3D max pooling, and 3D up-convolution layers, the input data size is a1, a2, a3, the number of channels is c, the filter size is f, i.e., the filter dimension is f c, and the filter number is n, then the final output of the 3-dimensional convolution is:

(a1-f+1)*(a2-f+1)*(a3-f+1)*n

there are an analysis path and a synthesis path. In the analysis path, each layer contains two convolution kernels of 3 × 3, each followed by an activation function (Relu), and then a maximum pooling of 2 × 2 in each dimension merges two steps. In the synthesis path, each layer consists of 2 × 2 upward convolutions, with 2 steps in each dimension, followed by two 3 × 3 convolutions, and then Relu. The shortcut connections from the equal resolution layers in the analysis path then provide the basic high resolution features of the composite path. In the last layer, 1 × 1 convolution reduces the number of output channels.

Further, the 3D boundary optimization process includes the following operations: inputting the second medical image data into a 3D convolution layer for 3D convolution operation to obtain a feature map; inputting the feature map into a 3D pooling layer for pooling and nonlinear activation to obtain a first feature map; and carrying out cascade operation on the first feature map to obtain a prediction result.

Wherein the data enhancement processing includes any one of: 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 clipping, and data enhancement based on simulating different illumination variations.

Wherein the image data comprises a data set of the brain, a data set of the cerebral arteries, and a data set of the cerebral veins.

Optionally, in a possible implementation manner, after performing a second preset process on the second medical image data to obtain the image data, the method further includes: acquiring an image quality score corresponding to the image data; screening enhanced data with an image quality score larger than a preset image quality score from the image data according to the image quality score; setting the enhancement data to VRDS 4D imaging data; displaying the VRDS 4D imaging data on an output device.

It can be seen that, in the above technical scheme, enhancement data with an image quality score larger than a preset image quality score is screened out from image data according to the image quality score by acquiring the image quality score corresponding to the image data, the enhancement data is set as VRDS 4D imaging data, and finally, the VRDS 4D imaging data is displayed on an output device, thereby assisting a doctor in making a quick diagnosis.

Optionally, in a possible embodiment, the analyzing the image data to locate the disease condition using the cerebrovascular disease analysis strategy includes: analyzing the cerebrovascular wall thicknesses of a plurality of cerebral vessels in the image data by adopting the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular wall thicknesses corresponding to the plurality of cerebral vessels, wherein the plurality of cerebral vessels are cerebral vessels corresponding to a plurality of parts of the brain; comparing each of the plurality of cerebrovascular wall thickness thinness with a preset cerebrovascular wall thickness set to respectively determine a plurality of preset cerebrovascular wall thickness subsets of which each of the plurality of cerebrovascular wall thickness thinness falls into the preset cerebrovascular wall thickness set; and positioning the disease according to the plurality of preset cerebral vessel wall thickness and thinness subsets.

Wherein the plurality of cerebral vessels comprises at least one cerebral artery or at least one cerebral vein. Further, the at least one cerebral artery may, for example, comprise at least one of: vertebral artery, internal carotid artery, etc. The at least one cerebral vein may, for example, comprise at least one of: cerebral great veins, diencephalon veins, brainstem veins, cerebellar veins, and the like.

Wherein each preset cerebrovascular vessel wall thickness sub-set in the preset cerebrovascular vessel wall thickness sub-sets comprises a plurality of preset cerebrovascular vessel wall thickness sub-sets corresponding to the same cerebrovascular vessel.

It can be seen that, in the above technical scheme, according to a plurality of preset cerebrovascular wall thickness and thinness subsets, disorders are located, so that more accurate disorder location is realized, and the accuracy of cerebrovascular disease identification is improved.

Optionally, in a possible embodiment, the analyzing the image data to locate the disease condition using the cerebrovascular disease analysis strategy includes: performing preset processing on the image data to obtain a plurality of characteristic data corresponding to a plurality of cerebral vessels, wherein each characteristic data in the plurality of characteristic data comprises a color, a shape, a cross-sectional area and a curvature corresponding to each cerebral vessel; analyzing each cerebral vessel in the plurality of cerebral vessels by adopting the cerebrovascular disease analysis strategy according to the plurality of characteristic data to obtain at least one cerebral vessel with abnormal first characteristic data, wherein the first characteristic data at least comprises the following color, shape, cross-sectional area and curvature; at least one cerebrovascular localization disorder that is abnormal according to the first characteristic data.

Wherein the preset processing comprises the steps of: establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule; starting from the origin of the coordinate system, detecting along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to preset distances respectively, recording the spatial position corresponding to a first pixel point when detecting that a gray value corresponding to the first pixel point belongs to a gray value corresponding to a cerebral vessel, and recording the spatial position corresponding to a second pixel point when detecting that a gray value corresponding to the second pixel point does not belong to a gray value corresponding to a cerebral vessel and a gray value corresponding to an adjacent pixel point of the second pixel point belongs to a gray value corresponding to a cerebral vessel; segmenting the image data according to the spatial positions corresponding to all the first pixel points and the spatial positions corresponding to all the second pixel points to obtain a plurality of cerebral vessels; and acquiring the color, the shape, the cross section and the curvature of each cerebral vessel in the plurality of cerebral vessels.

Wherein the curvature corresponding to each of the plurality of cerebral vessels is obtained by the medical imaging device executing the following steps, including: obtaining an outermost cerebrovascular cell data set for each of the plurality of cerebral vessels, each outermost cerebrovascular cell data set comprising a plurality of outermost cerebrovascular cell data; for each outermost cerebrovascular cell dataset, performing the steps of: acquiring a characteristic curve of the projection of the outermost layer cerebrovascular cell data set currently processed on any plane; selecting any point of the characteristic curve as a starting point; starting from the starting point, marking pixels continuously along the positive direction and the negative direction of the characteristic curve, stopping marking when a target pixel is marked, wherein the positive direction of the characteristic curve is the transverse positive direction of the image data, the negative direction of the characteristic curve is the transverse negative direction of the image data, the target pixel is a pixel with the maximum curvature change of a target cerebral vessel section, the target cerebral vessel section is a cerebral vessel of a target cerebral vessel between the starting point and a target space position, the target cerebral vessel corresponds to a currently processed outermost cerebral vessel cell data set, and the target space position is a position corresponding to the target pixel point; acquiring the corresponding curvature of the target cerebral vessel section; and setting the curvature corresponding to the target cerebral vessel section as the curvature corresponding to the target cerebral vessel.

It can be seen that, in the above technical scheme, according to at least one cerebrovascular positioning disorder with abnormal first characteristic data, more accurate positioning disorder is realized, and the accuracy of identifying cerebrovascular diseases is improved.

Referring to fig. 3, fig. 3 is a flowchart illustrating a method for analyzing a brain image based on a VRDS AI according to an embodiment of the present disclosure. As shown in fig. 3, an embodiment of the present application provides a method for analyzing the image data to locate a disease condition, including:

301. the medical imaging device analyzes the thicknesses of a plurality of cerebrovascular vessel sets located at a plurality of parts of the brain in the image data by adopting the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the cerebrovascular vessel sets, wherein each part of the plurality of parts of the brain in the image data comprises a plurality of cerebrovascular vessels, each cerebrovascular vessel set in the plurality of cerebrovascular vessel sets comprises a plurality of cerebrovascular vessels, and each cerebrovascular vessel thickness set in the plurality of cerebrovascular vessel thickness sets comprises a plurality of cerebrovascular vessel thicknesses corresponding to the plurality of cerebrovascular vessels.

Optionally, in a possible implementation manner, the analyzing, by using the cerebrovascular disease analysis strategy, thicknesses of a plurality of cerebrovascular vessel sets located at a plurality of portions of the brain in the image data to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets includes:

acquiring a cerebrovascular thickness analysis priority set, wherein the cerebrovascular thickness analysis priority set comprises a plurality of cerebrovascular thickness analysis priority subsets, and the cerebrovascular thickness analysis priority subsets correspond to the cerebrovascular sets one by one;

and analyzing the thicknesses of a plurality of cerebrovascular vessel sets positioned at a plurality of parts of the brain in the image data by adopting the cerebrovascular disease analysis strategy according to the cerebrovascular vessel thickness analysis priority set to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the cerebrovascular vessel sets.

The cerebrovascular thickness analysis priority set is obtained by randomly setting analysis sequence of the plurality of cerebrovascular sets by the medical imaging device.

It can be seen that, in the above technical solution, by obtaining the cerebrovascular vessel thickness analysis priority set, the cerebrovascular vessel thickness analysis strategies are adopted to analyze the thicknesses of the plurality of cerebrovascular vessel sets at the plurality of portions of the brain in the image data according to the cerebrovascular vessel thickness analysis priority set, so as to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets, thereby improving the analysis efficiency.

302. The medical imaging device compares a plurality of cerebrovascular vessel thicknesses corresponding to the plurality of cerebrovascular vessels in each of the plurality of cerebrovascular vessel thickness sets with a preset cerebrovascular vessel thickness set respectively to determine that each of the plurality of cerebrovascular vessel thickness sets comprises a plurality of preset cerebrovascular vessel thickness subsets in which the plurality of cerebrovascular vessel thicknesses corresponding to the plurality of cerebrovascular vessels respectively fall into the preset cerebrovascular vessel thickness set respectively.

Each preset cerebral vessel thickness subset in the plurality of preset cerebral vessel thickness subsets comprises a plurality of preset cerebral vessel thicknesses corresponding to the same cerebral vessel.

303. The medical imaging device locates the condition according to the plurality of preset cerebrovascular thickness subsets.

It can be seen that, in the above technical solution, the thicknesses of a plurality of cerebrovascular vessel sets located at a plurality of portions of a brain in image data are analyzed by using a cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets, each of the plurality of cerebrovascular vessel thickness sets including a plurality of cerebrovascular vessel thicknesses corresponding to a plurality of cerebrovascular vessels is compared with a preset cerebrovascular vessel thickness set respectively to determine that each of the plurality of cerebrovascular vessel thickness sets including a plurality of cerebrovascular vessel thicknesses corresponding to a plurality of cerebrovascular vessels falls into a plurality of preset cerebrovascular vessel thickness sets respectively, and finally, a disease condition is located according to the plurality of preset cerebrovascular vessel thickness sets, thereby realizing more accurate disease positioning and improving the accuracy of cerebrovascular disease identification.

Referring to fig. 4, fig. 4 is a flowchart illustrating another method for analyzing a brain image based on a VRDS AI according to an embodiment of the present disclosure. As shown in fig. 4, before analyzing thicknesses of a plurality of cerebrovascular vessel sets located at a plurality of portions of the brain in the image data by using the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets, the method according to an embodiment of the present application further includes:

401. the method comprises the steps that a medical imaging device obtains a plurality of pixel points, the gray value and the texture corresponding to each pixel point in the pixel points are different from each other, the gray value and the texture corresponding to each pixel point in the pixel points are respectively the gray value and the texture corresponding to any cerebrovascular cell data in each cerebrovascular cell layer, and each cerebrovascular cell layer forms a cerebrovascular;

402. 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, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule;

403. the medical imaging device divides the image data according to the gray values and textures corresponding to each pixel point in the plurality of pixel points from the origin of the coordinate system along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to preset distances, so as to obtain a plurality of cerebral vessel sets positioned at a plurality of parts of the brain in the image data.

Wherein the preset distance is determined according to the thickness of the cerebrovascular cell layer.

It can be seen that, in the above technical scheme, the high-efficiency segmentation of the cerebral vessels from the image data is realized, and the segmentation is performed according to the preset distance along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis, and the positive direction and the negative direction of the Z axis of the coordinate system, so that data loss is avoided, and the accuracy of the segmented cerebral vessels is improved.

Referring to fig. 5, an embodiment of the present application provides a schematic diagram of a medical imaging apparatus 500, and the medical imaging apparatus 500 may include:

a first obtaining module 501, configured to obtain a scan image of a brain of a user and disease manifestation information of the user;

A generating module 502, configured to generate image data including a cerebral blood vessel according to the scan image;

The generating module is specifically configured to perform a first preset process on the scanned image to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a data set of the cerebral vessels and a data set of the brain, the data set of the cerebral vessels comprises fusion data of the intersection positions of the cerebral arteries and the cerebral veins, the data set of the brain is a transfer function result of a cubic space of the brain surface and a tissue structure inside the brain, and the data set of the cerebral vessels is a transfer function result of a cubic space of the brain surface and the tissue structure inside the cerebral vessels; importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises a data set of the brain, a data set of the cerebral artery and a data set of the cerebral vein, and first data in the data set of the cerebral artery and second data in the data set of the cerebral vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position; and executing second preset processing on the second medical image data to obtain the image data.

A determining module 503, configured to determine cerebrovascular disease information corresponding to the disease manifestation information;

Optionally, the determining module is specifically configured to analyze the disease manifestation information to obtain a plurality of disease manifestation fields corresponding to the disease manifestation information; obtaining synonyms or synonyms of each disease manifestation field in the plurality of disease manifestation fields to obtain a plurality of disease manifestation field sets, wherein the ith disease manifestation field set in the plurality of disease manifestation field sets comprises synonyms or synonyms of the ith disease manifestation field in the plurality of disease manifestation fields, and i is a positive integer; looking up the cerebrovascular disease information matching the plurality of condition expression field sets from a cerebrovascular disease database.

Optionally, the determining module is specifically configured to generate a disease manifestation identifier corresponding to the disease manifestation information; searching the cerebrovascular disease information matched with the disease expression identification from a cerebrovascular disease database, wherein a plurality of disease expression identifications and a plurality of cerebrovascular disease information are stored in the cerebrovascular disease database in an associated manner, and the plurality of disease expression identifications correspond to the plurality of cerebrovascular disease information one by one.

A second obtaining module 504, configured to obtain a cerebrovascular disease analysis policy corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis policy library;

An analysis module 505 for analyzing the image data using the cerebrovascular disease analysis strategy to locate a disease condition, the disease condition comprising a brain tumor.

Optionally, the analysis module is specifically configured to analyze thicknesses of a plurality of cerebrovascular vessel sets located at a plurality of portions of the brain in the image data by using the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets, where each portion of the plurality of portions of the brain in the image data includes a plurality of cerebrovascular vessels, each cerebrovascular vessel set of the plurality of cerebrovascular vessel sets includes the plurality of cerebrovascular vessels, and each cerebrovascular vessel thickness set of the plurality of cerebrovascular vessel thickness sets includes a plurality of cerebrovascular vessel thicknesses corresponding to the plurality of cerebrovascular vessels; comparing each of the plurality of cerebrovascular fineness sets, including a plurality of cerebrovascular fineness corresponding to the plurality of cerebral vessels, with a preset cerebrovascular fineness set respectively to determine that each of the plurality of cerebrovascular fineness sets includes a plurality of preset cerebrovascular fineness subsets in which the plurality of cerebrovascular fineness corresponding to the plurality of cerebral vessels fall into the preset cerebrovascular fineness set respectively; locating the condition according to the plurality of preset cerebrovascular thickness subsets.

Optionally, the apparatus further includes a processing module, where the processing module is configured to obtain a plurality of pixel points, where a gray value and a texture corresponding to each pixel point in the plurality of pixel points are different from each other, the gray value and the texture corresponding to each pixel point in the plurality of pixel points are a gray value and a texture corresponding to any cerebrovascular cell data in each cerebrovascular cell layer, and each cerebrovascular cell layer constitutes a cerebrovascular; establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule; and starting from the origin of the coordinate system, dividing the image data according to the preset distance along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to the gray value and the texture corresponding to each pixel point in the plurality of pixel points so as to obtain a plurality of cerebral blood vessel sets of a plurality of parts of the brain in the image data.

Optionally, the analyzing module is specifically configured to obtain a cerebrovascular thickness analysis priority set, where the cerebrovascular thickness analysis priority set includes a plurality of cerebrovascular thickness analysis priority subsets, and the cerebrovascular thickness analysis priority subsets correspond to the cerebrovascular sets one to one; and analyzing the thicknesses of a plurality of cerebrovascular vessel sets positioned at a plurality of parts of the brain in the image data by adopting the cerebrovascular disease analysis strategy according to the cerebrovascular vessel thickness analysis priority set to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the cerebrovascular vessel sets.

Optionally, the analysis module is configured to perform preset processing on the image data to obtain a plurality of feature data corresponding to a plurality of cerebral vessels, where each feature data in the plurality of feature data includes a color, a shape, a structure, and a curvature corresponding to each cerebral vessel; analyzing each cerebral vessel in the plurality of cerebral vessels by adopting the cerebrovascular disease analysis strategy according to the plurality of characteristic data to obtain at least one cerebral vessel with abnormal first characteristic data, wherein the first characteristic data at least comprises the following color, shape, structure and bending degree; locating the condition based on the at least one cerebrovascular vessel with the first characteristic data anomaly.

Optionally, the preset processing includes the following steps: establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule; starting from the origin of the coordinate system, detecting along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to preset distances respectively, recording the spatial position corresponding to a first pixel point when detecting that a gray value corresponding to the first pixel point belongs to a gray value corresponding to a cerebral vessel, and recording the spatial position corresponding to a second pixel point when detecting that a gray value corresponding to the second pixel point does not belong to a gray value corresponding to a cerebral vessel and a gray value corresponding to an adjacent pixel point of the second pixel point belongs to a gray value corresponding to a cerebral vessel; segmenting the image data according to the spatial positions corresponding to all the first pixel points and the spatial positions corresponding to all the second pixel points to obtain a plurality of cerebral vessels; and acquiring the color, the shape, the structure and the bending degree corresponding to each cerebral vessel in the plurality of cerebral vessels.

Referring to fig. 6, fig. 6 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application. As shown in fig. 6, a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application may include:

a processor 601, such as a CPU.

The memory 602 may alternatively be a high speed RAM memory or a stable memory such as a disk memory.

A communication interface 603 for implementing connection communication between the processor 601 and the memory 602.

Those skilled in the art will appreciate that the configuration of the medical imaging device shown in FIG. 6 is not intended to be limiting and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.

As shown in fig. 6, the memory 602 may include therein an operating system, a network communication module, and a program for information processing. An operating system is a program that manages and controls the hardware and software resources of the medical imaging device, a program that supports personnel management, and the execution of other software or programs. The network communication module is used for communication among the components in the memory 602 and with other hardware and software in the medical imaging apparatus.

In the medical imaging apparatus shown in fig. 6, a processor 601 is configured to execute a program for information migration stored in a memory 602, and implement the following steps: acquiring a scan image of a user's brain and disease manifestation information of the user; generating image data including a cerebral blood vessel from the scan image; determining cerebrovascular disease information corresponding to the disease manifestation information; acquiring a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis strategy library; analyzing the image data using the cerebrovascular disease analysis strategy to locate a condition, the condition comprising a brain tumor.

For specific implementation of the medical imaging apparatus according to the present application, reference may be made to the above embodiments of the analysis method based on VRDS AI brain images, which are not described herein again.

The present application further provides a computer readable storage medium for storing a computer program, the stored computer program being executable by the processor to perform the steps of: acquiring a scan image of a user's brain and disease manifestation information of the user; generating image data including a cerebral blood vessel from the scan image; determining cerebrovascular disease information corresponding to the disease manifestation information; acquiring a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis strategy library; analyzing the image data using the cerebrovascular disease analysis strategy to locate a condition, the condition comprising a brain tumor.

For specific implementation of the computer-readable storage medium according to the present application, reference may be made to the above embodiments of the analysis method based on VRDS AI brain images, which are not described herein again.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art should understand that the present application is not limited by the order of acts described, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that the acts and modules involved are not necessarily required for this application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some interfaces, and may be in an electrical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.

The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a medical imaging apparatus, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

The analysis method based on the VRDS AI brain image is characterized by comprising the following steps:

acquiring a scan image of a user's brain and disease manifestation information of the user;

generating image data including a cerebral blood vessel from the scan image;

determining cerebrovascular disease information corresponding to the disease manifestation information;

acquiring a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from a cerebrovascular disease analysis strategy library;

analyzing the image data using the cerebrovascular disease analysis strategy to locate a condition, the condition comprising a brain tumor.
The method of claim 1, wherein said determining cerebrovascular disease information corresponding to said condition manifestation information comprises:

analyzing the disease manifestation information to obtain a plurality of disease manifestation fields corresponding to the disease manifestation information;

obtaining synonyms or synonyms of each disease manifestation field in the plurality of disease manifestation fields to obtain a plurality of disease manifestation field sets, wherein the ith disease manifestation field set in the plurality of disease manifestation field sets comprises synonyms or synonyms of the ith disease manifestation field in the plurality of disease manifestation fields, and i is a positive integer;

looking up the cerebrovascular disease information matching the plurality of condition expression field sets from a cerebrovascular disease database.
The method of claim 1, wherein said determining cerebrovascular disease information corresponding to said condition manifestation information comprises:

generating a disease manifestation identifier corresponding to the disease manifestation information;

searching the cerebrovascular disease information matched with the disease expression identification from a cerebrovascular disease database, wherein a plurality of disease expression identifications and a plurality of cerebrovascular disease information are stored in the cerebrovascular disease database in an associated manner, and the plurality of disease expression identifications correspond to the plurality of cerebrovascular disease information one by one.
The method of claim 1, wherein analyzing the image data to locate a condition using the cerebrovascular disease analysis strategy comprises:

analyzing thicknesses of a plurality of cerebrovascular vessel sets positioned at a plurality of parts of the brain in the image data by adopting the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the cerebrovascular vessel sets, wherein each part of the plurality of parts of the brain in the image data comprises a plurality of cerebrovascular vessels, each cerebrovascular vessel set in the plurality of cerebrovascular vessel sets comprises the plurality of cerebrovascular vessels, and each cerebrovascular vessel thickness set in the plurality of cerebrovascular vessel thickness sets comprises a plurality of cerebrovascular vessel thicknesses corresponding to the plurality of cerebrovascular vessels;

comparing each of the plurality of cerebrovascular fineness sets, including a plurality of cerebrovascular fineness corresponding to the plurality of cerebral vessels, with a preset cerebrovascular fineness set respectively to determine that each of the plurality of cerebrovascular fineness sets includes a plurality of preset cerebrovascular fineness subsets in which the plurality of cerebrovascular fineness corresponding to the plurality of cerebral vessels fall into the preset cerebrovascular fineness set respectively;

locating the condition according to the plurality of preset cerebrovascular thickness subsets.
The method of claim 4, wherein before analyzing the thickness of the plurality of cerebrovascular vessel sets at the plurality of locations in the brain in the image data using the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets, the method further comprises:

acquiring a plurality of pixel points, wherein the gray value and the texture corresponding to each pixel point in the plurality of pixel points are different from each other, the gray value and the texture corresponding to each pixel point in the plurality of pixel points are respectively the gray value and the texture corresponding to any cerebrovascular cell data in each cerebrovascular cell layer, and each cerebrovascular cell layer forms a cerebrovascular;

establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule;

and starting from the origin of the coordinate system, dividing the image data according to the preset distance along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to the gray value and the texture corresponding to each pixel point in the plurality of pixel points so as to obtain a plurality of cerebral blood vessel sets of a plurality of parts of the brain in the image data.
The method according to claim 4 or 5, wherein analyzing the thickness of a plurality of cerebrovascular vessel sets at a plurality of portions of the brain in the image data by using the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets comprises:

acquiring a cerebrovascular thickness analysis priority set, wherein the cerebrovascular thickness analysis priority set comprises a plurality of cerebrovascular thickness analysis priority subsets, and the cerebrovascular thickness analysis priority subsets correspond to the cerebrovascular sets one by one;

and analyzing the thicknesses of a plurality of cerebrovascular vessel sets positioned at a plurality of parts of the brain in the image data by adopting the cerebrovascular disease analysis strategy according to the cerebrovascular vessel thickness analysis priority set to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the cerebrovascular vessel sets.
The method of claim 1, wherein analyzing the image data to locate a condition using the cerebrovascular disease analysis strategy comprises:

performing preset processing on the image data to obtain a plurality of characteristic data corresponding to a plurality of cerebral vessels, wherein each characteristic data in the plurality of characteristic data comprises a color, a shape, a cross-sectional area and a curvature corresponding to each cerebral vessel;

analyzing each cerebral vessel in the plurality of cerebral vessels by adopting the cerebrovascular disease analysis strategy according to the plurality of characteristic data to obtain at least one cerebral vessel with abnormal first characteristic data, wherein the first characteristic data at least comprises the following color, shape, cross-sectional area and curvature;

locating the condition based on the at least one cerebrovascular vessel with the first characteristic data anomaly.
The method according to claim 7, wherein the pre-setting process comprises the steps of:

establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule;

starting from the origin of the coordinate system, detecting along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to preset distances respectively, recording the spatial position corresponding to a first pixel point when detecting that a gray value corresponding to the first pixel point belongs to a gray value corresponding to a cerebral vessel, and recording the spatial position corresponding to a second pixel point when detecting that a gray value corresponding to the second pixel point does not belong to a gray value corresponding to a cerebral vessel and a gray value corresponding to an adjacent pixel point of the second pixel point belongs to a gray value corresponding to a cerebral vessel;

segmenting the image data according to the spatial positions corresponding to all the first pixel points and the spatial positions corresponding to all the second pixel points to obtain a plurality of cerebral vessels;

and acquiring the color, the shape, the cross section and the curvature of each cerebral vessel in the plurality of cerebral vessels.
The method of any one of claims 1-8, wherein generating image data including cerebral blood vessels from the scan image comprises:

performing first preset processing on the scanned image to obtain a bitmap BMP data source;

importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a data set of the cerebral vessels and a data set of the brain, the data set of the cerebral vessels comprises fusion data of the intersection positions of the cerebral arteries and the cerebral veins, the data set of the brain is a transfer function result of a cubic space of the brain surface and a tissue structure inside the brain, and the data set of the cerebral vessels is a transfer function result of a cubic space of the brain surface and the tissue structure inside the cerebral vessels;

importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises a data set of the brain, a data set of the cerebral artery and a data set of the cerebral vein, and first data in the data set of the cerebral artery and second data in the data set of the cerebral vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position;

and executing second preset processing on the second medical image data to obtain the image data.
A medical imaging device, comprising:

a first acquisition module for acquiring a scan image of a user's brain and a user's disease manifestation information;

the generating module is used for generating image data comprising cerebral vessels according to the scanning image;

a determination module for determining cerebrovascular disease information corresponding to the disease manifestation information;

the second acquisition module is used for acquiring a cerebrovascular disease analysis strategy corresponding to the cerebrovascular disease information from the cerebrovascular disease analysis strategy library;

an analysis module for analyzing the image data using the cerebrovascular disease analysis strategy to locate a condition, the condition comprising a brain tumor.
The apparatus according to claim 10, wherein the determining module is specifically configured to parse the disease manifestation information to obtain a plurality of disease manifestation fields corresponding to the disease manifestation information; obtaining synonyms or synonyms of each disease manifestation field in the plurality of disease manifestation fields to obtain a plurality of disease manifestation field sets, wherein the ith disease manifestation field set in the plurality of disease manifestation field sets comprises synonyms or synonyms of the ith disease manifestation field in the plurality of disease manifestation fields, and i is a positive integer; looking up the cerebrovascular disease information matching the plurality of condition expression field sets from a cerebrovascular disease database.
The apparatus according to claim 10, wherein the determining module is specifically configured to generate a condition performance identifier corresponding to the condition performance information; and searching the cerebrovascular disease information matched with the disease expression identification from a cerebrovascular disease database, wherein a plurality of disease expression identifications and a plurality of cerebrovascular disease information are stored in the cerebrovascular disease database in an associated manner, and the plurality of disease expression identifications correspond to the plurality of cerebrovascular disease information one by one.
The apparatus according to claim 10, wherein the analysis module is specifically configured to analyze thicknesses of a plurality of cerebrovascular vessel sets located at a plurality of portions of the brain in the image data using the cerebrovascular disease analysis strategy to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the plurality of cerebrovascular vessel sets, wherein each portion of the plurality of portions of the brain in the image data includes a plurality of cerebrovascular vessels, each cerebrovascular vessel set of the plurality of cerebrovascular vessel sets includes a plurality of cerebrovascular vessels, and each cerebrovascular vessel thickness set of the plurality of cerebrovascular vessel thickness sets includes a plurality of cerebrovascular vessel thicknesses corresponding to the plurality of cerebrovascular vessels; comparing each of the plurality of cerebrovascular fineness sets, including a plurality of cerebrovascular fineness corresponding to the plurality of cerebral vessels, with a preset cerebrovascular fineness set respectively to determine that each of the plurality of cerebrovascular fineness sets includes a plurality of preset cerebrovascular fineness subsets in which the plurality of cerebrovascular fineness corresponding to the plurality of cerebral vessels fall into the preset cerebrovascular fineness set respectively; locating the condition according to the plurality of preset cerebrovascular thickness subsets.
The apparatus according to claim 10, further comprising a processing module, configured to obtain a plurality of pixel points, where a gray scale value and a texture corresponding to each of the plurality of pixel points are different from each other, the gray scale value and the texture corresponding to each of the plurality of pixel points are a gray scale value and a texture corresponding to any cerebrovascular cell data in each cerebrovascular cell layer, and each cerebrovascular cell layer constitutes a cerebrovascular vessel; establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule; and starting from the origin of the coordinate system, dividing the image data according to the preset distance along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to the gray value and the texture corresponding to each pixel point in the plurality of pixel points so as to obtain a plurality of cerebral blood vessel sets of a plurality of parts of the brain in the image data.
The apparatus according to claim 13 or 14, wherein the analyzing module is specifically configured to obtain a cerebrovascular thickness analysis priority set, wherein the cerebrovascular thickness analysis priority set comprises a plurality of cerebrovascular thickness analysis priority subsets, and the plurality of cerebrovascular thickness analysis priority subsets correspond to the plurality of cerebrovascular sets one-to-one; and analyzing the thicknesses of a plurality of cerebrovascular vessel sets positioned at a plurality of parts of the brain in the image data by adopting the cerebrovascular disease analysis strategy according to the cerebrovascular vessel thickness analysis priority set to obtain a plurality of cerebrovascular vessel thickness sets corresponding to the cerebrovascular vessel sets.
The device according to claim 10, wherein the analysis module is configured to perform preset processing on the image data to obtain a plurality of feature data corresponding to a plurality of cerebral vessels, and each feature data in the plurality of feature data includes a color, a shape, a structure, and a curvature corresponding to each cerebral vessel; analyzing each cerebral vessel in the plurality of cerebral vessels by adopting the cerebrovascular disease analysis strategy according to the plurality of characteristic data to obtain at least one cerebral vessel with abnormal first characteristic data, wherein the first characteristic data at least comprises the following color, shape, structure and bending degree; locating the condition based on the at least one cerebrovascular vessel with the first characteristic data anomaly.
The apparatus according to claim 16, wherein the preset process comprises the steps of: establishing a coordinate system according to the image data, wherein the origin of the coordinate system is any position of the brain, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow a right-hand spiral rule; starting from the origin of the coordinate system, detecting along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis and the positive direction and the negative direction of the Z axis of the coordinate system according to preset distances respectively, recording the spatial position corresponding to a first pixel point when detecting that a gray value corresponding to the first pixel point belongs to a gray value corresponding to a cerebral vessel, and recording the spatial position corresponding to a second pixel point when detecting that a gray value corresponding to the second pixel point does not belong to a gray value corresponding to a cerebral vessel and a gray value corresponding to an adjacent pixel point of the second pixel point belongs to a gray value corresponding to a cerebral vessel; segmenting the image data according to the spatial positions corresponding to all the first pixel points and the spatial positions corresponding to all the second pixel points to obtain a plurality of cerebral vessels; and acquiring the color, the shape, the structure and the bending degree corresponding to each cerebral vessel in the plurality of cerebral vessels.
The apparatus according to any one of claims 10 to 17, wherein the generating module is specifically configured to perform a first preset process on the scan image to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a data set of the cerebral vessels and a data set of the brain, the data set of the cerebral vessels comprises fusion data of the intersection positions of the cerebral arteries and the cerebral veins, the data set of the brain is a transfer function result of a cubic space of the brain surface and a tissue structure inside the brain, and the data set of the cerebral vessels is a transfer function result of a cubic space of the brain surface and the tissue structure inside the cerebral vessels; importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, wherein the second medical image data comprises a data set of the brain, a data set of the cerebral artery and a data set of the cerebral vein, and first data in the data set of the cerebral artery and second data in the data set of the cerebral vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position; and executing second preset processing on the second medical image data to obtain the image data.
A medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and generated as instructions for execution by the processor to perform the steps of the method of any of claims 1-9.
A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, which is executed by the processor, to implement the method of any of claims 1-9.