WO2021081850A1

WO2021081850A1 - Vrds 4d medical image-based spine disease recognition method, and related devices

Info

Publication number: WO2021081850A1
Application number: PCT/CN2019/114499
Authority: WO
Inventors: 李斯图尔特平; 李戴维伟
Original assignee: 未艾医疗技术(深圳)有限公司
Priority date: 2019-10-30
Filing date: 2019-10-30
Publication date: 2021-05-06
Also published as: CN114402395A

Abstract

A VRDS 4D medical image-based spine disease recognition method, comprising: acquiring a scanning image of the spine of a target user; processing the scanning image of the spine, so as to obtain target medical image data, the target medical image data comprising image data of the spine and image data of blood vessels; determining an abnormal blood vessel according to the image data of the spine and the image data of the blood vessels; recognizing a disease type of the spine according to the correlation between the abnormal blood vessel and the spine; and performing 4D medical imaging according to the target medical image data, and outputting the disease type of the spine.

Description

Vertebral disease recognition method and related device based on VRDS 4D medical image

Technical field

This application relates to the technical field of medical imaging devices, and in particular to a method and related devices for identifying spine diseases based on VRDS 4D medical images.

Background technique

Spinal cord ischemia refers to the lack of blood supply to the spinal cord, which damages neurons in the spinal cord, causing irreversible spinal cord function damage. Spinal cord ischemia is mostly spinal vascular malformations.

At present, doctors still use to watch and read continuous two-dimensional slice scan images, for example, CT (electronic computer tomography), MRI (magnetic resonance imaging), DTI (diffusion tensor imaging), PET (positron emission computed tomography) ), etc., in order to judge and analyze the patient’s spine disease. However, blood vessels in the spine are rich in distribution, and it is sometimes impossible to identify the abnormal types of spinal blood vessels just by directly viewing the two-dimensional slice data, which seriously affects the diagnosis of spine diseases by doctors and delays the treatment of spine diseases. With the rapid development of medical imaging technology, people have put forward new demands for medical imaging.

Summary of the invention

The embodiment of the present application provides a method and related device for identifying spine diseases based on VRDS 4D medical images, which is beneficial to improve the accuracy and efficiency of spine disease identification by the medical imaging device.

The first aspect of the embodiments of this application provides a method for identifying spine diseases based on VRDS 4D medical images, including:

Obtain a scanned image of the spine of the target user;

Processing the scanned image of the spine to obtain target medical image data, where the target medical image data includes image data of the spine and image data of blood vessels;

Determining an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;

Identifying the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine;

Perform 4D medical imaging according to the target medical image data, and output the disease type of the spine.

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

The acquiring unit is used to acquire a scanned image of the spine of the target user;

A processing unit, configured to process the scanned image of the spine to obtain target medical image data, wherein the target medical image data includes image data of the spine and image data of blood vessels;

A determining unit, configured to determine an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;

An identification unit, configured to identify the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine;

The output unit is configured to perform 4D medical imaging according to the target medical image data and output the disease type of the spine.

A third aspect of the embodiments of the present application provides a medical imaging device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are generated It is executed by the processor to execute the instructions of the steps in any one of the methods of the first aspect of the above claims.

The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, and the stored computer program is executed by the processor to implement the first aspect of the claims. Any of the methods.

It can be seen that in the above technical solution, the scan image of the spine of the target user is obtained, and secondly, the scan image of the spine is processed to obtain target medical image data, where the target medical image data includes the image data of the spine and the image of the blood vessel. Data, secondly, identify abnormal blood vessels based on the image data of the spine and blood vessels; secondly, identify the type of disease of the spine based on the relationship between the abnormal blood vessels and the spine; finally, perform 4D medical imaging based on the target medical image data and output the spine Type of disease. It can be seen that the medical imaging device in the present application can identify the type of disease of the spine by processing the scanned image of the spine, and output the type of disease of the spine, avoiding the situation that the observation based on the human eye is not accurate enough, and is beneficial to improve the medical imaging device to perform the spine The accuracy and efficiency of disease recognition.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

among them:

Fig. 1 is a schematic structural diagram of a VRDS 4D medical image intelligent analysis and processing system provided by an embodiment of the application;

FIG. 2 is a schematic flowchart of a method for identifying spine diseases based on VRDS 4D medical images according to an embodiment of the application;

FIG. 3 is a schematic diagram of a medical imaging device provided by an embodiment of the application;

FIG. 4 is a schematic structural diagram of a medical imaging device in a hardware operating environment related to an embodiment of the application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by a person of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Detailed descriptions are given below.

The terms "first", "second", and "third" in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific sequence. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

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. Correspondence. "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- For information such as CT, "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).

First, referring to Figure 1, Figure 1 is a schematic structural diagram of a VRDS-based 4D medical image intelligent analysis and processing system 100 provided by an embodiment of the present application. The system 100 includes a medical imaging device 110 and a network database 120. The medical imaging device 110 can Including the local medical imaging device 111 and/or the terminal medical imaging device 112, the local medical imaging device 111 or the terminal medical imaging device 112 is used to identify spine diseases based on the VRDS 4D medical image presented in the embodiment of the application based on the original DICOM data Based on the method, the identification and positioning of human spine diseases, four-dimensional volume rendering, and abnormal analysis are carried out to achieve four-dimensional three-dimensional imaging effects (the four-dimensional medical image specifically refers to the medical image including the internal spatial structure characteristics and external spatial structure characteristics of the displayed tissue The internal spatial structure feature refers to that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of target organs, blood vessels and other tissues, and the external spatial structure feature refers to the environmental features between tissues, including tissues The characteristics of the spatial position between the tissue and the tissue (including intersection, interval, fusion, etc., such as the edge structure characteristics of the intersection between the spine and the artery, etc.), the local medical imaging device 111 can also be used for the terminal medical imaging device 112 Edit the scanned image to form the transfer function result of the four-dimensional human body image. The transfer function result can include the transfer function result of the surface of the internal organs and the tissue structure in the internal organs of the human body, and the transfer function result of the cube space, as shown in the transfer function. The number, coordinates, color, transparency and other information of the array of cube edit box and arc edit required. 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.

When the user performs specific image display through the above-mentioned medical imaging device 110, he can select a display or a head-mounted display (Head-mounted Displays Set, HMDS) of virtual reality VR to display in combination with operating actions. 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.

The following is a detailed introduction to the spine disease identification method based on VRDS 4D medical images involved in the embodiments of the present application.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a method for recognizing spine diseases based on VRDS 4D medical imaging according to an embodiment of the present application, which is applied to the medical imaging device described in FIG. 1, as shown in FIG. 2, this embodiment The provided methods for identifying spine diseases based on VRDS 4D medical images include:

201. Acquire a scanned image of the spine of the target user.

Wherein, the scanned image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.

202. Process the scanned image of the spine to obtain target medical image data, where the target medical image data includes image data of the spine and image data of blood vessels.

In a possible example, processing the scanned image of the spine to obtain target medical image data includes: generating an image source of the spine according to the scanned image of the spine; and executing a first image source for the image source. Preset processing to obtain a bitmap BMP data source; import the BMP data source into a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes the image data of the spine and all The first image data of the blood vessel; the second image data of the blood vessel is filtered from the first image data of the blood vessel according to the image data of the spine to obtain second medical image data, wherein the second The medical image data includes the image data of the spine and the second image data of the blood vessel; the second medical image data is imported into a preset cross-vessel network model to obtain the third medical image data, wherein the third The medical image data includes the image data of the spine, the image data of the artery, and the image data of the vein; the target medical image data is obtained by performing a second preset processing on the third medical image data.

Optionally, generating the image source of the spine according to the scanned image of the spine includes: the medical imaging device acquires multiple scanned images that reflect the internal structural features of the target user's human body collected by medical equipment; and from the multiple scanned images Filter out at least one scanned image containing the spine, and use at least one scanned image as the target user's medical digital imaging and communication DICOM data; analyze the DICOM data to generate the target user's image source, the image source includes Texture 2D/3D image volume data .

Optionally, 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.

Among them, VRDS limited contrast adaptive histogram equalization includes: regional noise ratio limiting, global contrast limiting; the local histogram of the image source is divided into multiple partitions, for each partition, according to the accumulation of the neighborhood of the partition The slope of the histogram determines the slope of the transformation function, and the degree of contrast magnification around the pixel value of the partition is determined according to the gradient of the transformation function, and then the limit cropping process is performed according to the degree of contrast magnification to generate the distribution of the effective histogram. It also generates effective and available neighborhood consignment values, and evenly distributes these cropped parts of the histogram to other areas of the histogram.

Among them, hybrid partial differential denoising includes: different from Gaussian low-pass filtering (indiscriminately weakening the high-frequency components of the image, denoising will also produce image edge blurring), the isoilluminance formed by objects in natural images The line (including the edge) should be a smooth and smooth curve, that is, the absolute value of the curvature of these isoilluminance lines should be small enough. When the image is contaminated by noise, the local gray value of the image will fluctuate randomly, resulting in the unevenness of the isoilluminance line. Irregular oscillations, forming iso-illuminance lines with large local curvatures. According to this principle, the design uses VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising to protect the edges of the image and avoid the step effect in the smoothing process. The hybrid partial differential denoising model.

Among them, the VRDS Ai elastic deformation processing includes: superimposing positive and negative random distances on the original lattice to form a difference position matrix, and then the grayscale at each difference position forms a new lattice, which can realize the distortion of the image. Deform, and then rotate, distort, and translate the image.

It can be seen that in this example, the medical imaging device obtains the BMP data source by processing the original scanned image data, which increases the amount of information of the original data, and increases the depth information, and finally obtains data that meets the requirements of 4D medical image display.

Wherein, the VRDS medical network model is provided with a transfer function of the structural characteristics of the spine and a transfer function of the structural characteristics of the blood vessel. The BMP data source obtains first medical image data through the processing of the transfer function, and the first medical image data includes the spine The image data of and the first image data of the blood vessel, and the first image data of the blood vessel includes the fusion data of the intersection position of the artery and the vein.

Optionally, filtering the second image data of the blood vessel from the first image data of the blood vessel according to the image data of the spine includes: determining the abnormal position of the spine according to the image data of the spine; The abnormal position of the spine determines the blood vessel screening range; according to the blood vessel screening range, the second image data of the blood vessel is selected from the first image data of the blood vessel.

Specifically, the first image data of blood vessels includes the image data of all blood vessels around the spine. The abnormal position of the spine can be determined according to the image data of the spine. In this way, the blood vessel screening range can be determined according to the abnormal position of the spine, and the blood vessel screening range is the abnormal position of the spine. In the surrounding range, the second image data of the blood vessel is filtered from the first image data of the blood vessel according to the blood vessel screening range, that is, the image data of the blood vessel in the range around the abnormal position of the spine is filtered from the image data of all blood vessels around the spine. This can reduce the amount of subsequent data calculations and improve the accuracy of the identification of spine diseases.

Optionally, determining the blood vessel screening range according to the abnormal position of the spine includes: taking the abnormal position of the spine as the center of the circle, and intercepting a circular image according to a first radius to obtain the first circular range; and determining the first circular range; The number of blood vessels in the circular range; determine whether the number of blood vessels in the first circular range exceeds a preset blood vessel number threshold; if the number of blood vessels in the first circular range exceeds the preset blood vessel number threshold, then It is determined that the first circular range is the blood vessel screening range; if the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold, the abnormal position of the spine is taken as the center of the circle, and the A circular image is intercepted with two radii to obtain a second circular range, wherein the second radius is greater than the first radius, and the number of blood vessels in the second circular range exceeds the preset blood vessel number threshold.

For example, if the first radius is 3cm, the abnormal position of the spine is taken as the center, and the circular image is intercepted according to the radius of 3cm to obtain the first circular range. The number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold. Then re-determine the second radius, the second radius can be 5cm, the abnormal position of the spine is taken as the center, and the circular image is intercepted according to the radius of 5cm, and the second circular range is obtained. The number of blood vessels in the second circular range exceeds the expected Set a threshold for the number of blood vessels.

Optionally, importing the second medical image data into a preset cross-vessel network model to obtain third medical image data includes: importing the second medical image data into the cross-vessel network model, and the cross-vessel network model passes the following Operation to realize the data separation of arteries and veins: (1) Extract the fusion data of the intersection position; (2) Separate the fusion data based on the preset data separation algorithm for each fusion data to obtain independent arterial boundary point data and venous boundary points Data; (3) integrating multiple arterial boundary point data obtained after processing into the first data, and integrating multiple venous boundary point data obtained after processing into the second data. Finally, the third medical image data is obtained, and the third medical image data includes the image data of the spine, the image data of the arteries, and the image data of the veins.

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

Among them, the 2D boundary optimization processing includes: multiple sampling to obtain low-resolution information and high-resolution information. Among them, the low-resolution information can provide the contextual semantic information of the segmentation target in the entire image, that is, the characteristics that reflect the relationship between the target and the environment. These features are used to determine the object category, and the high-resolution information is used to provide more refined features, such as gradients, for the segmentation target.

The 3D boundary optimization processing includes: 3D convolution, 3D max pooling, and 3D upward convolution layer, the input data size is a1, a2, a3, the number of channels is c, the filter size is f, that is, the filter dimension is f*f*f*c, the number of filters is n, the final output of the 3-dimensional convolution is:

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

With analysis path and synthesis path. In the analysis path, 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. In the synthesis path, 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.

Among them, the data enhancement processing includes any one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random cut, and data enhancement based on Data enhancement to simulate different lighting changes.

It can be seen that in this example, the medical imaging device can process the BMP data source through the VRDS medical network model and the cross blood vessel network model, and combine boundary optimization and data enhancement processing to obtain target image data, which solves the problem that traditional medical imaging cannot achieve segmentation of arteries and arteries. The overall separation of veins improves the authenticity, comprehensiveness and refinement of medical image display.

203. Determine an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel.

In a possible example, determining the abnormal blood vessel according to the imaging data of the spine and the imaging data of the blood vessel includes: determining the position of the spinal cord according to the imaging data of the spine; determining the abnormal position of the spine and the spinal cord The positional relationship of the spine, wherein the positional relationship includes that the abnormal position of the spine is located in the dura of the spinal cord, the abnormal position of the spine is located around the spinal cord, and the abnormal position of the spine is located at the position of the spinal cord. Any one of internal or surface relationships; acquiring image data of a normal blood vessel corresponding to the positional relationship; comparing the image data of the blood vessel with the image data of the normal blood vessel to determine the abnormal blood vessel.

Optionally, if the positional relationship is that the abnormal position of the spine is located in the dura of the spinal cord, the imaging data of the normal spinal dural artery and the imaging data of the normal root vein are acquired; if the positional relationship is If the abnormal position of the spine is located around the spinal cord, at least one of the image data of the normal anterior spinal artery and the image data of the normal posterior spinal artery is obtained, and the image data of the normal anterior spinal vein and the normal posterior spinal vein are obtained. At least one item of image data; if the positional relationship is that the abnormal position of the spine is located inside or on the surface of the spinal cord, obtain image data of normal root medullary arteries, and obtain image data of normal intramedullary veins or normal Image data of the perimedullary vein.

Specifically, spinal vascular malformations include spinal dural arteriovenous atrophy, perimedullary arteriovenous malformation, and spinal arteriovenous malformations. Among them, spinal dural arteriovenous malformation is acquired, and perimedullary arteriovenous malformations and spinal arteriovenous malformations continue to be congenital. Sexual, the supplying artery for spinal dural arteriovenous atrophy is the spinal dural artery, the draining vein is the root vein, and the pathophysiology is characterized by chronic venous congestion; the supplying artery for the perimedullary arteriovenous atrophy is the anterior and/or posterior spinal artery, draining Veins are anterior and/or posterior spinal cord veins, and their pathophysiological manifestations are spinal cord parenchymal or subarachnoid hemorrhage, venous congestion, and space-occupying effect; the feeding artery of spinal arteriovenous malformation is the root medullary artery, and the draining vein is intramedullary or perimedullary Veins, pathophysiological manifestations are spinal cord parenchyma or subarachnoid hemorrhage, venous congestion, space-occupying effect.

Optionally, the image data of a blood vessel includes image data of an artery, and the image data of a normal blood vessel includes image data of a normal artery. The image data of the blood vessel is compared with the image data of the normal blood vessel to determine the abnormal blood vessel. , Including: acquiring the diameter of the artery according to the image data of the artery; comparing the image data of the artery with the image data of the normal artery to determine the degree of surface expansion of the artery; acquiring the target The physical parameters of the user, wherein the physical parameters of the target user include at least one of the height, weight, blood pressure, blood sugar, and heart rate of the target user; the diameter of the artery is determined according to the physical parameters of the target user The corresponding first weight and the second weight corresponding to the degree of surface expansion of the artery; the diameter of the artery and the degree of surface expansion of the artery are respectively weighted according to the first weight and the second weight , To obtain the comprehensive degree of expansion of the artery. When the comprehensive expansion degree of the artery exceeds a preset arterial expansion threshold, it is determined that the artery is an abnormal artery.

Optionally, the image data of the blood vessel further includes the image data of the vein, and the image data of the normal blood vessel also includes the image data of the normal vein. The image data of the blood vessel is compared with the image data of the normal blood vessel to determine the The abnormal blood vessel further includes: obtaining the diameter of the vein according to the image data of the vein; comparing the image data of the vein with the image data of the normal vein to determine the degree of surface expansion of the vein; The physical parameters of the target user determine the third weight corresponding to the diameter of the vein and the fourth weight corresponding to the degree of surface expansion of the vein; The diameter of the tube and the surface expansion degree of the vein are weighted to obtain the comprehensive expansion degree of the vein; when the comprehensive expansion degree of the vein exceeds a preset vein expansion threshold, the vein is determined to be an abnormal vein.

Among them, the diameter of the artery/vein can be obtained in the following way: segment the artery/vein according to the image data of the artery/vein to obtain the M segment of the artery/vein, where M is a positive integer; determine the M segment of the artery/vein The average diameter of each segment of artery/vein, where the difference between the average diameter of the i-th artery/vein and the average diameter of the (i+1)-th artery/vein is not less than the preset segment threshold, i It is a positive integer less than M; determine the weight corresponding to each segment of the artery/vein in the M segment artery/vein; according to the weight corresponding to each segment of the artery/vein in the M segment artery/vene, calculate each segment of the artery/vein in the M segment artery/vein The average diameter of the artery is weighted to obtain the diameter of the artery/venous.

Wherein, comparing the image data of the artery/vein with the image data of the normal artery/vein to determine the degree of surface expansion of the artery/vein includes: obtaining the surface characteristics of the artery/vein according to the image data of the artery/vein Determine the target surface area of the artery/vein according to the surface characteristics of the artery/vein, and obtain the first surface feature of the target surface area of the artery/vein; obtain the normal artery according to the image data of the normal artery/vein / The second surface feature of the target surface area of the vein; the first surface feature is compared with the second surface feature to determine the degree of surface expansion of the artery/vein.

Wherein, the method for determining the target surface area of the artery/vein according to the surface characteristics of the artery/vein may be: analyzing the feature point distribution of the surface area of the artery/vein according to the surface characteristics of the artery/vein; The surface area of the artery/vein is intercepted according to N different circle centers to obtain N circular surface partitions, where N is an integer greater than 3; determine each circular surface in the N circular surface partitions The number of feature points included in the partition; a target circular surface partition is selected from the N circular surface partitions, wherein the number of feature points included in the target circular surface partition is greater than the N circular surface partitions The number of feature points included in other circular surface divisions in the surface division; the target circular surface division is determined as the target surface area.

It can be seen that in this example, by screening the target surface area of the artery/vein, the surface characteristics of the target surface area are compared with the surface characteristics of the normal blood vessel. In this way, the complexity of feature comparison can be reduced, the comparison time can be shortened, and the comparison efficiency can be improved.

In a possible example, the image data of the blood vessel also includes the curvature of the blood vessel, and the method for determining the curvature of the blood vessel may be:

Establishing a coordinate system according to the third image data of the blood vessel, the origin of the coordinate system is any position of the blood vessel, and the X axis, Y axis and Z axis of the coordinate system are perpendicular to each other and follow the right-hand spiral rule;

Starting from the origin of the coordinate system, detection is performed 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 of the coordinate system according to a preset distance, respectively, Whenever it is detected that the gray value corresponding to the first pixel belongs to the gray value corresponding to the vascular cell data of the outermost layer of the blood vessel, the spatial position corresponding to the first pixel is recorded, and whenever the first pixel is detected The gray value corresponding to the two pixel points does not belong to the gray value corresponding to the blood vessel cell data of the outermost layer of the blood vessel, and the gray value corresponding to the adjacent pixels of the second pixel point belongs to the blood vessel of the outermost layer of the blood vessel When the gray value corresponding to the cell data is used, the spatial position corresponding to the second pixel point is recorded;

The third image data of the blood vessel is segmented according to the spatial positions corresponding to all the first pixels and the spatial positions corresponding to all the second pixels, so as to obtain a plurality of outermost parts corresponding to a plurality of blood vessels. Layer vascular cell data set, each outermost vascular cell data set includes multiple outermost vascular cell data;

For each outermost blood vessel cell data set, perform the following steps:

Obtain the characteristic curve projected on any plane of the currently processed outermost vascular cell data set; select any point on the characteristic curve as the starting point; starting from the starting point, follow the positive and negative directions of the characteristic curve The direction keeps marking the pixels, and the marking stops when the target pixel is marked. The positive direction of the characteristic curve is the transverse positive direction of the third image data of the blood vessel, and the reverse direction of the characteristic curve is the first horizontal direction of the blood vessel. In the transverse direction of the three image data, the target pixel is the pixel with the largest change in the curvature of the target blood vessel segment, the target blood vessel segment is the blood vessel between the starting point and the target spatial position of the target blood vessel, the target blood vessel Corresponding to the currently processed outermost blood vessel cell data set, the target spatial position is the position corresponding to the target pixel; acquiring the curvature corresponding to the target blood vessel segment; setting the curvature corresponding to the target blood vessel segment to be The target blood vessel corresponds to the degree of curvature.

Based on the above example, whether the blood vessel is an artery or a vein, comparing the image data of the blood vessel with the image data of the normal blood vessel to determine the abnormal blood vessel includes: acquiring the image data of the blood vessel The diameter of the blood vessel; compare the image data of the blood vessel with the image data of the normal blood vessel to determine the degree of surface expansion of the blood vessel; obtain the body parameters of the target user, wherein the body of the target user The parameters include at least one of the height, weight, blood pressure, blood sugar, and heart rate of the target user; the fifth weight corresponding to the diameter of the blood vessel and the degree of surface expansion of the blood vessel are determined according to the physical parameters of the target user The corresponding sixth weight, the seventh weight corresponding to the curvature of the blood vessel; according to the fifth weight, the sixth weight, and the seventh weight, the diameter of the blood vessel and the surface of the blood vessel are respectively The degree of expansion and the degree of curvature of the blood vessel are weighted to obtain the overall degree of expansion of the blood vessel. When the comprehensive expansion degree of the blood vessel exceeds a preset blood vessel expansion threshold, it is determined that the blood vessel is an abnormal blood vessel.

204. Identify the disease type of the spine according to the association relationship between the abnormal blood vessel and the spine.

In a possible example, identifying the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine includes: if the abnormal artery is a spinal dural artery and the abnormal vein is a root vein, determining Whether the abnormal artery supplies blood to the spinal cord, if the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as spinal arteriovenous atrophy; if the abnormal artery is the anterior spinal artery and the posterior spinal cord At least one of the arteries, and the abnormal vein is at least one of the anterior spinal cord vein and the posterior spinal vein, determining whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, then Identify the type of disease of the spine as perimedullary arteriovenous atrophy; if the abnormal artery is a root medullary artery, and the abnormal vein is an intramedullary vein or a perimedullary vein, determine whether the abnormal artery supplies blood to the spinal cord, If the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as spinal arteriovenous malformation.

205. Perform 4D medical imaging according to the target medical image data, and output the disease type of the spine.

Among them, 4D medical imaging refers to the presentation of 4-dimensional medical images.

In a possible example, performing 4D medical imaging according to the target medical image data includes: the medical imaging device selects enhancement data with a quality score greater than a preset score from the target medical image data as VRDS 4D imaging data; according to VRDS 4D imaging The data undergoes 4D medical imaging.

Among them, the quality score can be comprehensively evaluated from the following dimensions: average gradient, information entropy, visual information fidelity, peak signal-to-noise ratio PSNR, structural similarity SSIM, mean square error MSE, etc. For details, please refer to common image quality in the image field. The scoring algorithm will not be repeated here.

It can be seen that, in the embodiment of the present application, the scan image of the spine of the target user is acquired, and secondly, the scan image of the spine is processed to obtain the target medical image data. The target medical image data includes the image data of the spine and the blood vessel. Image data, secondly, identify abnormal blood vessels based on the image data of the spine and blood vessels; secondly, identify the disease type of the spine based on the relationship between the abnormal blood vessels and the spine; finally, perform 4D medical imaging based on the target medical image data and output the spine Type of disease. It can be seen that the medical imaging device in the present application can identify the type of disease of the spine by processing the scanned image of the spine, and output the type of disease of the spine, avoiding the situation that the observation based on the human eye is not accurate enough, and is beneficial to improve the medical imaging device to perform the spine The accuracy and efficiency of disease recognition.

Referring to FIG. 3, FIG. 3 is a schematic diagram of a medical imaging apparatus 300 provided by an embodiment of the present application. The medical imaging apparatus 300 may include:

The obtaining unit 301 is used to obtain a scanned image of the spine of the target user;

The processing unit 302 is configured to process the scanned image of the spine to obtain target medical image data, where the target medical image data includes image data of the spine and image data of blood vessels;

The determining unit 303 is configured to determine an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;

The identification unit 304 is configured to identify the disease type of the spine according to the association relationship between the abnormal blood vessel and the spine;

The output unit 305 is configured to perform 4D medical imaging according to the target medical image data and output the disease type of the spine.

In a possible example, the processing unit 302 is specifically configured to: generate an image source of the spine according to the scanned image of the spine; perform first preset processing on the image source to obtain a bitmap BMP data source; The BMP data source imports a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes image data of the spine and first image data of the blood vessel; The image data of the spine filters the second image data of the blood vessel from the first image data of the blood vessel to obtain the second medical image data, wherein the second medical image data includes the image data of the spine and The second image data of the blood vessel; the second medical image data is imported into a preset cross-vessel network model to obtain third medical image data, wherein the third medical image data includes the image data of the spine, The image data of the arteries and the image data of the veins; performing a second preset processing on the third medical image data to obtain the target medical image data.

In a possible example, the processing unit 302 is specifically configured to: determine the abnormal position of the spine according to the image data of the spine; determine the blood vessel screening range according to the abnormal position of the spine; The second image data of the blood vessel is filtered out of the first image data of the blood vessel.

In a possible example, the processing unit 302 is specifically configured to: take the abnormal position of the spine as the center of the circle and perform a circular image interception according to a first radius to obtain a first circle range; and determine the first circle The number of blood vessels in the first circular range; determine whether the number of blood vessels in the first circular range exceeds a preset blood vessel number threshold; if the number of blood vessels in the first circular range exceeds the preset blood vessel number threshold, determine The first circular range is the blood vessel screening range; if the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold, the abnormal position of the spine is taken as the center of the circle, and the second The radius is a circular image interception to obtain a second circular range, wherein the second radius is greater than the first radius, and the number of blood vessels in the second circular range exceeds the preset blood vessel number threshold.

In a possible example, the determining unit 303 is specifically configured to: determine the position of the spinal cord according to the image data of the spine; determine the positional relationship between the abnormal position of the spine and the spinal cord, wherein the positional relationship includes The abnormal position of the spine is located in the dura mater of the spinal cord, the abnormal position of the spine is located around the spinal cord, and the abnormal position of the spine is located inside or on the surface of the spinal cord; Image data of a normal blood vessel corresponding to the position relationship; comparing the image data of the blood vessel with the image data of the normal blood vessel to determine the abnormal blood vessel.

In a possible example, the determining unit 303 is specifically configured to: if the positional relationship is that the abnormal position of the spine is located in the dura of the spinal cord, obtain the image data of the normal spinal dural artery and the normal root Image data of the vein; if the positional relationship is that the abnormal position of the spine is located around the spinal cord, obtain at least one of the image data of the normal anterior spinal artery and the image data of the normal posterior spinal artery, and obtain the normal At least one of the image data of the anterior spinal cord vein and the image data of the normal posterior spinal vein; if the positional relationship is that the abnormal position of the spine is located inside or on the surface of the spinal cord, the image data of the normal root medullary artery is acquired , And obtain the imaging data of normal intramedullary veins or normal perimedullary veins.

In a possible example, the image data of the blood vessel includes the image data of the artery, the image data of the normal blood vessel includes the image data of the normal artery, and the determining unit 303 is specifically configured to: Data to obtain the diameter of the artery; compare the image data of the artery with the image data of the normal artery to determine the degree of surface expansion of the artery; obtain the body parameters of the target user, wherein the The body parameters of the target user include at least one of the height, weight, blood pressure, blood sugar, and heart rate of the target user; the first weight corresponding to the diameter of the artery and the artery are determined according to the body parameters of the target user The second weight corresponding to the degree of surface expansion of the artery; the diameter of the artery and the degree of surface expansion of the artery are respectively weighted according to the first weight and the second weight to obtain the comprehensive degree of expansion of the artery When the comprehensive expansion degree of the artery exceeds the preset arterial expansion threshold, it is determined that the artery is an abnormal artery.

In a possible example, the image data of the blood vessel further includes the image data of the vein, the image data of the normal blood vessel further includes the image data of the normal vein, and the determining unit 303 is specifically configured to: Obtain the diameter of the vein from the image data; compare the image data of the vein with the image data of the normal vein to determine the degree of surface expansion of the vein; determine the diameter of the vein according to the physical parameters of the target user The third weight corresponding to the diameter of the vein and the fourth weight corresponding to the degree of surface expansion of the vein; according to the third weight and the fourth weight, the diameter of the vein and the surface of the vein are respectively expanded The degree is weighted to obtain the comprehensive expansion degree of the vein; when the comprehensive expansion degree of the vein exceeds a preset vein expansion threshold, it is determined that the vein is an abnormal vein.

In a possible example, the identification unit 304 is specifically configured to: if the abnormal artery is a spinal dural artery and the abnormal vein is a root vein, determine whether the abnormal artery supplies blood to the spinal cord, if If the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as spinal arteriovenous atrophy; if the abnormal artery is at least one of the anterior spinal artery and the posterior spinal artery, and the abnormal vein is At least one of the anterior spinal vein and the posterior spinal vein to determine whether the abnormal artery supplies blood to the spinal cord. If the abnormal artery supplies blood to the spinal cord, then the disease type of the spine is identified as a perimedullary arteriovenous atrophy If the abnormal artery is a root medullary artery, and the abnormal vein is an intramedullary vein or a perimedullary vein, determine whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, identify The disease type of the spine is spinal arteriovenous malformation.

For the specific implementation of the medical imaging device involved in the present application, please refer to the foregoing embodiments of the method for recognizing spine diseases based on VRDS 4D medical images, which will not be repeated here.

Referring to FIG. 4, FIG. 4 is a schematic structural diagram of a medical imaging device in a hardware operating environment involved in an embodiment of the application. Wherein, as shown in FIG. 4, the medical imaging device in the hardware operating environment involved in the embodiment of the present application may include:

The processor 401, such as a CPU.

The memory 402, optionally, the memory may be a high-speed RAM memory, or a stable memory, such as a disk memory.

The communication interface 403 is used to implement connection and communication between the processor 401 and the memory 402.

Those skilled in the art can understand that the structure of the medical imaging device shown in FIG. 4 does not constitute a limitation to it, and may include more or less components than those shown in the figure, or a combination of certain components, or different component arrangements. .

As shown in FIG. 4, the memory 402 may include an operating system, a network communication module, and a program for identifying spinal diseases. The operating system is a program that manages and controls the hardware and software resources of the medical imaging device, and supports the operation of the spine disease recognition program and other software or programs. The network communication module is used to implement communication between various components in the memory 402 and communication with other hardware and software in the medical imaging device.

In the medical imaging apparatus shown in FIG. 4, the processor 401 is configured to execute the spine disease recognition program stored in the memory 402, and implement the following steps:

Obtain a scanned image of the spine of the target user;

The present application also provides a computer-readable storage medium for storing a computer program, and the computer program is executed by the processor to implement the following steps:

Obtain a scanned image of the spine of the target user;

For the specific implementation of the computer-readable storage medium involved in the present application, please refer to the foregoing embodiments of the method for recognizing spine diseases based on VRDS 4D medical images, which will not be repeated here.

It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not subject to the described sequence of actions. Limitation, because according to this application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in an embodiment, reference may be made to related descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, 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. In addition, 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.

The 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.

In addition, 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.

If 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. Based on this understanding, 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. .

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the embodiments are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

A method for identifying spine diseases based on VRDS 4D medical images, which is characterized in that it is applied to a medical imaging device, and the method includes:

Obtain a scanned image of the spine of the target user;

Processing the scanned image of the spine to obtain target medical image data, where the target medical image data includes image data of the spine and image data of blood vessels;

Determining an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;

Identifying the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine;

Perform 4D medical imaging according to the target medical image data, and output the disease type of the spine.
The method according to claim 1, wherein the processing the scanned image of the spine to obtain target medical image data comprises:

Generating the image source of the spine according to the scanned image of the spine;

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

Importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes image data of the spine and first image data of the blood vessel;

According to the image data of the spine, the second image data of the blood vessel is filtered from the first image data of the blood vessel to obtain second medical image data, wherein the second medical image data includes the image data of the spine Image data and second image data of the blood vessel;

Import the second medical image data into a preset cross blood vessel network model to obtain third medical image data, where the third medical image data includes image data of the spine, image data of arteries, and image data of veins ；

Performing a second preset process on the third medical image data to obtain the target medical image data.
The method according to claim 2, wherein the filtering out the second image data of the blood vessel from the first image data of the blood vessel according to the image data of the spine comprises:

Determining the abnormal position of the spine according to the image data of the spine;

Determining the blood vessel screening range according to the abnormal position of the spine;

The second image data of the blood vessel is filtered from the first image data of the blood vessel according to the blood vessel screening range.
The method according to claim 3, wherein the determining the blood vessel screening range according to the abnormal position of the spine comprises:

Taking the abnormal position of the spine as the center of the circle, and intercepting the circular image according to the first radius to obtain the first circular range;

Determining the number of blood vessels in the first circular range;

Judging whether the number of blood vessels in the first circular range exceeds a preset blood vessel number threshold;

If the number of blood vessels in the first circular range exceeds the preset blood vessel number threshold, determining that the first circular range is the blood vessel screening range;

If the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold, the abnormal position of the spine is taken as the center of the circle, and the circular image is intercepted according to the second radius to obtain the second circular range , Wherein the second radius is greater than the first radius, and the number of blood vessels in the second circular range exceeds the preset blood vessel number threshold.
The method according to claim 3 or 4, wherein the determining the abnormal blood vessel based on the image data of the spine and the image data of the blood vessel comprises:

Determining the position of the spinal cord according to the image data of the spine;

Determine the positional relationship between the abnormal position of the spine and the spinal cord, wherein the positional relationship includes that the abnormal position of the spine is located in the dura mater of the spinal cord, the abnormal position of the spine is located around the spinal cord, and The abnormal position of the spine is located in any relationship between the inside or the surface of the spinal cord;

Acquiring image data of normal blood vessels corresponding to the position relationship;

The image data of the blood vessel is compared with the image data of the normal blood vessel to determine the abnormal blood vessel.
The method according to claim 5, wherein said acquiring image data of a normal blood vessel corresponding to said positional relationship comprises:

If the positional relationship is that the abnormal position of the spine is located in the dura of the spinal cord, acquiring image data of the normal spinal dural artery and image data of the normal root vein;

If the positional relationship is that the abnormal position of the spine is located around the spinal cord, at least one of the image data of the normal anterior spinal artery and the image data of the normal posterior spinal artery is acquired, and the image of the normal anterior spinal vein is acquired At least one of the data and the imaging data of the normal posterior spinal cord vein;

If the positional relationship is that the abnormal position of the spine is located inside or on the surface of the spinal cord, image data of normal root medullary arteries are acquired, and image data of normal intramedullary veins or image data of normal perimedullary veins are acquired.
The method according to claim 5 or 6, wherein the image data of the blood vessel includes the image data of the artery, the image data of the normal blood vessel includes the image data of the normal artery, and the image data of the blood vessel is The comparison of the image data with the image data of the normal blood vessel to determine the abnormal blood vessel includes:

Acquiring the diameter of the artery according to the image data of the artery;

Comparing the image data of the artery with the image data of the normal artery to determine the degree of surface expansion of the artery;

Acquiring the physical parameters of the target user, where the physical parameters of the target user include at least one of the target user's height, weight, blood pressure, blood sugar, and heart rate;

Determining a first weight corresponding to the diameter of the artery and a second weight corresponding to the degree of surface expansion of the artery according to the physical parameters of the target user;

Weighting the diameter of the artery and the degree of surface expansion of the artery according to the first weight and the second weight, respectively, to obtain the comprehensive degree of expansion of the artery;

When the comprehensive expansion degree of the artery exceeds a preset arterial expansion threshold, it is determined that the artery is an abnormal artery.
The method according to claim 7, wherein the image data of the blood vessel further includes the image data of the vein, the image data of the normal blood vessel further includes the image data of the normal vein, and the image data of the blood vessel The comparison of the image data with the image data of the normal blood vessel to determine the abnormal blood vessel further includes:

Acquiring the diameter of the vein according to the image data of the vein;

Comparing the image data of the vein with the image data of the normal vein to determine the degree of surface expansion of the vein;

Determining, according to the physical parameters of the target user, a third weight corresponding to the diameter of the vein and a fourth weight corresponding to the degree of surface expansion of the vein;

Weighting the diameter of the vein and the degree of surface expansion of the vein respectively according to the third weight and the fourth weight to obtain the comprehensive degree of expansion of the vein;

When the comprehensive expansion degree of the vein exceeds a preset vein expansion threshold, it is determined that the vein is an abnormal vein.
The method according to claim 8, wherein the identifying the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine comprises:

If the abnormal artery is a spinal dural artery and the abnormal vein is a root vein, determine whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, identify the disease of the spine The type is spinal dural arteriovenous atrophy;

If the abnormal artery is at least one of the anterior spinal artery and the posterior spinal artery, and the abnormal vein is at least one of the anterior spinal cord and the posterior spinal vein, determining whether the abnormal artery supplies blood to the spinal cord, If the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as perimedullary arteriovenous atrophy;

If the abnormal artery is a root medullary artery, and the abnormal vein is an intramedullary vein or a perimedullary vein, determine whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, identify the The disease type of the spine is spinal arteriovenous malformation.
A medical imaging device, characterized in that the device comprises:

The acquiring unit is used to acquire a scanned image of the spine of the target user;

A processing unit, configured to process the scanned image of the spine to obtain target medical image data, wherein the target medical image data includes image data of the spine and image data of blood vessels;

A determining unit, configured to determine an abnormal blood vessel according to the image data of the spine and the image data of the blood vessel;

An identification unit, configured to identify the type of disease of the spine according to the association relationship between the abnormal blood vessel and the spine;

The output unit is configured to perform 4D medical imaging according to the target medical image data and output the disease type of the spine.
The device according to claim 10, wherein the processing unit is specifically configured to:

Generating the image source of the spine according to the scanned image of the spine;

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

Importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes image data of the spine and first image data of the blood vessel;

According to the image data of the spine, the second image data of the blood vessel is filtered from the first image data of the blood vessel to obtain second medical image data, wherein the second medical image data includes the image data of the spine Image data and second image data of the blood vessel;

Import the second medical image data into a preset cross blood vessel network model to obtain third medical image data, where the third medical image data includes image data of the spine, image data of arteries, and image data of veins ；

Performing a second preset process on the third medical image data to obtain the target medical image data.
The device according to claim 11, wherein the processing unit is specifically configured to:

Determining the abnormal position of the spine according to the image data of the spine;

Determining the blood vessel screening range according to the abnormal position of the spine;

The second image data of the blood vessel is filtered from the first image data of the blood vessel according to the blood vessel screening range.
The device according to claim 12, wherein the processing unit is specifically configured to:

Taking the abnormal position of the spine as the center of the circle, and intercepting the circular image according to the first radius to obtain the first circular range;

Determining the number of blood vessels in the first circular range;

Judging whether the number of blood vessels in the first circular range exceeds a preset blood vessel number threshold;

If the number of blood vessels in the first circular range exceeds the preset blood vessel number threshold, determining that the first circular range is the blood vessel screening range;

If the number of blood vessels in the first circular range does not exceed the preset blood vessel number threshold, the abnormal position of the spine is taken as the center of the circle, and the circular image is intercepted according to the second radius to obtain the second circular range , Wherein the second radius is greater than the first radius, and the number of blood vessels in the second circular range exceeds the preset blood vessel number threshold.
The device according to claim 12 or 13, wherein the determining unit is specifically configured to:

Determining the position of the spinal cord according to the image data of the spine;

Determine the positional relationship between the abnormal position of the spine and the spinal cord, wherein the positional relationship includes that the abnormal position of the spine is located in the dura mater of the spinal cord, the abnormal position of the spine is located around the spinal cord, and The abnormal position of the spine is located in any relationship between the inside or the surface of the spinal cord;

Acquiring image data of normal blood vessels corresponding to the position relationship;

The image data of the blood vessel is compared with the image data of the normal blood vessel to determine the abnormal blood vessel.
The device according to claim 14, wherein the determining unit is specifically configured to:

If the positional relationship is that the abnormal position of the spine is located in the dura of the spinal cord, acquiring image data of the normal spinal dural artery and image data of the normal root vein;

If the positional relationship is that the abnormal position of the spine is located around the spinal cord, at least one of the image data of the normal anterior spinal artery and the image data of the normal posterior spinal artery is acquired, and the image of the normal anterior spinal vein is acquired At least one of the data and the imaging data of the normal posterior spinal cord vein;

If the positional relationship is that the abnormal position of the spine is located inside or on the surface of the spinal cord, image data of normal root medullary arteries are acquired, and image data of normal intramedullary veins or image data of normal perimedullary veins are acquired.
The device according to claim 14 or 15, wherein the image data of the blood vessel includes the image data of the artery, the image data of the normal blood vessel includes the image data of the normal artery, and the determining unit is specifically configured to :

Acquiring the diameter of the artery according to the image data of the artery;

Comparing the image data of the artery with the image data of the normal artery to determine the degree of surface expansion of the artery;

Acquiring the physical parameters of the target user, where the physical parameters of the target user include at least one of the target user's height, weight, blood pressure, blood sugar, and heart rate;

Determining a first weight corresponding to the diameter of the artery and a second weight corresponding to the degree of surface expansion of the artery according to the physical parameters of the target user;

Weighting the diameter of the artery and the degree of surface expansion of the artery according to the first weight and the second weight, respectively, to obtain the comprehensive degree of expansion of the artery;

When the comprehensive expansion degree of the artery exceeds a preset arterial expansion threshold, it is determined that the artery is an abnormal artery.
The device according to claim 16, wherein the image data of the blood vessel further includes the image data of the vein, the image data of the normal blood vessel further includes the image data of the normal vein, and the determining unit is specifically configured to :

Acquiring the diameter of the vein according to the image data of the vein;

Comparing the image data of the vein with the image data of the normal vein to determine the degree of surface expansion of the vein;

Determining a third weight corresponding to the diameter of the vein and a fourth weight corresponding to the degree of surface expansion of the vein according to the physical parameters of the target user;

Weighting the diameter of the vein and the degree of surface expansion of the vein respectively according to the third weight and the fourth weight to obtain the comprehensive degree of expansion of the vein;

When the comprehensive expansion degree of the vein exceeds a preset vein expansion threshold, it is determined that the vein is an abnormal vein.
The device according to claim 17, wherein the identification unit is specifically configured to:

If the abnormal artery is the spinal dural artery and the abnormal vein is the root vein, determine whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, identify the disease of the spine Type is spinal dural arteriovenous atrophy;

If the abnormal artery is at least one of the anterior spinal artery and the posterior spinal artery, and the abnormal vein is at least one of the anterior spinal cord and the posterior spinal vein, determining whether the abnormal artery supplies blood to the spinal cord, If the abnormal artery supplies blood to the spinal cord, the disease type of the spine is identified as perimedullary arteriovenous atrophy;

If the abnormal artery is a root medullary artery, and the abnormal vein is an intramedullary vein or a perimedullary vein, determine whether the abnormal artery supplies blood to the spinal cord, and if the abnormal artery supplies blood to the spinal cord, identify the The disease type of the spine is spinal arteriovenous malformation.
A medical imaging device is characterized by 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 by the processor Execute to execute the instructions of the steps in any one of the methods of claims 1-9.
A computer-readable storage medium, characterized in that 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 method described in any one of claims 1-9 method.