WO2021081845A1

WO2021081845A1 - Vrds ai-based liver tumor and blood vessel analysis method and related product

Info

Publication number: WO2021081845A1
Application number: PCT/CN2019/114486
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: CN114286643A

Abstract

A VRDS AI-based liver tumor and blood vessel analysis method applied to a medical imaging device, comprising: acquiring a scanned image of the liver of a target user (201); generating an image data set of a liver tumor and an image data set of liver blood vessels on the basis of the scanned image (202); determining a blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels (203); and performing 4D medical imaging on the image data set of the liver tumor and the image data set of the liver blood vessels, so as to output the location of the liver tumor and liver blood vessels that have a blood supply relationship with the liver tumor (204). The invention addresses the problem of low blood supply relationship identification efficiency caused by two-dimensional scan images not being able to display the spatial structural characteristics of tumors and blood vessels, and thus improves the accuracy and convenience of determining blood supply relationships. A corresponding imaging device and storage medium are also disclosed.

Description

A VRDS AI-based liver tumor and blood vessel analysis method and related products

Technical field

This application relates to the technical field of medical imaging devices, in particular to a method for analyzing liver tumors and blood vessels and related products based on VRDS AI.

Background technique

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 diseased tissues such as tumors. However, it is not possible to determine the specific location of the tumor just by directly viewing the two-dimensional slice data, which seriously affects the doctor's diagnosis of the disease. With the rapid development of medical imaging technology, people have put forward new demands for medical imaging.

Summary of the invention

The embodiments of the present application provide a liver tumor and blood vessel analysis method and related products based on VRDS AI, which are beneficial to improve the accuracy and convenience of determining the blood supply relationship.

In the first aspect, the embodiments of the present application provide a method for analyzing liver tumors and blood vessels based on VRDS AI, which is applied to a medical imaging device, and includes:

Acquiring a scanned image of the liver of the target user, the scanned image including a liver tumor image and the liver blood vessel image;

Generating an image data set of the liver tumor and an image data set of the liver blood vessel according to the scan image, the image data set of the liver blood vessel including an image data set of the arteries of the liver and an image data set of the veins of the liver;

Determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data collection of the liver tumor and the image data collection of the liver blood vessels;

Perform 4D medical imaging on the image data collection of the liver tumor and the image data collection of the liver blood vessels to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor.

In the second aspect, the embodiments of the present application provide a liver tumor and blood vessel analysis device based on VRDS AI, which is applied to a medical imaging device, and the device includes:

An acquiring unit, configured to acquire a scanned image of the liver of the target user, the scanned image including a liver tumor image and the liver blood vessel image;

The processing unit is configured to generate an image data set of the liver tumor and an image data set of the liver blood vessel according to the scan image. The image data set of the liver blood vessel includes the image data set of the liver arteries and the blood vessels of the liver. Image data collection;

An extraction unit, configured to determine the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels;

The determining unit is configured to perform 4D medical imaging on the image data collection of the liver tumor and the image data collection of the liver blood vessels to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor.

In a third aspect, an embodiment of the present application provides an electronic 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 configured by The foregoing processor executes, and the foregoing program includes instructions for executing the steps in the first aspect of the embodiments of the present application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute Some or all of the steps described in one aspect.

In a fifth aspect, the embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute as implemented in this application. Example part or all of the steps described in the first aspect. The computer program product may be a software installation package.

It can be seen that in the above technical solution, firstly, the scanned image of the liver of the target user is acquired, and then the image data collection of liver tumors and the image data collection of liver blood vessels are generated according to the scanned images; secondly, the image data collection of liver tumors and liver blood vessels are generated. The image data collection of the liver tumor determines the blood supply relationship between the liver tumor and the liver blood vessel; the image data collection of the liver tumor and the image data collection of the liver blood vessel are subjected to 4D medical imaging to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor . Determine the blood supply relationship between the liver tumor and the liver blood vessels by analyzing the image data collection of the liver tumor and the image data collection of the liver blood vessels, avoiding the inability to show the spatial structure of the tumor and blood vessels due to the two-dimensional scan image The problem of low blood supply relationship recognition efficiency caused by characteristics improves the accuracy and convenience of determining the blood supply relationship.

Description of the drawings

The following will briefly introduce the drawings needed to be used in the description of the embodiments or the prior art.

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.

FIG. 1 is a schematic structural diagram of a liver tumor and blood vessel analysis system based on VRDS 4D medical imaging according to an embodiment of the present application;

2 is a schematic flowchart of a method for analyzing liver tumors and blood vessels based on VRDS AI according to an embodiment of the present application;

Fig. 3 is a schematic diagram of a liver image provided by an embodiment of the present application;

4 is a schematic structural diagram of a medical imaging device provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an embodiment of a device for analyzing liver tumors and blood vessels based on VRDS AI according to an embodiment of the present application.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the application, the technical solutions in the embodiments of the application will be clearly and completely described below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only It is a part of the embodiments of the present application, but not all of 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.

The terms "first" and "second" in the specification and claims of the present 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. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes unlisted steps or units, or optionally also includes Other steps or units inherent to these processes, methods, products or equipment.

Reference to "embodiments" herein means that a specific feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art clearly and implicitly understand that the embodiments described herein can be combined with other embodiments.

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).

Please refer to FIG. 1, which is a schematic structural diagram of a liver tumor and blood vessel analysis system 100 based on VRDS 4D medical imaging according to an embodiment of this application. The system 100 includes a medical imaging device 110 and a network database 120. The imaging device 110 may include a local medical imaging device 111 and/or a terminal medical imaging device 112. The local medical imaging device 111 or the terminal medical imaging device 112 is used for the VRDS 4D medical image presented in the embodiment of the present application based on the original DICOM data. Liver tumor and blood vessel analysis algorithm is based on the recognition, positioning, four-dimensional volume rendering, and abnormal analysis of liver tumor and blood vessels to realize the four-dimensional imaging effect (the four-dimensional medical image specifically refers to the internal spatial structure of the displayed tissue. Features and external spatial structure features. The internal spatial structure features refer to that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of tissues such as liver tumors and blood vessels. The external spatial structure feature refers to the relationship between the tissue and the tissue. The environmental characteristics of the local medical imaging device 111 relative to the terminal, including the spatial location characteristics between tissues (including crossing, spacing, fusion), etc., such as the edge structure characteristics of the crossing position between the liver and other organs and blood vessels, etc.) The medical imaging device 112 can also be used to edit the image source data to form the transfer function result of the four-dimensional human body image. The transfer function result may include the transfer function result of the liver tumor and blood vessel structure, and the transfer function result of the cube space, such as transfer function result. The cube edit box and arc edit array quantity, coordinates, color, transparency and other information needed by the function. The network database 120 may be, for example, a cloud server. 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 image source may be from multiple sources. A local medical imaging device 111 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 the liver, render real-time clipping effects, and (5) move the view up and down.

The following is a detailed introduction to the VRDS AI-based liver tumor and blood vessel analysis methods involved in the embodiments of the present application.

Please refer to FIG. 2, which is a schematic flowchart of an embodiment of a method for analyzing liver tumors and blood vessels based on VRDS AI according to an embodiment of this application. The VRDS AI-based liver tumor and blood vessel analysis method described in this embodiment includes the following steps:

201. Acquire a scanned image of the liver of a target user, where the scanned image includes a liver tumor image and the liver blood vessel image.

The above-mentioned target user may be any user or patient, and the above-mentioned liver scan image may include any of the following: CT image, MRI image, DTI image, PET-CT image, etc., which are not limited herein. The medical imaging device may acquire a scanned image of the liver reflecting the internal structure of the liver of the target user.

202. Generate an image data set of the liver tumor and an image data set of the liver blood vessel according to the scanned image, the image data set of the liver blood vessel including an image data set of hepatic arteries and an image data set of hepatic veins .

Among them, the scanned image of the liver collected by the medical imaging device can be input into the VRDS (Virtual Reality Doctor system, VRDS) system to obtain the liver 4D image data of the target user. The liver 4D image data includes the internal spatial structure characteristics of the liver. And the structural characteristics of the external space.

Optionally, processing the scanned image of the liver to obtain 4D image data of the liver of the target user includes: performing a first preset processing on the scanned image of the liver to obtain a bitmap BMP data source; The BMP data source is imported into the preset VRDS medical network model to obtain first medical image data. The first medical image data includes a liver data set and a blood vessel data set. The liver data set includes a liver tumor data set; A medical image data is imported into a preset cross blood vessel network model to obtain second medical image data, where the second medical image data includes a blood vessel data set; performing a second preset processing on the second medical image data to obtain the target 4D image, the 4D image data includes: liver 4D image data and blood vessel data set. The first preset processing may include at least one of the following operations: VRDS restricted contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing, etc., which are not limited here; the medical imaging device may be preset VRDS medical network model, the medical imaging device obtains the BMP data source by processing the scanned image data of the liver, which increases the amount of information of the original data, and increases the depth dimension information, and finally obtains data that meets the requirements of 4D medical image display.

In specific implementation, the medical imaging device imports the above-mentioned BMP data source into the preset VRDS medical network model, and can call each transfer function in the pre-stored transfer function set through the VRDS medical network model, and pass multiple transfer functions in the transfer function set. The function processes the above-mentioned BMP data source to obtain the first medical image data. The above-mentioned transfer function set may include the transfer function of the blood vessel and the transfer function of the liver preset by the reverse editor. In addition, the transfer function of the blood vessel may include: the transfer of the artery Function and the transfer function of the vein. In this way, the first medical image data obtained by the preset VRDS medical network model can improve the accuracy and efficiency of the obtained data.

Further, the medical imaging device may be preset with a cross-vessel network model, the preset cross-vessel network model may be a trained neural network model, and the above-mentioned first medical image data may be imported into the preset cross-vessel network model. The cross blood vessel network model is used for data segmentation, and the liver data set, artery data set and vein data set are obtained. The first data in the artery data set and the second data in the vein data set are independent of each other. The data associated with the intersection of arteries and blood vessels. The above second data is the data associated with the intersection of venous blood vessels. Finally, the second medical image data can be obtained. In this way, the data corresponding to the blood vessels can correspond to liver tumors through the cross-vessel network model. Data segmentation between the data.

Further, the foregoing second preset processing includes at least one of the following methods: 2D boundary optimization processing, 3D boundary optimization processing, data enhancement processing, etc., which are not limited here; the foregoing 2D boundary optimization processing includes the following operations: multiple sampling acquisition Low-resolution information and high-resolution information, where 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, and the segmentation target includes the liver and liver tumors. And liver blood vessels, liver blood vessels can include arteries and veins. These features are used to determine object categories. High-resolution information is used to provide more refined features for segmentation targets, such as gradients. Specifically, the medical imaging device can The second medical image is sampled multiple times to obtain low-resolution information and high-resolution information, so as to display the relationship between liver tumors and liver blood vessels in the second medical image. The multiple times may be preset times or Is the number of historical samples, etc. In this way, the target 4D image can be obtained by processing the above-mentioned second medical image data, and the target 4D image can include liver 4D image data and a blood vessel data set.

203. Determine the blood supply relationship between the liver tumor and the liver blood vessels according to the image data collection of the liver tumor and the image data collection of the liver blood vessels.

Among them, as shown in Figure 3, Figure 3 is a schematic diagram of the liver image, in which 301 is a liver tumor and 302 is a liver blood vessel. Liver tumors are generally supplied with blood by the hepatic artery, portal vein, and arterial collateral branches. Among them, the blood supply arteries of liver tumors are divided into central type, peripheral type, mixed type and low blood supply type. The portal vein is generally distributed around the liver tumor, with small branches extending to the center. Therefore, the internal spatial structure characteristics of the liver and liver tumors and the external spatial structure characteristics can be used to determine the blood supply relationship between the tumor and the blood vessels of the liver. The blood supply relationship also includes the blood supply vessels. The direction of blood flow, the size of blood supply, etc.

204. Perform 4D medical imaging on the image data collection of the liver tumor and the image data collection of the liver blood vessels to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor.

Wherein, the 4D medical imaging refers to presenting a 4-dimensional medical image.

It can be seen that, in the embodiment of the present application, the scan image of the liver of the target user is first obtained, and then the image data set of the liver tumor and the image data set of the liver blood vessels are generated according to the scanned image; secondly, the image data set of the liver tumor and the liver are generated. The blood vessel image data collection determines the blood supply relationship between liver tumors and liver blood vessels; 4D medical imaging is performed on the image data collection of liver tumors and the image data collection of liver blood vessels to output the location of liver tumors and the liver that has blood supply relationship with liver tumors Blood vessels. Determine the blood supply relationship between the liver tumor and the liver blood vessels by analyzing the image data collection of the liver tumor and the image data collection of the liver blood vessels, avoiding the inability to show the spatial structure of the tumor and blood vessels due to the two-dimensional scan image The problem of low blood supply relationship recognition efficiency caused by characteristics improves the accuracy and convenience of determining the blood supply relationship.

In a possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data collection of the liver tumor and the image data collection of the liver blood vessels includes: according to the image data collection of the liver Determine the location of the liver tumor of the target user by scanning images and the image data set of the liver tumor; determine the spatial coordinates of each image data in the image data set of the liver blood vessels; and determine the spatial coordinates of each image data The blood supply relationship between the liver tumor and the liver blood vessel is determined with the location of the liver tumor, and the blood supply relationship includes the absence of blood supply relationship and the existence of blood supply relationship.

Wherein, a spatial coordinate system is established based on the image data set of the liver tumor and the image data set of the liver blood vessels, the origin of the spatial coordinate system is any position of the liver, and the X axis and Y of the spatial coordinate system The axis and the Z axis are perpendicular to each other and follow the right-hand spiral rule; the 4D image data set of the liver, the 4D image data set of the liver tumor, and the 4D image data set of the liver blood vessels are obtained according to the scan image of the liver, and the 4D image data set of the liver is obtained The 4D image data set of the liver tumor and the spatial coordinate system determine the location of the liver tumor of the target user; then the blood supply relationship between the liver tumor and the liver blood vessel is determined according to the relative position of the liver tumor and the liver blood vessel, If the relative position is far away, there is no blood supply relationship between the liver tumor and the liver blood vessel, and if it is fusion or close proximity, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessel.

It can be seen that in this example, the medical imaging device analyzes the coordinate position between the liver tumor and the liver blood vessel through the liver image data collection, the liver tumor image data collection, and the liver blood vessel image data collection, so as to determine the liver tumor and the liver blood vessel. The blood supply relationship between the two improves the accuracy and convenience of determining the blood supply relationship between liver tumors and liver blood vessels.

In a possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the spatial coordinates of each image data and the position of the liver tumor includes: traversing each image According to the spatial coordinates of the data, a plurality of first target spatial position data corresponding to the liver tumor and a plurality of second target spatial position data corresponding to the liver vessel are obtained; according to the plurality of first target spatial position data and the A plurality of second target spatial position data determine the blood supply relationship between the liver tumor and the liver blood vessel.

Wherein, the second target spatial position data includes the spatial position data of the liver artery and the spatial position data of the liver vein. The blood supply relationship is determined according to the overlap rate of the plurality of first target spatial position data and the plurality of second target spatial position data.

In specific implementation, it can be detected whether the first target spatial position data and the second target spatial position data include the same data; if so, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessel; if Otherwise, it is determined that there is no blood supply relationship between the liver tumor and the liver blood vessels.

In specific implementation, starting from the origin of the spatial coordinate system, the positive and negative directions of the X-axis, the positive and negative directions of the Y-axis, and the positive direction of the Z-axis of the spatial coordinate system are respectively along the preset distances. The detection is performed in the opposite direction. Whenever the gray value corresponding to the first pixel is detected to belong to the gray value corresponding to the liver tumor data, the first target spatial position data corresponding to the first pixel is recorded, and whenever it is detected When the gray value corresponding to the second pixel does not belong to the gray value corresponding to the liver tumor data and the gray value corresponding to the adjacent pixel of the second pixel belongs to the gray value corresponding to the liver blood vessel data, record all The second target space position data corresponding to the second pixel; the image data is segmented according to all the first target space position data and all the second target space position data to obtain the liver The positional relationship between the tumor and the blood vessels in the liver.

It can be seen that in this example, the medical imaging device can comprehensively analyze the first target spatial position data of liver tumors and the second target spatial position data of liver blood vessels, and determine the blood supply according to the first target spatial position data and the second target spatial position data. Relationship, improve the comprehensiveness and accuracy of the diagnosis and analysis of the blood supply of liver tumors.

In a possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data includes: determining The coincidence rate of the plurality of first target spatial position data and the plurality of second target spatial position data; when the coincidence rate is a preset threshold, it is determined that there is no relationship between the liver tumor and the liver blood vessel Blood supply relationship; when the coincidence rate is greater than a preset threshold, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessels.

Wherein, the overlap rate of the position data of the liver tumor and the liver blood vessel is determined according to the plurality of first target spatial position data and the plurality of second target spatial position data, wherein, when the first target spatial position data is When the coordinates of the multiple second target space position data are completely the same, it is coincident. When the ratio of the overlapped spatial position data to all the first target spatial position data of the liver tumor is greater than a preset threshold, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessels. It is also possible to determine the adjacent relationship or the covering relationship between the liver tumor and the liver blood vessel based on the first target spatial position data and the second target spatial position data.

In a possible example, when the coincidence rate is greater than a preset threshold, after determining that there is a blood supply relationship between the liver tumor and the liver blood vessel, the method further includes: according to the multiple first target spatial position data and The multiple second target spatial position data determine the positional relationship between the liver tumor and the liver vessel; determine the blood supply type of the liver vessel to the liver tumor according to the positional relationship between the liver tumor and the liver vessel .

Wherein, the blood supply type includes the blood supply of the liver artery to the liver tumor and the blood supply of the non-hepatic artery to the liver tumor, wherein the blood supply of the non-hepatic artery to the liver tumor includes the blood supply of the liver vein to the liver tumor As well as collateral blood supply and so on.

In specific implementation, the first position relationship between the liver tumor and the liver artery is determined according to the multiple first target spatial position data and the spatial position data of the liver artery, and the first position relationship between the liver tumor and the liver artery is determined according to the multiple first target spatial position data and the liver vein. The spatial position data determines the second positional relationship between the liver tumor and the liver vein, and then determines the blood supply relationship between the liver tumor and the liver vessel according to the first positional relationship and the second positional relationship, Among them, the first positional relationship and the second positional relationship can comprehensively and accurately determine the blood supply relationship between the liver tumor and the liver blood vessels, thereby providing information support. The first positional relationship and the second positional relationship include coincidence, adjacent Or stay away. When the positional relationship is coincident, the blood supply type is central or mixed, when the positional relationship is adjacent, the blood supply type is peripheral, and when the positional relationship is far away, there is no blood supply relationship.

In a possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the spatial coordinates of each image data and the position of the liver tumor includes: according to each image data Obtain the gray value of the liver tumor and the gray value of the liver blood vessel; establish the gray three-dimensional space region corresponding to the spatial position data of the liver tumor according to the gray value of the liver tumor; The three-dimensional space region is divided into N layers of subspaces from top to bottom, where N is a positive integer greater than 1. Traverse the N layers of subspaces, and detect that the gray values corresponding to the image data pixels in each layer belong to the When the gray value of the liver blood vessels is used, the spatial coordinates corresponding to the pixel points of the image data are recorded; the blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates corresponding to the pixel points of the image data.

Among them, due to the different sizes of liver tumors, the diameter is generally 2mm to 20cm, so it is difficult to analyze the blood vessels in liver tumors. Corresponding can be established based on the multiple spatial position data corresponding to liver tumors and the gray value of liver tumors. The gray-scale three-dimensional space area, through which the position of the blood supply vessel in the liver tumor can be located. For example, the gray-scale three-dimensional space area is divided into N layers of subspaces from top to bottom, and each layer of subspace can be Corresponding to the tissue structure of one layer of liver tumor, the spatial position data of each layer of the above-mentioned N-layer subspace can be traversed, and the number of spatial layers corresponding to the liver tumor can be accurately located for each layer of liver tumor texture, and the Whether the blood supply vessels and the corresponding positions are included in the number of spatial layers is beneficial to improve the accuracy of locating liver vessels.

In a possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data collection of the liver tumor and the image data collection of the liver blood vessels includes: according to the liver tumor To identify the type of the liver tumor; query the preset blood supply relationship set to determine the blood supply vessel corresponding to the liver tumor, the blood supply relationship set includes the corresponding relationship between the type of the liver tumor and the blood supply vessel Determine the image data of the blood supply vessel according to the image data collection of the blood supply vessel and the liver blood vessel; determine the blood supply relationship according to the image data of the blood supply vessel and the image data set of the liver tumor.

Wherein, the type of the liver tumor is identified according to the image data set of the liver tumor, the image data of the liver tumor can be input into a preset liver tumor recognition model to obtain the type of the liver tumor; and the liver is queried according to the liver tumor type The blood supply vessels of tumors, for example, liver hemangioma consists of blood-filled sinusoids, and its blood supply vessels are hepatic artery and portal vein; focal nodular hyperplasia is a centrifugal blood supply, with one or more blood supply arteries from the center of the lesion to the surroundings Distributed radially and so on. Determine the image data of the blood supply vessels according to the image data set of the blood supply vessels and the liver vessels, for example, the blood supply vessels are the hepatic artery and the portal vein, determine the image data of the hepatic artery and the portal vein in the image data set of the liver vessels, and then according to The image data of hepatic artery and portal vein and the image data of liver tumor are set to determine the blood supply relationship.

It can be seen that, in this example, the medical imaging device determines the blood supply vessel of the liver tumor based on the liver tumor type, and then obtains the blood supply vessel imaging data, and then determines the blood supply relationship through the blood supply vessel imaging data and the imaging data of the liver tumor, which improves the determination of liver tumors. The accuracy and convenience of the blood supply relationship with the blood vessels of the liver.

In a possible example, the determining the blood supply relationship based on the image data of the blood supply vessel and the image data set of the liver tumor includes: performing segmentation processing on the blood supply vessel according to the image data of the blood supply vessel to obtain multiple segments Blood supply vessel segment; determine the connection relationship between each blood supply vessel segment and the liver tumor according to the image data collection of the blood supply vessel and the image data of the liver tumor; determine the blood supply relationship according to the connection relationship.

Wherein, the connection relationship includes direct connection, indirect connection, and no connection. The direct connection may be the fusion of blood supply vessels and penetrates through the liver tumor. The direct connection also includes the connection position, the connection angle, etc., the indirect connection may be The coating, surrounding, and the outside of the liver tumor supply blood to the liver tumor. The direct connection and indirect connection both indicate that there is a blood supply relationship between the liver tumor and the blood vessels of the liver, and the absence of connection means that there is no blood supply relationship. Assign a label value different from the gray value of the liver tumor to each segment of the blood supply blood vessel segment, and determine the relationship between each blood supply blood vessel segment and the liver tumor according to the label value and the liver tumor gray value. The connection relationship between each blood supply vessel segment and the liver tumor can be simultaneously detected, or the connection relationship between each blood supply vessel segment and the liver tumor can be sequentially detected according to the blood flow direction. The connection relationship and the blood supply relationship between the blood supply vessel and the liver tumor are determined according to the connection relationship between each blood supply vessel segment and the liver tumor.

Further, query the TACE strategy according to the blood supply relationship to determine the TACE injection point, the TACE strategy includes the corresponding relationship between the blood supply relationship and the TACE injection point; output the image data of the TACE injection point, wherein determining the TACE injection point includes: according to the blood supply The relationship determines the injection point. The blood supply relationship includes the existence of the blood supply relationship, the absence of the blood supply relationship, and the blood flow direction and blood supply volume of the blood supply vessel when the blood supply relationship exists. The blood supply relationship can be input into the preset neural network model to obtain the injection point. Or according to the relative distance between the liver tumor and the blood vessel of the liver, determine the resection range of the liver tumor, and design and plan the virtual operation plan.

It can be seen that in this example, the medical imaging device performs segmentation processing on the blood supply vessels through the blood supply blood vessel image data, and then determines the connection relationship through the image data of each blood supply vessel and the liver tumor, which improves the determination of the relationship between the liver tumor and the liver blood vessel. The accuracy and convenience of the blood supply relationship.

In a possible example, the generating the image data set of the liver tumor and the image data set of the liver blood vessel according to the scan image includes: performing a first preset processing on the scan image to obtain a bitmap BMP Data source; import the BMP data source into a preset VRDS medical network model to obtain first medical image data, the first medical image data includes a liver data set and a liver tumor data set; the first medical image data Importing a preset cross blood vessel network model to obtain second medical image data, where the second medical image data includes a liver blood vessel data set; performing a second preset processing on the second medical image data to obtain the image data set, The image data set includes liver 4D image data, liver tumor 4D image data, and blood vessel data set.

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 problem of the overall separation of veins in the medical field improves the authenticity, comprehensiveness and refinement of medical image display.

Consistent with the above, please refer to FIG. 4, which is a schematic structural diagram of a medical imaging apparatus 400 provided by an embodiment of this application. As shown in the figure, the medical imaging apparatus 400 includes a processor 410, a memory 420, a communication interface 430, and One or more programs 421, wherein the one or more programs 421 are stored in the above-mentioned memory 420 and are configured to be executed by the above-mentioned processor 410, and the one or more programs 421 include: instruction:

It can be seen that through the medical imaging device provided in the embodiments of the present application, the scanned image of the liver of the target user can be obtained through VRDS 4D imaging technology, and then the image data set of liver tumor and the image data set of liver blood vessels are generated based on the scanned image. ; Secondly, determine the blood supply relationship between liver tumor and liver blood vessels according to the image data collection of liver tumors and liver blood vessels; perform 4D medical imaging on the image data collection of liver tumors and the image data collection of liver blood vessels to output the liver The location of the tumor and the blood vessels of the liver that have a blood supply relationship with the liver tumor. Determine the blood supply relationship between the liver tumor and the liver blood vessels by analyzing the image data collection of the liver tumor and the image data collection of the liver blood vessels, avoiding the inability to show the spatial structure of the tumor and blood vessels due to the two-dimensional scan image The problem of low blood supply relationship recognition efficiency caused by characteristics improves the accuracy and convenience of determining the blood supply relationship.

In a possible example, in the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels, the program further includes Instructions for performing the following operations: determine the location of the liver tumor of the target user according to the scanned image of the liver and the image data set of the liver tumor; determine the value of each image data in the image data set of the liver blood vessel Spatial coordinates; determining the blood supply relationship between the liver tumor and the liver blood vessels according to the space coordinates of each image data and the location of the liver tumor, and the blood supply relationship includes the absence of blood supply relationship and the existence of blood supply relationship.

In a possible example, in terms of determining the blood supply relationship between the liver tumor and the liver blood vessel based on the spatial coordinates of each image data and the location of the liver tumor, the program further includes using Instructions for performing the following operations:

By traversing the spatial coordinates of each image data, a plurality of first target spatial position data corresponding to the liver tumor and a plurality of second target spatial position data corresponding to the liver blood vessel are obtained;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the plurality of first target spatial position data and the plurality of second target spatial position data.

In a possible example, in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data, The program also includes instructions for performing the following operations:

Determining the coincidence rate of the plurality of first target spatial position data and the plurality of second target spatial position data;

When the coincidence rate is a preset threshold, it is determined that there is no blood supply relationship between the liver tumor and the liver blood vessels;

When the coincidence rate is greater than a preset threshold, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessels.

In a possible example, based on the abnormal data, the program further includes instructions for performing the following operations: when the coincidence rate is greater than a preset threshold, determine the difference between the liver tumor and the liver blood vessel After there is a blood supply relationship, determine the position relationship between the liver tumor and the liver blood vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data;

The blood supply type of the liver tumor to the liver tumor is determined according to the positional relationship between the liver tumor and the liver blood vessel.

Acquiring the gray value of the liver tumor and the gray value of the liver blood vessel according to each of the image data;

Establishing a gray-scale three-dimensional space region corresponding to the spatial position data of the liver tumor according to the gray value of the liver tumor;

Dividing the gray-scale three-dimensional space region from top to bottom into N layers of subspaces, where N is a positive integer greater than 1;

Traversing the N layers of subspace, and when it is detected that the gray value corresponding to the image data pixel in each layer belongs to the gray value of the liver blood vessel, recording the spatial coordinates corresponding to the image data pixel;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates corresponding to the pixel points of the image data.

In a possible example, in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels, the program further Include instructions to perform the following actions:

Identifying the type of the liver tumor according to the image data set of the liver tumor;

Query a preset blood supply relationship set to determine the blood supply vessel corresponding to the liver tumor, the blood supply relationship set including the correspondence between the type of the liver tumor and the blood supply vessel;

Determining the image data of the blood supply vessel according to the image data collection of the blood supply vessel and the liver vessel;

The blood supply relationship is determined according to the image data of the blood supply vessel and the image data collection of the liver tumor.

In a possible example, in terms of determining the blood supply relationship based on the image data of the blood supply vessel and the image data set of the liver tumor, the program further includes instructions for performing the following operations:

Performing segment processing on the blood supply vessel according to the image data of the blood supply vessel to obtain multiple blood supply vessel segments;

Determining the connection relationship between each blood supply vessel segment and the liver tumor according to the image data collection of the blood supply vessel and the image data of the liver tumor;

The blood supply relationship is determined according to the connection relationship.

In a possible example, in terms of generating the image data set of the liver tumor and the image data set of the liver blood vessel according to the scanned image, the program further includes instructions for performing the following operations:

Perform 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, where the first medical image data includes a liver data set and a liver tumor data set;

Importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, where the second medical image data includes a liver blood vessel data set;

The second preset processing is performed on the second medical image data to obtain the image data set, and the image data set includes liver 4D image data, liver tumor 4D image data, and blood vessel data set.

Consistent with the above, the following is a device that implements the above VRDS AI-based liver tumor and blood vessel analysis method, and the details are as follows:

Please refer to FIG. 5, which is a schematic structural diagram of an embodiment of a liver tumor and blood vessel analysis device based on VRDS AI provided by an embodiment of this application. The VRDS AI-based liver tumor and blood vessel analysis device described in this embodiment includes: an acquisition unit 501, a processing unit 502, a determination unit 503, and an extraction unit 504, which are specifically as follows:

The obtaining unit 501 is configured to obtain a scanned image of the liver of a target user, the scanned image including a liver tumor image and the liver blood vessel image;

The processing unit 502 is configured to generate an image data set of the liver tumor and an image data set of the liver blood vessel according to the scan image, and the image data set of the liver blood vessel includes an image data set of liver arteries and blood vessels and hepatic veins. Image data collection;

The determining unit 503 is configured to determine the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels;

The extraction unit 504 is configured to perform 4D medical imaging on the image data set of the liver tumor and the image data set of the liver blood vessels to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor.

It can be seen that through the VRDS AI-based liver tumor and blood vessel analysis device described in the embodiments of this application, the scanned image of the liver of the target user can be first obtained through the VRDS 4D imaging technology, and then the image data set of the liver tumor is generated based on the scanned image. And the image data collection of liver blood vessels; secondly, the blood supply relationship between liver tumor and liver blood vessels is determined according to the image data collection of liver tumors and liver blood vessels; the image data collection of liver tumors and the image data collection of liver blood vessels are performed 4D medical imaging to output the location of liver tumors and the blood vessels of the liver that have a blood supply relationship with the liver tumors. Determine the blood supply relationship between the liver tumor and the liver blood vessels by analyzing the image data collection of the liver tumor and the image data collection of the liver blood vessels, avoiding the inability to show the spatial structure of the tumor and blood vessels due to the two-dimensional scan image The problem of low blood supply relationship recognition efficiency caused by characteristics improves the accuracy and convenience of determining the blood supply relationship.

In a possible example, in terms of determining the blood supply relationship between the liver tumor and the liver blood vessel based on the image data set of the liver tumor and the image data set of the liver blood vessel, the determining unit 503 Specifically used for:

Determining the location of the liver tumor of the target user according to the scanned image of the liver and the image data set of the liver tumor;

Determining the spatial coordinates of each image data in the image data set of the liver blood vessels;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates of each image data and the location of the liver tumor, and the blood supply relationship includes no blood supply relationship and a blood supply relationship.

In a possible example, in terms of determining the blood supply relationship between the liver tumor and the liver blood vessel according to the spatial coordinates of each image data and the position of the liver tumor, the determining unit 503 specifically further Used for:

The blood supply relationship between the liver tumor and the liver blood vessel is determined according to the plurality of first target spatial position data and the plurality of second target spatial position data.

In a possible example, in the aspect of determining the blood supply relationship between the liver tumor and the liver vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data, The determining unit 503 is specifically configured to:

In a possible example, when the coincidence rate is greater than a preset threshold, after determining that there is a blood supply relationship between the liver tumor and the liver blood vessel, the determining unit 503 is specifically further configured to:

Determining the positional relationship between the liver tumor and the liver blood vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data;

In a possible example, in terms of determining the blood supply relationship between the liver tumor and the liver blood vessel based on the image data set of the liver tumor and the image data set of the liver blood vessel, the determining unit 503 Specifically also used for:

In a possible example, in the aspect of determining the blood supply relationship based on the image data of the blood supply vessel and the image data set of the liver tumor, the processing unit 502 is specifically configured to:

In a possible example, in the aspect of generating the image data set of the liver tumor and the image data set of the liver blood vessel according to the scan image, the processing unit 502 is specifically configured to:

It is understandable that because the embodiment of the liver tumor and blood vessel analysis method based on VRDS AI and the embodiment of the liver tumor and blood vessel analysis device based on VRDS AI are different presentation forms of the same technical concept, therefore, the method embodiment part of this application The content should be synchronously adapted to the device embodiment part, and will not be repeated here.

An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes the computer to execute any VRDS AI-based liver described in the above method embodiment. Part or all of the steps in tumor and blood vessel analysis methods.

The embodiments of the present application also provide a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to execute the method described in the foregoing method embodiment. Part or all of the steps of any liver tumor and blood vessel analysis method based on VRDS AI.

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 limited by the described sequence of actions. Because according to this application, some steps can be performed in other order or at the same time. 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 may be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units 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 units, and may be in electrical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be realized in the form of hardware or software program module.

If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer readable memory. Based on this understanding, the technical solution of the present application essentially or the part that contributes to the existing technology 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 memory. A number of instructions are included to enable a computer device (which may be a personal computer, a server, 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 foregoing memory includes: U disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable memory, and the memory can include: a flash disk , ROM, RAM, magnetic disk or CD, etc.

The embodiments of the application are described in detail above, and specific examples are used in this article to illustrate the principles and implementation of the application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the application; at the same time, for Those of ordinary skill in the art, based on the ideas of the application, will have changes in the specific implementation and the scope of application. In summary, the content of this specification should not be construed as limiting the application.

Claims

A method for analyzing liver tumors and blood vessels based on VRDS AI, which is characterized in that it is applied to a medical imaging device and includes:

Acquiring a scanned image of the liver of the target user, the scanned image including a liver tumor image and the liver blood vessel image;

Generating an image data set of the liver tumor and an image data set of the liver blood vessel according to the scan image, the image data set of the liver blood vessel including an image data set of the arteries of the liver and an image data set of the veins of the liver;

Determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data collection of the liver tumor and the image data collection of the liver blood vessels;

Perform 4D medical imaging on the image data collection of the liver tumor and the image data collection of the liver blood vessels to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor.
The method according to claim 1, wherein the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels comprises :

Determining the location of the liver tumor of the target user according to the scanned image of the liver and the image data set of the liver tumor;

Determining the spatial coordinates of each image data in the image data set of the liver blood vessels;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates of each image data and the location of the liver tumor, and the blood supply relationship includes no blood supply relationship and a blood supply relationship.
The method according to claim 2, wherein the determining the blood supply relationship between the liver tumor and the liver blood vessel according to the spatial coordinates of each image data and the location of the liver tumor comprises:

By traversing the spatial coordinates of each image data, a plurality of first target spatial position data corresponding to the liver tumor and a plurality of second target spatial position data corresponding to the liver blood vessel are obtained;

The blood supply relationship between the liver tumor and the liver blood vessel is determined according to the plurality of first target spatial position data and the plurality of second target spatial position data.
The method according to claim 3, wherein the determination of the distance between the liver tumor and the liver blood vessel is based on the plurality of first target spatial position data and the plurality of second target spatial position data. Blood supply relationship, including:

Determining the coincidence rate of the plurality of first target spatial position data and the plurality of second target spatial position data;

When the coincidence rate is a preset threshold, it is determined that there is no blood supply relationship between the liver tumor and the liver blood vessels;

When the coincidence rate is greater than a preset threshold, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessels.
The method according to claim 4, wherein when the coincidence rate is greater than a preset threshold, after determining that there is a blood supply relationship between the liver tumor and the liver blood vessel, the method further comprises:

Determining the positional relationship between the liver tumor and the liver blood vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data;

The blood supply type of the liver tumor to the liver tumor is determined according to the positional relationship between the liver tumor and the liver blood vessel.
The method according to claim 2, wherein the determining the blood supply relationship between the liver tumor and the liver blood vessel according to the spatial coordinates of each image data and the location of the liver tumor comprises:

Acquiring the gray value of the liver tumor and the gray value of the liver blood vessel according to each of the image data;

Establishing a gray-scale three-dimensional space region corresponding to the spatial position data of the liver tumor according to the gray value of the liver tumor;

Dividing the gray-scale three-dimensional space region from top to bottom into N layers of subspaces, where N is a positive integer greater than 1;

Traversing the N layers of subspace, and when it is detected that the gray value corresponding to the image data pixel in each layer belongs to the gray value of the liver blood vessel, recording the spatial coordinates corresponding to the image data pixel;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates corresponding to the pixel points of the image data.
The method according to claim 1, wherein the determining the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels comprises :

Identifying the type of the liver tumor according to the image data set of the liver tumor;

Query a preset blood supply relationship set to determine the blood supply vessel corresponding to the liver tumor, the blood supply relationship set including the correspondence between the type of the liver tumor and the blood supply vessel;

Determining the image data of the blood supply vessel according to the image data collection of the blood supply vessel and the liver vessel;

The blood supply relationship is determined according to the image data of the blood supply vessel and the image data collection of the liver tumor.
The method according to claim 7, wherein the determining the blood supply relationship based on the image data of the blood supply vessel and the image data set of the liver tumor comprises:

Performing segment processing on the blood supply vessel according to the image data of the blood supply vessel to obtain multiple blood supply vessel segments;

Determining the connection relationship between each blood supply vessel segment and the liver tumor according to the image data collection of the blood supply vessel and the image data of the liver tumor;

The blood supply relationship is determined according to the connection relationship.
The method according to claim 1, wherein said generating an image data set of the liver tumor and an image data set of the liver blood vessel according to the scan image comprises:

Perform 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, where the first medical image data includes a liver data set and a liver tumor data set;

Importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, where the second medical image data includes a liver blood vessel data set;

The second preset processing is performed on the second medical image data to obtain the image data set, and the image data set includes liver 4D image data, liver tumor 4D image data, and blood vessel data set.
A liver tumor and blood vessel analysis device based on VRDS AI, which is characterized in that it is applied to a medical imaging device, and the device includes:

An acquiring unit, configured to acquire a scanned image of the liver of the target user, the scanned image including a liver tumor image and the liver blood vessel image;

The processing unit is configured to generate an image data set of the liver tumor and an image data set of the liver blood vessel according to the scan image. The image data set of the liver blood vessel includes the image data set of the liver arteries and the blood vessels of the liver. Image data collection;

A determining unit, configured to determine the blood supply relationship between the liver tumor and the liver blood vessels according to the image data set of the liver tumor and the image data set of the liver blood vessels;

The extraction unit is configured to perform 4D medical imaging on the image data collection of the liver tumor and the image data collection of the liver blood vessels to output the location of the liver tumor and the liver blood vessels that have a blood supply relationship with the liver tumor.
The device according to claim 10, wherein in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessel based on the image data set of the liver tumor and the image data set of the liver blood vessel , The determining unit is specifically configured to:

Determining the location of the liver tumor of the target user according to the scanned image of the liver and the image data set of the liver tumor;

Determining the spatial coordinates of each image data in the image data set of the liver blood vessels;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates of each image data and the location of the liver tumor, and the blood supply relationship includes no blood supply relationship and a blood supply relationship.
The apparatus according to claim 11, wherein in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessel according to the spatial coordinates of each image data and the position of the liver tumor, The determining unit is also specifically used for:

By traversing the spatial coordinates of each image data, a plurality of first target spatial position data corresponding to the liver tumor and a plurality of second target spatial position data corresponding to the liver blood vessel are obtained;

The blood supply relationship between the liver tumor and the liver blood vessel is determined according to the plurality of first target spatial position data and the plurality of second target spatial position data.
The apparatus according to claim 12, wherein the determination is made between the liver tumor and the liver blood vessel based on the plurality of first target spatial position data and the plurality of second target spatial position data. In terms of blood supply relationship, the determining unit is specifically configured to:

Determining the coincidence rate of the plurality of first target spatial position data and the plurality of second target spatial position data;

When the coincidence rate is a preset threshold, it is determined that there is no blood supply relationship between the liver tumor and the liver blood vessels;

When the coincidence rate is greater than a preset threshold, it is determined that there is a blood supply relationship between the liver tumor and the liver blood vessels.
The device according to claim 13, wherein when the coincidence rate is greater than a preset threshold, after determining that there is a blood supply relationship between the liver tumor and the liver blood vessel, the determining unit is specifically further configured to:

Determining the positional relationship between the liver tumor and the liver blood vessel according to the plurality of first target spatial position data and the plurality of second target spatial position data;

The blood supply type of the liver tumor to the liver tumor is determined according to the positional relationship between the liver tumor and the liver blood vessel.
The device according to claim 11, characterized in that, in terms of determining the blood supply relationship between the liver tumor and the liver blood vessel according to the spatial coordinates of each image data and the position of the liver tumor, The determining unit is also specifically used for:

Acquiring the gray value of the liver tumor and the gray value of the liver blood vessel according to each of the image data;

Establishing a gray-scale three-dimensional space region corresponding to the spatial position data of the liver tumor according to the gray value of the liver tumor;

Dividing the gray-scale three-dimensional space region from top to bottom into N layers of subspaces, where N is a positive integer greater than 1;

Traversing the N layers of subspace, and when it is detected that the gray value corresponding to the image data pixel in each layer belongs to the gray value of the liver blood vessel, recording the spatial coordinates corresponding to the image data pixel;

The blood supply relationship between the liver tumor and the liver blood vessels is determined according to the spatial coordinates corresponding to the pixel points of the image data.
The device according to claim 10, wherein in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessel based on the image data set of the liver tumor and the image data set of the liver blood vessel , The determining unit is specifically further used for:

Identifying the type of the liver tumor according to the image data set of the liver tumor;

Query a preset blood supply relationship set to determine the blood supply vessel corresponding to the liver tumor, the blood supply relationship set including the correspondence between the type of the liver tumor and the blood supply vessel;

Determining the image data of the blood supply vessel according to the image data collection of the blood supply vessel and the liver vessel;

The blood supply relationship is determined according to the image data of the blood supply vessel and the image data collection of the liver tumor.
The apparatus according to claim 10, wherein, in the aspect of determining the blood supply relationship based on the image data of the blood supply vessel and the image data set of the liver tumor, the processing unit is specifically configured to:

Performing segment processing on the blood supply vessel according to the image data of the blood supply vessel to obtain multiple blood supply vessel segments;

Determining the connection relationship between each blood supply vessel segment and the liver tumor according to the image data collection of the blood supply vessel and the image data of the liver tumor;

The blood supply relationship is determined according to the connection relationship.
The apparatus according to claim 17, wherein, in terms of generating the image data collection of the liver tumor and the image data collection of the liver blood vessel according to the scan image, the processing unit is specifically configured to:

Perform 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, where the first medical image data includes a liver data set and a liver tumor data set;

Importing the first medical image data into a preset cross blood vessel network model to obtain second medical image data, where the second medical image data includes a liver blood vessel data set;

The second preset processing is performed on the second medical image data to obtain the image data set, and the image data set includes liver 4D image data, liver tumor 4D image data, and blood vessel data set.
A medical imaging device, characterized by comprising a processor, a memory, a communication interface, and one or more programs, the one or more programs are stored in the memory and configured to be executed by the processor The program includes instructions for executing the steps in the method according to any one of claims 1-9.
A computer-readable storage medium, characterized by storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the method according to any one of claims 1-9.