CN114286643A

CN114286643A - Liver tumor and blood vessel analysis method based on VRDS AI and related product

Info

Publication number: CN114286643A
Application number: CN201980099792.3A
Authority: CN
Inventors: 斯图尔特平·李; 戴维伟·李
Original assignee: Weiai Medical Technology Shenzhen Co ltd
Current assignee: Cao Sheng
Priority date: 2019-10-30
Filing date: 2019-10-30
Publication date: 2022-04-05
Also published as: WO2021081845A1

Abstract

A VRDS AI based liver tumor and blood vessel analysis method applied to a medical imaging device, comprising: acquiring a scan image (201) of a liver of a target user; generating an image data set of a liver tumor and an image data set of a liver blood vessel from the scan image (202); determining a blood supply relationship (203) between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel; 4D medical imaging is performed on the image data set of the liver tumor and the image data set of the liver blood vessels to output a liver tumor position and the liver blood vessels having a blood supply relationship with the liver tumor (204). Therefore, the problem of low blood supply relationship identification efficiency caused by the fact that the two-dimensional scanning image cannot show the space structure characteristics of the tumor and the blood vessel is solved, and the accuracy and convenience for determining the blood supply relationship are improved. Corresponding imaging devices and storable media are also disclosed.

Description

Liver tumor and blood vessel analysis method based on VRDS AI and related product

Technical Field

The application relates to the technical field of medical imaging devices, in particular to a liver tumor and blood vessel analysis method based on VRDS AI and a related product.

Background

Currently, doctors still use the view of continuous two-dimensional slice scan images, such as CT (computed tomography), MRI (magnetic resonance imaging), DTI (diffusion tensor imaging), PET (positron emission tomography), etc., to judge and analyze the pathological tissues, such as tumors, of patients. However, the specific location of the tumor cannot be determined by simply looking directly at the two-dimensional slice data, which seriously affects the diagnosis of the disease by the physician. With the rapid development of medical imaging technology, people put new demands on medical imaging.

Disclosure of Invention

The embodiment of the application provides a liver tumor and blood vessel analysis method based on VRDS AI and a related product, which are beneficial to improving the accuracy and convenience of determining blood supply relationship.

In a first aspect, an embodiment of the present application provides a VRDS AI-based liver tumor and blood vessel analysis method, applied to a medical imaging apparatus, including:

acquiring a scanned image of a liver of a target user, wherein the scanned image comprises 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 scanning image, wherein the image data set of the liver blood vessel comprises an image data set of a liver artery blood vessel and an image data set of a liver vein blood vessel;

determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel;

and 4D medical imaging is carried out on the image data set of the liver tumor and the image data set of the liver blood vessels so as to output the position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor.

In a second aspect, the present application provides a VRDS AI-based liver tumor and blood vessel analysis apparatus, applied to a medical imaging apparatus, the apparatus including:

an acquisition unit, configured to acquire a scan image of a liver of a target user, where the scan image includes a liver tumor image and the liver blood vessel image;

the processing unit is used for generating an image data set of the liver tumor and an image data set of the liver blood vessel according to the scanning image, wherein the image data set of the liver blood vessel comprises an image data set of a liver artery blood vessel and an image data set of a liver vein blood vessel;

the extraction unit is used for determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel;

the determining unit is used for carrying out 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 position of the liver tumor and the liver blood vessels which have blood supply relation 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, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.

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

It can be seen that, in the above technical solution, a scan image of a liver of a target user is obtained, and then an image data set of a liver tumor and an image data set of a liver blood vessel are generated according to the scan image; secondly, determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel; and 4D medical imaging is carried out on the image data set of the liver tumor and the image data set of the liver blood vessels so as to output the position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor. The blood supply relationship between the liver tumor and the liver blood vessel is determined by analyzing the image data set of the liver tumor and the image data set of the liver blood vessel, so that the problem of low blood supply relationship identification efficiency caused by the fact that a two-dimensional scanning image cannot show the space structure characteristics of the tumor and the blood vessel is solved, and the accuracy and convenience for determining the blood supply relationship are improved.

Drawings

Reference will now be made in brief to the drawings that are needed in describing embodiments or prior art.

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

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

fig. 2 is a schematic flow chart of a VRDS AI-based liver tumor and blood vessel analysis method according to an embodiment of the present application;

fig. 3 is a schematic diagram of a liver image according to an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a medical imaging apparatus provided in an embodiment of the present application;

fig. 5 is a schematic structural diagram of an embodiment of a liver tumor and blood vessel analysis device based on VRDS AI according to an embodiment of the present application.

Detailed Description

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

The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

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

Referring to fig. 1, a schematic structural diagram of a system 100 for analyzing liver tumor and blood vessel based on VRDS 4D medical image provided by an embodiment of the present application is shown, where the system 100 includes a medical imaging device 110 and a network database 120, where the medical imaging device 110 may include a local medical imaging device 111 and/or a terminal medical imaging device 112, and the local medical imaging device 111 or the terminal medical imaging device 112 is configured to perform identification, positioning, four-dimensional volume rendering, and anomaly analysis of liver tumor and blood vessel based on raw DICOM data and based on a liver tumor and blood vessel analysis algorithm of the VRDS 4D medical image presented by the embodiment of the present application, so as to achieve a four-dimensional stereo imaging effect (the 4-dimensional medical image specifically refers to a medical image including internal spatial structural features and external spatial structural features of a displayed tissue, the internal spatial structural features refer to slice data inside the tissue not lost, that is, the medical imaging device may present the internal structure of the tissues such as liver tumor and blood vessel, and the external spatial structural characteristics refer to the environmental characteristics between the tissues, including the spatial position characteristics (including intersection, spacing, fusion) between the tissues, and the like, the edge structural characteristics of the intersection position between the organs such as liver and blood vessel, and the like), the local medical imaging device 111 may also be used to edit the image source data with respect to the terminal medical imaging device 112, to form the transfer function result of the four-dimensional human body image, which may include the transfer function result of the liver tumor and blood vessel structure, and the transfer function result of the cubic space, such as the number of sets of cubic edit boxes and arc edits required by the transfer function, coordinates, colors, transparency, and the like. The network database 120 may be, for example, a cloud server, and the like, and the network database 120 is configured to store a map source generated by parsing the raw DICOM data and a transfer function result of the four-dimensional human body image edited by the local medical imaging apparatus 111, where the map source may be from a plurality of local medical imaging apparatuses 111 to implement interactive diagnosis of a plurality of doctors.

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

The following is a detailed description of the liver tumor and blood vessel analysis method based on VRDS AI according to the embodiment of the present application.

Please refer to fig. 2, which is a flowchart illustrating an embodiment of a VRDS AI-based liver tumor and blood vessel analysis method according to the present application. The VRDS AI-based liver tumor and blood vessel analysis method described in this example includes the following steps:

and 201, acquiring a scanning image of the liver of the target user, wherein the scanning image comprises a liver tumor image and the liver blood vessel image.

The target user may be any user or patient, and the scan image of the liver may include any one of: CT images, MRI images, DTI images, PET-CT images, etc., without limitation. The medical imaging device may acquire a scan image of the liver reflecting the internal structure of the liver of the target user.

And 202, generating an image data set of the liver tumor and an image data set of the liver blood vessel according to the scanning image, wherein the image data set of the liver blood vessel comprises an image data set of a liver artery blood vessel and an image data set of a liver vein blood vessel.

A VRDS (Virtual Reality vector system) system may be input for the scan image of the liver collected by the medical imaging device to obtain the liver 4D image data of the target user, where the liver 4D image data includes an internal spatial structure feature and an external spatial structure feature of the liver.

Optionally, processing the scanned image of the liver to obtain 4D image data of the liver of the target user includes: executing first preset processing on the scanned image of the liver to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a liver data set and a blood vessel data set, and the liver data set comprises 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, wherein the second medical image data comprises a blood vessel data set; executing a second preset process on the second medical image data to obtain the target 4D image, wherein the 4D image data includes: liver 4D image data and a blood vessel data set. Wherein the first preset processing may include at least one of the following operations: VRDS-limited contrast adaptive histogram equalization, hybrid partial differential de-noising, VRDS Ai elastic deformation processing, and the like, without limitation; the VRDS medical network model can be preset in the medical imaging device, the medical imaging device obtains a BMP data source by processing the scanned image data of the liver, the information amount of original data is improved, depth dimension information is increased, and data meeting the display requirement of 4D medical images are finally obtained.

In a specific implementation, the medical imaging apparatus imports the BMP data source into a preset VRDS medical network model, can call each transfer function in a pre-stored transfer function set through the VRDS medical network model, and processes the BMP data source through a plurality of transfer functions in the transfer function set to obtain first medical image data, where the transfer function set may include a transfer function of a blood vessel and a transfer function of a liver preset by a reverse editor, and in addition, the transfer function of the blood vessel may include: the transfer function of the artery and the transfer function of the vein are obtained, so that the first medical image data is obtained by presetting the VRDS medical network model, and the accuracy and the efficiency of obtaining the data can be improved.

Furthermore, a cross blood vessel network model can be preset in the medical imaging device, the preset cross blood vessel network model can be a trained neural network model, the first medical image data can be imported into the preset cross blood vessel network model, data segmentation can be performed through the cross blood vessel network model, a data set of the liver, an artery data set and a vein data set can be obtained, first data in the artery data set and second data in the vein data set are independent of each other, the first data are data associated with the cross position of an artery blood vessel, the second data are data associated with the cross position of a vein blood vessel, and finally, second medical image data can be obtained, so that data segmentation between data corresponding to the blood vessel and data corresponding to the liver tumor can be achieved through the cross blood vessel network model.

Further, the second preset processing includes at least one of the following methods: 2D boundary optimization processing, 3D boundary optimization processing, data enhancement processing, and the like, which are not limited herein; the 2D boundary optimization process includes the following operations: the method comprises the steps of obtaining low-resolution information and high-resolution information through multiple sampling, wherein the low-resolution information can provide context semantic information of a segmented target in an entire image, namely, characteristics reflecting the relationship between the target and the environment, the segmented target comprises the liver, a liver tumor, a liver blood vessel and the like, the liver blood vessel can comprise an artery and a vein, the characteristics are used for judging object types, the high-resolution information is used for providing more fine characteristics such as gradients and the like for the segmented target, specifically, the medical imaging device can conduct multiple sampling on the second medical image to obtain the low-resolution information and the high-resolution information so as to display the relationship between the liver tumor and the liver blood vessel in the second medical image, and the multiple sampling can be preset times, historical sampling times and the like. Thus, processing the second medical image data may result in a target 4D image, which may include liver 4D image data and a blood vessel data set.

203, determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel.

As shown in fig. 3, fig. 3 is a schematic diagram of a liver image, in which 301 is a liver tumor and 302 is a liver blood vessel. Liver tumors are generally supplied by hepatic artery, portal vein, arterial side branch, etc. Wherein, the blood supply artery of liver tumor is divided into central type, peripheral type, mixed type and blood supply lacking type. The portal vein is generally distributed around the liver tumor and extends to the center with tiny branches, so the blood supply relationship between the tumor and the liver blood vessel can be determined through the internal space structure characteristics and the external space structure characteristics of the liver and the liver tumor, and the blood supply relationship also comprises the blood flow direction in the blood supply blood vessel, the blood supply amount and the like.

And 204, carrying out 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 position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor.

Wherein the 4D medical imaging refers to presenting 4-dimensional medical images.

It can be seen that, in the embodiment of the present application, a scan image of a liver of a target user is obtained first, and then an image data set of a liver tumor and an image data set of a liver blood vessel are generated according to the scan image; secondly, determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel; and 4D medical imaging is carried out on the image data set of the liver tumor and the image data set of the liver blood vessels so as to output the position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor. The blood supply relationship between the liver tumor and the liver blood vessel is determined by analyzing the image data set of the liver tumor and the image data set of the liver blood vessel, so that the problem of low blood supply relationship identification efficiency caused by the fact that a two-dimensional scanning image cannot show the space structure characteristics of the tumor and the blood vessel is solved, and the accuracy and convenience for determining the blood supply relationship are improved.

In one possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel includes: determining the liver tumor position of the target user according to the scanned liver image and the image data set of the liver tumor; determining a spatial coordinate of each image data in the set of image data of the liver vessel; and determining blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate of each image data and the position of the liver tumor, wherein the blood supply relationship comprises no blood supply relationship and no blood supply relationship.

Establishing a space coordinate system according to the image data set of the liver tumor and the image data set of the liver blood vessel, wherein the origin of the space coordinate system is any position of the liver, and the X axis, the Y axis and the Z axis of the space coordinate system are mutually perpendicular and follow a right-hand spiral rule; obtaining a 4D image data set of a liver, a 4D image data set of a liver tumor and a 4D image data set of a liver blood vessel according to the scanned image of the liver, and determining the liver tumor position of the target user according to the 4D image data set of the liver, the 4D image data set of the liver tumor and the space coordinate system; and then determining the blood supply relationship between the liver tumor and the liver blood vessel according to the relative position of the liver tumor and the liver blood vessel, wherein if the relative position is far away, the blood supply relationship does not exist between the liver tumor and the liver blood vessel, and if the relative position is fusion or close, the blood supply relationship exists between the liver tumor and the liver blood vessel.

In this example, the medical imaging apparatus analyzes the coordinate position between the liver tumor and the liver blood vessel through the image data set of the liver, the image data set of the liver tumor, and the image data set of the liver blood vessel, so as to determine the blood supply relationship between the liver tumor and the liver blood vessel, thereby improving the accuracy and convenience of determining the blood supply relationship between the liver tumor and the liver blood vessel.

In one possible example, 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 liver tumor position includes: obtaining a plurality of first target space position data corresponding to the liver tumor and a plurality of second target space position data corresponding to the liver blood vessel by traversing the space coordinates of each image data; determining a blood supply relationship between the liver tumor and the liver blood vessel from the plurality of first target spatial location data and the plurality of second target spatial location data.

Wherein the second target spatial position data comprises spatial position data of a hepatic artery and spatial position data of a hepatic vein. And determining blood supply relation according to the coincidence rate of the plurality of first target space position data and the plurality of second target space position data.

In a specific implementation, whether the first target spatial position data and the second target spatial position data include the same data or not may be detected; if yes, determining that a blood supply relation exists between the liver tumor and the liver blood vessel; and if not, determining that the blood supply relationship does not exist between the liver tumor and the liver blood vessel.

In the specific implementation, starting from the origin of the spatial coordinate system, detection is performed along the positive direction and the negative direction of the X axis, the positive direction and the negative direction of the Y axis, and the positive direction and the negative direction of the Z axis of the spatial coordinate system according to preset distances, first target spatial position data corresponding to a first pixel point is recorded whenever a gray value corresponding to the first pixel point is detected to belong to a gray value corresponding to liver tumor data, and second target spatial position data corresponding to a second pixel point is recorded whenever a gray value corresponding to the second pixel point is detected not to belong to a gray value corresponding to liver tumor data and a gray value corresponding to an adjacent pixel point of the second pixel point belongs to a gray value corresponding to liver blood vessel data; and segmenting the image data according to all the first target space position data and all the second target space position data to obtain the position relation between the liver tumor and the liver blood vessel.

In this example, the medical imaging device can comprehensively analyze the first target spatial position data of the liver tumor and the second target spatial position data of the liver blood vessel, and determine the blood supply relationship according to the first target spatial position data and the second target spatial position data, so as to improve the comprehensiveness and accuracy of the blood supply diagnosis analysis of the liver tumor.

In one possible example, the determining a blood supply relationship between the liver tumor and the liver blood vessel from the plurality of first target spatial location data and the plurality of second target spatial location data comprises: determining a rate of coincidence of the first plurality of target spatial location data and the second plurality of target spatial location data; when the coincidence rate is a preset threshold value, determining that no blood supply relation exists between the liver tumor and the liver blood vessel; and when the coincidence rate is larger than a preset threshold value, determining that a blood supply relation exists between the liver tumor and the liver blood vessel.

Determining the coincidence rate of the position data of the liver tumor and the liver blood vessel according to the plurality of first target space position data and the plurality of second target space position data, wherein when the coordinates of the first target space position data and the plurality of second target space position data are completely the same, the coincidence is determined. Determining that a blood supply relationship exists between the liver tumor and the liver blood vessel when the ratio of the coincident spatial position data to all first target spatial position data of the liver tumor is greater than a preset threshold value. The adjacent relation or the coating relation of the liver tumor and the liver blood vessel can be determined according to the first target space position data and the second target space position data.

In one possible example, when the coincidence rate is greater than a preset threshold, after determining that a blood supply relationship exists between the liver tumor and the liver blood vessel, the method further includes: determining the position relation of the liver tumor and the liver blood vessel according to the plurality of first target space position data and the plurality of second target space position data; and determining the blood supply type of the liver blood vessel to the liver tumor according to the position relation of the liver tumor and the liver blood vessel.

Wherein the blood supply types comprise blood supply of liver artery to the liver tumor and blood supply of non-liver artery to the liver tumor, wherein the blood supply of non-liver artery to the liver tumor comprises blood supply of liver vein to the liver tumor, collateral blood supply and the like.

In specific implementation, a first position relation between the liver tumor and the liver artery is determined according to a plurality of first target space position data and space position data of the liver artery, a second position relation between the liver tumor and the liver vein is determined according to the plurality of first target space position data and space position data of the liver vein, and then a blood supply relation between the liver tumor and the liver blood vessel is determined according to the first position relation and the second position relation, wherein the blood supply relation between the liver tumor and the liver blood vessel can be comprehensively and accurately determined according to the first position relation and the second position relation, so that information support is provided, and the first position relation and the second position relation are overlapped, adjacent or far away. When the positional relationship is coincidence, the blood supply type is central type or mixed type, when the positional relationship is adjacent, the blood supply type is peripheral type, and when the positional relationship is distant, the blood supply relationship is not present.

In one possible example, 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 liver tumor position includes: acquiring the gray value of the liver tumor and the gray value of the liver blood vessel according to each image data; establishing a gray scale three-dimensional space region corresponding to the spatial position data of the liver tumor according to the gray scale value of the liver tumor; dividing the gray scale three-dimensional space region into N layers of subspaces from top to bottom, wherein N is a positive integer greater than 1; traversing the N layers of subspaces, and recording the spatial coordinates corresponding to the image data pixel points when detecting that the gray values corresponding to the image data pixel points in each layer belong to the gray values of the liver blood vessels; and determining the blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate corresponding to the image data pixel point.

The size of liver tumor is different, the diameter is generally 2 mm-20 cm, so the blood vessel in the liver tumor is difficult to analyze, a corresponding gray scale three-dimensional space area can be established according to a plurality of spatial position data corresponding to the liver tumor and the gray scale value of the liver tumor, the position of blood supply vessel in the liver tumor can be positioned through the space position area, for example, the gray scale three-dimensional space area is divided into N layers of subspaces from top to bottom, each layer of subspace can correspond to the tissue structure of one layer of liver tumor, the spatial position data of each layer of the N layers of subspaces can be traversed, the spatial layer number corresponding to the liver tumor can be accurately positioned according to the texture of each layer of liver tumor, and whether the spatial layer number contains the blood supply vessel and the corresponding position is determined, thereby being beneficial to improving the accuracy of positioning the liver vessel.

In one possible example, the determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel includes: identifying the type of the liver tumor according to the image data set of the liver tumor; inquiring a preset blood supply relationship set, and determining a blood supply vessel corresponding to the liver tumor, wherein the blood supply relationship set comprises the corresponding relationship between the type of the liver tumor and the blood supply vessel; determining image data of the blood supply vessel according to the image data sets of the blood supply vessel and the liver vessel; and determining blood supply relation according to the image data of the blood supply vessel and the image data set of the liver tumor.

The type of the liver tumor is identified according to the image data set of the liver tumor, and the image data of the liver tumor can be input into a preset liver tumor identification model to obtain the type of the liver tumor; inquiring blood supply vessels of the liver tumor according to the type of the liver tumor, wherein the liver tumor consists of blood sinuses full of blood, and the blood supply vessels are hepatic artery and portal vein; focal nodule proliferation is a centrifugal blood supply, and one or more blood supply arteries are radially distributed from the center of a lesion to the periphery. Determining image data of the blood supply vessel according to the image data sets of the blood supply vessel and the liver vessel, for example, the blood supply vessel is a hepatic artery and a portal vein, determining image data of the hepatic artery and the portal vein in the image data sets of the liver vessel, and further determining a blood supply relationship according to the image data of the hepatic artery and the portal vein and the image data sets of liver tumors.

In this example, the medical imaging device determines the blood supply vessel of the liver tumor according to the liver tumor type, and then obtains the blood supply vessel image data, and then determines the blood supply relationship according to the blood supply vessel image data and the liver tumor image data, thereby improving the accuracy and convenience of determining the blood supply relationship between the liver tumor and the liver vessel.

In one possible example, the determining a blood supply relationship from the image data of the blood supply vessel and the image data set of the liver tumor comprises: carrying out segmentation processing on the blood supply vessel according to the image data of the blood supply vessel to obtain a plurality of blood supply vessel segments; determining the connection relation between each blood supply vessel segment and the liver tumor according to the image data set of the blood supply vessel and the image data of the liver tumor; and determining blood supply relation according to the connection relation.

The connection relation comprises direct connection, indirect connection and no connection, the direct connection can be blood supply vessel fusion and run through the liver tumor, the direct connection also comprises a connection position, a connection angle and the like, the indirect connection can be cladding and surrounding, the liver tumor is externally supplied with blood for the liver tumor, the direct connection and the indirect connection all represent that the blood supply relation exists between the liver tumor and the liver vessel, and the no connection is no blood supply relation. And giving a mark value different from the gray value of the liver tumor to each segmented blood supply vessel segment, and determining the connection relation between each segment of blood supply vessel segment and the liver tumor according to the mark value and the gray value of the liver tumor, wherein the connection relation between each segment of blood supply vessel segment and the liver tumor can be detected simultaneously, or the connection relation between each segment of blood supply vessel segment and the liver tumor can be detected sequentially according to the blood flow direction. And determining the connection relationship and blood supply relationship between the blood supply vessel and the liver tumor according to the connection relationship between each blood supply vessel section and the liver tumor.

Further, querying a TACE strategy according to the blood supply relationship, and determining a TACE injection point, wherein the TACE strategy comprises the corresponding relationship between the blood supply relationship and the TACE injection point; outputting image data of the TACE injection point, wherein the step of determining the TACE injection point comprises the following steps: determining an injection point according to a blood supply relationship, wherein the blood supply relationship comprises the existence of the blood supply relationship, the nonexistence of the blood supply relationship, the blood flow direction, the blood supply quantity and the like of a blood supply vessel when the blood supply relationship exists, and the blood supply relationship can be input into a preset neural network model to obtain the injection point; or determining the resection range of the liver tumor according to the relative distance between the liver tumor and the liver blood vessel, and designing and planning a virtual operation scheme.

In this example, the medical imaging apparatus performs segmentation processing on the blood supply vessels through the image data of the blood supply vessels, and then determines the connection relationship through the image data of each blood supply vessel and the liver tumor, so that the accuracy and convenience of determining the blood supply relationship between the liver tumor and the liver vessel are improved.

In one possible example, the generating the image data set of the liver tumor and the image data set of the liver blood vessel from the scan image comprises: executing first preset processing on the scanned image to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a 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, wherein the second medical image data comprises a liver blood vessel data set; and executing second preset processing aiming at the second medical image data to obtain the image data set, wherein the image data set comprises liver 4D image data, liver tumor 4D image data and a blood vessel data set.

In this example, the medical imaging device can process the BMP data source through the VRDS medical network model and the cross vascular network model, and obtain target image data by combining boundary optimization and data enhancement processing, thereby solving the problem that the traditional medical image cannot realize the medical field in which the whole artery and vein are separated, and improving the authenticity, comprehensiveness and refinement degree of medical image display.

In accordance with the above, please refer to fig. 4, which is a schematic structural diagram of a medical imaging apparatus 400 provided in an embodiment of the present 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 memory 420 and configured to be executed by the processor 410, and the one or more programs 421 include instructions for:

It can be seen that, with the medical imaging apparatus provided in the embodiment of the present application, a scan image of the liver of a target user may be first obtained through the VRDS 4D imaging technology, and then an image data set of a liver tumor and an image data set of liver blood vessels are generated according to the scan image; secondly, determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel; and 4D medical imaging is carried out on the image data set of the liver tumor and the image data set of the liver blood vessels so as to output the position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor. The blood supply relationship between the liver tumor and the liver blood vessel is determined by analyzing the image data set of the liver tumor and the image data set of the liver blood vessel, so that the problem of low blood supply relationship identification efficiency caused by the fact that a two-dimensional scanning image cannot show the space structure characteristics of the tumor and the blood vessel is solved, and the accuracy and convenience for determining the blood supply relationship are improved.

In one possible example, the method further comprises, in the determining a blood supply relationship between the liver tumor and the liver vessel from the set of image data of the liver tumor and the set of image data of the liver vessel, instructions for: determining the liver tumor position of the target user according to the scanned liver image and the image data set of the liver tumor; determining a spatial coordinate of each image data in the set of image data of the liver vessel; and determining blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate of each image data and the position of the liver tumor, wherein the blood supply relationship comprises no blood supply relationship and no blood supply relationship.

In one possible example, in the determining the blood supply relationship between the liver tumor and the liver blood vessels from the spatial coordinates of each image data and the liver tumor location, the program further comprises instructions for:

obtaining a plurality of first target space position data corresponding to the liver tumor and a plurality of second target space position data corresponding to the liver blood vessel by traversing the space coordinates of each image data;

determining a blood supply relationship between the liver tumor and the liver blood vessel from the plurality of first target spatial location data and the plurality of second target spatial location data.

In one possible example, in the determining a blood supply relationship between the liver tumor and the liver vessel from the plurality of first target spatial location data and the plurality of second target spatial location data, the program further comprises instructions for:

determining a rate of coincidence of the first plurality of target spatial location data and the second plurality of target spatial location data;

when the coincidence rate is a preset threshold value, determining that no blood supply relation exists between the liver tumor and the liver blood vessel;

and when the coincidence rate is larger than a preset threshold value, determining that a blood supply relation exists between the liver tumor and the liver blood vessel.

In one possible example, the program further includes instructions for, in accordance with the exception data: when the coincidence rate is larger than a preset threshold value, after the blood supply relationship between the liver tumor and the liver blood vessel is determined, determining the position relationship between the liver tumor and the liver blood vessel according to the plurality of first target space position data and the plurality of second target space position data;

and determining the blood supply type of the liver blood vessel to the liver tumor according to the position relation of 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 image data;

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

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

traversing the N layers of subspaces, and recording the spatial coordinates corresponding to the image data pixel points when detecting that the gray values corresponding to the image data pixel points in each layer belong to the gray values of the liver blood vessels;

and determining the blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate corresponding to the image data pixel point.

In one possible example, in the determining a blood supply relationship between the liver tumor and the liver vessel from the image data set of the liver tumor and the image data set of the liver vessel, the program further includes instructions for:

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

inquiring a preset blood supply relationship set, and determining a blood supply vessel corresponding to the liver tumor, wherein the blood supply relationship set comprises the corresponding relationship between the type of the liver tumor and the blood supply vessel;

determining image data of the blood supply vessel according to the image data sets of the blood supply vessel and the liver vessel;

and determining blood supply relation according to the image data of the blood supply vessel and the image data set of the liver tumor.

In one possible example, in the determining a donor relationship from the image data of the donor vessels and the image data set of the liver tumor, the program further comprises instructions for:

carrying out segmentation processing on the blood supply vessel according to the image data of the blood supply vessel to obtain a plurality of blood supply vessel segments;

determining the connection relation between each blood supply vessel segment and the liver tumor according to the image data set of the blood supply vessel and the image data of the liver tumor;

and determining blood supply relation according to the connection relation.

In one possible example, in the generating of the image data set of the liver tumor and the image data set of the liver blood vessels from the scan image, the program further comprises instructions for:

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

importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a 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, wherein the second medical image data comprises a liver blood vessel data set;

and executing second preset processing aiming at the second medical image data to obtain the image data set, wherein the image data set comprises liver 4D image data, liver tumor 4D image data and a blood vessel data set.

In accordance with the above, the following is a device for implementing the VRDS AI-based liver tumor and blood vessel analysis method, specifically 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 according to the present application. The liver tumor and blood vessel analysis device based on VRDS AI described in the embodiment comprises: the acquiring unit 501, the processing unit 502, the determining unit 503 and the extracting unit 504 are specifically as follows:

an obtaining unit 501, configured to obtain a scanned image of a liver of a target user, where the scanned image includes a liver tumor image and the liver blood vessel image;

a processing unit 502, configured to generate an image data set of the liver tumor and an image data set of the liver blood vessel according to the scanned image, where the image data set of the liver blood vessel includes an image data set of a liver artery blood vessel and an image data set of a liver vein blood vessel;

a determining unit 503, configured to determine, according to the image data set of the liver tumor and the image data set of the liver blood vessel, a blood supply relationship between the liver tumor and the liver blood vessel;

an extracting unit 504, configured to perform 4D medical imaging on the image data set of the liver tumor and the image data set of the liver blood vessel to output the position of the liver tumor and the liver blood vessel having a blood supply relationship with the liver tumor.

It can be seen that, with the liver tumor and blood vessel analysis device based on VRDS AI described in the embodiment of the present application, a scan image of the liver of a target user can be first obtained through the VRDS 4D imaging technology, and then an image data set of the liver tumor and an image data set of the liver blood vessel are generated according to the scan image; secondly, determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel; and 4D medical imaging is carried out on the image data set of the liver tumor and the image data set of the liver blood vessels so as to output the position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor. The blood supply relationship between the liver tumor and the liver blood vessel is determined by analyzing the image data set of the liver tumor and the image data set of the liver blood vessel, so that the problem of low blood supply relationship identification efficiency caused by the fact that a two-dimensional scanning image cannot show the space structure characteristics of the tumor and the blood vessel is solved, and the accuracy and convenience for determining the blood supply relationship are improved.

In one possible example, in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel, the determining unit 503 is specifically configured to:

determining the liver tumor position of the target user according to the scanned liver image and the image data set of the liver tumor;

determining a spatial coordinate of each image data in the set of image data of the liver vessel;

and determining blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate of each image data and the position of the liver tumor, wherein the blood supply relationship comprises no blood supply relationship and no blood supply relationship.

In one possible example, 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 liver tumor position, the determining unit 503 is further specifically configured to:

In one 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 determining unit 503 is specifically configured to:

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

determining the position relation of the liver tumor and the liver blood vessel according to the plurality of first target space position data and the plurality of second target space position data;

In one possible example, in the aspect of determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel, the determining unit 503 is specifically further configured to:

In one possible example, in the aspect of determining a blood supply relationship according to 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:

and determining blood supply relation according to the connection relation.

In one 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 from the scan image, the processing unit 502 is specifically configured to:

It can be understood that, since the embodiment of the VRDS AI-based liver tumor and blood vessel analysis method and the embodiment of the VRDS AI-based liver tumor and blood vessel analysis device are different representations of the same technical concept, the contents of the method embodiment section in the present application should be synchronously adapted to the device embodiment section, and are not repeated herein.

Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the VRDS AI-based liver tumor and blood vessel analysis methods as described in the above method embodiments.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the VRDS AI based liver tumor and blood vessel analysis methods as set out in the above method embodiments.

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

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

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.

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

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.

The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

A liver tumor and blood vessel analysis method based on VRDS AI is characterized in that the method is applied to a medical imaging device and comprises the following steps:

acquiring a scanned image of a liver of a target user, wherein the scanned image comprises 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 scanning image, wherein the image data set of the liver blood vessel comprises an image data set of a liver artery blood vessel and an image data set of a liver vein blood vessel;

determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel;

and 4D medical imaging is carried out on the image data set of the liver tumor and the image data set of the liver blood vessels so as to output the position of the liver tumor and the liver blood vessels which have blood supply relation with the liver tumor.
The method of claim 1, wherein determining the blood supply relationship between the liver tumor and the liver vessel from the image data set of the liver tumor and the image data set of the liver vessel comprises:

determining the liver tumor position of the target user according to the scanned liver image and the image data set of the liver tumor;

determining a spatial coordinate of each image data in the set of image data of the liver vessel;

and determining blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate of each image data and the position of the liver tumor, wherein the blood supply relationship comprises no blood supply relationship and no blood supply relationship.
The method of claim 2, wherein determining the blood supply relationship between the liver tumor and the liver blood vessels based on the spatial coordinates of each image data and the liver tumor location comprises:

obtaining a plurality of first target space position data corresponding to the liver tumor and a plurality of second target space position data corresponding to the liver blood vessel by traversing the space coordinates of each image data;

determining a blood supply relationship between the liver tumor and the liver blood vessel from the plurality of first target spatial location data and the plurality of second target spatial location data.
The method of claim 3, wherein determining a blood supply relationship between the liver tumor and the liver vessel from the plurality of first target spatial location data and the plurality of second target spatial location data comprises:

determining a rate of coincidence of the first plurality of target spatial location data and the second plurality of target spatial location data;

when the coincidence rate is a preset threshold value, determining that no blood supply relation exists between the liver tumor and the liver blood vessel;

and when the coincidence rate is larger than a preset threshold value, determining that a blood supply relation exists between the liver tumor and the liver blood vessel.
The method of claim 4, wherein when the coincidence rate is greater than a preset threshold, after determining that a blood supply relationship exists between the liver tumor and the liver blood vessel, further comprising:

determining the position relation of the liver tumor and the liver blood vessel according to the plurality of first target space position data and the plurality of second target space position data;

and determining the blood supply type of the liver blood vessel to the liver tumor according to the position relation of the liver tumor and the liver blood vessel.
The method of claim 2, wherein determining the blood supply relationship between the liver tumor and the liver blood vessels based on the spatial coordinates of each image data and the liver tumor location comprises:

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

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

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

traversing the N layers of subspaces, and recording the spatial coordinates corresponding to the image data pixel points when detecting that the gray values corresponding to the image data pixel points in each layer belong to the gray values of the liver blood vessels;

and determining the blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate corresponding to the image data pixel point.
The method of claim 1, wherein determining the blood supply relationship between the liver tumor and the liver vessel from the image data set of the liver tumor and the image data set of the liver vessel comprises:

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

inquiring a preset blood supply relationship set, and determining a blood supply vessel corresponding to the liver tumor, wherein the blood supply relationship set comprises the corresponding relationship between the type of the liver tumor and the blood supply vessel;

determining image data of the blood supply vessel according to the image data sets of the blood supply vessel and the liver vessel;

and determining blood supply relation according to the image data of the blood supply vessel and the image data set of the liver tumor.
The method of claim 7, wherein determining a blood supply relationship from the image data of the blood-supplying vessel and the image data set of the liver tumor comprises:

carrying out segmentation processing on the blood supply vessel according to the image data of the blood supply vessel to obtain a plurality of blood supply vessel segments;

determining the connection relation between each blood supply vessel segment and the liver tumor according to the image data set of the blood supply vessel and the image data of the liver tumor;

and determining blood supply relation according to the connection relation.
The method of claim 1, wherein generating the image data set of the liver tumor and the image data set of the liver blood vessels from the scan image comprises:

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

importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a 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, wherein the second medical image data comprises a liver blood vessel data set;

and executing second preset processing aiming at the second medical image data to obtain the image data set, wherein the image data set comprises liver 4D image data, liver tumor 4D image data and a blood vessel data set.
A VRDS AI based liver tumor and blood vessel analysis device for use in a medical imaging device, the device comprising:

an acquisition unit, configured to acquire a scan image of a liver of a target user, where the scan image includes a liver tumor image and the liver blood vessel image;

the processing unit is used for generating an image data set of the liver tumor and an image data set of the liver blood vessel according to the scanning image, wherein the image data set of the liver blood vessel comprises an image data set of a liver artery blood vessel and an image data set of a liver vein blood vessel;

the determining unit is used for determining the blood supply relationship between the liver tumor and the liver blood vessel according to the image data set of the liver tumor and the image data set of the liver blood vessel;

and the extraction unit is used for carrying out 4D medical imaging on the image data set of the liver tumor and the image data set of the liver blood vessel so as to output the position of the liver tumor and the liver blood vessel which has a blood supply relation with the liver tumor.
The apparatus according to claim 10, wherein, in the determining of the blood supply relationship between the liver tumor and the liver vessel from the image data set of the liver tumor and the image data set of the liver vessel, the determining unit is specifically configured to:

determining the liver tumor position of the target user according to the scanned liver image and the image data set of the liver tumor;

determining a spatial coordinate of each image data in the set of image data of the liver vessel;

and determining blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate of each image data and the position of the liver tumor, wherein the blood supply relationship comprises no blood supply relationship and no blood supply relationship.
The apparatus according to claim 11, wherein in said 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 liver tumor position, the determining unit is further configured to:

obtaining a plurality of first target space position data corresponding to the liver tumor and a plurality of second target space position data corresponding to the liver blood vessel by traversing the space coordinates of each image data;

determining a blood supply relationship between the liver tumor and the liver blood vessel from the plurality of first target spatial location data and the plurality of second target spatial location data.
The apparatus according to claim 12, wherein in said determining the blood supply relationship between the liver tumor and the liver vessel from the plurality of first target spatial position data and the plurality of second target spatial position data, the determining unit is specifically configured to:

determining a rate of coincidence of the first plurality of target spatial location data and the second plurality of target spatial location data;

when the coincidence rate is a preset threshold value, determining that no blood supply relation exists between the liver tumor and the liver blood vessel;

and when the coincidence rate is larger than a preset threshold value, determining that a blood supply relation exists between the liver tumor and the liver blood vessel.
The apparatus according to claim 13, wherein after determining that there is a blood supply relationship between the liver tumor and the liver blood vessel when the coincidence ratio is greater than a preset threshold, the determining unit is further configured to:

determining the position relation of the liver tumor and the liver blood vessel according to the plurality of first target space position data and the plurality of second target space position data;

and determining the blood supply type of the liver blood vessel to the liver tumor according to the position relation of the liver tumor and the liver blood vessel.
The apparatus according to claim 11, wherein in said 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 liver tumor position, the determining unit is further configured to:

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

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

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

traversing the N layers of subspaces, and recording the spatial coordinates corresponding to the image data pixel points when detecting that the gray values corresponding to the image data pixel points in each layer belong to the gray values of the liver blood vessels;

and determining the blood supply relationship between the liver tumor and the liver blood vessel according to the space coordinate corresponding to the image data pixel point.
The apparatus according to claim 10, wherein, in the determining of the blood supply relationship between the liver tumor and the liver vessel from the image data set of the liver tumor and the image data set of the liver vessel, the determining unit is further specifically configured to:

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

inquiring a preset blood supply relationship set, and determining a blood supply vessel corresponding to the liver tumor, wherein the blood supply relationship set comprises the corresponding relationship between the type of the liver tumor and the blood supply vessel;

determining image data of the blood supply vessel according to the image data sets of the blood supply vessel and the liver vessel;

and determining blood supply relation according to the image data of the blood supply vessel and the image data set of the liver tumor.
The apparatus of claim 10, wherein in said determining a donor relationship from said set of image data of donor vessels and image data of liver tumors, said processing unit is specifically configured to:

carrying out segmentation processing on the blood supply vessel according to the image data of the blood supply vessel to obtain a plurality of blood supply vessel segments;

determining the connection relation between each blood supply vessel segment and the liver tumor according to the image data set of the blood supply vessel and the image data of the liver tumor;

and determining blood supply relation according to the connection relation.
The apparatus according to claim 17, wherein the processing unit, in the generating of the image data set of the liver tumor and the image data set of the liver vessels from the scan image, is specifically configured to:

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

importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprises a 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, wherein the second medical image data comprises a liver blood vessel data set;

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