CN114340497A

CN114340497A - Intestinal tumor and blood vessel analysis method based on VRDS AI medical image and related device

Info

Publication number: CN114340497A
Application number: CN201980099768.XA
Authority: CN
Inventors: 戴维伟·李; 斯图尔特平·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: 2022-04-12
Also published as: WO2021081842A1

Abstract

An intestinal tumor and blood vessel analysis method based on VRDS AI medical images and a related device comprise: acquiring a scanned image of an intestinal tract of a user, wherein the scanned image further includes an intestinal tumor and blood vessels around the intestinal tract (S201); generating image data of the intestinal tract, image data of intestinal tumor and image data of blood vessel from the scan image (S202); determining the position area of the intestinal tumor in the intestinal tract and the quantity and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels; 4D medical imaging is performed on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels to output the location area and the number and distribution of the blood-supplying vessels (S203). The method and the related device for analyzing the intestinal tumor and the blood vessel can improve the diagnosis efficiency of intestinal diseases.

Description

Intestinal tumor and blood vessel analysis method based on VRDS AI medical image and related device

Technical Field

The application relates to the technical field of medical imaging devices, in particular to a method and a related device for analyzing intestinal tumors and blood vessels based on VRDS AI medical images.

Background

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

Disclosure of Invention

The embodiment of the application provides an intestinal tumor and blood vessel analysis method and a related device based on VRDS AI medical images, and the diagnosis efficiency of intestinal diseases is improved by implementing the embodiment of the application.

In a first aspect, an intestinal tumor and blood vessel analysis method based on VRDS AI medical images is provided, including:

acquiring a scanned image of a user's intestinal tract, wherein the scanned image further includes an intestinal tumor and blood vessels surrounding the intestinal tract;

generating image data of the intestinal tract, image data of the intestinal tumor and image data of blood vessels according to the scanning image;

determining the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels;

and 4D medical imaging is carried out on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the position area and the number and distribution of the blood supply vessels.

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

the device comprises an acquisition module, a display module and a processing module, wherein the acquisition module is used for acquiring a scanning image of the intestinal tract of a user, and the scanning image further comprises intestinal tumors and blood vessels around the intestinal tract;

the generation module is used for generating the image data of the intestinal tract, the image data of intestinal tumor and the image data of blood vessel according to the scanning image;

the determining module is used for determining the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels;

and the output module is used for carrying out 4D medical imaging on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the number and distribution of the position area and the blood supply vessels.

A third aspect of embodiments of the present application provides a medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are generated as instructions to be executed by the processor to perform the steps of the method of any of the first aspects of the claims above.

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

It can be seen that, in the above technical solution, a scanned image of an intestinal tract of a user is obtained, where the scanned image further includes an intestinal tumor and blood vessels around the intestinal tract, and the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels are generated according to the scanned image, and then the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor are determined according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels, so as to implement rapid diagnosis of intestinal tract diseases, and avoid a problem of low diagnosis efficiency of intestinal tract diseases caused by that a two-dimensional slice scanned image cannot present spatial structure characteristics of the intestinal tract. Meanwhile, 4D medical imaging is carried out on the image data of the intestinal tract, the image data of intestinal tumor and the image data of blood vessels, so that the number and distribution of position areas and blood supply vessels are output, doctors can conveniently position diseases, and the diagnosis efficiency of intestinal diseases is improved.

Drawings

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

Wherein:

fig. 1 is a schematic structural diagram of an intestinal tumor and blood vessel analysis system based on VRDS AI medical images according to an embodiment of the present application;

fig. 2A is a schematic flowchart of a method for analyzing intestinal tumor and blood vessel based on VRDS AI medical image according to an embodiment of the present disclosure;

fig. 2B is a schematic diagram of a coordinate system according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of another intestinal tumor and blood vessel analysis method based on VRDS AI medical images according to an embodiment of the present disclosure;

FIG. 4 is a schematic view of a medical imaging apparatus provided by an embodiment of the present application;

fig. 5 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application.

Detailed Description

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

The following are detailed below.

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

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

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

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

Referring to fig. 2A, fig. 2A is a schematic flow chart of a method for analyzing intestinal tumor and blood vessel based on VRDS AI medical image according to an embodiment of the present application. As shown in fig. 2A, an intestinal tumor and blood vessel analysis method based on VRDS AI medical images according to an embodiment of the present disclosure may include:

201. a medical imaging device acquires a scanned image of a user's intestinal tract, wherein the scanned image further includes an intestinal tumor and blood vessels surrounding the intestinal tract.

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

202. The medical imaging device generates the image data of the intestinal tract, the image data of intestinal tumor and the image data of blood vessel according to the scanning image.

Among them, blood vessels include arteries and veins. Further, the artery may include, for example: superior mesenteric artery, inferior mesenteric artery, etc. The vein may include, for example, a mesenteric vein or the like.

The image data of the intestinal tract comprises three-dimensional space image data of the intestinal tract, the image data of the intestinal tumor comprises three-dimensional space image data of the intestinal tumor, and the image data of the blood vessel comprises three-dimensional space image data of the blood vessel.

203. The medical imaging device determines the number and distribution of the intestinal tumor in the position area of the intestinal tract and the blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels.

In one possible embodiment, the determining the location area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels includes: determining the position area according to the image data of the intestinal tract and the image data of the intestinal tumor; determining the position area of each blood vessel in the image data of the blood vessel to obtain a plurality of pieces of blood vessel position information; determining the number and distribution of the blood-supplying vessels according to the position area and the plurality of vessel position information.

For example, the image data of the blood vessels includes a plurality of blood vessels, where a first blood vessel is any one of the plurality of blood vessels, and if the blood vessel position information corresponding to the first blood vessel matches with the position area of the intestinal tumor in the intestinal tract, the first blood vessel is determined to be the blood supply vessel of the intestinal tumor.

Further, the determining the number and distribution of the blood supply vessels according to the position area and the plurality of vessel position information comprises: matching the location area with the plurality of vessel location information; and determining the number and distribution of the blood supply vessels according to the matching result.

Wherein each of the plurality of blood vessel position information comprises a plurality of spatial positions of each blood vessel distributed in the image data of the blood vessel.

For example, the first blood vessel position information is any one of the plurality of blood vessel position information, and the first blood vessel position information may include, for example: (X1, Y1, Z1), (X2, Y2, Z2), and (X3, Y3, Z3), and the like.

It can be seen that, in the above technical solution, a location area is determined according to the image data of the intestinal tract and the image data of the intestinal tumor, then, a location area of each blood vessel in the image data of the blood vessel is determined to obtain a plurality of pieces of blood vessel location information, and finally, the number and distribution of blood supply vessels are determined according to the location area and the plurality of pieces of blood vessel location information, so that the accuracy of determining the location area of the intestinal tumor in the intestinal tract and the number and distribution of the blood supply vessels of the intestinal tumor is improved.

Further, optionally, in a possible embodiment, the determining the location area according to the image data of the intestinal tract and the image data of the intestinal tumor includes: comparing the image data of the intestinal tract with the image data of the intestinal tumor to determine first image data matched with the image data of the intestinal tumor in the image data of the intestinal tract; determining a spatial position of the first image data in the image data of the intestinal tract; setting the spatial position as the position region.

According to the technical scheme, the image data of the intestinal tract and the image data of the intestinal tumor are compared to determine the first image data matched with the image data of the intestinal tumor in the image data of the intestinal tract, so that the spatial position of the first image data in the image data of the intestinal tract is determined, the position area is further determined, and the accuracy of determining the position area of the intestinal tumor in the intestinal tract is improved.

Optionally, in a possible embodiment, the comparing the image data of the intestinal tract with the image data of the intestinal tumor to determine first image data, which matches the image data of the intestinal tumor, in the image data of the intestinal tract includes: segmenting the image data of the intestinal tract according to the category of the intestinal tract to obtain a plurality of image subdata of the intestinal tract; executing the following steps for each image subdata of the plurality of image subdata of the intestinal tract, wherein the steps comprise: determining a target intestinal tract included in the currently processed image subdata; acquiring template image subdata according to a target intestinal tract included in the currently processed image subdata, wherein the template image subdata comprises image data of the target intestinal tract in a healthy state; comparing the currently processed image subdata with the template image subdata; if the currently processed image subdata is not matched with the template image subdata, second image subdata which is not matched with the template image subdata in the currently processed image subdata is obtained, and the second image subdata is compared with the image data of the intestinal tumor to determine that the second image subdata is the first image data.

For example, the target intestinal tract is a small intestine, and the template image sub-data includes image data of the small intestine in a healthy state.

Optionally, in a possible implementation, the comparing the currently processed image sub-data with the template image sub-data includes: establishing a first coordinate system according to the currently processed image subdata, wherein the origin of the first coordinate system is the center of the target intestinal tract, and the X axis, the Y axis and the Z axis of the first coordinate system are mutually vertical and follow the right-hand spiral rule; starting from the origin of the first coordinate system, extracting a plurality of first intestinal cell layers from the currently processed image subdata according to preset distances along the positive direction and the negative direction of the Z axis of the first coordinate system respectively; and comparing the plurality of layers of first intestinal tract cell layers with the plurality of layers of second intestinal tract cell layers, wherein the plurality of layers of second intestinal tract cell layers are extracted from the template image subdata.

Each first intestinal tract cell layer comprises a first intestinal tract cell data set and characteristic data corresponding to the first intestinal tract cell data set, wherein the characteristic data corresponding to the first intestinal tract cell data set comprises a shape corresponding to each first intestinal tract cell data in the first intestinal tract cell data set, a size corresponding to each first intestinal tract cell data in the first intestinal tract cell data set and a spatial position where each first intestinal tract cell data in the first intestinal tract cell data set is located;

each second intestinal tract cell layer comprises a second intestinal tract cell data set and characteristic data corresponding to the second intestinal tract cell data set, and the characteristic data corresponding to the second intestinal tract cell data set comprises a shape corresponding to each second intestinal tract cell data in the second intestinal tract cell data set, a size corresponding to each second intestinal tract cell data in the second intestinal tract cell data set and a spatial position where each second intestinal tract cell data in the second intestinal tract cell data set is located.

Wherein, first intestinal cell layer is in arbitrary one deck intestinal cell layer in the first intestinal cell layer of multilayer, second intestinal cell layer is in the second intestinal cell layer of multilayer with the cell layer of first intestinal cell layer associative relationship, associative relationship is the spatial position that every first intestinal cell data in the first intestinal cell data set that first intestinal cell layer included was located matches with the spatial position that every second intestinal cell data in the second intestinal cell data set that second intestinal cell layer included was located, will first intestinal cell layer with the contrast of second intestinal cell layer includes: acquiring the first intestinal tract cell data set included in the first intestinal tract cell layer and characteristic data corresponding to the first intestinal tract cell data set; and respectively comparing the shape and the size corresponding to each first intestinal tract cell data in the first intestinal tract cell data set with the shape and the size corresponding to each second intestinal tract cell data in the second intestinal tract cell data set.

It can be seen that, in the above technical solution, by comparing the currently processed image sub-data with the template image sub-data, the abnormal image data in the currently processed image sub-data can be more accurately found, and the data comparison efficiency is improved.

Optionally, in a possible embodiment, the determining, according to the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessels, the number and distribution of the intestinal tumor in the location area of the intestinal tract and the blood supply vessels of the intestinal tumor includes: determining the position area according to the image data of the intestinal tract and the image data of the intestinal tumor; determining the connection position and the connection angle of each blood vessel and the intestinal tumor according to the image data of the blood vessel and the image data of the intestinal tumor; and determining the number and distribution of the blood supply vessels according to the connection positions and the connection angles.

It can be seen that, in the above technical solution, the position area is determined according to the image data of the intestinal tract and the image data of the intestinal tumor, the connection position and the connection angle of each blood vessel and the intestinal tumor are determined according to the image data of the blood vessel and the image data of the intestinal tumor, and finally, the number and the distribution of the blood supply vessels are determined according to the connection position and the connection angle, so that the accuracy of determining the position area of the intestinal tumor in the intestinal tract and the number and the distribution of the blood supply vessels of the intestinal tumor is improved.

204. The medical imaging device carries out 4D medical imaging on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the position area and the number and distribution of the blood supply vessels.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Optionally, in a possible implementation manner, the first data is extracted by the medical imaging device from the fused data including the intersection positions of the artery and the vein in the data set of the blood vessel, and the fused data is separated by using a preset data separation algorithm to obtain artery boundary point data.

And extracting the second data from the medical imaging device to obtain fusion data of the intersection positions of the artery and the vein in the data set of the blood vessel, and separating the fusion data by adopting a preset data separation algorithm to obtain vein boundary point data.

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

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

Wherein the segmentation targets comprise intestinal tracts, arteries and veins.

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

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

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

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

Wherein the data enhancement processing includes any one of: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random clipping, and data enhancement based on simulating different illumination variations.

Optionally, in a possible embodiment, the first data set of the intestinal tract further includes fused data of the crossing position of the intestinal tract, and the processing the second medical image data to obtain the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessel includes: importing the second medical image data into a preset cross intestinal network model to obtain third medical image data, wherein the third medical image data comprise a second data set of the intestinal tract, a data set of the artery and a data set of the vein, and the second data set of the intestinal tract comprises separation data of a cross position of the intestinal tract, surface characteristics of the intestinal tract and a data set of the intestinal tumor; performing a second predetermined process on the third medical image data to obtain image data of the intestinal tract, image data of the intestinal tumor, and image data of the blood vessel.

It can be seen that, in the above technical solution, the second medical image data is imported into the preset cross-bowel network model to obtain the third medical image data, the third medical image data includes the second data set of the bowel, the data set of the artery and the data set of the vein, the second data set of the bowel includes the separation data of the cross position of the bowel, the surface characteristics of the bowel and the data set of the intestinal tumor, then the second preset processing is performed on the third medical image data to obtain the image data of the bowel, the image data of the intestinal tumor and the image data of the blood vessel, so that the medical image data better restores the original organ or tissue, the authenticity of the medical image data is improved, the doctor can conveniently locate the disease, and the diagnosis efficiency of the intestinal disease is improved

Optionally, in a possible embodiment, the performing 4D medical imaging on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels to output the location area and the number and distribution of the blood-supplying vessels includes: respectively acquiring a plurality of image quality scores corresponding to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessel; screening a plurality of enhanced data with an image quality score larger than a preset image quality score from the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessel according to the plurality of image quality scores; 4D medical imaging the plurality of enhancement data to output the location area and the number and distribution of the donating vessels.

The plurality of image quality scores include a plurality of image quality scores corresponding to the image data of the intestinal tract, a plurality of image quality scores corresponding to the image data of the intestinal tumor, and a plurality of image quality scores corresponding to the image data of the blood vessel.

The plurality of enhanced data comprise enhanced data of which the image quality score is greater than the preset image quality score in the image data of the intestinal tract, enhanced data of which the image quality score is greater than the preset image quality score in the image data of the intestinal tumor and enhanced data of which the image quality score is greater than the preset image quality score in the image data of the blood vessel.

It can be seen that, in the above technical solution, a plurality of image quality scores corresponding to the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessel are respectively obtained; screening a plurality of enhanced data with the image quality scores larger than a preset image quality score from the image data of the intestinal tract, the image data of intestinal tumor and the image data of blood vessel according to the image quality scores; and 4D medical imaging is carried out on the plurality of enhanced data to output the position area and the number and distribution of blood supply vessels, so that a doctor is assisted to carry out quick diagnosis and the diagnosis efficiency of the intestinal diseases is improved. Meanwhile, 4D medical imaging is carried out by adopting the enhanced data, and the image definition and precision are improved.

Optionally, in a possible implementation, the method further includes: establishing a coordinate system according to the image data of the intestinal tumor, wherein the origin of the coordinate system is the center of the intestinal tumor, and the X axis, the Y axis and the Z axis of the coordinate system are mutually vertical and follow the right-hand spiral rule; starting from the origin of the coordinate system, extracting multiple intestinal tumor cell layers from the image data of the intestinal tumor according to preset distances along the positive direction and the negative direction of the Z axis of the coordinate system respectively, wherein each intestinal tumor cell layer comprises an intestinal tumor cell data set; performing preset processing on each intestinal tumor cell layer in the multiple intestinal tumor cell layers to obtain a first intestinal tumor cell data set, wherein the first intestinal tumor cell data set is the intestinal tumor cell data of the outermost layer in the intestinal tumors; determining a growth cycle corresponding to the intestinal tumor from the first intestinal tumor cell dataset; when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library; calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor; and outputting the image data of the intestinal tumor.

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

Referring to fig. 2B, fig. 2B is a schematic diagram of a coordinate system provided by an embodiment of the present application, as shown in fig. 2B, where the origin of the coordinate system is the center of the intestinal tumor, and the X-axis, the Y-axis and the Z-axis of the coordinate system are perpendicular to each other and follow the right-hand spiral rule.

Optionally, in a possible embodiment, the determining a growth cycle corresponding to the intestinal tumor according to the first intestinal tumor cell data set includes: acquiring a spatial position corresponding to each first intestinal tumor cell data in the first intestinal tumor cell data set; determining the position relation between each first intestinal tumor cell data and the intestinal tract according to the spatial position corresponding to each first intestinal tumor cell data in the first intestinal tumor cell data set; and determining the growth cycle corresponding to the intestinal tumor according to the position relation.

Wherein the positional relationship comprises one of: each first intestinal tumor cell data is inside the intestinal tract or each first intestinal tumor cell data is outside the intestinal tract.

The growth cycle may include, for example, stages 0, i, ii, iii and iv, with stage 0: cancer is in the early stage, cancer cells are present only in the innermost layer of the intestine, stage i: cancer cells invade many areas of the inner wall of the colon intestine; and stage II: cancer cells have gone beyond the intestinal range, spread to peripheral tissues, but not to lymph nodes; stage III: cancer cells have spread to peripheral lymph nodes but not to other parts of the body. And IV, period: cancer cells have spread to other parts of the body.

Wherein the preset growth cycle is the time for improving the self-healing probability by cutting off the intestinal tumor.

It can be seen that, in the above technical scheme, the growth cycle corresponding to the intestinal tumor is determined according to the position relationship, so that the accuracy of determining the growth cycle is improved.

Optionally, in a possible implementation, each intestinal tumor cell layer includes characteristic data corresponding to the intestinal tumor cell data set, the characteristic data corresponding to the intestinal tumor cell data set includes a shape corresponding to each intestinal tumor cell data in the intestinal tumor cell data set and a size corresponding to each intestinal tumor cell data in the intestinal tumor cell data set, and the preset processing includes the following steps: acquiring outermost intestinal tumor cell data corresponding to the intestinal tumors from an intestinal tumor cell database, wherein the outermost intestinal tumor cell data comprise shapes and sizes corresponding to the outermost intestinal tumor cell data, and the intestinal tumor cell database comprises outermost intestinal tumor cell data corresponding to each of a plurality of intestinal tumors in different growth cycles; second intestinal tumor cell data similar in shape and size to the outermost intestinal tumor cell data are extracted from each intestinal tumor cell layer.

According to the technical scheme, the acquisition of the intestinal tumor cell data of the outermost layer of the intestinal tumor is realized, and the accuracy of the acquisition of the intestinal tumor cell data of the outermost layer of the intestinal tumor is improved through the comparison of the shape and the size.

The intestinal tumor simulated resection strategy library comprises a plurality of intestinal tumor growth periods and a plurality of intestinal tumor simulated resection strategies corresponding to the position areas of the intestinal tract of the intestinal tumors, and each intestinal tumor simulated resection strategy is different from each other.

The image data for ablating the intestinal tumor may comprise, for example, video data for ablating the intestinal tumor.

According to the technical scheme, when the growth cycle is not matched with the preset growth cycle, the intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area is determined from the intestinal tumor simulated resection strategy library, then the intestinal tumor simulated resection algorithm is called according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor, and finally the image data for resection of the intestinal tumor is output, so that the accuracy of the intestinal tumor simulated resection process is improved, and the image data for resection of the intestinal tumor is provided for doctors so as to improve the success rate of intestinal tumor resection.

Referring to fig. 3, fig. 3 is a schematic flow chart of another intestinal tumor and blood vessel analysis method based on VRDS AI medical images according to an embodiment of the present application. As shown in fig. 3, the method includes:

301. the medical imaging device determines the growth cycle corresponding to the intestinal tumor according to the number and distribution of the blood supply vessels.

302. And when the growth cycle does not match with the preset growth cycle, the medical imaging device determines an intestinal tumor simulated excision strategy corresponding to the growth cycle and the position area from the intestinal tumor simulated excision strategy library.

303. And the medical imaging device calls an intestinal tumor simulated resection algorithm to process the intestinal tumor according to the intestinal tumor simulated resection strategy so as to generate image data for resecting the intestinal tumor.

304. A medical imaging device outputs image data for ablating the intestinal tumor.

According to the technical scheme, the growth cycle corresponding to the intestinal tumor is determined according to the number and distribution of blood supply vessels, then the intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area is determined from the intestinal tumor simulated resection strategy library when the growth cycle is not matched with the preset growth cycle, then the intestinal tumor simulated resection algorithm is called according to the intestinal tumor simulated resection strategy to process the intestinal tumor to generate the image data for resecting the intestinal tumor, and finally the image data for resecting the intestinal tumor is output, so that the accuracy of the intestinal tumor simulated resection process is improved, and the image data for resecting the intestinal tumor is provided for a doctor to improve the success rate of intestinal tumor resection.

Referring to fig. 4, an embodiment of the present application provides a schematic diagram of a medical imaging apparatus 400, and the medical imaging apparatus 400 may include:

an obtaining module 401, configured to obtain a scanned image of an intestinal tract of a user, where the scanned image further includes an intestinal tumor and blood vessels around the intestinal tract;

A generating module 402, configured to generate image data of the intestinal tract, image data of an intestinal tumor, and image data of a blood vessel according to the scan image;

Optionally, the generating module is specifically configured to perform a first preset process on the scanned image to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprise a first data set of the intestinal tract and a data set of the blood vessel, the first data set of the intestinal tract comprises a data set of the intestinal tumor, the data set of the blood vessel comprises fusion data of the intersection position of the artery and the vein, the first data set of the intestinal tract is a transfer function result of a cubic space of tissue structures on the surface of the intestinal tract and in the intestinal tract, and the data set of the blood vessel is a transfer function result of a cubic space of the tissue structures on the surface of the blood vessel and in the blood vessel; 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 first data set of the intestinal tract, a data set of the artery and a data set of the vein, the first data in the data set of the artery and the second data in the data set of the vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position; processing the second medical image data to obtain image data of the intestinal tract, image data of the intestinal tumor and image data of the blood vessel.

A determining module 403, configured to determine, according to the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessels, the number and distribution of the intestinal tumor in the location area of the intestinal tract and the blood supply vessels of the intestinal tumor;

optionally, the determining module is specifically configured to determine the location area according to the image data of the intestinal tract and the image data of the intestinal tumor; determining the position area of each blood vessel in the image data of the blood vessel to obtain a plurality of pieces of blood vessel position information; determining the number and distribution of the blood-supplying vessels according to the position area and the plurality of vessel position information.

Optionally, the determining module is specifically configured to compare the image data of the intestinal tract with the image data of the intestinal tumor to determine first image data, which is matched with the image data of the intestinal tumor, in the image data of the intestinal tract; determining a spatial position of the first image data in the image data of the intestinal tract; setting the spatial position as the position region.

Optionally, the determining module is specifically configured to segment the image data of the intestinal tract according to the category to which the intestinal tract belongs, so as to obtain a plurality of image subdata of the intestinal tract; executing the following steps for each image subdata of the plurality of image subdata of the intestinal tract, wherein the steps comprise: determining a target intestinal tract included in the currently processed image subdata; acquiring template image subdata according to a target intestinal tract included in the currently processed image subdata, wherein the template image subdata comprises image data of the target intestinal tract in a healthy state; comparing the currently processed image subdata with the template image subdata; if the currently processed image subdata is not matched with the template image subdata, second image subdata which is not matched with the template image subdata in the currently processed image subdata is obtained, and the second image subdata is compared with the image data of the intestinal tumor to determine that the second image subdata is first image data.

Optionally, the determining module is specifically configured to determine the location area according to the image data of the intestinal tract and the image data of the intestinal tumor; determining the connection position and the connection angle of each blood vessel and the intestinal tumor according to the image data of the blood vessel and the image data of the intestinal tumor, wherein each blood vessel is connected with the intestinal tumor in the image data of the blood vessel; and determining the number and distribution of the blood supply vessels according to the connection positions and the connection angles.

An output module 404, configured to perform 4D medical imaging on the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessels, so as to output the location area and the number and distribution of the blood supply vessels.

Optionally, the apparatus further comprises an excision module for determining a growth cycle corresponding to the intestinal tumor according to the number and distribution of the blood supply vessels; when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library; calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor; and outputting the image data of the intestinal tumor.

Optionally, the apparatus further includes a resection module, configured to establish a coordinate system according to the image data of the intestinal tumor, where an origin of the coordinate system is a center of the intestinal tumor, and an X-axis, a Y-axis, and a Z-axis of the coordinate system are perpendicular to each other and follow a right-handed spiral rule; starting from the origin of the coordinate system, extracting multiple intestinal tumor cell layers from the image data of the intestinal tumor according to preset distances along the positive direction and the negative direction of the Z axis of the coordinate system respectively, wherein each intestinal tumor cell layer comprises an intestinal tumor cell data set; performing preset processing on each intestinal tumor cell layer in the multiple intestinal tumor cell layers to obtain a first intestinal tumor cell data set, wherein the first intestinal tumor cell data set is the intestinal tumor cell data of the outermost layer in the intestinal tumors; determining a growth cycle corresponding to the intestinal tumor from the first intestinal tumor cell dataset; when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library; calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor; and outputting the image data of the intestinal tumor.

Wherein each layer of intestinal tumor cell layer comprises characteristic data corresponding to the intestinal tumor cell data set, the characteristic data corresponding to the intestinal tumor cell data set comprises a shape corresponding to each intestinal tumor cell data in the intestinal tumor cell data set and a size corresponding to each intestinal tumor cell data in the intestinal tumor cell data set, and the presetting process comprises the following steps:

acquiring outermost intestinal tumor cell data corresponding to the intestinal tumors from an intestinal tumor cell database, wherein the outermost intestinal tumor cell data comprise shapes and sizes corresponding to the outermost intestinal tumor cell data, and the intestinal tumor cell database comprises outermost intestinal tumor cell data corresponding to each of a plurality of intestinal tumors in different growth cycles;

second intestinal tumor cell data similar in shape and size to the outermost intestinal tumor cell data are extracted from each intestinal tumor cell layer.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application. As shown in fig. 5, a medical imaging apparatus in a hardware operating environment according to an embodiment of the present application may include:

a processor 501, such as a CPU.

The memory 502 may alternatively be a high speed RAM memory or a stable memory such as a disk memory.

A communication interface 503 for implementing connection communication between the processor 501 and the memory 502.

Those skilled in the art will appreciate that the configuration of the medical imaging device shown in FIG. 5 is not intended to be limiting and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.

As shown in fig. 5, the memory 502 may include therein an operating system, a network communication module, and a program for information processing. An operating system is a program that manages and controls the hardware and software resources of the medical imaging device, a program that supports personnel management, and the execution of other software or programs. The network communication module is used to enable communication between the components within the memory 502, as well as with other hardware and software within the medical imaging apparatus.

In the medical imaging apparatus shown in fig. 5, the processor 501 is configured to execute the program for migrating information stored in the memory 502, and implement the following steps: acquiring a scanned image of a user's intestinal tract, wherein the scanned image further includes an intestinal tumor and blood vessels surrounding the intestinal tract; generating image data of the intestinal tract, image data of the intestinal tumor and image data of blood vessels according to the scanning image; determining the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels; and 4D medical imaging is carried out on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the position area and the number and distribution of the blood supply vessels.

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

The present application further provides a computer readable storage medium for storing a computer program, the stored computer program being executable by the processor to perform the steps of: acquiring a scanned image of a user's intestinal tract, wherein the scanned image further includes an intestinal tumor and blood vessels surrounding the intestinal tract; generating image data of the intestinal tract, image data of the intestinal tumor and image data of blood vessels according to the scanning image; determining the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels; and 4D medical imaging is carried out on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the position area and the number and distribution of the blood supply vessels.

For specific implementation of the computer-readable storage medium according to the present application, reference may be made to the above-mentioned embodiments of the method for analyzing intestinal tumor and blood vessel based on VRDS AI medical image, which are not described herein again.

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

Claims

The method for analyzing the intestinal tumor and the blood vessel based on the VRDS AI medical image is characterized by comprising the following steps:

acquiring a scanned image of a user's intestinal tract, wherein the scanned image further includes an intestinal tumor and blood vessels surrounding the intestinal tract;

generating image data of the intestinal tract, image data of the intestinal tumor and image data of blood vessels according to the scanning image;

determining the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels;

and 4D medical imaging is carried out on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the position area and the number and distribution of the blood supply vessels.
The method of claim 1, wherein determining the location area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor from the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessels comprises:

determining the position area according to the image data of the intestinal tract and the image data of the intestinal tumor;

determining the position area of each blood vessel in the image data of the blood vessel to obtain a plurality of pieces of blood vessel position information;

determining the number and distribution of the blood-supplying vessels according to the position area and the plurality of vessel position information.
The method of claim 2, wherein said determining the location region from the image data of the intestinal tract and the image data of the intestinal tumor comprises:

comparing the image data of the intestinal tract with the image data of the intestinal tumor to determine first image data matched with the image data of the intestinal tumor in the image data of the intestinal tract;

determining a spatial position of the first image data in the image data of the intestinal tract;

setting the spatial position as the position region.
The method of claim 3, wherein comparing the image data of the intestinal tract with the image data of the intestinal tumor to determine a first image data of the intestinal tract that matches the image data of the intestinal tumor comprises:

segmenting the image data of the intestinal tract according to the category of the intestinal tract to obtain a plurality of image subdata of the intestinal tract;

executing the following steps for each image subdata of the plurality of image subdata of the intestinal tract, wherein the steps comprise:

determining a target intestinal tract included in the currently processed image subdata; acquiring template image subdata according to a target intestinal tract included in the currently processed image subdata, wherein the template image subdata comprises image data of the target intestinal tract in a healthy state; comparing the currently processed image subdata with the template image subdata; if the currently processed image subdata is not matched with the template image subdata, second image subdata which is not matched with the template image subdata in the currently processed image subdata is obtained, and the second image subdata is compared with the image data of the intestinal tumor to determine that the second image subdata is the first image data.
The method of claim 1, wherein determining the location area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor from the image data of the intestinal tract, the image data of the intestinal tumor, and the image data of the blood vessels comprises:

determining the position area according to the image data of the intestinal tract and the image data of the intestinal tumor;

determining the connection position and the connection angle of each blood vessel and the intestinal tumor according to the image data of the blood vessel and the image data of the intestinal tumor, wherein each blood vessel is connected with the intestinal tumor in the image data of the blood vessel;

and determining the number and distribution of the blood supply vessels according to the connection positions and the connection angles.
The method according to any one of claims 1-5, further comprising:

determining the growth cycle corresponding to the intestinal tumor according to the number and distribution of the blood supply vessels;

when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library;

calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor;

and outputting the image data of the intestinal tumor.
The method according to any one of claims 1-5, further comprising:

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

starting from the origin of the coordinate system, extracting multiple intestinal tumor cell layers from the image data of the intestinal tumor according to preset distances along the positive direction and the negative direction of the Z axis of the coordinate system respectively, wherein each intestinal tumor cell layer comprises an intestinal tumor cell data set;

performing preset processing on each intestinal tumor cell layer in the multiple intestinal tumor cell layers to obtain a first intestinal tumor cell data set, wherein the first intestinal tumor cell data set is the intestinal tumor cell data of the outermost layer in the intestinal tumors;

determining a growth cycle corresponding to the intestinal tumor from the first intestinal tumor cell dataset;

when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library;

calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor;

and outputting the image data of the intestinal tumor.
The method according to claim 7, wherein each intestinal tumor cell layer includes characteristic data corresponding to the intestinal tumor cell data set, the characteristic data corresponding to the intestinal tumor cell data set includes a shape corresponding to each intestinal tumor cell data in the intestinal tumor cell data set and a size corresponding to each intestinal tumor cell data in the intestinal tumor cell data set, and the preset processing includes the following steps:

acquiring outermost intestinal tumor cell data corresponding to the intestinal tumors from an intestinal tumor cell database, wherein the outermost intestinal tumor cell data comprise shapes and sizes corresponding to the outermost intestinal tumor cell data, and the intestinal tumor cell database comprises outermost intestinal tumor cell data corresponding to each of a plurality of intestinal tumors in different growth cycles;

second intestinal tumor cell data similar in shape and size to the outermost intestinal tumor cell data are extracted from each intestinal tumor cell layer.
The method of claim 1, wherein generating the image data of the intestinal tract, the intestinal tumor and the blood vessel from the scan image comprises:

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

importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprise a first data set of the intestinal tract and a data set of the blood vessel, the first data set of the intestinal tract comprises a data set of the intestinal tumor, the data set of the blood vessel comprises fusion data of the intersection position of the artery and the vein, the first data set of the intestinal tract is a transfer function result of a cubic space of tissue structures on the surface of the intestinal tract and in the intestinal tract, and the data set of the blood vessel is a transfer function result of a cubic space of the tissue structures on the surface of the blood vessel and in the blood vessel;

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 first data set of the intestinal tract, a data set of the artery and a data set of the vein, the first data in the data set of the artery and the second data in the data set of the vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position;

processing the second medical image data to obtain image data of the intestinal tract, image data of the intestinal tumor and image data of the blood vessel.
A medical imaging device, comprising:

the device comprises an acquisition module, a display module and a processing module, wherein the acquisition module is used for acquiring a scanning image of the intestinal tract of a user, and the scanning image further comprises intestinal tumors and blood vessels around the intestinal tract;

the generation module is used for generating the image data of the intestinal tract, the image data of intestinal tumor and the image data of blood vessel according to the scanning image;

the determining module is used for determining the position area of the intestinal tumor in the intestinal tract and the number and distribution of blood supply vessels of the intestinal tumor according to the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels;

and the output module is used for carrying out 4D medical imaging on the image data of the intestinal tract, the image data of the intestinal tumor and the image data of the blood vessels so as to output the number and distribution of the position area and the blood supply vessels.
The device according to claim 10, wherein the determination module is configured to determine the location area based on the image data of the intestinal tract and the image data of the intestinal tumor; determining the position area of each blood vessel in the image data of the blood vessel to obtain a plurality of pieces of blood vessel position information; determining the number and distribution of the blood-supplying vessels according to the position area and the plurality of vessel position information.
The apparatus according to claim 11, wherein the determining module is specifically configured to compare the image data of the intestinal tract with the image data of the intestinal tumor to determine first image data of the intestinal tract that matches the image data of the intestinal tumor; determining a spatial position of the first image data in the image data of the intestinal tract; setting the spatial position as the position region.
The device according to claim 12, wherein the determining module is specifically configured to segment the image data of the intestinal tract according to a category to which the intestinal tract belongs, so as to obtain a plurality of image subdata of the intestinal tract; executing the following steps for each image subdata of the plurality of image subdata of the intestinal tract, wherein the steps comprise: determining a target intestinal tract included in the currently processed image subdata; acquiring template image subdata according to a target intestinal tract included in the currently processed image subdata, wherein the template image subdata comprises image data of the target intestinal tract in a healthy state; comparing the currently processed image subdata with the template image subdata; if the currently processed image subdata is not matched with the template image subdata, second image subdata which is not matched with the template image subdata in the currently processed image subdata is obtained, and the second image subdata is compared with the image data of the intestinal tumor to determine that the second image subdata is first image data.
The device according to claim 10, wherein the determination module is configured to determine the location area based on the image data of the intestinal tract and the image data of the intestinal tumor; determining the connection position and the connection angle of each blood vessel and the intestinal tumor according to the image data of the blood vessel and the image data of the intestinal tumor, wherein each blood vessel is connected with the intestinal tumor in the image data of the blood vessel; and determining the number and distribution of the blood supply vessels according to the connection positions and the connection angles.
The apparatus according to any one of claims 10-14, further comprising an ablation module for determining a growth cycle corresponding to the intestinal tumor based on the number and distribution of the donor vessels; when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library; calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor; and outputting the image data of the intestinal tumor.
The apparatus according to any one of claims 10-14, further comprising an ablation module configured to establish a coordinate system from the image data of the intestinal tumor, the origin of the coordinate system being the center of the intestinal tumor, the X-axis, Y-axis and Z-axis of the coordinate system being perpendicular to each other and following a right-handed helical rule; starting from the origin of the coordinate system, extracting multiple intestinal tumor cell layers from the image data of the intestinal tumor according to preset distances along the positive direction and the negative direction of the Z axis of the coordinate system respectively, wherein each intestinal tumor cell layer comprises an intestinal tumor cell data set; performing preset processing on each intestinal tumor cell layer in the multiple intestinal tumor cell layers to obtain a first intestinal tumor cell data set, wherein the first intestinal tumor cell data set is the intestinal tumor cell data of the outermost layer in the intestinal tumors; determining a growth cycle corresponding to the intestinal tumor from the first intestinal tumor cell dataset; when the growth cycle is not matched with a preset growth cycle, determining an intestinal tumor simulated resection strategy corresponding to the growth cycle and the position area from an intestinal tumor simulated resection strategy library; calling an intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor so as to generate image data for resection of the intestinal tumor; and outputting the image data of the intestinal tumor.
The apparatus according to claim 16, wherein each layer of the intestinal tumor cell layer includes characteristic data corresponding to the intestinal tumor cell data set, the characteristic data corresponding to the intestinal tumor cell data set includes a shape corresponding to each intestinal tumor cell data in the intestinal tumor cell data set and a size corresponding to each intestinal tumor cell data in the intestinal tumor cell data set, and the preset processing includes the following steps:

acquiring outermost intestinal tumor cell data corresponding to the intestinal tumors from an intestinal tumor cell database, wherein the outermost intestinal tumor cell data comprise shapes and sizes corresponding to the outermost intestinal tumor cell data, and the intestinal tumor cell database comprises outermost intestinal tumor cell data corresponding to each of a plurality of intestinal tumors in different growth cycles;

second intestinal tumor cell data similar in shape and size to the outermost intestinal tumor cell data are extracted from each intestinal tumor cell layer.
The apparatus according to claim 1, wherein the generating module is specifically configured to perform a first preset process on the scan image to obtain a bitmap BMP data source; importing the BMP data source into a preset VRDS medical network model to obtain first medical image data, wherein the first medical image data comprise a first data set of the intestinal tract and a data set of the blood vessel, the first data set of the intestinal tract comprises a data set of the intestinal tumor, the data set of the blood vessel comprises fusion data of the intersection position of the artery and the vein, the first data set of the intestinal tract is a transfer function result of a cubic space of tissue structures on the surface of the intestinal tract and in the intestinal tract, and the data set of the blood vessel is a transfer function result of a cubic space of the tissue structures on the surface of the blood vessel and in the blood vessel; 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 first data set of the intestinal tract, a data set of the artery and a data set of the vein, the first data in the data set of the artery and the second data in the data set of the vein are independent of each other, the first data is data associated with the cross position, and the second data is data associated with the cross position; processing the second medical image data to obtain image data of the intestinal tract, image data of the intestinal tumor and image data of the blood vessel.
A medical imaging apparatus comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and generated as instructions for execution by the processor to perform the steps of the method of any of claims 1-9.
A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, which is executed by the processor, to implement the method of any of claims 1-9.