WO2021081842A1 - Procédé d'analyse de néoplasme intestinal et de système vasculaire basé sur une image médicale d'intelligence artificielle (ia) vrds, et dispositif associé - Google Patents

Procédé d'analyse de néoplasme intestinal et de système vasculaire basé sur une image médicale d'intelligence artificielle (ia) vrds, et dispositif associé Download PDF

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WO2021081842A1
WO2021081842A1 PCT/CN2019/114482 CN2019114482W WO2021081842A1 WO 2021081842 A1 WO2021081842 A1 WO 2021081842A1 CN 2019114482 W CN2019114482 W CN 2019114482W WO 2021081842 A1 WO2021081842 A1 WO 2021081842A1
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data
intestine
intestinal
image data
tumor
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PCT/CN2019/114482
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English (en)
Chinese (zh)
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李戴维伟
李斯图尔特平
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未艾医疗技术(深圳)有限公司
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Priority to CN201980099768.XA priority Critical patent/CN114340497A/zh
Priority to PCT/CN2019/114482 priority patent/WO2021081842A1/fr
Publication of WO2021081842A1 publication Critical patent/WO2021081842A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to intestinal tumor and blood vessel analysis methods and related devices based on VRDS AI medical imaging.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • DTI Diffusion Tensor Imaging
  • Computed Tomography Positron Emission Computed Tomography
  • the embodiments of the present application provide methods and related devices for analyzing intestinal tumors and blood vessels based on VRDS AI medical images. Implementing the embodiments of the present application can improve the diagnosis efficiency of intestinal diseases.
  • the first aspect of the embodiments of the present application provides a method for intestinal tumor and blood vessel analysis based on VRDS AI medical imaging, including:
  • a second aspect of the embodiments of the present application provides a medical imaging device, including:
  • An acquisition module for acquiring a scanned image of the user's intestine, wherein the scanned image further includes intestinal tumors and blood vessels around the intestine;
  • a generating module for generating image data of the intestine, image data of the intestine tumor, and image data of the blood vessel according to the scanned image;
  • the determining module is used to determine the location area of the intestine tumor in the intestine and the blood supply vessel of the intestine tumor according to the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel. Quantity and distribution;
  • the output module is used to perform 4D medical imaging on the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel to output the location area and the number and distribution of the blood supply vessel.
  • a third aspect of the embodiments of the present application provides a medical imaging device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are generated It is executed by the processor to execute the instructions of the steps in any one of the methods of the first aspect of the above claims.
  • the fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, and the stored computer program is executed by the processor to implement the first aspect of the claims. Any of the methods.
  • a scanned image of the user’s intestine is acquired, where the scanned image also includes the intestinal tumor and blood vessels around the intestine, and the image data of the intestine, the image data of the intestinal tumor and the blood vessels are generated according to the scanned image. Then, according to the image data of the intestine, the image data of the intestinal tumor, and the image data of the blood vessel, determine the location area of the intestinal tumor and the number and distribution of the blood supply vessels of the intestinal tumor, so as to realize the rapid response to the intestinal disease Diagnosis, to avoid the problem of low efficiency in diagnosing intestinal diseases due to the inability of the two-dimensional slice scan image to show the spatial structure of the intestine.
  • 4D medical imaging is performed on intestinal imaging data, intestinal tumor imaging data, and blood vessel imaging data to output the location area and the number and distribution of blood vessels, which facilitates doctors to locate symptoms and improves the diagnosis efficiency of intestinal diseases.
  • FIG. 1 is a schematic structural diagram of an intestinal tumor and blood vessel analysis system based on VRDS AI medical imaging according to an embodiment of the application;
  • 2A is a schematic flowchart of a method for analyzing intestinal tumors and blood vessels based on VRDS AI medical imaging according to an embodiment of the application;
  • 2B is a schematic diagram of a coordinate system provided by an embodiment of this application.
  • FIG. 3 is a schematic flowchart of yet another method for analyzing intestinal tumors and blood vessels based on VRDS AI medical imaging according to an embodiment of the application;
  • FIG. 4 is a schematic diagram of a medical imaging device provided by an embodiment of the application.
  • FIG. 5 is a schematic structural diagram of a medical imaging device in a hardware operating environment related to an embodiment of the application.
  • the medical imaging devices involved in the embodiments of this application refer to various instruments that use various media as information carriers to reproduce the internal structure of the human body as images.
  • the image information and the actual structure of the human body have spatial and temporal distributions.
  • DICOM data refers to the original image file data that reflects the internal structural characteristics of the human body collected by medical equipment, which can include electronic computed tomography CT, magnetic resonance MRI, diffusion tensor imaging DTI, and positron emission computed tomography PET-
  • image source refers to the Texture2D/3D image volume data generated by analyzing the original DICOM data.
  • VRDS refers to the Virtual Reality Doctor system (VRDS for short).
  • Fig. 1 is a schematic structural diagram of an intestinal tumor and blood vessel analysis system 100 based on VRDS AI medical imaging according to an embodiment of the present application.
  • the system 100 includes a medical imaging device 110 and a network database 120.
  • the medical imaging The device 110 may include a local medical imaging device 111 and/or a terminal medical imaging device 112.
  • the local medical imaging device 111 or the terminal medical imaging device 112 is used for the VRDS AI medical imaging based on the original DICOM data presented in the embodiment of this application.
  • the analysis algorithm of intestinal tumors and blood vessels Based on the analysis algorithm of intestinal tumors and blood vessels, it carries out the recognition, positioning, four-dimensional volume rendering, and abnormal analysis of the human intestinal image area to realize the four-dimensional stereo imaging effect (the four-dimensional medical image specifically refers to the medical image including the inside of the displayed tissue Spatial structural features and external spatial structural features.
  • the internal spatial structural features mean that the slice data inside the tissue is not lost, that is, the medical imaging device can present the internal structure of tissues such as intestines and blood vessels.
  • the external spatial structural characteristics refer to tissues and tissues.
  • the environmental characteristics between the tissues including the spatial location characteristics between tissues (including intersections, intervals, fusions, etc., such as the edge structure characteristics of the intersection between arteries and veins, etc.), the local medical imaging device 111 is relative to the terminal
  • the medical imaging device 112 can also be used to edit the scanned image to form a transfer function result of a four-dimensional human body image.
  • the transfer function result may include the transfer function result of the surface of the human intestine and the tissue structure in the human intestine, and the transfer of the cube space. Function results, such as the number of cube edit boxes and arc edit arrays, coordinates, colors, transparency and other information required by the transfer function.
  • the network database 120 may be, for example, a cloud medical imaging device, etc.
  • the network database 120 is used to store the image source generated by analyzing the original DICOM data and the transfer function result of the four-dimensional human body image edited by the local medical imaging device 111.
  • the scanned image may be from Multiple local medical imaging devices 111 are used to realize interactive diagnosis of multiple doctors.
  • HMDS head-mounted Displays Set
  • the operating actions refer to the user’s actions through the medical imaging device.
  • External ingestion equipment such as mouse, keyboard, tablet (portable android device, Pad), iPad (internet portable apple device), etc., operate and control the four-dimensional human image to achieve human-computer interaction.
  • the operation actions include at least the following One: (1) Change the color and/or transparency of a specific organ/tissue, (2) Position the zoom view, (3) Rotate the view, realize the multi-view 360-degree observation of the four-dimensional human body image, (4) "Enter” Observe the internal structure of human organs, render real-time clipping effects, and (5) move the view up and down.
  • FIG. 2A is a schematic flowchart of a method for analyzing intestinal tumors and blood vessels based on VRDS AI medical imaging according to an embodiment of the application.
  • an intestinal tumor and blood vessel analysis method based on VRDS AI medical imaging provided by an embodiment of the present application may include:
  • a medical imaging device acquires a scanned image of a user's intestine, where the scanned image further includes an intestinal tumor and blood vessels around the intestine.
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the medical imaging device generates image data of the intestine, image data of the intestine tumor, and image data of the blood vessel according to the scanned image.
  • blood vessels include arteries and veins.
  • the arteries may include, for example, superior mesenteric artery, inferior mesenteric artery, and the like.
  • the veins may include mesenteric veins and the like, for example.
  • the image data of the intestine includes the three-dimensional image data of the intestine
  • the image data of the intestinal tumor includes the three-dimensional image data of the intestinal tumor
  • the image data of the blood vessel includes the three-dimensional image data of the blood vessel. Spatial image data.
  • the medical imaging device determines the location area of the intestine tumor in the intestine and the blood supply vessel of the intestine tumor according to the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel. Quantity and distribution.
  • the location area of the intestine tumor in the intestine is determined according to the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel.
  • the number and distribution of blood supply vessels of the intestinal tumor includes: determining the location area according to the image data of the intestine and the image data of the intestine tumor; determining the location area of each blood vessel in the image data of the blood vessel , To obtain multiple blood vessel location information; determine the number and distribution of the blood supply vessels according to the location area and the multiple blood vessel location information.
  • the image data of the blood vessel includes multiple blood vessels, where the first blood vessel is any one of the multiple blood vessels. It is assumed that the blood vessel position information corresponding to the first blood vessel is related to the intestinal tumor in the intestinal tract. If the position and area match of, it is determined that the first blood vessel is the blood supply vessel of the intestinal tumor.
  • the determining the number and distribution of the blood supply vessels according to the position area and the position information of the plurality of blood vessels includes: matching the position area and the position information of the plurality of blood vessels; and determining according to the matching result The number and distribution of the blood supply vessels.
  • each blood vessel position information in the multiple blood vessel position information includes multiple spatial positions of each blood vessel distributed in the image data of the blood vessel.
  • the first blood vessel position information is any one of the multiple blood vessel position information, and the first blood vessel position information may include: (X1, Y1, Z1), (X2, Y2, Z2), and ( X3, Y3, Z3) and so on.
  • the location area is determined according to the image data of the intestine and the image data of the intestine tumor, and then the location area of each blood vessel in the image data of the blood vessel is determined to obtain multiple blood vessel location information.
  • the number and distribution of blood supply vessels are determined according to the location area and the location information of multiple blood vessels, thereby improving the accuracy of determining the location area of the bowel tumor in the intestine and the number and distribution of the blood supply blood vessels of the bowel tumor.
  • the determining the location area according to the imaging data of the intestine and the imaging data of the intestinal tumor includes: combining the imaging data of the intestine Compare with the image data of the intestine tumor to determine the first image data matching the image data of the intestine tumor in the image data of the intestine; determine the image of the first image data in the intestine The spatial position in the data; the spatial position is set as the position area.
  • the intestinal imaging data is compared with the intestinal tumor imaging data to determine the first image data matching the intestinal tumor imaging data in the intestinal imaging data, thereby determining the first image data.
  • the spatial position of the image data in the image data of the intestine, thereby determining the location area improves the accuracy of determining the location area of the intestine tumor in the intestine.
  • the imaging data of the intestine is compared with the imaging data of the intestine tumor to determine the difference between the imaging data of the intestine and the intestine tumor.
  • the first image data matched by the image data includes: segmenting the image data of the intestine according to the category to which the intestine belongs to obtain multiple image sub-data of the intestine;
  • Each image sub-data in the image sub-data performs the following steps, including: determining the target intestine included in the currently processed image sub-data; obtaining template image sub-data according to the target intestine included in the currently processed image sub-data, wherein The template image sub-data includes image data of the target intestine in a healthy state; compare the currently processed image sub-data with the template image sub-data; if the currently processed image sub-data does not match the template image sub-data , Acquire the second image sub-data that does not match the template image sub-data in the currently processed image sub-data, and compare the second image sub-data with the image
  • the template image sub-data includes image data of the small intestine in a healthy state.
  • 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 sub-data, and the first coordinate system
  • the origin of a coordinate system is the center of the target intestine, and the X, Y, and Z axes of the first coordinate system are perpendicular to each other and follow the right-hand spiral law; starting from the origin of the first coordinate system, follow the The preset distance is along the positive and negative directions of the Z axis of the first coordinate system to extract multiple layers of first intestinal cell layers from the currently processed image sub-data; and combine the multiple layers of first intestinal cell layers with A comparison is made with multiple second intestinal cell layers, which are extracted from the template image sub-data.
  • each layer of the first intestinal cell layer includes a first intestinal cell data set and feature data corresponding to the first intestinal cell data set, and the feature data corresponding to the first intestinal cell data set includes the first intestinal cell data set.
  • the shape corresponding to each first intestinal cell data in the first intestinal cell data set, the size corresponding to each first intestinal cell data in the first intestinal cell data set, and each in the first intestinal cell data set The spatial location of the first intestinal cell data;
  • Each second intestinal cell layer includes a second intestinal cell data set and feature data corresponding to the second intestinal cell data set, and the feature data corresponding to the second intestinal cell data set includes the second intestine
  • the first intestinal cell layer is any intestinal cell layer in the multi-layer first intestinal cell layer
  • the second intestinal cell layer is the multi-layer second intestinal cell layer and the The first intestinal cell layer has an associated cell layer, and the associated relationship is the spatial position and location of each first intestinal cell data in the first intestinal cell data set included in the first intestinal cell layer.
  • the second intestinal cell data set included in the second intestinal cell layer matches the spatial position of each second intestinal cell data set, and the first intestinal cell layer is compared with the second intestinal cell layer , Including: acquiring the first intestinal cell data set included in the first intestinal cell layer and characteristic data corresponding to the first intestinal cell data set; and separately collecting the first intestinal cell data set for each The shape and size corresponding to the first intestinal cell data are compared with the shape and size corresponding to each second intestinal cell data in the second intestinal cell data set.
  • the position of the intestinal tumor in the intestine is determined based on the imaging data of the intestine, the imaging data of the intestinal tumor, and the imaging data of the blood vessel
  • the area and the number and distribution of blood supply vessels of the intestinal tumor include: determining the location area according to the imaging data of the intestine and the imaging data of the intestinal tumor; and determining the location area according to the imaging data of the blood vessel and the intestinal tumor Determine the connection position and connection angle of each blood vessel with the intestinal tumor from the image data; determine the number and distribution of the blood supply blood vessels according to the connection position and the connection angle.
  • the location area is determined based on the image data of the intestine and the image data of the intestinal tumor, and then the connection position and angle of connection between each blood vessel and the intestine tumor are determined according to the image data of the blood vessel and the image data of the intestine tumor. Finally, the number and distribution of blood supply vessels are determined according to the connection position and connection angle, which improves the accuracy of determining the location area of intestinal tumors in the intestine and the number and distribution of blood supply vessels for intestinal tumors.
  • the medical imaging device performs 4D medical imaging on the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel to output the location area and the number and distribution of the blood supply vessels.
  • a scanned image of the user’s intestine is acquired, where the scanned image also includes the intestinal tumor and blood vessels around the intestine, and the image data of the intestine, the image data of the intestinal tumor and the blood vessels are generated according to the scanned image. Then, according to the image data of the intestine, the image data of the intestinal tumor, and the image data of the blood vessel, determine the location area of the intestinal tumor and the number and distribution of the blood supply vessels of the intestinal tumor, so as to realize the rapid response to the intestinal disease Diagnosis, to avoid the problem of low efficiency in diagnosing intestinal diseases due to the inability of the two-dimensional slice scan image to show the spatial structure of the intestine.
  • 4D medical imaging is performed on intestinal imaging data, intestinal tumor imaging data, and blood vessel imaging data to output the location area and the number and distribution of blood vessels, which facilitates doctors to locate symptoms and improves the diagnosis efficiency of intestinal diseases.
  • said generating the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel according to the scan image includes: performing a first operation on the scan image. Preset processing to obtain a bitmap BMP data source; import the BMP data source into a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes a first data set of the intestine and The data set of the blood vessel, the first data set of the intestine includes the data set of the intestinal tumor, the data set of the blood vessel includes the fusion data of the intersection position of the artery and the vein, and the first data of the intestine
  • the set is the transfer function result of the cube space of the tissue structure inside the intestine and the surface of the intestine
  • the data set of the blood vessel is the transfer function result of the cube space of the tissue structure inside the blood vessel and the surface of the blood vessel Importing the first medical image data into a preset cross-vascular network model to obtain second
  • the first preset processing includes at least one of the following operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, and VRDS Ai elastic deformation processing.
  • the VRDS limited contrast adaptive histogram equalization includes the following steps: performing regional noise ratio limiting and global contrast limiting on the image source; dividing the local histogram of the image source into multiple partitions; The slope of the cumulative histogram of the neighborhood of each of the multiple partitions determines multiple slopes of the multiple transformation functions; the pixels of each of the multiple partitions are determined according to the multiple slopes.
  • the degree of contrast magnification around the value; according to the degree of contrast magnification around the pixel value of each of the multiple partitions, the multiple partitions are subject to limited cropping processing to obtain the distribution of the effective histogram and the effectively usable neighborhood
  • the value of the size; the histogram cut by the limit is evenly distributed to other areas of the local histogram of the image source.
  • the hybrid partial differential denoising includes the following steps: the image source is processed through VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, so that the curvature of the image edge is less than the preset curvature, which can protect the edge of the image, and The mixed partial differential denoising model that can avoid the step effect in the smoothing process;
  • the VRDS Ai elastic deformation processing includes the following steps: acquiring the image dot matrix of the image source, superimposing the positive and negative random distances on the image dot matrix to form a difference position matrix, and for each of the difference position matrix Perform grayscale processing on each difference position to obtain a new difference position matrix, so as to realize the distortion inside the image, and then perform rotation, distortion, and translation operations on the image.
  • the hybrid partial differential denoising is processed by the medical imaging device using a CDD and a high-order denoising model to process the image source.
  • the CDD model (Curvature Driven Diffusions) model is formed by introducing a curvature drive on the basis of the TV (Total Variation) model, which solves the problem that the TV model cannot repair the visual connectivity of the image.
  • the high-order denoising refers to denoising the image based on a partial differential equation (PDE) method.
  • the image source is subjected to a noise filtering effect according to the specified differential equation function change to obtain the BMP data source.
  • the solution of the partial differential equation is the BMP data source obtained after high-order denoising.
  • the PDE-based image denoising method has the characteristics of anisotropic diffusion, so it can perform different degrees in different regions of the image source. The effect of diffusion, so as to achieve the effect of suppressing noise while protecting the edge texture information of the image.
  • the medical imaging device uses at least one of the following image processing operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing, which improves the execution efficiency of image processing, and Improve image quality and protect the edge texture of the image.
  • the performing the first preset processing on the scanned image to obtain the bitmap BMP data source includes: setting the scanned image as the user's medical digital imaging and communication DICOM Data; parsing the DICOM data to generate the image source of the user, the image source including texture 2D/3D image volume data; performing the first preset processing on the image source to obtain the BMP data source.
  • the DICOM Digital Imaging and Communications in Medicine
  • the medical imaging device first acquires multiple scanned images that reflect the internal structural characteristics of the user's intestines, and can screen out at least one suitable scanned image containing the intestinal tract based on clarity, accuracy, etc. Further processing is performed on the scanned image to obtain a bitmap BMP data source.
  • the medical imaging device can obtain a bitmap BMP data source after filtering, parsing, and first preset processing based on the acquired scanned image, which improves the accuracy and clarity of medical image imaging.
  • the medical imaging device processes the scanned image into image data that can reflect the spatial structure characteristics of the intestine through a series of data processing, and the venous image data and arterial image data at the crossing position are independent of each other, supporting three-dimensional Accurate presentation of space improves the accuracy and comprehensiveness of data processing.
  • the importing the BMP data source into the preset VRDS medical network model to obtain the first medical image data includes: importing the BMP data source into the preset VRDS medical network model , Call each transfer function in the set of pre-stored transfer functions through the VRDS medical network model, and process the BMP data source through multiple transfer functions in the transfer function set to obtain the first medical image data.
  • the function set includes the transfer function of the intestine and the transfer function of the blood vessel preset by a reverse editor.
  • BMP full name Bitmap
  • DDB device-dependent bitmap
  • DIB device-independent bitmap
  • the VRDS medical network model is a preset network model, and its training method includes the following three steps: image sampling and scale scaling; 3D convolutional neural network feature extraction and scoring; medical imaging device evaluation and network training.
  • first sampling will be required to obtain N BMP data sources, and then M BMP data sources will be extracted from the N BMP data sources at a preset interval. It needs to be explained that the preset interval can be flexibly set according to the usage scenario.
  • Sample M from N then scale the sampled M BMP data sources to a fixed size (for example, the length is S pixels, the width is S pixels), and the resulting processing result is used as the input of the 3D convolutional neural network .
  • M BMP data sources are used as the input of the 3D convolutional neural network.
  • a 3D convolutional neural network is used to perform 3D convolution processing on the BMP data source to obtain a feature map.
  • the medical imaging device extracts the first data from the blood vessel data set including the fusion data of the intersection of the artery and the vein, and uses a preset data separation algorithm to separate The fusion data is separated to obtain arterial boundary point data.
  • the second data is extracted by the medical imaging device from the fusion data of the intersection position of the artery and the vein in the blood vessel data set, and the fusion data is separated by using a preset data separation algorithm to obtain the vein boundary point data.
  • the second preset processing includes at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization processing, and data enhancement processing.
  • the 2D boundary optimization processing includes: multiple sampling to obtain low-resolution information and high-resolution information, where the low-resolution information can provide contextual semantic information of the segmentation target in the entire image, that is, reflecting the segmentation target and the environment The features of the relationship between these features are used to determine the object category, and the high-resolution information is used to provide more refined features, such as gradients, for the segmentation target.
  • segmentation targets include intestines, arteries and veins.
  • the 3D boundary optimization processing includes: 3D convolution, 3D max pooling, and 3D upward convolution layer, the input data size is a1, a2, a3, the number of channels is c, the filter size is f, that is, the filter dimension is f*f*f*c, the number of filters is n, the final output of the 3-dimensional convolution is:
  • each layer contains two 3*3*3 convolution kernels, each of which is followed by an activation function (Relu), and then there is a maximum pooling of 2*2*2 in each dimension to merge the two Steps.
  • each layer is composed of 2*2*2 upward convolutions, with a step size of 2 in each dimension, and then two 3*3*3 convolutions, and then Relu. Then in the analysis path, the shortcut connections of equal resolution layers provide the basic high-resolution features of the synthesized path. In the last layer, 1*1*1 convolution reduces the number of output channels.
  • the 3D boundary optimization processing includes the following operations: inputting the second medical image data into a 3D convolution layer to perform a 3D convolution operation to obtain a feature map; inputting the feature map to a 3D pooling layer for pooling And non-linear activation to obtain a first feature map; cascading the first feature map to obtain a prediction result.
  • the data enhancement processing includes any one of the following: data enhancement based on arbitrary angle rotation, data enhancement based on histogram equalization, data enhancement based on white balance, data enhancement based on mirroring operation, data enhancement based on random cut And data enhancement based on simulating different lighting changes.
  • the first data set of the intestines further includes fusion data of the cross positions of the intestines
  • the second medical image data is processed to obtain all the data.
  • the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel include: importing the second medical image data into a preset cross-intestinal network model to obtain third medical image data, so
  • the third medical image data includes a second data set of the intestine, a data set of the arteries, and a data set of the veins, and the second data set of the intestine includes the separation of the cross positions of the intestines Data, the surface features of the intestine and the data collection of the intestinal tumor; performing a second preset processing on the third medical image data to obtain the image data of the intestine, the image data of the intestine tumor, and The image data of the blood vessel.
  • the second medical image data is imported into the preset cross-intestinal network model to obtain the third medical image data.
  • the third medical image data includes the second data set of the intestine and the data set of the arteries.
  • the second data set of the intestine includes the separation data of the cross position of the intestine, the surface characteristics of the intestine and the data set of the intestinal tumor.
  • the second preset processing is performed on the third medical image data to Obtain intestinal imaging data, intestinal tumor imaging data, and blood vessel imaging data, so that the medical imaging data can better restore the original organs or tissues, improve the authenticity of medical imaging data, facilitate doctors to locate symptoms, and improve intestinal diseases Diagnostic efficiency
  • the 4D medical imaging is performed on the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel to output the location area and
  • the quantity and distribution of the blood supply vessels include: acquiring image data of the intestine, the image data of the intestine tumor, and multiple image quality scores corresponding to the image data of the blood vessel, respectively; according to the multiple image quality The score selects a plurality of enhanced data with an image quality score greater than a preset image quality score from the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel; Perform 4D medical imaging to output the location area and the number and distribution of the blood supply vessels.
  • the multiple image quality scores include multiple image quality scores corresponding to the image data of the intestine, multiple image quality scores corresponding to the image data of the intestine tumor, and multiple image quality scores corresponding to the image data of the blood vessel.
  • Image quality score corresponding to the image data of the intestine, multiple image quality scores corresponding to the image data of the intestine tumor, and multiple image quality scores corresponding to the image data of the blood vessel.
  • the plurality of enhancement data includes enhancement data with an image quality score greater than the preset image quality score in the image data of the intestine, and an image quality score greater than the preset image quality in the image data of the bowel tumor
  • the scored enhancement data and the enhancement data of the image data of the blood vessel with an image quality score greater than the preset image quality score are provided.
  • multiple image quality scores corresponding to the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel are obtained respectively; according to the multiple image quality scores, the image data of the intestine, Intestinal tumor image data and blood vessel image data, multiple enhancement data with image quality scores greater than the preset image quality score are screened out; multiple enhancement data are subjected to 4D medical imaging to output the location area and the number and distribution of blood vessels, Thereby assisting doctors in rapid diagnosis and improving the diagnosis efficiency of intestinal diseases. At the same time, the use of enhanced data for 4D medical imaging improves image clarity and accuracy.
  • the method further includes: establishing a coordinate system according to the imaging data of the intestinal tumor, the origin of the coordinate system is the center of the intestinal tumor, and the X of the coordinate system
  • the axis, Y axis, and Z axis are perpendicular to each other and follow the right-handed spiral rule; starting from the origin of the coordinate system, follow the preset distance along the positive and negative directions of the Z axis of the coordinate system from the bowel tumor
  • Multi-layer intestinal tumor cell layers are extracted from the image data, and each intestinal tumor cell layer includes an intestinal tumor cell data set; preset processing is performed on each intestinal tumor cell layer in the multi-layer intestinal tumor cell layer to obtain the first An intestinal tumor cell data set, where the first intestinal tumor cell data set is the outermost intestinal tumor cell data in the intestinal tumor; determining the growth cycle corresponding to the intestinal tumor according to the first intestinal tumor cell data set; When the growth cycle does not match the preset growth cycle, determine the intestinal tumor simulated resection strategy corresponding to the growth cycle and the location area
  • the preset distance is determined according to the thickness of the intestinal tumor cell layer.
  • Figure 2B is a schematic diagram of a coordinate system provided by an embodiment of the application. As shown in Figure 2B, the origin of the coordinates is the center of the bowel tumor, and the X-axis, Y-axis and Z-axis of the coordinate system are perpendicular to each other and follow the right hand The law of spirals.
  • the determining the growth cycle corresponding to the intestinal tumor according to the first intestinal tumor cell data set includes: acquiring each first intestinal tumor cell data set in the first intestinal tumor cell data set.
  • the spatial position corresponding to the intestinal tumor cell data determine the positional relationship between each first intestinal tumor cell data and the intestine according to the spatial position corresponding to each first intestinal tumor cell data in the first intestinal tumor cell data set; according to The positional relationship determines the growth cycle corresponding to the intestinal tumor.
  • the positional relationship includes one of the following: each first intestinal tumor cell data is located inside the intestine or each first intestinal tumor cell data is located outside the intestine.
  • the growth cycle can include, for example, stage 0, stage I, stage II, stage III and stage IV.
  • Stage 0 cancer is at an early stage and cancer cells only exist in the innermost layer of the intestine.
  • Stage I cancer cells invade the inner wall of the colon and intestine. Many areas; stage II: cancer cells have spread to the surrounding tissues but have not spread to the lymph nodes; stage III: cancer cells have spread to the peripheral lymph nodes, but have not spread to other parts of the body.
  • Stage IV Cancer cells have spread to other parts of the body.
  • the preset growth cycle is the time required to increase the probability of self-healing by removing the intestinal tumor.
  • each intestinal tumor cell layer includes characteristic data corresponding to the intestinal tumor cell data set, and the characteristic data corresponding to the intestinal tumor cell data set includes the intestinal tumor cell data.
  • the preset processing includes the following steps: obtaining the intestinal tumor corresponding to the intestinal tumor from the intestinal tumor cell database
  • the outermost intestinal tumor cell data includes the shape and size corresponding to the outermost intestinal tumor cell data
  • the intestinal tumor cell database includes each intestinal tumor in a variety of intestinal tumors.
  • the outermost intestinal tumor cell data corresponding to the tumor in different growth cycles; the second intestinal tumor cell data whose shape and size are similar to the data of the outermost intestinal tumor cell are extracted from each intestinal tumor cell layer.
  • the intestinal tumor simulated resection strategy library includes a variety of intestinal tumor growth cycles and a variety of intestinal tumor locations in the intestine corresponding to a variety of intestinal tumor simulated resection strategies. Each intestinal tumor simulated resection strategy is different from each other.
  • the image data of the resection of the intestine tumor may include, for example, video data of the resection of the intestine tumor.
  • the intestinal tumor simulated resection strategy corresponding to the growth cycle and location area is determined from the intestinal tumor simulated resection strategy database when the growth cycle does not match the preset growth cycle, and then the intestinal tumor simulated resection strategy is determined according to the intestinal tumor
  • the simulated resection strategy calls the intestinal tumor simulation resection algorithm to process the intestinal tumor to generate the image data for the resection of the intestinal tumor.
  • the image data of the resection of the intestinal tumor is output, which improves the accuracy of the simulated resection of the intestinal tumor and provides the doctor with resection. Intestinal tumor imaging data to improve the success rate of intestinal tumor resection.
  • FIG. 3 is a schematic flowchart of another method for analyzing bowel tumors and blood vessels based on VRDS AI medical imaging according to an embodiment of the application. Among them, as shown in Figure 3, it includes:
  • the medical imaging device determines the growth cycle corresponding to the intestinal tumor according to the number and distribution of the blood supply vessels.
  • the growth cycle can include, for example, stage 0, stage I, stage II, stage III and stage IV.
  • Stage 0 cancer is at an early stage and cancer cells only exist in the innermost layer of the intestine.
  • Stage I cancer cells invade the inner wall of the colon and intestine. Many areas; stage II: cancer cells have spread to the surrounding tissues but have not spread to the lymph nodes; stage III: cancer cells have spread to the peripheral lymph nodes, but have not spread to other parts of the body.
  • Stage IV Cancer cells have spread to other parts of the body.
  • the medical imaging device determines the intestinal tumor simulated resection strategy corresponding to the growth period and the location area from the intestinal tumor simulated resection strategy library.
  • the preset growth cycle is the time required to increase the probability of self-healing by removing the intestinal tumor.
  • the intestinal tumor simulated resection strategy library includes a variety of intestinal tumor growth cycles and multiple intestinal tumor simulated resection strategies corresponding to the location area of multiple intestinal tumors in the intestine. Each intestinal tumor simulated resection strategy is different from each other.
  • the medical imaging device invokes an intestinal tumor simulated resection algorithm to process the intestinal tumor according to the intestinal tumor simulated resection strategy to generate image data for resection of the intestinal tumor.
  • the image data of the resection of the intestine tumor may include, for example, video data of the resection of the intestine tumor.
  • the medical imaging device outputs image data for resection of the intestinal tumor.
  • the growth cycle of intestinal tumors is determined according to the number and distribution of blood supply vessels. Then, when the growth cycle does not match the preset growth cycle, the growth cycle is determined from the intestinal tumor simulation resection strategy library.
  • Improve the accuracy of the process of simulating the resection of intestinal tumors and provide doctors with imaging data of the resection of intestinal tumors to improve the success rate of intestinal tumor resection.
  • the medical imaging apparatus 400 may include:
  • the obtaining module 401 is configured to obtain a scanned image of the user's intestine, where the scanned image further includes intestinal tumors and blood vessels around the intestine;
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the generating module 402 is configured to generate the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel according to the scanned image;
  • blood vessels include arteries and veins.
  • the arteries may include, for example, superior mesenteric artery, inferior mesenteric artery, and the like.
  • the veins may include mesenteric veins and the like, for example.
  • the image data of the intestine includes the three-dimensional image data of the intestine
  • the image data of the intestinal tumor includes the three-dimensional image data of the intestinal tumor
  • the image data of the blood vessel includes the three-dimensional image data of the blood vessel. Spatial image data.
  • the generating module is specifically configured to perform first preset processing on the scanned image to obtain a bitmap BMP data source; import the BMP data source into a preset VRDS medical network model to obtain the first medical image Data, the first medical image data includes a first data set of the intestine and a data set of the blood vessel, the first data set of the intestine includes a data set of the intestine tumor, and the data of the blood vessel
  • the set includes the fusion data of the intersection position of the artery and the vein
  • the first data set of the intestine is the transfer function result of the cube space of the tissue structure of the intestine surface and the inside of the intestine
  • the data set of the blood vessel Is the transfer function result of the cube space of the blood vessel surface and the tissue structure inside the blood vessel
  • the first medical image data is imported into the preset cross blood vessel network model to obtain the second medical image data.
  • the image data includes a first data set of the intestine, a data set of the arteries, and a data set of the veins, and the first data in the arterial data set and the second data in the vein data set
  • the data are independent of each other, the first data is data associated with the intersection position, and the second data is data associated with the intersection position; the second medical image data is processed to obtain the intestinal tract Image data of, image data of the bowel tumor, and image data of the blood vessel.
  • the determining module 403 is configured to determine the location area of the intestine tumor in the intestine and the blood supply vessel of the intestine tumor according to the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel The number and distribution of;
  • the determining module is specifically configured to determine the location area according to the image data of the intestine and the image data of the intestine tumor; determine the location area of each blood vessel in the image data of the blood vessel to Obtain multiple blood vessel location information; determine the number and distribution of the blood supply vessels according to the location area and the multiple blood vessel location information.
  • the determining module is specifically configured to compare the image data of the intestine with the image data of the intestine tumor to determine that the image data of the intestine matches the image data of the intestine tumor Determine the spatial position of the first image data in the image data of the intestinal tract; set the spatial position as the position area.
  • the determining module is specifically configured to segment the image data of the intestine according to the category to which the intestine belongs to obtain multiple image sub-data of the intestine;
  • Each of the image sub-data performs the following steps, including: determining the target intestine included in the currently processed image sub-data; obtaining template image sub-data according to the target intestine included in the currently processed image sub-data, wherein, The template image sub-data includes image data of the target intestine in a healthy state; compare the currently processed image sub-data with the template image sub-data; if the currently processed image sub-data is different from the template image sub-data Match, acquire the second image sub-data that does not match the template image sub-data in the currently processed image sub-data, and compare the second image sub-data with the image data of the bowel tumor to determine the The second image sub-data is the first image data.
  • the determining module is specifically configured to determine the location area according to the imaging data of the intestine and the imaging data of the intestinal tumor; determine the location area according to the imaging data of the blood vessel and the imaging data of the intestinal tumor.
  • the connection position and connection angle between each blood vessel and the intestine tumor, and each blood vessel is a blood vessel connected to the intestine tumor in the image data of the blood vessel; the connection position and the connection angle are determined according to the connection position and the connection angle.
  • the output module 404 is configured to perform 4D medical imaging on the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel to output the location area and the number and distribution of the blood supply vessels.
  • the device further includes a resection module, which is used to determine the growth cycle of the bowel tumor according to the number and distribution of the blood supply vessels; when the growth cycle does not match a preset growth cycle At the time, determine the intestinal tumor simulated resection strategy corresponding to the growth cycle and the location area from the intestinal tumor simulated resection strategy library; call the intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor To generate image data for resection of the intestinal tumor; output image data for resection of the intestine tumor.
  • a resection module which is used to determine the growth cycle of the bowel tumor according to the number and distribution of the blood supply vessels; when the growth cycle does not match a preset growth cycle At the time, determine the intestinal tumor simulated resection strategy corresponding to the growth cycle and the location area from the intestinal tumor simulated resection strategy library; call the intestinal tumor simulated resection algorithm according to the intestinal tumor simulated resection strategy to process the intestinal tumor To generate image data for resection
  • the device further includes a resection module, and the resection module is configured to establish a coordinate system according to the image data of the bowel tumor, the origin of the coordinate system is the center of the bowel tumor, and the coordinate system
  • the X-axis, Y-axis, and Z-axis are perpendicular to each other and follow the right-handed spiral rule; starting from the origin of the coordinate system, from the intestinal tumor along the positive and negative directions of the Z-axis of the coordinate system at a preset distance, Multi-layer intestinal tumor cell layers are extracted from the image data of the intestine tumor cell layer, and each intestinal tumor cell layer includes an intestinal tumor cell data set; preset processing is performed for each intestinal tumor cell layer in the multi-layer intestinal tumor cell layer to obtain the first An intestinal tumor cell data set, where the first intestinal tumor cell data set is the outermost intestinal tumor cell data in the intestinal tumor; the growth cycle corresponding to the intestinal tumor is determined according to the first intestinal tumor cell data set When the growth cycle does not match the preset growth cycle, determine the intestinal tumor
  • each intestinal tumor cell layer includes feature data corresponding to the intestinal tumor cell data set
  • the feature data corresponding to the intestinal tumor cell data set includes the shape and the corresponding shape of each intestinal tumor cell data in the intestinal tumor cell data set.
  • the size corresponding to each intestinal tumor cell data in the intestinal tumor cell data set, the preset processing includes the following steps:
  • the outermost intestinal tumor cell data includes the shape and size corresponding to the outermost intestinal tumor cell data.
  • the tumor cell database includes the outermost intestinal tumor cell data corresponding to each intestinal tumor in a different growth cycle in a variety of intestinal tumors;
  • the second intestinal tumor cell data whose shape and size are similar to the data of the outermost intestinal tumor cell are extracted from each intestinal tumor cell layer.
  • FIG. 5 is a schematic structural diagram of a medical imaging apparatus in a hardware operating environment involved in an embodiment of the application.
  • the medical imaging device in the hardware operating environment involved in the embodiment of the present application may include:
  • the processor 501 is, for example, a CPU.
  • the memory 502 optionally, the memory may be a high-speed RAM memory, or a stable memory, such as a disk memory.
  • the communication interface 503 is used to implement connection and communication between the processor 501 and the memory 502.
  • FIG. 5 does not constitute a limitation to it, and may include more or less components than those shown in the figure, or a combination of certain components, or different component arrangements. .
  • the memory 502 may include an operating system, a network communication module, and an information processing program.
  • the operating system is a program that manages and controls the hardware and software resources of the medical imaging device, and supports the operation of personnel management programs and other software or programs.
  • the network communication module is used to implement communication between various components in the memory 502 and communication with other hardware and software in the medical imaging device.
  • the processor 501 is configured to execute the information migration program stored in the memory 502, and implement the following steps: Obtain a scanned image of the user's intestines, where the scanned image also includes intestinal tumors And the blood vessels around the intestine; generate the image data of the intestine, the image data of the intestine tumor, and the image data of the blood vessel according to the scan image; according to the image data of the intestine, the image data of the intestine tumor The imaging data and the imaging data of the blood vessels determine the location area of the intestine tumor in the intestine and the number and distribution of the blood supply vessels of the intestinal tumor; the imaging data of the intestine and the imaging of the intestine tumor The data and the image data of the blood vessel are subjected to 4D medical imaging to output the number and distribution of the location area and the blood supply blood vessel.
  • the present application also provides a computer-readable storage medium for storing a computer program, and the stored computer program is executed by the processor to implement the following steps: Obtain a scan of the user’s intestines Image, wherein the scanned image further includes an intestine tumor and blood vessels around the intestine; according to the scanned image, image data of the intestine, image data of the intestine tumor, and image data of the blood vessel are generated; The imaging data of the intestine, the imaging data of the intestine tumor, and the imaging data of the blood vessel determine the location area of the intestine tumor in the intestine and the number and distribution of blood supply vessels of the intestine tumor; The image data of the intestine, the image data of the intestinal tumor, and the image data of the blood vessel are subjected to 4D medical imaging to output the location area and the number and distribution of the blood supply vessels.

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Abstract

L'invention concerne un procédé d'analyse de néoplasme intestinal et de système vasculaire basé sur une image médicale d'intelligence artificielle (IA) VRDS, et un dispositif associé. Le procédé consiste à : obtenir une image balayée d'un canal intestinal d'un utilisateur, l'image balayée comprenant en outre un néoplasme intestinal et un vaisseau sanguin autour du canal intestinal (S201) ; générer des données d'image du canal intestinal, des données d'image du néoplasme intestinal, et des données d'image du vaisseau sanguin selon l'image balayée (S202) ; et déterminer une zone de position du néoplasme intestinal dans le canal intestinal et le nombre et la distribution des vaisseaux d'alimentation du néoplasme intestinal selon les données d'image du canal intestinal, les données d'image du néoplasme intestinal, et les données d'image du vaisseau sanguin, et effectuer une imagerie médicale 4D sur les données d'image du canal intestinal, les données d'image du néoplasme intestinal, et les données d'image du vaisseau sanguin pour afficher le néoplasme intestinal et le nombre et la distribution des vaisseaux d'alimentation (S203). Le procédé d'analyse de néoplasme intestinal et de système vasculaire et le dispositif associé peuvent améliorer l'efficacité diagnostique de maladies entériques.
PCT/CN2019/114482 2019-10-30 2019-10-30 Procédé d'analyse de néoplasme intestinal et de système vasculaire basé sur une image médicale d'intelligence artificielle (ia) vrds, et dispositif associé WO2021081842A1 (fr)

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CN201980099768.XA CN114340497A (zh) 2019-10-30 2019-10-30 基于vrds ai医学影像的肠肿瘤与血管分析方法和相关装置
PCT/CN2019/114482 WO2021081842A1 (fr) 2019-10-30 2019-10-30 Procédé d'analyse de néoplasme intestinal et de système vasculaire basé sur une image médicale d'intelligence artificielle (ia) vrds, et dispositif associé

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101120381A (zh) * 2005-02-14 2008-02-06 皇家飞利浦电子股份有限公司 用于确定靶向给药的注入点的设备和方法
CN101861126A (zh) * 2007-11-20 2010-10-13 皇家飞利浦电子股份有限公司 脉管形成的可视化
WO2015101797A1 (fr) * 2013-12-31 2015-07-09 General Electric Company Procédé d'imagerie tridimensionnel d'une zone limitée du système vasculaire d'un patient
US20180353149A1 (en) * 2015-08-06 2018-12-13 Case Western Reserve University Characterizing lung nodule risk with quantitative nodule and perinodular radiomics
CN109949899A (zh) * 2019-02-28 2019-06-28 未艾医疗技术(深圳)有限公司 图像三维测量方法、电子设备、存储介质及程序产品

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101120381A (zh) * 2005-02-14 2008-02-06 皇家飞利浦电子股份有限公司 用于确定靶向给药的注入点的设备和方法
CN101861126A (zh) * 2007-11-20 2010-10-13 皇家飞利浦电子股份有限公司 脉管形成的可视化
WO2015101797A1 (fr) * 2013-12-31 2015-07-09 General Electric Company Procédé d'imagerie tridimensionnel d'une zone limitée du système vasculaire d'un patient
US20180353149A1 (en) * 2015-08-06 2018-12-13 Case Western Reserve University Characterizing lung nodule risk with quantitative nodule and perinodular radiomics
CN109949899A (zh) * 2019-02-28 2019-06-28 未艾医疗技术(深圳)有限公司 图像三维测量方法、电子设备、存储介质及程序产品

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