WO2020168694A1 - 基于VRDS 4D医学影像的肿瘤Ai处理方法及产品 - Google Patents

基于VRDS 4D医学影像的肿瘤Ai处理方法及产品 Download PDF

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WO2020168694A1
WO2020168694A1 PCT/CN2019/101156 CN2019101156W WO2020168694A1 WO 2020168694 A1 WO2020168694 A1 WO 2020168694A1 CN 2019101156 W CN2019101156 W CN 2019101156W WO 2020168694 A1 WO2020168694 A1 WO 2020168694A1
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data
target
tumor
medical image
medical
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PCT/CN2019/101156
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English (en)
French (fr)
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李斯图尔特平
李戴维伟
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未艾医疗技术(深圳)有限公司
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Priority to US17/432,507 priority Critical patent/US20220180507A1/en
Priority to EP19916480.7A priority patent/EP3929935A4/en
Priority to AU2019430773A priority patent/AU2019430773B2/en
Publication of WO2020168694A1 publication Critical patent/WO2020168694A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
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    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
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Definitions

  • This application relates to the technical field of medical imaging devices, in particular to a tumor Ai processing method and product based on VRDS 4D medical imaging.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • DTI Diffusion Tensor Imaging
  • Positron Emission Computed Tomography Computed Tomography
  • PET Magnetic Resonance Imaging
  • PET Positron Emission Computed Tomography
  • PET PET
  • doctors still watch and read continuous two-dimensional slice scan images to judge and analyze the patient's diseased tissues such as tumors.
  • the two-dimensional slice scan image cannot show the spatial structural characteristics of the tumor, which affects the doctor's diagnosis of the disease.
  • people have put forward new demands for medical imaging.
  • the embodiments of the present application provide a tumor Ai processing method and product based on VRDS 4D medical imaging, in order to improve the accuracy and efficiency of the medical imaging device for tumor recognition.
  • an embodiment of the present application provides a tumor Ai processing method based on VRDS 4D medical imaging, which is applied to a medical imaging device; the method includes:
  • the target medical image data includes at least a data set of the target organ and a data set of blood vessels around the target organ, and the blood vessel data set includes arterial data
  • the data collection of the collection and/or the vein, and the data of the intersection position of the artery and the vein are independent of each other, and the data collection of the target organ is a cube space of the surface of the target organ and the tissue structure inside the target organ
  • the data set of the blood vessel is the result of the transfer function of the cube space of the blood vessel surface and the tissue structure inside the blood vessel;
  • the embodiments of the present application provide a VRDS 4D medical image-based tumor processing device, which is applied to a medical imaging device;
  • the VRDS 4D medical image-based tumor processing device includes a processing unit and a communication unit, wherein,
  • the processing unit is configured to import the bitmap BMP data source associated with the target organ of the target user into a preset VRDS medical network model to obtain first medical image data, where the first medical image data includes data of the target organ Collection and a data collection of blood vessels surrounding the target organ, the blood vessel data collection including fusion data of the intersection of arteries and veins; and a data collection for combining the first medical image data with the prestored target tissue
  • the gray value template is compared to determine normal data and abnormal data in the first medical image data, the target tissue includes the target organ and the blood vessel; and used to determine the tumor of the target user according to the abnormal data And is used to perform 4D medical imaging according to the normal data and abnormal data through the communication unit, and output the attribute information of the tumor.
  • an embodiment 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 configured by the above Executed by a processor, the above-mentioned program includes instructions for executing steps in any method of the first aspect of the embodiments of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, wherein the foregoing computer-readable storage medium stores a computer program for electronic data exchange, wherein the foregoing computer program enables a computer to execute In one aspect, some or all of the steps described in any method.
  • embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute For example, some or all of the steps described in any method of the first aspect.
  • the computer program product may be a software installation package.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images associated with the target organ of the target user, and secondly, generates the target medical imaging data based on the BMP data source, and again, determines the target medical The abnormal data in the image data. Once again, the attribute information of the target user’s tumor is determined according to the abnormal data. Finally, 4D medical imaging is performed according to the target medical image data, and the attribute information of the tumor is output.
  • the target medical image data includes at least the target organ’s attribute information. The data collection and the data collection of blood vessels around the target organ.
  • the blood vessel data collection includes the data collection of arteries and/or the data collection of veins, and the data at the intersection of the arteries and veins are independent of each other.
  • the data collection of the target organ is the target organ
  • the transfer function result of the cube space of the tissue structure inside the surface and the target organ, and the data collection of the blood vessel is the transfer function result of the cube space of the tissue structure inside the blood vessel. It can be seen that the medical imaging device in this application can identify and locate tumors and perform 4D medical imaging, which is beneficial to improve the accuracy and efficiency of tumor recognition.
  • FIG. 1 is a schematic structural diagram of a VRDS-based 4D medical image intelligent analysis and processing system provided by an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a tumor Ai processing method based on VRDS 4D medical imaging provided by an embodiment of the present application;
  • FIG. 3 is a schematic structural diagram of a medical imaging device provided by an embodiment of the present application.
  • FIG. 4 is a block diagram of functional units of a tumor processing device based on VRDS 4D medical imaging provided by an embodiment of the present 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 collected by medical equipment that reflects the internal structural characteristics of the human body. It 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 a VRDS-based 4D medical image intelligent analysis and processing system 100 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 device 110 may include The local medical imaging device 111 and/or the terminal medical imaging device 112, the local medical imaging device 111 or the terminal medical imaging device 112 are used to be based on raw DICOM data, and the VRDS 4D medical imaging-based tumor recognition algorithm presented in the embodiment of the application is Basically, the recognition, positioning and four-dimensional 4D volume rendering of human tumor regions are carried out to achieve the 4D stereo imaging effect (the four-dimensional medical image specifically refers to the medical image including the internal spatial structural features and external spatial structural features of the displayed tissue.
  • the internal space Structural characteristics mean that the slice data inside the tissue has not been lost, that is, the medical imaging device can present the internal structure of target organs, blood vessels and other tissues.
  • the external spatial structural characteristics refer to the environmental characteristics between tissues, including those between tissues. Spatial location characteristics (including intersection, interval, fusion, etc., such as the edge structure characteristics of the intersection between the kidney and the artery, etc.).
  • the local medical imaging device 111 can also be used to perform image source data processing relative to the terminal medical imaging device 112. Edit to form the transfer function result of the four-dimensional human body image.
  • the transfer function result can include the transfer function result of the surface of the internal organs and the tissue structure of the internal organs, and the transfer function result of the cube space, such as the cube required for the transfer function
  • the network database 120 may be, for example, a cloud server.
  • the network database 120 is used to store the image source generated by analyzing the original DICOM data and the transfer function result of the four-dimensional human body image edited by the local medical imaging device 111.
  • the image source may be from multiple sources.
  • a local medical imaging device 111 to realize interactive diagnosis of multiple doctors.
  • the operation action refers to the user’s medical imaging
  • the external intake equipment of the device such as a mouse, keyboard, etc., controls the operation of the four-dimensional human body image to realize human-computer interaction.
  • the operation action includes at least one of the following: (1) Change the color of a specific organ/tissue and / Or transparency, (2) positioning zoom view, (3) rotating view, realizing multi-view 360-degree observation of four-dimensional human body image, (4) "entering" human organs to observe internal structure, real-time clipping effect rendering, (5) Move the view up and down.
  • FIG 2 is a schematic flow diagram of a VRDS 4D medical imaging-based tumor Ai processing method according to an embodiment of the present application, which is applied to the medical imaging device described in Figure 1; as shown in the figure, this is based on VRDS Tumor Ai processing methods for 4D medical images include:
  • the medical imaging device determines a bitmap BMP data source according to multiple scanned images associated with the target organ of the target user;
  • the target organ may be an organ such as the kidney, for example.
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the medical imaging device generates target medical image data according to the BMP data source, and the target medical image data includes at least a data set of the target organ and a data set of blood vessels around the target organ.
  • the data set includes the data set of the artery and/or the data set of the vein, and the data of the intersection position of the artery and the vein are independent of each other, and the data set of the target organ is the surface of the target organ and the target organ
  • a transfer function result of the cube space of the internal tissue structure, and the data set of the blood vessel is the result of the transfer function of the cube space of the blood vessel surface and the tissue structure inside the blood vessel;
  • the medical imaging device generating target medical image data according to the BMP data source includes: the medical imaging device imports 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 data set of the target organ and a data set of the blood vessel, and the blood vessel data set includes fusion data of the intersection of an artery and a vein; and the first medical image
  • the image data is imported into the preset cross blood vessel network model to obtain second medical image data
  • the second medical image data includes the data set of the target organ, the data set of the artery and the data set of the vein, and
  • the first data in the arterial data set and the second data in the vein data set are independent of each other, the first data is data associated with the intersection position, and the second data is related to the intersection position Associated data; performing a first preset process on the second medical image data to obtain target medical image data, the first preset process including at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization processing, data enhancement Processing, the
  • the VRDS medical network model is provided with a transfer function of the structural characteristics of the target organ and a transfer function of the structural characteristics of the blood vessel
  • the BMP data source obtains the first medical image data through the processing of the transfer function
  • the cross blood vessel network model passes the following Operation to realize the data separation of arteries and veins: (1) Extract the fusion data of the intersection position; (2) Separate the fusion data based on the preset data separation algorithm for each fusion data to obtain independent arterial boundary point data and venous boundary point data Data; (3) integrating multiple arterial boundary point data obtained after processing into the first data, and integrating multiple venous boundary point data obtained after processing into the second data.
  • the 2D boundary optimization processing includes: multiple sampling to obtain low-resolution information and high-resolution information, where the low-resolution information can provide the contextual semantic information of the segmented target in the entire image, that is, reflect the relationship between the target and the environment These features are used for object category judgment, and high-resolution information is used to provide more refined features for segmentation targets, such as gradients.
  • 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 follows an activation function (Relu), and then in each dimension there is a 2*2*2 maximum pooling and merging 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 the 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 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 medical imaging device can process the BMP data source through the VRDS medical network model and the cross blood vessel network model, and combine boundary optimization and data enhancement processing to obtain target image data, which solves the problem that traditional medical imaging cannot achieve segmentation of arteries and arteries.
  • the problem of the overall separation of veins in the medical field improves the authenticity, comprehensiveness and refinement of medical image display.
  • the medical imaging device determines the bitmap BMP data source according to multiple scanned images associated with the target organ of the target user, including: the medical imaging device acquires the internal body of the target user collected by medical equipment A plurality of scanned images with structural characteristics; at least one scanned image containing the target organ is selected from the multiple scanned images, and the at least one scanned image is used as the medical digital imaging and communication DICOM data of the target user;
  • the DICOM data is parsed to generate the image source of the target user, the image source includes texture 2D/3D image volume data;
  • the second preset processing is performed on the image source to obtain the BMP data source, the second preset
  • the 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: regional noise ratio limiting, global contrast limiting; dividing the local histogram of the image source into multiple partitions, for each partition, according to the neighborhood of the partition
  • the slope of the cumulative histogram determines the slope of the transformation function, and the degree of contrast magnification around the pixel value of the partition is determined according to the gradient of the transformation function, and then the limit cropping process is performed according to the degree of contrast magnification to generate the distribution of the effective histogram.
  • the effective and usable neighborhood size values are also generated, and these cropped parts of the histogram are evenly distributed to other areas of the histogram.
  • the hybrid partial differential denoising includes: different from Gaussian low-pass filtering (indiscriminately attenuating the high-frequency components of the image, denoising will also produce blurring of the edges of the image), formed by objects in natural images
  • the isoilluminance lines (including edges) should be sufficiently smooth and smooth curves, that is, the absolute value of the curvature of these isoilluminance lines should be small enough.
  • the local gray value of the image will fluctuate randomly, resulting in isoilluminance. Irregular oscillations of the lines form iso-illuminance lines with large local curvatures.
  • the design is achieved through VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, which can protect the edge of the image and avoid the appearance in the smoothing process.
  • Mixed partial differential denoising model of step effect is achieved through VRDS Ai curvature drive and VRDS Ai high-order hybrid denoising, which can protect the edge of the image and avoid the appearance in the smoothing process.
  • the VRDS Ai elastic deformation processing includes: superimposing positive and negative random distances on the original dot matrix to form a difference position matrix, and then the grayscale at each difference position forms a new dot matrix, which can realize the internal image Distortion and deformation of the image, and then rotate, distort, and translate the image.
  • the medical imaging device obtains the BMP data source by processing the original scanned image data, which increases the amount of information of the original data, and increases the depth dimension information, and finally obtains data that meets the requirements of 4D medical image display.
  • S203 The medical imaging device determines abnormal data in the target medical image data
  • the method for the medical imaging device to determine the abnormal data in the target medical image data may be: comparing the target medical image data with a pre-stored gray value template of the target tissue to obtain a comparison For abnormal data whose results are not matched, the target tissue includes the target organ and the blood vessel.
  • the medical imaging device determines the attribute information of the tumor of the target user according to the abnormal data
  • the attribute information of the tumor includes any one of the following: type attribute, size attribute, and location attribute.
  • the attribute information includes location attributes; the medical imaging device determines the attribute information of the tumor of the target user according to the abnormal data, including: the medical imaging device detects the tissue associated with the abnormal data If it is detected that the tissue associated with the abnormal data includes only the target organ, then the location attribute of the tumor is determined to be an internal tumor; if it is detected that the tissue associated with the abnormal data includes the target organ and the blood vessel, It is determined that the location attribute of the tumor is an external tumor.
  • the tissue associated with the current abnormal data only includes the kidney, that is, the abnormal data is within the spatial range of the kidney, it is determined that the tumor is a tumor inside the kidney.
  • the medical imaging device can accurately determine the location information of the tumor through the correlation analysis of the tissue described in the data. Due to the high accuracy of the data comparison, the difference in the observation of the doctor and the human eye is avoided, and the accuracy of the tumor location recognition is improved. Degree and efficiency.
  • the attribute information includes a type attribute
  • the medical imaging device determining the attribute information of the tumor of the target user according to the abnormal data includes: the medical imaging device determines that the boundary is abnormal in the abnormal data Data; determine the boundary characteristics of the tumor according to the boundary abnormality data; if the boundary characteristics are detected to be smooth and continuous, then the target user’s tumor type attribute is determined to be a benign tumor; if the boundary characteristics are detected to be non
  • the smooth continuous characteristic determines that the type attribute of the target user’s tumor is malignant.
  • tumors are new organisms formed by the body under the action of various tumor-causing factors, and cells in local tissues lose normal regulation of their growth at the gene level, resulting in abnormal proliferation and differentiation.
  • Benign tumors refer to tumors that have no ability to infiltrate and metastasize. Benign tumors often have envelopes with clear borders, showing smooth and continuous characteristics.
  • Malignant tumor is a kind of cellular disease. Its main feature is the sudden mutation of hereditary genes, which causes the continuous abnormal and excessive proliferation of cells to form a mass. Due to its invasion of surrounding tissues and metastasis to other organs, the boundary is usually unclear and non-smooth Continuous characteristics.
  • the medical imaging device determines the boundary characteristics of the tumor by analyzing the boundary data of the tumor, and accurately determines the tumor type according to the boundary characteristics, which is beneficial to improve the accuracy and efficiency of tumor type recognition.
  • the attribute information includes a size attribute
  • the medical imaging device determining the attribute information of the tumor of the target user according to the abnormal data includes: the medical imaging device determines each abnormality in the common data The spatial coordinate information of the data; the size attribute of the tumor of the target user is determined according to the spatial coordinate information of each abnormal data.
  • the growth characteristics of the tumor can be accurately obtained, thereby providing accurate information support for the treatment of the tumor.
  • the medical imaging device can accurately calculate the size of the tumor based on its spatial coordinate information, and the tumor size can accurately reflect the growth state of the tumor, thereby providing accurate information support for the treatment of the tumor.
  • the medical imaging device performs 4D medical imaging according to the target medical image data, and outputs the attribute information of the tumor.
  • the 4D medical imaging refers to presenting four-dimensional medical images.
  • the medical imaging device performing 4D medical imaging according to the target medical image data includes: the medical imaging device selects enhancement data with a quality score greater than a preset score from the target medical image data as VRDS 4D imaging data.
  • the quality score can be comprehensively evaluated from the following dimensions: average gradient, information entropy, visual information fidelity, peak signal-to-noise ratio PSNR, structural similarity SSIM, mean square error MSE, etc., for details, please refer to the common image field The image quality scoring algorithm will not be repeated here.
  • the medical imaging device further performs data screening through quality scores to improve the imaging effect.
  • the specific implementation manner for the medical imaging device to output the attribute information of the tumor may be: when the medical imaging device detects a selection operation for the tumor location, displaying the The attribute information is output at the preset position of the display screen of the tumor image.
  • the selection operation may be selected by the user through a mouse, or selected through a head-mounted VR device to control the viewing angle, etc., which is not uniquely limited here. It can be seen that the triggering process of the display of the attribute information is convenient and efficient.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images associated with the target organ of the target user, and secondly, generates the target medical imaging data based on the BMP data source, and again, determines the target medical The abnormal data in the image data. Once again, the attribute information of the target user’s tumor is determined according to the abnormal data. Finally, 4D medical imaging is performed according to the target medical image data, and the attribute information of the tumor is output.
  • the target medical image data includes at least the target organ’s attribute information. The data collection and the data collection of blood vessels around the target organ.
  • the blood vessel data collection includes the data collection of arteries and/or the data collection of veins, and the data at the intersection of the arteries and veins are independent of each other.
  • the data collection of the target organ is the target organ
  • the transfer function result of the cube space of the tissue structure inside the surface and the target organ, and the data collection of the blood vessel is the transfer function result of the cube space of the tissue structure inside the blood vessel. It can be seen that the medical imaging device in this application can identify and locate tumors and perform 4D medical imaging, which is beneficial to improve the accuracy and efficiency of tumor recognition.
  • FIG. 3 is a schematic structural diagram of a medical imaging apparatus 300 provided by an embodiment of the present application.
  • the imaging device 300 includes a processor 310, a memory 320, a communication interface 330, and one or more programs 321, where the one or more programs 321 are stored in the memory 320 and are configured to be executed by the processor 310,
  • the one or more programs 321 include instructions for performing the following steps;
  • the target medical image data including at least the target organ data set and the A data set of blood vessels around a target organ, the data set of blood vessels includes a data set of arteries and/or a data set of veins, and the data on the crossing positions of the arteries and the veins are independent of each other.
  • the data set is the transfer function result of the cube space of the surface of the target organ and the tissue structure inside the target organ
  • the data set of the blood vessel is the transfer function of the cube space of the tissue structure inside the blood vessel and the surface of the blood vessel Result; and determine the abnormal data in the target medical image data; and determine the attribute information of the tumor of the target user according to the abnormal data; and perform 4D medical imaging according to the target medical image data, and output the tumor Property information.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images associated with the target organ of the target user, and secondly, generates the target medical imaging data based on the BMP data source, and again, determines the target medical The abnormal data in the image data. Once again, the attribute information of the target user’s tumor is determined according to the abnormal data. Finally, 4D medical imaging is performed according to the target medical image data, and the attribute information of the tumor is output.
  • the target medical image data includes at least the target organ’s attribute information. The data collection and the data collection of blood vessels around the target organ.
  • the blood vessel data collection includes the data collection of arteries and/or the data collection of veins, and the data at the intersection of the arteries and veins are independent of each other.
  • the data collection of the target organ is the target organ
  • the transfer function result of the cube space of the tissue structure inside the surface and the target organ, and the data collection of the blood vessel is the transfer function result of the cube space of the tissue structure inside the blood vessel. It can be seen that the medical imaging device in this application can identify and locate tumors and perform 4D medical imaging, which is beneficial to improve the accuracy and efficiency of tumor recognition.
  • the instructions in the program are specifically used to perform the following operations: detecting the abnormality The tissue associated with the data; and for determining that the location attribute of the tumor is an internal tumor if the tissue associated with the abnormal data is detected to include only the target organ; and for determining the location attribute of the tumor as an internal tumor if the abnormal data is detected
  • the tissue includes the target organ and the blood vessel, and the location attribute of the tumor is determined to be an external tumor.
  • the attribute information includes a type attribute
  • the instructions in the program are specifically used to perform the following operations: determining the abnormality The abnormal boundary data in the data; and used to determine the boundary characteristic of the tumor based on the abnormal boundary data; and used to determine that the type attribute of the tumor of the target user is benign if the boundary characteristic is detected to be a smooth continuous characteristic Tumor; and for determining that the type attribute of the tumor of the target user is a malignant tumor if the boundary characteristic is detected as a non-smooth continuous characteristic.
  • the attribute information includes a size attribute; in terms of determining the attribute information of the tumor of the target user according to the abnormal data, the instructions in the program are specifically used to perform the following operations: determining the normal Spatial coordinate information of each abnormal data in the data; and used to determine the size attribute of the tumor of the target user according to the spatial coordinate information of each abnormal data.
  • the instructions in the program are specifically used to perform the following operations: when a selection operation for the tumor location is detected, displaying the The attribute information is output at a preset position of the display screen of the tumor image.
  • the instructions in the program are specifically used to perform the following operations: import the BMP data source into a preset VRDS medical network model Obtain first medical image data, where the first medical image data includes a data set of the target organ and a data set of the blood vessel, and the blood vessel data set includes fusion data of the intersection of an artery and a vein; and It is used to import the first medical image data into a preset cross-vascular network model to obtain second medical image data.
  • the second medical image data includes the data set of the target organ and the data set of the artery and all The data set of the vein, and the first data in the arterial data set and the second data of the vein data set are independent of each other, the first data is data associated with the intersection position, and the first data The second data is data associated with the intersection position; and is used to perform a first preset process on the second medical image data to obtain target medical image data, and the first preset process includes at least one of the following operations: 2D Boundary optimization processing, 3D boundary optimization processing, and data enhancement processing.
  • the target medical image data includes a data set of the target organ, a data set of the artery, and a data set of the vein.
  • the instructions in the program are specifically used to perform the following operations: filter the target medical image data with a quality score greater than a preset
  • the scored enhancement data is used as VRDS 4D imaging data.
  • the medical imaging apparatus includes hardware structures and/or software modules corresponding to each function.
  • this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.
  • the embodiment of the present application may divide the medical imaging device into functional units according to the foregoing method examples.
  • each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.
  • FIG. 4 is a block diagram of functional units of the VRDS 4D medical image-based tumor processing apparatus 400 involved in the embodiment of the present application.
  • the VRDS 4D medical image-based tumor processing device 400 is applied to a medical imaging device.
  • the VRDS 4D medical image-based tumor processing device 400 includes a processing unit 401 and a communication unit 402, wherein,
  • the processing unit 401 is configured to import the bitmap BMP data source associated with the target organ of the target user into the preset VRDS medical network model to obtain the first medical image data.
  • the first medical image data includes the information of the target organ A data set and a data set of blood vessels around the target organ, the blood vessel data set including fusion data of the intersection of arteries and veins; and used to combine the first medical image data with the pre-stored target tissue
  • the gray value template is compared to determine normal data and abnormal data in the first medical image data.
  • the target tissue includes the target organ and the blood vessel; and the target user is determined according to the abnormal data Attribute information of the tumor; and used to perform 4D medical imaging according to the normal data and abnormal data through the communication unit 402, and output the attribute information of the tumor.
  • the VRDS 4D medical image-based tumor processing device 400 further includes a storage unit 403, the processing unit 401 may be a processor, the communication unit 402 may be a transceiver, and the storage unit may be a memory.
  • the medical imaging device first determines the bitmap BMP data source based on the multiple scanned images associated with the target organ of the target user, and secondly, generates the target medical imaging data based on the BMP data source, and again, determines the target medical The abnormal data in the image data. Once again, the attribute information of the target user’s tumor is determined according to the abnormal data. Finally, 4D medical imaging is performed according to the target medical image data, and the attribute information of the tumor is output.
  • the target medical image data includes at least the target organ’s attribute information. The data collection and the data collection of blood vessels around the target organ.
  • the blood vessel data collection includes the data collection of arteries and/or the data collection of veins, and the data at the intersection of the arteries and veins are independent of each other.
  • the data collection of the target organ is the target organ
  • the transfer function result of the cube space of the tissue structure inside the surface and the target organ, and the data collection of the blood vessel is the transfer function result of the cube space of the tissue structure inside the blood vessel. It can be seen that the medical imaging device in this application can identify and locate tumors and perform 4D medical imaging, which is beneficial to improve the accuracy and efficiency of tumor recognition.
  • the attribute information includes a location attribute
  • the processing unit 401 is specifically configured to: detect the abnormal data associated And for determining that the location attribute of the tumor is internal tumor if it is detected that the tissue associated with the abnormal data includes only the target organ; and for determining that the tissue associated with the abnormal data includes the The target organ and the blood vessel determine the location attribute of the tumor as an external tumor.
  • the attribute information includes a type attribute
  • the processing unit 401 is specifically configured to: determine the boundary in the abnormal data Abnormal data; and for determining the boundary characteristics of the tumor based on the boundary abnormality data; and for determining the type attribute of the tumor of the target user as a benign tumor if the boundary characteristics are detected to be smooth and continuous; and If it is detected that the boundary characteristic is a non-smooth continuous characteristic, it is determined that the type attribute of the tumor of the target user is a malignant tumor.
  • the attribute information includes a size attribute; in terms of determining the attribute information of the tumor of the target user according to the abnormal data, the processing unit 401 is specifically configured to: determine each of the common data Spatial coordinate information of the abnormal data; and used to determine the size attribute of the tumor of the target user according to the spatial coordinate information of each abnormal data.
  • the processing unit 401 is specifically configured to: when a selection operation for the tumor location is detected, display the tumor image The preset position of the display screen outputs the attribute information.
  • the processing unit 401 is specifically configured to: import the BMP data source into a preset VRDS medical network model to obtain the first Medical image data, where the first medical image data includes a data set of the target organ and a data set of the blood vessel, and the blood vessel data set includes fusion data at the intersection of an artery and a vein; and The first medical image data is imported into a preset cross-vessel network model to obtain second medical image data.
  • the second medical image data includes a data set of the target organ, a data set of the artery, and data of the vein Collection, and the first data in the arterial data collection and the second data in the vein data collection are independent of each other, the first data is data associated with the intersection position, and the second data is and The data associated with the cross position; and for performing a first preset processing on the second medical image data to obtain target medical image data, the first preset processing includes at least one of the following operations: 2D boundary optimization processing, 3D boundary optimization processing and data enhancement processing, the target medical image data includes a data set of the target organ, a data set of the artery, and a data set of the vein.
  • the processing unit 401 is specifically configured to: filter the target medical image data for enhanced data with a quality score greater than a preset score As VRDS 4D imaging data.
  • An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method as recorded in the above method embodiment ,
  • the aforementioned computer includes a medical imaging device.
  • the embodiments of the present application also provide a computer program product.
  • the above-mentioned computer program product includes a non-transitory computer-readable storage medium storing a computer program. Part or all of the steps of the method.
  • the computer program product may be a software installation package, and the computer includes a medical imaging device.
  • the disclosed device may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the above-mentioned units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory.
  • the technical solution of the present application essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, A number of instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the foregoing methods of the various embodiments of the present application.
  • the aforementioned memory includes: U disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.
  • the program can be stored in a computer-readable memory, and the memory can include: flash disk , Read-only memory (English: Read-Only Memory, abbreviation: ROM), random access device (English: Random Access Memory, abbreviation: RAM), magnetic disk or optical disc, etc.

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Abstract

本申请实施例公开了一种基于VRDS 4D医学影像的肿瘤Ai处理方法及产品，应用于医学成像装置；方法包括：根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源；根据BMP数据源生成目标医学影像数据；确定目标医学影像数据中异常数据；根据异常数据确定目标用户的肿瘤的属性信息；根据目标医学影像数据进行4D医学成像，并输出肿瘤的属性信息。本申请实施例有利于提高肿瘤识别的准确度和效率。

Description

基于VRDS 4D医学影像的肿瘤Ai处理方法及产品 技术领域
本申请涉及医学成像装置技术领域，具体涉及一种基于VRDS 4D医学影像的肿瘤Ai处理方法及产品。
背景技术
目前，医生通过电子计算机断层扫描(Computed Tomography，CT)、磁共振成像(Magnetic Resonance Imaging，MRI)、弥散张量成像(Diffusion Tensor Imaging，DTI)、正电子发射型计算机断层显像(Positron Emission Computed Tomography，PET)等技术获取病变组织的形态、位置、拓扑结构等信息。医生仍然采用观看阅读连续的二维切片扫描图像，以此对患者的病变组织如肿瘤进行判断分析。然而，两维切片扫描图像无法呈现出肿瘤的空间结构特性，影响到医生对疾病的诊断。随着医学成像技术的飞速发展，人们对医学成像提出了新的需求。
发明内容
本申请实施例提供了一种基于VRDS 4D医学影像的肿瘤Ai处理方法及产品，以期提高医学成像装置进行肿瘤识别的准确度和效率。
第一方面，本申请实施例提供一种基于VRDS 4D医学影像的肿瘤Ai处理方法，应用于医学成像装置；所述方法包括：
根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源；
根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且所述动脉和所述静脉的交叉位置的数据相互独立，所述目标器官的数据集合为所述目标器官表面和所述目标器官内部的组织结构的立方体空间的传递函数结果，所述血 管的数据集合为所述血管表面和所述血管内部的组织结构的立方体空间的传递函数结果；
确定所述目标医学影像数据中异常数据；
根据所述异常数据确定目标用户的肿瘤的属性信息；
根据所述目标医学影像数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
第二方面，本申请实施例提供一种基于VRDS 4D医学影像的肿瘤处理装置，应用于医学成像装置；所述基于VRDS 4D医学影像的肿瘤处理装置包括处理单元和通信单元，其中，
所述处理单元，用于将目标用户的目标器官关联的位图BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；以及用于将所述第一医学影像数据与预存的所述目标组织的灰度值模板进行比较，确定所述第一医学影像数据中的正常数据和异常数据，所述目标组织包括所述目标器官和所述血管；以及用于根据所述异常数据确定目标用户的肿瘤的属性信息；以及用于通过所述通信单元根据所述正常数据和异常数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
第三方面，本申请实施例提供一种医学成像装置，包括处理器、存储器、通信接口以及一个或多个程序，其中，上述一个或多个程序被存储在上述存储器中，并且被配置由上述处理器执行，上述程序包括用于执行本申请实施例第一方面任一方法中的步骤的指令。
第四方面，本申请实施例提供了一种计算机可读存储介质，其中，上述计算机可读存储介质存储用于电子数据交换的计算机程序，其中，上述计算机程序使得计算机执行如本申请实施例第一方面任一方法中所描述的部分或全部步骤。
第五方面，本申请实施例提供了一种计算机程序产品，其中，上述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质，上述计算机 程序可操作来使计算机执行如本申请实施例第一方面任一方法中所描述的部分或全部步骤。该计算机程序产品可以为一个软件安装包。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，再次，确定目标医学影像数据中异常数据，再次，根据异常数据确定目标用户的肿瘤的属性信息，最后，根据目标医学影像数据进行4D医学成像，并输出肿瘤的属性信息，其中，目标医学影像数据至少包括目标器官的数据集合和目标器官周围的血管的数据集合，血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且动脉和静脉的交叉位置的数据相互独立，目标器官的数据集合为目标器官表面和目标器官内部的组织结构的立方体空间的传递函数结果，血管的数据集合为血管表面和血管内部的组织结构的立方体空间的传递函数结果。可见，本申请中的医学成像装置能够识别、定位肿瘤并进行4D医学成像，有利于提高肿瘤识别的准确度和效率。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种基于VRDS 4D医学影像智能分析处理系统的结构示意图；
图2是本申请实施例提供的一种基于VRDS 4D医学影像的肿瘤Ai处理方法的流程示意图；
图3是本申请实施例提供的一种医学成像装置的结构示意图；
图4是本申请实施例提供的一种基于VRDS 4D医学影像的肿瘤处理装置的功能单元组成框图。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其他步骤或单元。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
本申请实施例所涉及到的医学成像装置是指利用各种不同媒介作为信息载体，将人体内部的结构重现为影像的各种仪器，其影像信息与人体实际结构有着空间和时间分布上的对应关系。“DICOM数据”是指通过医疗设备采集的反映人体内部结构特征的原始图像文件数据，可以包括电子计算机断层扫描CT、核磁共振MRI、弥散张量成像DTI、正电子发射型计算机断层显像PET-CT等信息，“图源”是指解析原始DICOM数据生成的Texture2D/3D图像体数据。“VRDS”是指虚拟现实医用系统(Virtual Reality Doctor system，简称为VRDS)。
请参阅图1，图1是本申请实施例提供了一种基于VRDS 4D医学影像智能分析处理系统100的结构示意图，该系统100包括医学成像装置110和网络数据库120，其中医学成像装置110可以包括本地医学成像装置111和/或终端医学成像装置112，本地医学成像装置111或终端医学成像装置112用于基于原始DICOM数据，以本申请实施例所呈现的基于VRDS 4D医学影像的肿瘤识别算 法为基础，进行人体肿瘤区域的识别、定位和四维4D体绘制，实现4D立体成像效果(该四维医学影像具体是指医学影像包括所显示组织的内部空间结构特征及外部空间结构特征，所述内部空间结构特征是指组织内部的切片数据未丢失，即医学成像装置可以呈现目标器官、血管等组织的内部构造，外部空间结构特性是指组织与组织之间的环境特征，包括组织与组织之间的空间位置特性(包括交叉、间隔、融合)等，如肾脏与动脉之间的交叉位置的边缘结构特性等)，本地医学成像装置111相对于终端医学成像装置112还可以用于对图源数据进行编辑，形成四维人体图像的传递函数结果，该传递函数结果可以包括人体内脏器官表面和人体内脏器官内的组织结构的传递函数结果，以及立方体空间的传递函数结果，如传递函数所需的立方编辑框与弧线编辑的数组数量、坐标、颜色、透明度等信息。网络数据库120例如可以是云服务器等，该网络数据库120用于存储解析原始DICOM数据生成的图源，以及本地医学成像装置111编辑得到的四维人体图像的传递函数结果，图源可以是来自于多个本地医学成像装置111以实现多个医生的交互诊断。
用户通过上述医学成像装置110进行具体的图像显示时，可以选择显示器和/或虚拟现实VR的头戴式显示器(Head mounted Displays Set，HMDS)结合操作动作进行显示，操作动作是指用户通过医学成像装置的外部摄入设备，如鼠标、键盘等，对四维人体图像进行的操作控制，以实现人机交互，该操作动作包括以下至少一种：(1)改变某个具体器官/组织的颜色和/或透明度，(2)定位缩放视图，(3)旋转视图，实现四维人体图像的多视角360度观察，(4)“进入”人体器官内部观察内部构造，实时剪切效果渲染，(5)上下移动视图。
下面对本申请实施例涉及到的基于VRDS 4D医学影像的肿瘤识别算法进行详细介绍。
请参阅图2，图2是本申请实施例提供了一种基于VRDS 4D医学影像的肿瘤Ai处理方法的流程示意图，应用于如图1所述的医学成像装置；如图所示，本基于VRDS 4D医学影像的肿瘤Ai处理方法包括：
S201，医学成像装置根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源；
其中，所述目标器官例如可以是肾脏等器官。所述扫描图像包括以下任意一种：CT图像、MRI图像、DTI图像、PET-CT图像。
S202，所述医学成像装置根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且所述动脉和所述静脉的交叉位置的数据相互独立，所述目标器官的数据集合为所述目标器官表面和所述目标器官内部的组织结构的立方体空间的传递函数结果，所述血管的数据集合为所述血管表面和所述血管内部的组织结构的立方体空间的传递函数结果；
在本可能的示例中，所述医学成像装置根据所述BMP数据源生成目标医学影像数据，包括：所述医学成像装置将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；将所述第一医学影像数据导入预设的交叉血管网络模型，得到第二医学影像数据，所述第二医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据；针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
其中，所述VRDS医学网络模型设置有目标器官的结构特性的传递函数和血管的结构特性的传递函数，BMP数据源通过传递函数的处理得到第一医学影像数据，所述交叉血管网络模型通过以下操作实现动脉和静脉的数据分离：(1)提取交叉位置的融合数据；(2)针对每个融合数据基于预设数据分离算法分离该融合数据，得到相互独立的动脉边界点数据和静脉边界点数据；(3)将处理后得到的多个动脉边界点数据整合为第一数据，将处理后得到的多个静脉边界 点数据整合为第二数据。
其中，所述2D边界优化处理包括：多次采样获取低分辨率信息和高分辨率信息，其中，低分辨率信息能够提供分割目标在整个图像中上下文语义信息，即反映目标与环境之间关系的特征，这些特征用于物体类别判断，高分辨率信息用于为分割目标提供更加精细的特征，如梯度等。
所述3D边界优化处理包括：3D卷积、3D最大池化和3D向上卷积层，输入数据的大小为a1、a2、a3，通道数为c，过滤器大小为f，即过滤器维度为f*f*f*c，过滤器数量为n，则3维卷积最终输出为：
(a1-f+1)*(a2-f+1)*(a3-f+1)*n
具有分析路径和合成路径。在分析路径中，每一层包含两个3*3*3的卷积核，每一个都跟随一个激活函数(Relu)，然后在每个维度上有2*2*2的最大池化合并两个步长。在合成路径中，每个层由2*2*2的向上卷积组成，每个维度上步长都为2，接着，两个3*3*3的卷积，然后Relu。然后在分析路径中从相等分辨率层的shortcut连接提供了合成路径的基本高分辨特征。在最后一层中，1*1*1卷积减少了输出通道的数量。
其中，所述数据增强处理包括以下任意一种：基于任意角度旋转的数据增强、基于直方图均衡的数据增强、基于白平衡的数据增强、基于镜像操作的数据增强、基于随机剪切的数据增强和基于模拟不同光照变化的数据增强。
可见，本示例中，医学成像装置能够通过VRDS医学网络模型、交叉血管网络模型对BMP数据源进行处理，结合边界优化和数据增强处理得到目标影像数据，解决了传统的医学影像无法实现分割动脉和静脉的整体分离的医学领域的问题，提高医学影像显示的真实性、全面性和精细化程度。
在本可能的示例中，所述医学成像装置根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，包括：所述医学成像装置获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标器官的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源 执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
其中，所述VRDS限制对比度自适应直方图均衡包括：区域噪音比度限幅、全局对比度限幅；将图源的局部直方图划分多个分区，针对每个分区，根据该分区的邻域的累积直方图的斜度确定变换函数的斜度，根据该变换函数的斜度确定该分区的像素值周边的对比度放大程度，然后根据该对比度放大程度进行限度裁剪处理，产生有效直方图的分布，同时也产生有效可用的邻域大小的取值，将这些裁剪掉的部分直方图均匀的分布到直方图的其他区域。
其中，所述混合偏微分去噪包括：不同于高斯低通滤波(对图像的高频成分不加区别的减弱，去噪的同时会产生图像边缘模糊化)，自然图像中的物体所形成的等照度线(包括边缘)应该是足够光滑顺畅的曲线，即这些等照度线的曲率的绝对值应该足够小，当图像受到噪声污染后，图像的局部灰度值会发生随机起伏，导致等照度线的不规则震荡，形成局部曲率很大的等照度线，根据这一原理，设计通过VRDS Ai曲率驱动和VRDS Ai高阶混合去噪，实现即可保护图像边缘、又可以避免平滑过程中出现阶梯效应的混合偏微分去噪模型。
其中，所述VRDS Ai弹性变形处理包括：在原有点阵上，叠加正负向随机距离形成差值位置矩阵，然后在每个差值位置上的灰度，形成新的点阵，可以实现图像内部的扭曲变形，另外再对图像进行旋转、扭曲、平移等操作。
可见，本示例中，医学成像装置通过对原始扫描图像数据的处理，得到BMP数据源，提高了原始数据的信息量，且增加了深度维度信息，最终得到符合4D医学影像显示需求的数据。
S203，所述医学成像装置确定所述目标医学影像数据中异常数据；
具体实现中，所述医学成像装置确定所述目标医学影像数据中异常数据的实现方式可以是：将所述目标医学影像数据与预存的所述目标组织的灰度值模板进行比对，得到比对结果为不匹配的异常数据，所述目标组织包括所述目标器官和所述血管。
S204，所述医学成像装置根据所述异常数据确定目标用户的肿瘤的属性信 息；
其中，所述肿瘤的属性信息包括以下任意一种：类型属性、大小属性和位置属性。
在本可能的示例中，所述属性信息包括位置属性；所述医学成像装置根据所述异常数据确定目标用户的肿瘤的属性信息，包括：所述医学成像装置检测所述异常数据所关联的组织；若检测到所述异常数据所关联的组织仅包括所述目标器官，则确定肿瘤的位置属性为内肿瘤；若检测到所述异常数据所关联的组织包括所述目标器官和所述血管，则确定所述肿瘤的位置属性为外肿瘤。
其中，以肾脏为例，若当前的异常数据所关联的组织仅包括肾脏，即异常数据在肾脏的空间范围内，则确定该肿瘤为肾脏内部的肿瘤。
可见，本示例中，医学成像装置能够通过数据所述组织的关联性分析，准确确定肿瘤的位置信息，由于数据比对准确度高，避免医生人眼观察的差异性，提高肿瘤位置识别的准确度和效率。
在本可能的示例中，所述属性信息包括类型属性；所述医学成像装置根据所述异常数据确定目标用户的肿瘤的属性信息，包括：所述医学成像装置确定所述异常数据中的边界异常数据；根据所述边界异常数据确定所述肿瘤的边界特性；若检测到所述边界特性为光滑连续特性，则确定目标用户的肿瘤的类型属性为良性肿瘤；若检测到所述边界特性为非光滑连续特性，则确定目标用户的肿瘤的类型属性为恶性肿瘤。
其中，肿瘤是机体在各种致瘤因素作用下，局部组织的细胞在基因水平上失去对其生长的正常调控导致异常增生与分化而形成的新生物。良性肿瘤(benign tumor)是指无浸润和转移能力的肿瘤。良性肿瘤常具有包膜，边界清楚，呈现光滑连续特性。恶性肿瘤是一种细胞性疾病，其主要特点是遗传基因突发变异，致使细胞持续性异常过度增生形成肿块，由于其向周围组织侵犯和向其他脏器转移，通常边界不清，呈现非光滑连续特性。
可见，本示例中，医学成像装置通过分析肿瘤的边界数据，确定肿瘤的边界特性，并根据边界特性准确确定肿瘤的类型，有利于提高肿瘤类型识别的准确度和效率。
在本可能的示例中，所述属性信息包括大小属性；所述医学成像装置根据所述异常数据确定目标用户的肿瘤的属性信息，包括：所述医学成像装置确定所述常数据中每个异常数据的空间坐标信息；根据所述每个异常数据的空间坐标信息确定目标用户的肿瘤的大小属性。
其中，通过周期性的分析肿瘤的大小，能够准确得到该肿瘤的成长特性，进而为肿瘤的治疗提供精准的信息支撑。
可见，本示例中，医学成像装置能够基于肿瘤的空间坐标信息准确计算其大小，肿瘤大小能够精确反映肿瘤的生长状态，进而为肿瘤的治疗提供准确的信息支撑。
S205，所述医学成像装置根据所述目标医学影像数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
其中，所述4D医学成像是指呈现四维医学影像。
在本可能的示例中，所述医学成像装置根据所述目标医学影像数据进行4D医学成像，包括：所述医学成像装置从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据。
其中，所述质量评分可以从以下维度进行综合评价，平均梯度、信息熵、视觉信息保真度、峰值信噪比PSNR、结构相似性SSIM、均方误差MSE等，具体可以参考图像领域的常见图像质量评分算法，此处不再赘述。
可见，本示例中，医学成像装置通过质量评分进一步进行数据筛选，提高成像效果。
在本可能的示例中，所述医学成像装置输出所述肿瘤的所述属性信息的具体实现方式可以是：所述医学成像装置在检测到针对所述肿瘤位置的选择操作时，在显示所述肿瘤影像的显示屏的预设位置输出所述属性信息。
其中，所述选择操作具体可以由用户通过鼠标选取，或者通过头戴式VR设备控制视角进行选择等，此处不做唯一限定，可见该属性信息的显示的触发过程便捷高效。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标 医学影像数据，再次，确定目标医学影像数据中异常数据，再次，根据异常数据确定目标用户的肿瘤的属性信息，最后，根据目标医学影像数据进行4D医学成像，并输出肿瘤的属性信息，其中，目标医学影像数据至少包括目标器官的数据集合和目标器官周围的血管的数据集合，血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且动脉和静脉的交叉位置的数据相互独立，目标器官的数据集合为目标器官表面和目标器官内部的组织结构的立方体空间的传递函数结果，血管的数据集合为血管表面和血管内部的组织结构的立方体空间的传递函数结果。可见，本申请中的医学成像装置能够识别、定位肿瘤并进行4D医学成像，有利于提高肿瘤识别的准确度和效率。
与上述图2、图3、图4所示的实施例一致的，请参阅图3，图3是本申请实施例提供的一种医学成像装置300的结构示意图，如图所示，所述医学成像装置300包括处理器310、存储器320、通信接口330以及一个或多个程序321，其中，所述一个或多个程序321被存储在上述存储器320中，并且被配置由上述处理器310执行，所述一个或多个程序321包括用于执行以下步骤的指令；
根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源；以及根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且所述动脉和所述静脉的交叉位置的数据相互独立，所述目标器官的数据集合为所述目标器官表面和所述目标器官内部的组织结构的立方体空间的传递函数结果，所述血管的数据集合为所述血管表面和所述血管内部的组织结构的立方体空间的传递函数结果；以及确定所述目标医学影像数据中异常数据；以及根据所述异常数据确定目标用户的肿瘤的属性信息；以及根据所述目标医学影像数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，再次，确定目标医学影像数据中异常数据，再次，根据异常数据确定目标用户的肿瘤的属性信息，最后，根据目标医学影像数据进行4D医 学成像，并输出肿瘤的属性信息，其中，目标医学影像数据至少包括目标器官的数据集合和目标器官周围的血管的数据集合，血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且动脉和静脉的交叉位置的数据相互独立，目标器官的数据集合为目标器官表面和目标器官内部的组织结构的立方体空间的传递函数结果，血管的数据集合为血管表面和血管内部的组织结构的立方体空间的传递函数结果。可见，本申请中的医学成像装置能够识别、定位肿瘤并进行4D医学成像，有利于提高肿瘤识别的准确度和效率。
在一个可能的示例中，在所述属性信息包括位置属性；所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述程序中的指令具体用于执行以下操作：检测所述异常数据所关联的组织；以及用于若检测到所述异常数据所关联的组织仅包括所述目标器官，则确定肿瘤的位置属性为内肿瘤；以及用于若检测到所述异常数据所关联的组织包括所述目标器官和所述血管，则确定所述肿瘤的位置属性为外肿瘤。
在一个可能的示例中，所述属性信息包括类型属性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述程序中的指令具体用于执行以下操作：确定所述异常数据中的边界异常数据；以及用于根据所述边界异常数据确定所述肿瘤的边界特性；以及用于若检测到所述边界特性为光滑连续特性，则确定目标用户的肿瘤的类型属性为良性肿瘤；以及用于若检测到所述边界特性为非光滑连续特性，则确定目标用户的肿瘤的类型属性为恶性肿瘤。
在一个可能的示例中，所述属性信息包括大小属性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述程序中的指令具体用于执行以下操作：确定所述常数据中每个异常数据的空间坐标信息；以及用于根据所述每个异常数据的空间坐标信息确定目标用户的肿瘤的大小属性。
在一个可能的示例中，在所述输出所述肿瘤的所述属性信息方面，所述程序中的指令具体用于执行以下操作：在检测到针对所述肿瘤位置的选择操作时，在显示所述肿瘤影像的显示屏的预设位置输出所述属性信息。
在一个可能的示例中，在所述根据所述BMP数据源生成目标医学影像数据方面，所述程序中的指令具体用于执行以下操作：将所述BMP数据源导入预设 的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；以及用于将所述第一医学影像数据导入预设的交叉血管网络模型，得到第二医学影像数据，所述第二医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据；以及用于针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
在一个可能的示例中，在所述根据所述目标医学影像数据进行4D医学成像方面，所述程序中的指令具体用于执行以下操作：从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据。
上述主要从方法侧执行过程的角度对本申请实施例的方案进行了介绍。可以理解的是，医学成像装置为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。本领域技术人员应该很容易意识到，结合本文中所提供的实施例描述的各示例的单元及算法步骤，本申请能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
本申请实施例可以根据上述方法示例对医学成像装置进行功能单元的划分，例如，可以对应各个功能划分各个功能单元，也可以将两个或两个以上的功能集成在一个处理单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。需要说明的是，本申请实施例中对单元的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
图4是本申请实施例中所涉及的基于VRDS 4D医学影像的肿瘤处理装置400的功能单元组成框图。该基于VRDS 4D医学影像的肿瘤处理装置400应用于医学成像装置，该基于VRDS 4D医学影像的肿瘤处理装置400包括处理单元401和通信单元402，其中，
所述处理单元401，用于将目标用户的目标器官关联的位图BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；以及用于将所述第一医学影像数据与预存的所述目标组织的灰度值模板进行比较，确定所述第一医学影像数据中的正常数据和异常数据，所述目标组织包括所述目标器官和所述血管；以及用于根据所述异常数据确定目标用户的肿瘤的属性信息；以及用于通过所述通信单元402根据所述正常数据和异常数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
所述基于VRDS 4D医学影像的肿瘤处理装置400还包括存储单元403，所述处理单元401可以是处理器，所述通信单元402可以是收发器，所述存储单元可以是存储器。
可以看出，本申请实施例中，医学成像装置首先根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，其次，根据BMP数据源生成目标医学影像数据，再次，确定目标医学影像数据中异常数据，再次，根据异常数据确定目标用户的肿瘤的属性信息，最后，根据目标医学影像数据进行4D医学成像，并输出肿瘤的属性信息，其中，目标医学影像数据至少包括目标器官的数据集合和目标器官周围的血管的数据集合，血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且动脉和静脉的交叉位置的数据相互独立，目标器官的数据集合为目标器官表面和目标器官内部的组织结构的立方体空间的传递函数结果，血管的数据集合为血管表面和血管内部的组织结构的立方体空间的传递函数结果。可见，本申请中的医学成像装置能够识别、定位肿瘤并进行4D医学成像，有利于提高肿瘤识别的准确度和效率。
在一个可能的示例中，所述属性信息包括位置属性；在所述根据所述异常 数据确定目标用户的肿瘤的属性信息方面，所述处理单元401具体用于：检测所述异常数据所关联的组织；以及用于若检测到所述异常数据所关联的组织仅包括所述目标器官，则确定肿瘤的位置属性为内肿瘤；以及用于若检测到所述异常数据所关联的组织包括所述目标器官和所述血管，则确定所述肿瘤的位置属性为外肿瘤。
在一个可能的示例中，所述属性信息包括类型属性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述处理单元401具体用于：确定所述异常数据中的边界异常数据；以及用于根据所述边界异常数据确定所述肿瘤的边界特性；以及用于若检测到所述边界特性为光滑连续特性，则确定目标用户的肿瘤的类型属性为良性肿瘤；以及用于若检测到所述边界特性为非光滑连续特性，则确定目标用户的肿瘤的类型属性为恶性肿瘤。
在一个可能的示例中，所述属性信息包括大小属性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述处理单元401具体用于：确定所述常数据中每个异常数据的空间坐标信息；以及用于根据所述每个异常数据的空间坐标信息确定目标用户的肿瘤的大小属性。
在一个可能的示例中，在所述输出所述肿瘤的所述属性信息方面，所述处理单元401具体用于：在检测到针对所述肿瘤位置的选择操作时，在显示所述肿瘤影像的显示屏的预设位置输出所述属性信息。
在一个可能的示例中，在所述根据所述BMP数据源生成目标医学影像数据方面，所述处理单元401具体用于：将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；以及用于将所述第一医学影像数据导入预设的交叉血管网络模型，得到第二医学影像数据，所述第二医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据；以及用于针对所述第二医学影像数据执行第一预设处理得到目标医学影像 数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
在一个可能的示例中，在所述根据所述目标医学影像数据进行4D医学成像方面，所述处理单元401具体用于：从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据。
本申请实施例还提供一种计算机存储介质，其中，该计算机存储介质存储用于电子数据交换的计算机程序，该计算机程序使得计算机执行如上述方法实施例中记载的任一方法的部分或全部步骤，上述计算机包括医学成像装置。
本申请实施例还提供一种计算机程序产品，上述计算机程序产品包括存储了计算机程序的非瞬时性计算机可读存储介质，上述计算机程序可操作来使计算机执行如上述方法实施例中记载的任一方法的部分或全部步骤。该计算机程序产品可以为一个软件安装包，上述计算机包括医学成像装置。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本申请并不受所描述的动作顺序的限制，因为依据本申请，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本申请所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置，可通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如上述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性或其它的形式。
上述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为 单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
上述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储器中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储器中，包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本申请各个实施例上述方法的全部或部分步骤。而前述的存储器包括：U盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储器中，存储器可以包括：闪存盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取器(英文：Random Access Memory，简称：RAM)、磁盘或光盘等。
以上对本申请实施例进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

Claims (20)

  1. 一种基于VRDS 4D医学影像的肿瘤Ai处理方法，其特征在于，应用于医学成像装置；所述方法包括：
    根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源；
    根据所述BMP数据源生成目标医学影像数据，所述目标医学影像数据至少包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉的数据集合和/或静脉的数据集合，且所述动脉和所述静脉的交叉位置的数据相互独立，所述目标器官的数据集合为所述目标器官表面和所述目标器官内部的组织结构的立方体空间的传递函数结果，所述血管的数据集合为所述血管表面和所述血管内部的组织结构的立方体空间的传递函数结果；
    确定所述目标医学影像数据中异常数据；
    根据所述异常数据确定目标用户的肿瘤的属性信息；
    根据所述目标医学影像数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
  2. 根据权利要求1所述的方法，其特征在于，所述属性信息包括位置属性；所述根据所述异常数据确定目标用户的肿瘤的属性信息，包括：
    检测所述异常数据所关联的组织；
    若检测到所述异常数据所关联的组织仅包括所述目标器官，则确定肿瘤的位置属性为内肿瘤；
    若检测到所述异常数据所关联的组织包括所述目标器官和所述血管，则确定所述肿瘤的位置属性为外肿瘤。
  3. 根据权利要求1所述的方法，其特征在于，所述属性信息包括类型属性；所述根据所述异常数据确定目标用户的肿瘤的属性信息，包括：
    确定所述异常数据中的边界异常数据；
    根据所述边界异常数据确定所述肿瘤的边界特性；
    若检测到所述边界特性为光滑连续特性，则确定目标用户的肿瘤的类型属性为良性肿瘤；
    若检测到所述边界特性为非光滑连续特性，则确定目标用户的肿瘤的类型属性为恶性肿瘤。
  4. 根据权利要求1所述的方法，其特征在于，所述属性信息包括大小属性；所述根据所述异常数据确定目标用户的肿瘤的属性信息，包括：
    确定所述常数据中每个异常数据的空间坐标信息；
    根据所述每个异常数据的空间坐标信息确定目标用户的肿瘤的大小属性。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，所述输出所述肿瘤的所述属性信息，包括：
    在检测到针对所述肿瘤位置的选择操作时，在显示所述肿瘤影像的显示屏的预设位置输出所述属性信息。
  6. 根据权利要求1-5任一项所述的方法，其特征在于，所述根据所述BMP数据源生成目标医学影像数据，包括：
    将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；
    将所述第一医学影像数据导入预设的交叉血管网络模型，得到第二医学影像数据，所述第二医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述交叉位置关联的数据，所述第二数据为与所述交叉位置关联的数据；
    针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
  7. 根据权利要求6所述的方法，其特征在于，所述根据所述目标医学影像数据进行4D医学成像，包括：
    从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据。
  8. 根据权利要求1所述的方法，其特征在于，所述根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源，包括：
    获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标器官的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；
    解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture2D/3D图像体数据；
    针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
  9. 根据权利要求1所述的方法，其特征在于，所述确定所述目标医学影像数据中异常数据，包括：
    将所述目标医学影像数据与预存的所述目标组织的灰度值模板进行比对，得到比对结果为不匹配的异常数据，所述目标组织包括所述目标器官和所述血管。
  10. 一种基于VRDS 4D医学影像的肿瘤处理装置，其特征在于，应用于医学成像装置；所述基于VRDS 4D医学影像的肿瘤处理装置包括处理单元和通信单元，其中，
    所述处理单元，用于将目标用户的目标器官关联的位图BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述目标器官周围的血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；以及用于将所述第一医学影像数据与预存的所述目标组织的灰度值模板进行比较，确定所述第一医学影像数据中的正常数据和异常数据，所述目标组织包括所述目标器官和所述血管；以及用于根据所述异常数据确定目标用户的肿瘤的属性信息；以及用于通过所述通信单元根据所述正常数据和异常数据进行4D医学成像，并输出所述肿瘤的所述属性信息。
  11. 根据权利要求10所述的装置，其特征在于，所述属性信息包括位置属 性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述处理单元具体用于：检测所述异常数据所关联的组织；若检测到所述异常数据所关联的组织仅包括所述目标器官，则确定肿瘤的位置属性为内肿瘤；若检测到所述异常数据所关联的组织包括所述目标器官和所述血管，则确定所述肿瘤的位置属性为外肿瘤。
  12. 根据权利要求10所述的装置，其特征在于，所述属性信息包括类型属性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述处理单元具体用于：确定所述异常数据中的边界异常数据；根据所述边界异常数据确定所述肿瘤的边界特性；若检测到所述边界特性为光滑连续特性，则确定目标用户的肿瘤的类型属性为良性肿瘤；若检测到所述边界特性为非光滑连续特性，则确定目标用户的肿瘤的类型属性为恶性肿瘤。
  13. 根据权利要求10所述的装置，其特征在于，所述属性信息包括大小属性；在所述根据所述异常数据确定目标用户的肿瘤的属性信息方面，所述处理单元具体用于：确定所述常数据中每个异常数据的空间坐标信息；根据所述每个异常数据的空间坐标信息确定目标用户的肿瘤的大小属性。
  14. 根据权利要求10-13任一项所述的装置，其特征在于，在所述输出所述肿瘤的所述属性信息方面，所述通信单元具体用于：在检测到针对所述肿瘤位置的选择操作时，在显示所述肿瘤影像的显示屏的预设位置输出所述属性信息。
  15. 根据权利要求10-14任一项所述的装置，其特征在于，在所述根据所述BMP数据源生成目标医学影像数据方面，所述处理单元具体用于：将所述BMP数据源导入预设的VRDS医学网络模型，得到第一医学影像数据，所述第一医学影像数据包括所述目标器官的数据集合和所述血管的数据集合，所述血管的数据集合中包括动脉和静脉的交叉位置的融合数据；将所述第一医学影像数据导入预设的交叉血管网络模型，得到第二医学影像数据，所述第二医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合，且所述动脉的数据集合中的第一数据和所述静脉的数据集合的第二数据相互独立，所述第一数据为与所述交叉位置关联的数据，所述第二数据为 与所述交叉位置关联的数据；针对所述第二医学影像数据执行第一预设处理得到目标医学影像数据，所述第一预设处理包括以下至少一种操作：2D边界优化处理、3D边界优化处理、数据增强处理，所述目标医学影像数据包括所述目标器官的数据集合和所述动脉的数据集合以及所述静脉的数据集合。
  16. 根据权利要求15所述的装置，其特征在于，在所述根据所述目标医学影像数据进行4D医学成像方面，所述通信单元具体用于：从所述目标医学影像数据中筛选质量评分大于预设评分的增强数据作为VRDS 4D成像数据。
  17. 根据权利要求10所述的装置，其特征在于，在所述根据目标用户的目标器官关联的多张扫描图像确定位图BMP数据源方面，所述处理单元具体用于：获取通过医疗设备采集的反映目标用户的人体内部结构特征的多张扫描图像；从所述多张扫描图像中筛选出包含所述目标器官的至少一张扫描图像，将所述至少一张扫描图像作为目标用户的医学数字成像和通信DICOM数据；解析所述DICOM数据生成目标用户的图源，所述图源包括纹理Texture 2D/3D图像体数据；针对所述图源执行第二预设处理得到所述BMP数据源，所述第二预设处理包括以下至少一种操作：VRDS限制对比度自适应直方图均衡、混合偏微分去噪、VRDS Ai弹性变形处理。
  18. 根据权利要求10所述的装置，其特征在于，在所述确定所述目标医学影像数据中异常数据方面，所述处理单元具体用于：将所述目标医学影像数据与预存的所述目标组织的灰度值模板进行比对，得到比对结果为不匹配的异常数据，所述目标组织包括所述目标器官和所述血管。
  19. 一种医学成像装置，其特征在于，包括处理器、存储器、通信接口，以及一个或多个程序，所述一个或多个程序被存储在所述存储器中，并且被配置由所述处理器执行，所述程序包括用于执行如权利要求1-9任一项所述的方法中的步骤的指令。
  20. 一种计算机可读存储介质，其特征在于，存储用于电子数据交换的计算机程序，其中，所述计算机程序使得计算机执行如权利要求1-9任一项所述的方法。
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