WO2021081839A1 - Procédé à base de vrds 4d pour l'analyse de l'affection d'un patient, et produits associés - Google Patents

Procédé à base de vrds 4d pour l'analyse de l'affection d'un patient, et produits associés Download PDF

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Publication number
WO2021081839A1
WO2021081839A1 PCT/CN2019/114475 CN2019114475W WO2021081839A1 WO 2021081839 A1 WO2021081839 A1 WO 2021081839A1 CN 2019114475 W CN2019114475 W CN 2019114475W WO 2021081839 A1 WO2021081839 A1 WO 2021081839A1
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target
blood vessel
image data
data
disease
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PCT/CN2019/114475
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English (en)
Chinese (zh)
Inventor
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 PCT/CN2019/114475 priority Critical patent/WO2021081839A1/fr
Priority to CN201980099991.4A priority patent/CN114341996A/zh
Publication of WO2021081839A1 publication Critical patent/WO2021081839A1/fr

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    • 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

Definitions

  • This application relates to the technical field of medical imaging devices, in particular to a VRDS 4D-based disease analysis method and related products.
  • CT electronic computer tomography
  • MRI magnetic resonance imaging
  • DTI diffusion tensor imaging
  • PET positron emission computed tomography
  • the embodiments of the present application provide a VRDS 4D-based disease analysis method and related products, which are beneficial to improve the efficiency of disease analysis.
  • the embodiments of the present application provide a VRDS 4D-based disease analysis method, including:
  • each human organ corresponds to at least one scanned image
  • the human organ includes at least one of the following: ears, nose, and throat;
  • the disease condition analysis is performed on the diseased part, and the target disease condition analysis result is obtained.
  • an embodiment of the present application provides a VRDS 4D-based disease analysis device, including:
  • the acquiring unit is used to acquire scanned images of multiple human organs of the target user to obtain multiple scanned images, and each human organ corresponds to at least one scanned image;
  • a processing unit configured to process the multiple scanned images to obtain a target 4D image corresponding to the target user, where the target 4D image includes target image data corresponding to the target user;
  • An identification unit configured to identify an onset location corresponding to the target user based on the target image data, where the onset location is one or more of the multiple human organs;
  • an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured by The foregoing processor executes, and the foregoing program includes instructions for executing the steps in the first aspect of the embodiments of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute Some or all of the steps described in one aspect.
  • the embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute as implemented in this application.
  • the computer program product may be a software installation package.
  • FIG. 1 is a schematic structural diagram of a VRDS 4D-based disease analysis and processing system 100 provided by an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of an embodiment of a VRDS 4D-based disease analysis method 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 schematic structural diagram of an embodiment of a VRDS 4D-based disease analysis device 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 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 a VRDS 4D-based disease analysis and processing system 100 according to an embodiment of this application.
  • the system 100 includes a medical imaging device 110 and a network database 120.
  • the medical imaging device 110 can Including 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 is used to base on the original DICOM data, based on the VRDS 4D disease analysis algorithm presented in the embodiment of this application, Perform identification, positioning, four-dimensional volume rendering, and abnormal analysis of human organs to achieve four-dimensional three-dimensional imaging effects (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 is not lost, that is, the medical imaging device can present the internal structure of human organs, blood vessels and other tissues.
  • the external spatial structural characteristics refer to the environmental characteristics between tissues, including tissues and tissues. Spatial location characteristics (including crossing, spacing, fusion), etc., such as the edge structure characteristics of the crossing position between the ears, nose, and throat and blood vessels, etc.), the local medical imaging device 111 is relative to the terminal medical imaging device 112 It can also be used to edit the image source data to form the transfer function result of the four-dimensional human body image.
  • the transfer function result can include the transfer function result of the surface of the human organ and the tissue structure in the human organ, and the transfer function result of the cube space, such as The cube edit box and arc edit array quantity, coordinates, color, transparency and other information required by 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.
  • HMDS head mounted Displays Set
  • the operation actions refer to the user’s actions through the medical imaging device.
  • External ingestion devices such as mouse, keyboard, tablet (portable android device, Pad), iPad (internetportable apple device), etc., operate and control the four-dimensional human body image to achieve human-computer interaction.
  • the operation action includes at least one of the following : (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” the human organ Internal observation of internal structure, real-time cutting effect rendering, (5) Move the view up and down.
  • FIG. 2 is a schematic flowchart of an embodiment of a VRDS 4D-based disease analysis method provided by an embodiment of this application.
  • the VRDS 4D-based disease analysis method described in this embodiment includes the following steps:
  • the medical imaging device obtains scanned images of multiple human organs of a target user, and obtains multiple scanned images. Each human organ corresponds to at least one scanned image.
  • the human organ includes at least one of the following: ears, nose, and Throat
  • the above-mentioned human organs may be at least one of the following: ears, nose, throat, brain, kidneys and other organs, which are not limited here, and the above-mentioned scanned images may include any of the following: CT images, MRI images, DTI images , PET-CT images, etc., are not limited here.
  • the medical imaging device can collect multiple scanned images reflecting the internal structure of multiple human organs of the target user, and each human organ corresponds to at least one scanned image.
  • the medical imaging device processes the multiple scanned images to obtain a target 4D image corresponding to the target user, where the target 4D image includes target image data corresponding to the target user;
  • the target image data may include a human organ data set and a blood vessel data set, and the human organ data set may include data corresponding to multiple human organs.
  • the multiple scanned images are processed to obtain a target 4D image corresponding to the target user, where the target 4D image includes target image data corresponding to the target user, and may include the following steps:
  • the first medical image data includes a human organ data set and a blood vessel data set, and the human organ data set includes the Multiple data corresponding to multiple human organs;
  • the foregoing first preset processing may include at least one of the following operations: VRDS restricted contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing, etc., which are not limited here; the medical imaging device may be preset Suppose the VRDS medical network model, the medical imaging device obtains the BMP data source by processing multiple 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.
  • the medical imaging device imports the above-mentioned BMP data source into the preset VRDS medical network model, through which each transfer function in the set of pre-stored transfer functions can be called through the VRDS medical network model, and processed by multiple transfer functions in the transfer function set
  • the above-mentioned BMP data source obtains the first medical image data
  • the above-mentioned transfer function set may include the transfer function of the blood vessel and the transfer function of the above-mentioned multiple human organs preset by a reverse editor, wherein the transfer function of the above-mentioned multiple human organs It may include at least one of the following: the transfer function of the ear, the transfer function of the nose, and the transfer function of the larynx. In this way, obtaining the first medical image data through the preset VRDS medical network model can improve the accuracy and efficiency of the obtained data.
  • the medical imaging device may be preset with a cross-vessel network model
  • the preset cross-vessel network model may be a trained neural network model
  • the above-mentioned first medical image data may be imported into the preset cross-vessel network model.
  • the cross blood vessel network model performs data segmentation to obtain ear data set, nose data set, throat data set, and blood vessel data set.
  • the blood vessel data set includes the data associated with the cross positions of the above-mentioned organs and blood vessels.
  • the second medicine can be obtained.
  • Image data in this way, can achieve data segmentation between data corresponding to blood vessels and data corresponding to multiple human organs by crossing the blood vessel network model, so as to obtain data information corresponding to different human organs.
  • the blood vessel centerline data of the blood vessel can be determined from the above-mentioned blood vessel data set.
  • the blood vessel centerline may refer to the line between each center point in the blood vessel section.
  • the method for extracting the blood vessel centerline may include the following At least one: manual calibration, distance transformation, topology refinement, etc., which are not limited here.
  • multiple formation directions corresponding to multiple blood vessels can be determined from the data of the blood vessel intersection part in the blood vessel data set, and based on the multiple The formation direction determines the position and direction of the blood vessel centerline of each blood vessel in the above-mentioned blood vessel in the multiple blood vessels.
  • the blood vessel centerline data set corresponding to each blood vessel can be obtained according to the above-mentioned extraction method, and the blood vessel centerline data set includes multiple The blood vessel centerline of each blood vessel, in this way, the blood vessel centerline can be prepared for the subsequent determination of the location of the disease.
  • the foregoing second preset processing includes at least one of the following methods: 2D boundary optimization processing, 3D boundary optimization processing, data enhancement processing, etc., which are not limited here; the foregoing 2D boundary optimization processing includes: multiple sampling to obtain low resolution Rate information and high-resolution information.
  • the low-resolution information can provide the contextual semantic information of the segmentation target in the entire image, that is, the features that reflect the relationship between the segmentation target and the environment. These features are used for object category judgment, high resolution
  • the rate information is used to provide more refined features for segmentation targets, such as gradients.
  • the segmentation targets may include ear, nose, and throat and blood vessels.
  • the second medical image data can be processed to obtain a 4D image of the target, and the target 4D image may include Target image data.
  • the target image data may include at least one of the following: a blood vessel data set, an ear data set, a nose data set, and a throat data set.
  • the medical imaging device identifies an onset location corresponding to the target user based on the target image data, where the onset location is one or more of the multiple human organs;
  • the above-mentioned disease site may include at least one of the following: nose, ears, throat, etc., which are not limited here.
  • the above-mentioned disease site can be understood as a specific site of disease in multiple human organs, which can be multiple people.
  • the disease type corresponding to the diseased part may be a cyst or a polyp, etc., which is not limited here.
  • identifying the diseased location corresponding to the target user based on the target image data may include the following steps:
  • the blood vessel centerline data set determine the blood vessel distribution data set corresponding to the multiple human organs, wherein each of the human organs corresponds to one blood vessel distribution data;
  • the onset location corresponding to the target user According to the multiple image data, identify the onset location corresponding to the target user.
  • the above-mentioned target image data includes at least a blood vessel data set. Since the scanned image obtained by scanning includes images of multiple blood vessels, multiple target blood vessel data sets associated with multiple human organs can be selected based on the blood vessel data set. The multiple target blood vessels corresponding to the multiple target blood vessel data sets are all connected to multiple human organs, and the blood vessel centerline data sets corresponding to the multiple target blood vessels can be obtained through the multiple target blood vessel data sets.
  • the blood vessel centerline data Concentration includes multiple blood vessel center lines corresponding to multiple target blood vessels. Since each target blood vessel has a width, if the target blood vessel has a disease, the contour will become different, which is not conducive to observing the bifurcation and position of the target blood vessel.
  • the centerline of the blood vessel can be introduced, and then the specific position of the target blood vessel can be obtained through the position of the blood vessel centerline.
  • the corresponding distribution of multiple target blood vessels connected to multiple human organs can be obtained, and
  • a blood vessel distribution data set, the blood vessel distribution data set includes the position of the blood vessel, the shape feature of the blood vessel, and so on.
  • multiple target organs connected to blood vessels can be obtained according to the above-mentioned blood vessel distribution data set.
  • the target organs can include ears, nose, throat, etc., and the multiple target organs can be target organs with disease. In this way, it can be determined The specific location of the lesion in the target organ is conducive to subsequent disease analysis.
  • the identifying the onset location corresponding to the target user based on the multiple image data may include the following steps:
  • the feature information includes at least one of the following: color, shape, position, and size;
  • multiple image data corresponding to multiple target organs can be generated to generate multiple feature information corresponding to the multiple target organs, and the location of the disease can be determined based on the feature information.
  • the feature information may include at least one of the following: Color, shape, position and size, etc., are not limited here.
  • the foregoing step 34 determining multiple target organs according to the blood vessel distribution data set, may include the following steps:
  • A1. Obtain a blood vessel radius data set corresponding to the multiple human organs in the blood vessel data set;
  • A2 according to the blood vessel radius data set, determine a blood vessel distortion distribution data set corresponding to a blood vessel radius in the blood vessel distribution data set that exceeds a preset threshold;
  • A3. Determine the vascular distortion range through the vascular distortion distribution data set
  • A4. Determine the multiple target organs according to the blood vessel distortion distribution data set and the blood vessel distortion range.
  • the above-mentioned blood vessel data set may include data associated with the intersection of blood vessels and blood vessels.
  • the blood vessel data set Through the blood vessel data set, multiple blood vessels connected to multiple human organs can be determined, and images corresponding to the multiple blood vessels can be obtained to determine the above
  • a blood vessel radius data set can be obtained, and the blood vessel radius data set includes the radii of multiple blood vessels corresponding to multiple human organs.
  • the above preset threshold can be set by the user or the system defaults.
  • the preset threshold can be understood as the radius of a normal blood vessel. If a lesion such as hemangioma is formed in the blood vessel, the radius of the blood vessel will be larger than the radius of the normal blood vessel. Then the blood vessel can be classified as a deformed blood vessel, or the contour shape of the blood vessel can be combined to determine whether the blood vessel is a deformed blood vessel.
  • the blood vessel radius exceeding the preset threshold can be filtered from the blood vessel radius data set, and the blood vessel radius can be determined to exceed the preset threshold.
  • a plurality of deformed blood vessels with a threshold value can be obtained, and the deformed blood vessel distribution data set in the above-mentioned blood vessel distribution data set corresponding to the multiple deformed blood vessels can be obtained.
  • the data set includes the distribution data corresponding to each deformed blood vessel.
  • the distribution data includes blood vessel bifurcation information, Position information, etc., to obtain the distribution information corresponding to the above-mentioned multiple distorted blood vessels, and determine the multiple distortion positions of the blood vessel distortion according to the blood vessel distortion distribution data set corresponding to the multiple distorted blood vessels, and according to the multiple distortion positions and the multiple distorted blood vessels
  • the distribution information of multiple target organs with lesions can be determined.
  • the foregoing step 37, identifying the diseased parts in the multiple target organs according to the multiple characteristic information may include the following steps:
  • the aforementioned preset disease location recognition model can be set by the user or the system defaults
  • the preset disease location recognition model can be a convolutional neural network
  • the aforementioned preset probability value can be set by the user or the system defaults
  • the above feature information It may include at least one of the following: morphological features, color features, location features, size features, etc., which are not limited here; specifically, multiple feature probabilities corresponding to the multiple features can be used to determine the excess of the multiple feature probabilities.
  • At least one piece of feature information corresponding to the preset probability value, and the location corresponding to the at least one piece of feature information is the onset location among the multiple onset target organs. In this way, the recognition efficiency can be improved.
  • identifying the onset location corresponding to the target user may further include the following steps:
  • the target image data generate first target image data corresponding to any human organ i among the plurality of human organs, the first target image data includes at least k pieces of spatial position data, and the k pieces
  • the spatial position data corresponds to k first image data, and each of the spatial position data corresponds to one first image data, where k is a positive integer;
  • the medical imaging device can determine the spatial location area corresponding to each human organ data in the multiple human organs according to the human organ data set in the target image data, and determine each human body according to the spatial location area corresponding to each human organ data The position coordinates corresponding to the organ data. In this way, k spatial position data corresponding to any human organ i can be obtained, and each spatial position data can correspond to a first image data in the target image data.
  • the identifying the onset location corresponding to the target user according to the k spatial location data may include the following steps:
  • the site is the site of the disease, where p is an integer less than k.
  • the abnormal first image data can be obtained, and the abnormal part can be obtained from the abnormal first image data, and the abnormal part is the diseased part, where k is positive Integer, p is an integer less than k.
  • the abnormal data corresponding to the onset location can be detected through multiple spatial location data, so that the onset location can be identified, and the recognition accuracy is improved.
  • the medical imaging device performs a disease analysis on the diseased part, and obtains a target disease analysis result.
  • the diseased site after identifying the diseased site, the diseased site can be analyzed to obtain the target diseased analysis result.
  • the medicine can be prescribed according to the obtained diseased analysis result, or the diseased analysis result can be used as an auxiliary treatment plan to help the doctor confirm the diagnosis.
  • step 204 performing disease analysis on the diseased site to obtain the target disease analysis result, may include the following steps:
  • a disease feature database can be preset in the medical imaging device, and multiple disease types can be preset in the preset disease database, and each disease type can correspond to a set of preset disease feature information.
  • the disease types can include at least one of the following : Ear tumors, nose tumors, throat tumors, etc., which are not limited here; specifically, multiple features corresponding to the onset location can be put into the above-mentioned preset disease feature database for matching, and multiple matching values are obtained.
  • a matching value is multiple ratio values obtained by matching multiple preset disease feature information corresponding to each disease in the preset disease feature database, and the disease type corresponding to the largest matching value among the multiple matching values is selected as the target disease type
  • the above-mentioned medical imaging device can also preset the mapping relationship between the disease type and the disease analysis result. According to the mapping relationship, the target disease analysis result corresponding to the target disease type can be determined, and multiple predictions corresponding to each disease type can be determined.
  • Set disease feature information to generate disease analysis data which can obtain multiple disease analysis data for multiple disease types, and preset disease analysis results for each disease analysis data.
  • the preset disease analysis results can correspond to different disease severity. In this way, the target disease analysis result corresponding to the target disease type can be obtained, and the efficiency of disease analysis can be improved, and the efficiency of disease treatment can be improved.
  • the medical imaging device can first obtain scanned images of multiple human organs of the target user, and obtain multiple scanned images, each of which corresponds to at least one
  • the above-mentioned human organs include at least one of the following: ears, nose, and throat.
  • the multiple scanned images are processed to obtain a target 4D image corresponding to the target user, and the target 4D image includes a target image corresponding to the target user Based on the target image data, identify the affected part of the target user.
  • the affected part is one or more parts of multiple human organs.
  • the diseased part is analyzed to obtain the target disease analysis result. In this way, the scanned image
  • the analysis and processing of the target user can obtain the diseased location of the target user, and perform the disease analysis for the diseased location to obtain the disease analysis result, which is beneficial to improve the accuracy and efficiency of the disease analysis.
  • FIG. 3 is a schematic structural diagram of a medical imaging device 300 provided by an embodiment of the application.
  • the medical imaging device 300 includes a processor 310, a memory 320, a communication interface 330, One or more programs 321, wherein the one or more programs 321 are stored in the above-mentioned memory 320 and are configured to be executed by the above-mentioned processor 310, and the one or more programs 321 include: instruction:
  • each human organ corresponds to at least one scanned image
  • the human organ includes at least one of the following: ears, nose, and throat;
  • the disease condition analysis is performed on the diseased part, and the target disease condition analysis result is obtained.
  • scanned images of multiple human organs of the target user can be obtained, and multiple scanned images can be obtained.
  • Each human organ corresponds to at least one scanned image.
  • the aforementioned human organs include At least one of the following: ears, nose, and throat, process multiple scanned images to obtain a target 4D image corresponding to the target user.
  • the target 4D image includes target image data corresponding to the target user. Based on the target image data, identify The diseased part corresponding to the target user, the diseased part is one or more parts of multiple human organs, the diseased part is analyzed for the diseased part, and the target diseased analysis result is obtained.
  • the scanned image can be analyzed and processed to obtain the target user's
  • the location of the disease and the analysis of the disease condition for the location of the disease to obtain the results of the disease analysis, which is beneficial to improve the accuracy and efficiency of the disease analysis.
  • the program further includes a method for performing the following operations The instructions:
  • multiple target organs are determined, and the multiple target organs include at least one of the following: ears, nose, and throat;
  • the onset location corresponding to the target user is identified.
  • the program further includes instructions for performing the following operations:
  • the feature information including at least one of the following: color, shape, position, and size;
  • the onset location corresponding to the target user is identified.
  • the program further includes instructions for performing the following operations:
  • the multiple target organs are determined according to the blood vessel distortion distribution data set and the blood vessel distortion range.
  • the program further includes instructions for performing the following operations:
  • an onset location in the multiple target organs is determined.
  • the program further includes instructions for performing the following operations:
  • the target condition analysis result corresponding to the target user is determined.
  • the program further includes instructions for performing the following operations:
  • first target image data corresponding to any human organ i among the plurality of human organs is generated, the first target image data includes at least k spatial position data, and the k spatial positions The data corresponds to k first image data, and each of the spatial position data corresponds to one first image data, where k is a positive integer;
  • the k pieces of spatial location data identify the onset location corresponding to the target user.
  • the program further includes instructions for performing the following operations:
  • the target 4D image includes target image data corresponding to the target user
  • the program further Include instructions to perform the following actions:
  • the first medical image data includes a human organ data set and a blood vessel data set, and the human organ data set includes the plurality of people Multiple body organ data corresponding to the body organ;
  • FIG. 4 is a schematic structural diagram of an embodiment of a VRDS 4D-based disease analysis device provided by an embodiment of this application.
  • the VRDS 4D-based disease analysis device described in this embodiment includes: an acquisition unit 401, a processing unit 402, an identification unit 403, and an analysis unit 404, which are specifically as follows:
  • the acquiring unit 401 is configured to acquire scanned images of multiple human organs of the target user to obtain multiple scanned images, and each human organ corresponds to at least one scanned image;
  • the processing unit 402 is configured to process the multiple scanned images to obtain a target 4D image corresponding to the target user, and the target 4D image includes target image data corresponding to the target user;
  • the identification unit 403 is configured to identify an onset location corresponding to the target user based on the target image data, where the onset location is one or more of the multiple human organs;
  • the analysis unit 404 is configured to perform a disease analysis on the diseased part to obtain a target disease analysis result.
  • the VRDS 4D-based disease analysis device described in the embodiments of this application can obtain scanned images of multiple human organs of the target user to obtain multiple scanned images, and each human organ corresponds to at least one scanned image.
  • the above-mentioned human organs include at least one of the following: ears, nose, and throat.
  • the multiple scanned images are processed to obtain a target 4D image corresponding to the target user.
  • the target 4D image includes target image data corresponding to the target user.
  • Image data identify the affected part of the target user.
  • the affected part is one or more parts of multiple human organs, and analyze the condition of the affected part to obtain the target condition analysis result.
  • the scanned image can be analyzed and processed.
  • the identification unit 403 is specifically configured to:
  • multiple target organs are determined, and the multiple target organs include at least one of the following: ears, nose, and throat;
  • the onset location corresponding to the target user is identified.
  • the identification unit 403 is specifically further configured to:
  • the feature information including at least one of the following: color, shape, position, and size;
  • the onset location corresponding to the target user is identified.
  • the identification unit 403 is specifically further configured to:
  • the multiple target organs are determined according to the blood vessel distortion distribution data set and the blood vessel distortion range.
  • the identification unit 403 is specifically further configured to:
  • an onset location in the multiple target organs is determined.
  • the above analysis unit 404 is specifically configured to:
  • the target condition analysis result corresponding to the target user is determined.
  • the identification unit 403 is specifically further configured to:
  • first target image data corresponding to any human organ i among the plurality of human organs is generated, the first target image data includes at least k spatial position data, and the k spatial positions The data corresponds to k first image data, and each of the spatial position data corresponds to one first image data, where k is a positive integer;
  • the k pieces of spatial location data identify the onset location corresponding to the target user.
  • the identification unit 403 is specifically further configured to:
  • the target 4D image includes target image data corresponding to the target user, the processing unit 402 Specifically used for:
  • the first medical image data includes a human organ data set and a blood vessel data set, and the human organ data set includes the plurality of people Multiple body organ data corresponding to the body organ;
  • each program module of the VRDS 4D-based disease analysis device of this embodiment can be implemented according to the method in the above method embodiment.
  • An embodiment of the present application also provides a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program enables the computer to execute any VRDS 4D-based condition as recorded in the above method embodiment Part or all of the steps of the analytical method.
  • the embodiments of the present application also provide a computer program product.
  • the computer program product includes a non-transitory computer-readable storage medium storing a computer program.
  • the computer program is operable to cause a computer to execute the method described in the foregoing method embodiment. Part or all of the steps of any disease analysis method based on VRDS 4D.
  • the disclosed device may be implemented in other ways.
  • the device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be realized in the form of hardware or software program module.
  • the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer readable memory.
  • the technical solution of the present application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory.
  • a number of instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.
  • the foregoing memory includes: U disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.
  • the program can be stored in a computer-readable memory, and the memory can include: a flash disk , ROM, RAM, magnetic disk or CD, etc.

Landscapes

  • Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé basé sur la VRDS 4D pour l'analyse de l'affection d'un patient, et des produits associés, appliqué à un dispositif d'imagerie médicale. Le procédé consiste à : acquérir des images scannées d'une pluralité d'organes humains d'un utilisateur cible, de manière à obtenir une pluralité d'images scannées, les organes humains comprenant au moins l'un des suivants : l'oreille, le nez et la gorge ; traiter la pluralité d'images scannées pour obtenir une image 4D cible correspondant à l'utilisateur cible, l'image 4D cible comprenant des données d'image cible correspondant à l'utilisateur cible ; identifier un site d'invasion correspondant à l'utilisateur cible sur la base des données d'image cible, le site d'invasion étant un ou plusieurs sites parmi la pluralité d'organes humains ; et analyser l'affection du patient par rapport au site d'invasion, de façon à obtenir un résultat d'analyse cible.
PCT/CN2019/114475 2019-10-30 2019-10-30 Procédé à base de vrds 4d pour l'analyse de l'affection d'un patient, et produits associés WO2021081839A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/CN2019/114475 WO2021081839A1 (fr) 2019-10-30 2019-10-30 Procédé à base de vrds 4d pour l'analyse de l'affection d'un patient, et produits associés
CN201980099991.4A CN114341996A (zh) 2019-10-30 2019-10-30 基于vrds 4d的病情分析方法及相关产品

Applications Claiming Priority (1)

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PCT/CN2019/114475 WO2021081839A1 (fr) 2019-10-30 2019-10-30 Procédé à base de vrds 4d pour l'analyse de l'affection d'un patient, et produits associés

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WO2021081839A1 true WO2021081839A1 (fr) 2021-05-06

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Cited By (1)

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US11348228B2 (en) 2017-06-26 2022-05-31 The Research Foundation For The State University Of New York System, method, and computer-accessible medium for virtual pancreatography

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CN109472780A (zh) * 2018-10-26 2019-03-15 强联智创（北京）科技有限公司 一种颅内动脉瘤图像的形态学参数的测量方法及系统
CN109544534A (zh) * 2018-11-26 2019-03-29 上海联影智能医疗科技有限公司 一种病灶图像检测装置、方法和计算机可读存储介质
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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CN108447046A (zh) * 2018-02-05 2018-08-24 龙马智芯（珠海横琴）科技有限公司 病灶的检测方法和装置、设备、计算机可读存储介质
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11348228B2 (en) 2017-06-26 2022-05-31 The Research Foundation For The State University Of New York System, method, and computer-accessible medium for virtual pancreatography

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