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WO2021030994A1 - Procédé et produits de reconnaissance basés sur une image veineuse ia vrds - Google Patents

Procédé et produits de reconnaissance basés sur une image veineuse ia vrds Download PDF

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Publication number
WO2021030994A1
WO2021030994A1 PCT/CN2019/101164 CN2019101164W WO2021030994A1 WO 2021030994 A1 WO2021030994 A1 WO 2021030994A1 CN 2019101164 W CN2019101164 W CN 2019101164W WO 2021030994 A1 WO2021030994 A1 WO 2021030994A1
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WIPO (PCT)
Prior art keywords
vein
sub
image
vena cava
inferior vena
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PCT/CN2019/101164
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English (en)
Chinese (zh)
Inventor
李斯图尔特平
李戴维伟
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VR Doctor Medical Technology Shenzhen Co Ltd
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VR Doctor Medical Technology Shenzhen Co Ltd
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Priority to PCT/CN2019/101164 priority Critical patent/WO2021030994A1/fr
Priority to CN201980099716.2A priority patent/CN114364323A/zh
Publication of WO2021030994A1 publication Critical patent/WO2021030994A1/fr
Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves

Definitions

  • This application relates to the technical field of medical imaging devices, and in particular to a method and product based on VRDS AI vein image recognition.
  • the embodiments of the present application provide a method and product for identifying a vein image based on VRDS AI, in order to improve the security of identification by a medical imaging device.
  • an embodiment of the present application provides a method for identifying vein images based on VRDS AI, which is applied to a medical imaging device; the method includes:
  • an embodiment of the present application provides a medical imaging device, which includes a processing unit and a communication unit, wherein:
  • the processing unit is used to obtain a scanned image of a target user's target part including the inferior vena cava through the communication unit; and used to process the scanned image to obtain a target image of the inferior vena cava;
  • the target image generates a first feature data set of the inferior vena cava, the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user; and the first feature data is used to
  • the collection is compared with the pre-stored original feature data collection to obtain a comparison result; and used to perform a preset operation according to the comparison result.
  • 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 obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of this application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • FIG. 1 is a schematic structural diagram of a medical image intelligent analysis and processing system based on VRDS Ai according to an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a method for identifying vein images based on VRDS AI according to 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 medical imaging 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 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 Ai medical image intelligent analysis and processing system 100 based on 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 based on the original DICOM data, based on the identity recognition based on the VRDS AI vein image presented in the embodiment of this application , To perform the identification, positioning, four-dimensional volume rendering and identification comparison and identity recognition of the human inferior vena cava, to achieve four-dimensional stereo imaging effects (the four-dimensional medical image specifically refers to the medical image including the internal spatial structure characteristics of the displayed tissue and the external space Structural characteristics, the internal spatial 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 target organs, blood vessels and other tissues, and the external spatial structural characteristics refer to the environmental characteristics between tissues
  • the transfer function result may include the transfer function result of the surface of the internal organs and the tissue structure in the internal organs of the human body, 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 operating actions refer to the user’s actions through the medical imaging device.
  • An external intake device such as a mouse, keyboard, etc., controls the operation of 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 of a specific organ/tissue 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) moving up and down view.
  • FIG 2 is a schematic flowchart of a VRDS AI vein image recognition 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, the VRDS AI vein image Image recognition methods include:
  • the medical imaging device acquires a scanned image of a target part of the target user including the inferior vena cava;
  • the scan image includes any one of the following: CT image, MRI image, DTI image, PET-CT image.
  • the medical imaging device processes the scanned image to obtain a target image of the inferior vena cava
  • the medical imaging device generates a first characteristic data set of the inferior vena cava according to the target image, where the first characteristic data set is used to reflect the physiological characteristics of the inferior vena cava of the target user;
  • the physiological characteristics of the inferior vena cava refer to specific types of characteristic data that can reflect the unique identity of the user set based on a priori data.
  • the formation of the inferior vena cava of the human body includes complex connection processes and various embryonic stages.
  • the degenerative process of veins is the main conduit through which the veins of the lower extremities and abdominal organs return to the right atrium. Therefore, R&D personnel can use big data test analysis to find the uniqueness that can be used to identify the user’s identity from the various physiological characteristics of the inferior vena cava Single or multiple types of characteristic data.
  • This setting principle is similar to the setting principle of fingerprint features, but it is more complicated than the setting principle of fingerprint features, because fingerprints are the surface tissue of the user's skin, and their physiological characteristics are only limited to the associated features of the fingerprint texture, and are generally two-dimensional. Due to its high degree of complexity in distribution and the complicated internal environment of the human body, traditional two-dimensional image analysis methods are difficult to extract effective physiological characteristic data. Medical imaging devices are required to extract comprehensive and accurate images of the inferior vena cava. Based on this data, we can further analyze the image to obtain effective physiological characteristic data.
  • the medical imaging device compares the first feature data set with a pre-stored original feature data set to obtain a comparison result
  • the original feature data set includes a single type of feature data
  • the original feature data set includes multiple types of feature data
  • the comparing the first feature data set with a pre-stored original feature data set includes: calculating the data of each category The reference matching degree of the feature data; the weighted summation is assigned according to the matching degree weights of the multiple types of feature data to obtain the comprehensive matching degree; the comparison result is obtained according to the comprehensive matching degree and the preset matching degree threshold.
  • the matching weight distribution of the multiple types of feature data can be empirical values, or the age range of the target user can be predicted based on the target image of the inferior vena cava of the current user, and then the weight corresponding to the age range can be queried Distribution, which can avoid the influence of the inferior vena cava differences of different ages on the recognition results and improve the accuracy of comparison.
  • multiple types of feature data include feature data A, feature data B, feature data C, and feature data D.
  • the age groups are divided into three age groups: Q1, Q2, and Q3, and then each can be obtained based on big data statistical analysis.
  • the distribution of exclusive matching weights for age groups is shown in Table 1.
  • the weight of the feature data associated with the spatial structure characteristics of the inferior vena cava can be set higher. This is because such feature data involves the three-dimensional spatial distribution characteristics of the inferior vena cava.
  • the three-dimensional spatial distribution characteristics are difficult to collect and accurately analyze by general devices, so it is very difficult for illegal users to obtain this type of characteristic data.
  • This setting can make the final matching degree calculation result difficult to be easily tampered with and affect the accuracy. Improve safety.
  • S205 The medical imaging device performs a preset operation according to the comparison result.
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of the present application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • the inferior vena cava includes a main vein and a sub-vene
  • the main vein in question refers to the superior and inferior vena cava and the central vein that enters the right atrium
  • the sub-vein refers to the connection between the main vein and The inferior vena cava of the kidney
  • the medical imaging device generates a first feature data set of the inferior vena cava according to the target image, including: the medical imaging device generates at least the following of the inferior vena cava according to the target image Characteristic data of an attribute: the number of the sub-vein, the shape of the main vein and/or the sub-vein, the spatial position relationship between the main vein and the sub-vein, and the spatial position relationship of the sub-vein Generating the first feature data set according to the feature data of the at least one attribute.
  • the number of sub veins refers to the number of sub veins connected to the kidney
  • the shape refers to the contour characteristics, radius, etc. of the inferior vena cava
  • the spatial position relationship refers to the intersection, convergence, and neighboring between veins. , Far away, or relative distance and other positional relationship description information.
  • the medical imaging device will call the cross network model to perform spatial separation processing on the fusion data of the cross positions of the veins during the data processing process before generating the target image, so the data processing process Then the cross characteristics between different inferior vena cava can be obtained, and can be pre-stored in the first feature data set. This avoids repeated processing and improves processing efficiency.
  • the medical imaging device can perform accurate identity recognition by analyzing characteristics such as the number, shape, and spatial position relationship of the inferior vena cava.
  • the at least one attribute includes the quantity; the medical imaging device generates characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, including: the medical imaging The device determines the images of the main vein, the first kidney, and the second kidney in the target image; analyzes the image between the main vein and the first kidney in the target image to obtain the image of the child vein The first number; analyze the image between the main vein and the second kidney in the target image to obtain the second number of the sub-veins; determine the sum of the first number and the second number Is the characteristic data of the stated quantity.
  • the second number corresponding to the second kidney can be set to zero, which improves processing efficiency.
  • the medical imaging device can accurately obtain the number of sub-veins of the current user by analyzing the images of the sub-veins, thereby providing a basis for subsequent identification and comparison and improving accuracy and safety.
  • the at least one attribute includes the shape, and the shape includes the shape of a main vein and/or a sub-vein; the medical imaging device generates the following information of the inferior vena cava according to the target image.
  • the characteristic data of at least one attribute includes: the medical imaging device determines the image of the main vein and/or the sub-vein in the target image; according to the image of the main vein and/or the sub-vein Determine the outline and radius of the main vein and/or the sub-vein; determine the outline and the radius as characteristic data of the shape.
  • the characteristic data of the contour can be specifically described by the trend of the vein, etc.
  • the trend can describe the angle of each segment relative to the reference line, or directly describe the curvature distribution of the vein, etc., which is not uniquely limited here.
  • the characteristic data of the radius can be specifically described by the radius of one or more reference positions.
  • the medical imaging device can accurately obtain the characteristic data of the shape of the main vein and/or sub-vein of the current user by analyzing the images of the main vein and the sub-vein, so as to provide a basis for the subsequent identification and comparison and improve the accuracy. And security.
  • the at least one attribute includes the spatial position relationship between the main vein and the sub vein; the medical imaging device generates at least one of the following attributes of the inferior vena cava according to the target image
  • the characteristic data includes: the medical imaging device determines the main vein and the image of each sub-vein in the target image; and determines the main vein and the image of each sub-vein in the target image
  • the connection position and connection angle of the main vein and each sub-vein; the connection position and the connection angle are determined as the spatial position relationship between the main vein and the sub-vein.
  • the medical imaging device can accurately obtain the characteristic data of the spatial position relationship between the main vein and the sub-vein of the current user by analyzing the images of the main vein and the sub-vein, so as to provide a basis for the subsequent identification and comparison and improve the accuracy and safety.
  • the at least one attribute includes the spatial position relationship of the sub-vein; the medical imaging device generates feature data of at least one of the following attributes of the inferior vena cava according to the target image, including : The medical imaging device determines the images of the main vein, the first kidney, and the second kidney in the target image; analyzes the image between the main vein and the first kidney to obtain a first sub-vein set Analyzing the image between the main vein and the second kidney to obtain a second sub-vein set; determining the first positional relationship between any two sub-veins in the first sub-vein set, and determining the first The second positional relationship between any two sub-veins in the two sub-vein sets; the first positional relationship and the second positional relationship are determined as the spatial positional relationship of the sub-veins.
  • first positional relationship and the second positional relationship include at least one of the following: positional relationship description information such as bifurcation, winding staggered, non-contact, contact, adjacent, or relative distance of a specific position.
  • the medical imaging device can accurately obtain the characteristic data of the spatial position relationship of the current user's sub-vein by analyzing the image of the sub-vein, so as to provide a basis for subsequent identification comparison and improve accuracy and safety.
  • processing the scanned image by the medical imaging device to obtain the target image of the inferior vena cava includes: the medical imaging device generates a bitmap BMP data source according to the scanned image; and according to the BMP The data source generates first venous image data, the first venous image data includes an original data set of the inferior vena cava, and the original data set is an image of the surface of the inferior vena cava and the tissue structure inside the inferior vena cava A transfer function result in a cube space; generating second vein image data according to the first vein image data, the second vein image data including a segmentation data set of the inferior vena cava, the segmentation data set having a cross position relationship Mutually independent image data of the inferior vena cava; processing the second venous image data to obtain a target image of the inferior vena cava.
  • the specific implementation of generating a bitmap BMP data source according to the scanned image includes: using the scanned image as the medical digital imaging and communication DICOM data of the target user; parsing the DICOM data to generate the image source of the target user, the The image source includes texture 2D/3D image volume data; the BMP data source is obtained by performing first preset processing on the image source, and the first preset processing includes at least one of the following operations: VRDS restricted contrast adaptive histogram Image equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing.
  • the DICOM Digital Imaging and Communications in Medicine
  • the medical imaging device first acquires multiple scanned images that reflect the internal structural characteristics of the target user's human body, and can screen out at least one suitable scanned image that contains the target organ through sharpness and accuracy. Further processing is performed on the scanned image to obtain a bitmap BMP data source. It can be seen that, in this example, the medical imaging device can obtain a bitmap BMP data source after filtering, parsing, and first preset processing based on the acquired scanned image, which improves the accuracy and clarity of medical image imaging.
  • the VRDS limited contrast adaptive histogram equalization includes the following steps: regional noise ratio limiting, global contrast limiting; dividing the local histogram of the image source into multiple partitions, and for each partition, according to the neighbors of the partition.
  • the slope of the cumulative histogram of the domain determines the slope of the transformation function, and the degree of contrast amplification around the pixel value of the partition is determined according to the slope of the transformation function, and then the limit cropping process is performed according to the degree of contrast amplification to generate the effective histogram.
  • the hybrid partial differential denoising includes the following steps: driving by VRDS Ai curvature and The VRDS Ai high-order hybrid denoising makes the curvature of the image edge less than the preset curvature, and realizes a hybrid partial differential denoising model that can protect the image edge and avoid the step effect in the smoothing process;
  • the VRDS Ai elastic deformation processing includes The following steps: On the image dot matrix, superimpose the positive and negative random distances to form a difference position matrix, and then form a new dot matrix with the grayscale at each difference position, which can realize the distortion and deformation of the image, and then the image Perform rotation, twist, and translation operations.
  • the hybrid partial differential denoising can use CDD and high-order denoising models to process the image source;
  • the CDD model (Curvature Driven Diffusions) model is based on the TV (Total Variation) model with the introduction of curvature drive and It solves the problem that the TV model cannot repair the visual connectivity of the image.
  • high-order denoising refers to denoising the image based on the partial differential equation (PDE) method.
  • the image source perform noise filtering according to the specified differential equation function change, thereby filtering out the noise in the image source, and the solution of the partial differential equation is the BMP data source obtained after denoising
  • the PDE-based image denoising method has the characteristics of anisotropic diffusion, so it can perform different degrees of diffusion in different regions of the image source, thereby achieving the effect of suppressing noise while protecting the edge texture information of the image.
  • the medical imaging device uses at least one of the following image processing operations: VRDS limited contrast adaptive histogram equalization, hybrid partial differential denoising, VRDS Ai elastic deformation processing, which improves the execution efficiency of image processing, and Improve the image quality and protect the edge texture of the image.
  • the generating the first vein image data according to the BMP data source includes: importing the BMP data source into a preset VRDS medical network model, and invoking the pre-stored delivery through the VRDS medical network model For each transfer function in the function set, the BMP data source is processed by multiple transfer functions in the transfer function set to obtain the first venous image data, and the transfer function set includes all presets set by a reverse editor. Describe the transfer function of the inferior vena cava.
  • BMP full name Bitmap
  • DDB device-dependent bitmap
  • DIB device-independent bitmap
  • the VRDS medical network model is a preset network model, and its training method includes the following three steps: image sampling and scale scaling; 3D convolutional neural network feature extraction and scoring; medical imaging device evaluation and network training.
  • sampling is required to obtain N BMP data sources, and then M BMP data sources are extracted from N BMP data sources at a preset interval. It needs to be explained that the preset interval can be flexibly set according to the usage scenario.
  • Sample M from N then scale the sampled M BMP data sources to a fixed size (for example, the length is S pixels and the width is S pixels), and the resulting processing result is used as the input of the 3D convolutional neural network .
  • M BMP data sources are used as the input of the 3D convolutional neural network.
  • a 3D convolutional neural network is used to perform 3D convolution processing on the BMP data source to obtain a feature map.
  • the generating second vein image data according to the first vein image data includes: importing the first vein image data into a preset cross blood vessel network model, and pass the cross blood vessel network model Perform spatial segmentation processing on the original data at the intersection to obtain mutually independent image data of multiple inferior vena cava at the intersection; update the original data set through the independent image data to obtain a second vein Image data.
  • the processing the second vein image data to obtain the target image of the inferior vena cava includes: performing at least one of the following processing operations on the second vein image data to obtain the inferior vena cava
  • the target image 2D boundary optimization processing, 3D boundary optimization processing, data enhancement processing.
  • the 2D boundary optimization processing includes the following operations: multiple sampling to obtain low-resolution information and high-resolution information, where the low-resolution information can provide contextual semantic information of the segmented target in the entire image, that is, reflect the relationship between the target and the environment. Characteristics of the inter-relationship, the segmentation target includes the target vein.
  • the 3D boundary optimization processing includes the following operations: putting the second medical image data into a 3D convolution layer to perform a 3D convolution operation to obtain a feature map; the 3D pooling layer compresses the feature map and performs Non-linear activation; cascade operation is performed on the compressed feature maps to obtain the prediction result image output by the model.
  • the data enhancement processing includes at least 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 operations, data enhancement based on random cut, and data enhancement based on Data enhancement to simulate different lighting changes.
  • the method before acquiring the scanned image of the target part of the target user including the inferior vena cava, the method further includes: entering the original feature data set.
  • FIG. 3 is a schematic structural diagram of a medical imaging apparatus 300 provided by an embodiment of the present application.
  • the medical imaging apparatus 300 includes a processor 310, a memory 320, a communication interface 330, and 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 The program 321 includes instructions for performing the following steps;
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of the present application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • the inferior vena cava includes a main vein and a sub-vene
  • the main vein in question refers to the superior and inferior vena cava and the central vein that enters the right atrium
  • the sub-vein refers to the connection between the main vein and The inferior vena cava of the kidney
  • the instructions in the program are specifically used to perform the following operations: generating the inferior vena cava based on the target image Characteristic data of at least one of the following attributes of the vena cava: the number of the sub-vein, the shape of the main vein and/or the sub-vein, the spatial position relationship between the main vein and the sub-vein, the sub-vein The spatial position relationship of the veins; and for generating the first feature data set according to the feature data of the at least one attribute.
  • the at least one attribute includes the number; in terms of generating characteristic data of at least one of the following attributes of the inferior vena cava according to the target image, the instructions in the program specifically Used to perform the following operations: determine the images of the main vein, the first kidney, and the second kidney in the target image; and analyze the difference between the main vein and the first kidney in the target image Image to obtain the first number of the sub-veins; and used to analyze the image between the main vein and the second kidney in the target image to obtain the second number of the sub-veins; and The sum of the first quantity and the second quantity is determined as the characteristic data of the quantity.
  • the at least one attribute includes the shape, and the shape includes the shape of a main vein and/or a sub-vein; and at least one of the following of the inferior vena cava is generated according to the target image
  • the instructions in the program are specifically used to perform the following operations: determine the image of the main vein and/or the sub-vein in the target image; /Or the image of the sub-vein determines the outline and radius of the main vein and/or the sub-vein; and feature data for determining the outline and the radius as the shape.
  • the at least one attribute includes the spatial position relationship between the main vein and the sub-vein; and the characteristics of the following at least one attribute of the inferior vena cava are generated according to the target image
  • the instructions in the program are specifically used to perform the following operations: determine the image of the main vein and each sub-vein in the target image; Images of each sub-vein to determine the connection position and connection angle of the main vein and each sub-vein; and used to determine the connection position and the connection angle as the spatial position of the main vein and the sub-vein relationship.
  • the at least one attribute includes the spatial position relationship of the sub-vein; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava according to the target image, the The instructions in the program are specifically used to perform the following operations: determine the images of the main vein, the first kidney, and the second kidney in the target image; and analyze the difference between the main vein and the first kidney.
  • Image to obtain a first sub-vein set analyze the image between the main vein and the second kidney to obtain a second sub-vein set; and used to determine the distance between any two sub-veins in the first sub-vein set
  • the first positional relationship of the second sub-vein set, and the second positional relationship between any two sub-veins in the second sub-vein set; and the first positional relationship and the second positional relationship are used to determine the sub-vein The spatial relationship of veins.
  • the instructions in the program are specifically used to perform the following operations: generating a bitmap BMP data source according to the scanned image And used to generate first venous image data according to the BMP data source, the first venous image data including the original data set of the inferior vena cava, the original data set being the surface of the inferior vena cava and the The transfer function result of the cube space of the tissue structure inside the inferior vena cava; and for generating second vein image data according to the first vein image data, the second vein image data including the segmentation data set of the inferior vena cava
  • the segmentation data set has mutually independent image data of the inferior vena cava in a cross position relationship; and is used to process the second venous image data to obtain a target image of the inferior vena cava.
  • 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 the functional unit composition of the medical imaging device 400 involved in an embodiment of the present application. It includes a processing unit 401 and a communication unit 402, wherein,
  • the processing unit 401 is configured to obtain a scanned image of a target part of the target user including the inferior vena cava through the communication unit 402; and to process the scanned image to obtain a target image of the inferior vena cava; and Generate a first feature data set of the inferior vena cava according to the target image, the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user; and used to compare the first The feature data set is compared with the pre-stored original feature data set to obtain a comparison result; and used to perform a preset operation according to the comparison result.
  • the medical imaging apparatus 400 may further include a storage unit 403 for storing program codes and data of the electronic device.
  • the processing unit 401 may be a processor
  • the communication unit 402 may be a touch screen or a transceiver
  • the storage unit 403 may be a memory.
  • the medical imaging device first obtains a scanned image of the target user's target part including the inferior vena cava, and secondly, processes the scanned image to obtain a target image of the inferior vena cava, and again, generates the inferior vena cava based on the target image
  • the first feature data set of veins the first feature data set is used to reflect the physiological characteristics of the inferior vena cava of the target user, and then the first feature data set is compared with the pre-stored original feature data set to obtain the comparison result, Finally, perform preset operations based on the comparison results.
  • the medical imaging device of the present application analyzes the image features of the user's inferior vena cava and compares the image features to achieve identity recognition to complete the preset operation. Due to the physiological feature verification mechanism on the surface of the human body, the image features of the inferior vena cava inside the human body are difficult to be forged by illegal users, which is beneficial to improve the security of identity recognition.
  • the inferior vena cava includes a main vein and a sub-vene
  • the main vein in question refers to the superior and inferior vena cava and the central vein that enters the right atrium
  • the sub-vein refers to the connection between the main vein and The inferior vena cava of the kidney
  • the processing unit 401 is specifically configured to: generate the following information of the inferior vena cava according to the target image Characteristic data of at least one attribute: the number of the sub veins, the shape of the main vein and/or the sub vein, the spatial position relationship between the main vein and the sub vein, the spatial position of the sub vein Relationship; and for generating the first feature data set according to feature data of the at least one attribute.
  • the at least one attribute includes the quantity; in terms of generating characteristic data of the following at least one attribute of the inferior vena cava according to the target image, the processing unit 401 specifically uses To: determine the images of the main vein, the first kidney and the second kidney in the target image; and to analyze the images between the main vein and the first kidney in the target image to obtain all The first number of the sub-veins; and used to analyze the image between the main vein and the second kidney in the target image to obtain the second number of the sub-veins; and used to compare the first The sum of the quantity and the second quantity is determined as the characteristic data of the quantity.
  • the at least one attribute includes the shape, and the shape includes the shape of a main vein and/or a sub-vein; and at least one of the following of the inferior vena cava is generated according to the target image
  • the processing unit 401 is specifically configured to: determine the image of the main vein and/or the sub-vein in the target image; The image of the sub-vein determines the outline and radius of the main vein and/or the sub-vein; and feature data for determining the outline and the radius as the shape.
  • the at least one attribute includes the spatial position relationship between the main vein and the sub-vein; and the characteristics of the following at least one attribute of the inferior vena cava are generated according to the target image
  • the processing unit 401 is specifically configured to: determine the image of the main vein and each sub-vein in the target image; and to determine the image of the main vein and the image of each sub-vein , Determining the connection position and connection angle of the main vein and each of the sub veins; and determining the connection position and the connection angle as the spatial position relationship between the main vein and the sub vein.
  • the at least one attribute includes the spatial position relationship of the sub-vein; in the aspect of generating feature data of at least one of the following attributes of the inferior vena cava according to the target image, the The processing unit 401 is specifically configured to: determine the images of the main vein, the first kidney and the second kidney in the target image; and to analyze the images between the main vein and the first kidney to obtain the first A sub-vein set, which analyzes the image between the main vein and the second kidney to obtain a second sub-vein set; and is used to determine the first position between any two sub-veins in the first sub-vein set Relationship, and determining a second positional relationship between any two sub-veins in the second sub-vein set; and used to determine the first positional relationship and the second positional relationship as the spatial position of the sub-vein relationship.
  • the processing unit 401 is specifically configured to: generate a bitmap BMP data source according to the scanned image; and First venous image data is generated according to the BMP data source, the first venous image data includes the original data set of the inferior vena cava, the original data set is the surface of the inferior vena cava and the interior of the inferior vena cava The result of the transfer function in the cube space of the tissue structure; and for generating second vein image data according to the first vein image data, the second vein image data including the segmentation data set of the inferior vena cava, the segmentation The data set has mutually independent image data of the inferior vena cava in a cross position relationship; and is used to process the second venous image data to obtain a target image of the inferior vena cava.
  • 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.
  • the above-mentioned computer program is operable to cause a computer to execute any of the methods described in the above-mentioned method embodiments. 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.
  • each unit in each embodiment 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 various 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

L'invention concerne un procédé et des produits de reconnaissance basés sur une image veineuse IA VRDS (100, 300, 400) destinés à être utilisés dans un dispositif d'imagerie médicale. Le procédé comprend les étapes consistant à : acquérir une image scannée d'une partie cible contenant la veine cave inférieure d'un utilisateur cible (S201) ; traiter l'image scannée pour obtenir une image cible de la veine cave inférieure (S202) ; générer un premier ensemble de données de caractéristiques de la veine cave inférieure en fonction de l'image cible (S203), le premier ensemble de données de caractéristiques étant utilisé pour refléter les caractéristiques physiologiques de la veine cave inférieure de l'utilisateur cible ; comparer le premier ensemble de données de caractéristiques à un ensemble de données de caractéristiques initiales pré-stockées pour obtenir un résultat de comparaison (S204) ; et réaliser une opération prédéfinie en fonction du résultat de la comparaison (S205). Le procédé facilite l'amélioration de la sécurité de la reconnaissance d'identité du dispositif d'imagerie médicale.
PCT/CN2019/101164 2019-08-16 2019-08-16 Procédé et produits de reconnaissance basés sur une image veineuse ia vrds Ceased WO2021030994A1 (fr)

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CN201980099716.2A CN114364323A (zh) 2019-08-16 2019-08-16 基于vrds ai静脉影像的识别方法及产品

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KR102216126B1 (ko) * 2014-07-31 2021-02-16 삼성전자주식회사 정맥 인증을 이용하여 동작하는 웨어러블 디바이스 및 웨어러블 디바이스의 동작 방법
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WO2013163605A1 (fr) * 2012-04-26 2013-10-31 Dbmedx Inc. Appareil à ultrasons et procédé de surveillance de vaisseaux corporels
CN104424647A (zh) * 2013-09-04 2015-03-18 三星电子株式会社 用于对医学图像进行配准的方法和设备
CN107405083A (zh) * 2015-02-12 2017-11-28 方德里创新研究第有限公司 用于心力衰竭监测的可植入式设备和相关方法
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CN109561880A (zh) * 2016-08-02 2019-04-02 皇家飞利浦有限公司 用于确定心输出量的系统和方法

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