WO2021017272A1 - Procédé et dispositif d'annotation d'une image de pathologie, appareil informatique et support d'informations - Google Patents

Procédé et dispositif d'annotation d'une image de pathologie, appareil informatique et support d'informations Download PDF

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WO2021017272A1
WO2021017272A1 PCT/CN2019/116925 CN2019116925W WO2021017272A1 WO 2021017272 A1 WO2021017272 A1 WO 2021017272A1 CN 2019116925 W CN2019116925 W CN 2019116925W WO 2021017272 A1 WO2021017272 A1 WO 2021017272A1
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image
format
interest
region
preset
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Chinese (zh)
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杨光
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平安科技（深圳）有限公司
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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
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/51—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • 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
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies

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  • This application relates to the field of artificial intelligence technology, and in particular to a pathological image labeling method, device, computer equipment and storage medium.
  • the labeling tools on the market such as LabelMe, etc.
  • pathological images such as ndpi, tif, oct and other image formats
  • the inventor realized that because The data processed by many AI models is pathological data, which cannot be supported by traditional labeling tools and cannot adapt to diversified labeling requirements. As a result, users cannot label pathological images normally, which affects the work efficiency of users.
  • the embodiments of the present application provide a pathological image labeling method, device, computer equipment, and storage medium to solve the problem that pathological images cannot be accurately labelled, which affects the work efficiency of users.
  • a method for marking pathological images includes:
  • the image format is the conventional format, determining the pathological image corresponding to the conventional format as the region of interest image;
  • the image format is the target format, perform down-sampling processing on the pathological image corresponding to the target format, and use the down-sampled pathological image as the region of interest image;
  • the application type is matched with the description information in the preset annotation library, and the annotation strategy corresponding to the description information that is successfully matched is selected to annotate the region of interest image, wherein the preset annotation library contains the description Information and the labeling strategy corresponding to the description information.
  • a pathological image marking device including:
  • the first acquisition module is used to acquire the pathological image to be labeled from the preset image library
  • the recognition module is used to recognize the image format of the pathological image, and determine whether it is a pathological image in a target format or a pathological image in a conventional format;
  • the conventional format module is configured to determine the pathological image corresponding to the conventional format as the region of interest image if the image format is the conventional format;
  • the target format module is configured to, if the image format is the target format, perform down-sampling processing on the pathological image corresponding to the target format, and use the down-sampled pathological image as the region of interest image;
  • the second obtaining module is configured to receive the labeling request of the operating user, and obtain the application type corresponding to the labeling request from the preset type library;
  • An annotation module configured to match the application type with the description information in a preset annotation library, and select an annotation strategy corresponding to the description information that is successfully matched to annotate the region of interest image, wherein the preset annotation library Contains the description information and the labeling strategy corresponding to the description information.
  • a computer device comprising a memory, a processor, and computer readable instructions stored in the memory and capable of running on the processor, and the processor implements the above pathological image labeling method when the processor executes the computer readable instructions A step of.
  • a non-volatile computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the above pathological image labeling method is implemented step.
  • FIG. 1 is a flowchart of a pathological image labeling method provided by an embodiment of the present application
  • FIG. 2 is a flowchart of step S2 in FIG. 1;
  • FIG. 3 is a flowchart of step S4 in FIG. 1;
  • FIG. 4 is a flowchart of step S42 in Figure 3;
  • FIG. 5 is a flowchart of step S422 in FIG. 4;
  • FIG. 6 is a flowchart of graying a pathological image in a pathological image labeling method provided by an embodiment of the present application
  • FIG. 7 is a flowchart of storing the annotation information of the pathological image in the pathological image annotation method provided by the embodiment of the present application.
  • Fig. 8 is a schematic diagram of a pathological image labeling device provided by an embodiment of the present application.
  • Fig. 9 is a basic structural block diagram of a computer device provided by an embodiment of the present application.
  • the pathological image labeling method provided in this application is applied to the server, and the server can be implemented by an independent server or a server cluster composed of multiple servers.
  • a method for marking pathological images is provided, which includes the following steps:
  • the preset image library refers to a database dedicated to storing pathological images.
  • S2 Perform image format recognition on the pathological image, and determine whether it is the pathological image in the target format or the pathological image in the conventional format.
  • the file extension corresponding to the pathological image is obtained, and the image format is identified by matching the file extension with the preset extension, and then it is determined whether the image format of the pathological image is the target format or the regular format.
  • the file extension is also called the file extension, which is a mechanism used by the operating system to mark the file type.
  • the default extension refers to the file extension set according to the user's needs, which can be the file extension of ndpi.
  • the pathological image corresponding to the conventional format is determined as the region of interest image.
  • the image format of the pathological image is recognized according to step S2, and when the image format is recognized as the conventional format, the pathological image corresponding to the conventional format is determined as the region of interest image.
  • the conventional format can specifically refer to the image format of oct and tif, and can also be set according to the actual needs of the user, and there is no limitation here.
  • the image format of the pathological image is recognized according to step S2.
  • the image format is recognized as the target format
  • the pathological image corresponding to the target format is down-sampled, and the down-sampling processed
  • the pathological image is determined as the region of interest image.
  • the target format mainly refers to an image format of ndpi.
  • S5 Receive the labeling request from the operating user, and obtain the application type corresponding to the labeling request from the preset type library.
  • the annotation request corresponding to the user is obtained, and the application type corresponding to the annotation request is obtained from the preset type library, where the preset type
  • the library refers to a database specially used to store a labeling request and the application type corresponding to the labeling request.
  • the application types corresponding to the labeling request are mainly divided into three types, namely, classification application, detection application, and segmentation application.
  • the corresponding labeling requests are "category”, “detection”, and “segmentation”.
  • Classification refers to the classification of the input image category in a certain sense.
  • Detection refers to drawing a rectangular frame, circular frame, etc. on a certain area of an input image.
  • Segmentation refers to drawing a free pen closed curve to a certain area of an input image.
  • S6 Match the application type with the description information in the preset annotation library, and select the annotation strategy corresponding to the successfully matched description information to annotate the image of the region of interest.
  • the preset annotation library contains the description information and the corresponding description information. Labeling strategy.
  • the labeling request corresponding to the application type obtained in step S5 is matched with the description information in the preset labeling library.
  • the labeling request is the same as the description information, it indicates that the matching is successful, and the labeling strategy pair corresponding to the description information is selected Annotate the image of the region of interest.
  • the labeling strategy corresponding to the description information of "category” is to label the image of the region of interest based on the image format. If the image format is a regular format, the entire region of interest image is directly labeled with preset label information; if the image format is the target Format, it is detected that the user clicks on the relevant area on the image of the area of interest, and the area selected by the user is marked with preset label information.
  • the preset label information can be a noun or a symbol, and its specific settings can be set according to the actual needs of the user.
  • the region of interest image can be labeled with multiple same preset label information, or the region of interest image can be labeled with multiple different preset label information .
  • the labeling strategy corresponding to the description information of "detection” is to obtain a preselection box, and mark the preselection box for the area selected by the user based on the detection that the user clicks on the relevant area on the image of the region of interest on the client.
  • the preselection box may be a rectangular box or a circular box, and there is no limitation here.
  • the labeling strategy corresponding to "segmentation" in the description information is to obtain the preset free pen tool.
  • the preset free pen tool mainly refers to a tool used to mark an image with a closed curve.
  • the scale, setWorldMatrix, and translate functions in the interface of the object QPainter in the Qt framework can also be used to implement the translation function, rotation function, and zoom function of the image, respectively.
  • step S2 the image format identification of the pathological image, and determining whether it is the pathological image in the target format or the pathological image in the conventional format includes the following steps:
  • the file extension corresponding to the pathological image is directly obtained from the preset type table.
  • the preset type table refers to a data table specially used for storing file extensions corresponding to pathological images.
  • the file extension acquired in step S21 is compared with the preset extension.
  • the image format corresponding to the pathological image is determined to be the conventional format.
  • the image format of the pathological image corresponding to the file extension is determined to be the conventional format.
  • the file extension corresponding to pathology image A is oct. If the default extension is ndpi, compare the file extension oct with the preset extension ndpi. Since oct and ndpi are different, the image format of pathology image A is determined It is a regular format.
  • the image format of the pathological image corresponding to the file extension is determined as the target format.
  • the file extension corresponding to pathology image B is ndpi. If the preset extension name is ndpi, compare the file extension ndpi with the preset extension ndpi. Since both are ndpi, the image format of pathology image B is determined as Target format.
  • the image format of the conventional format or the target format is determined. In this way, accurate recognition of the image format is realized, and the accuracy of subsequent determination of the region of interest image for the image format is improved.
  • step S4 that is, if the image format is the target format, the pathological image corresponding to the target format is down-sampled, and the down-sampled pathological image is used as the region of interest image including The following steps:
  • the pathological image corresponding to the target format is imported into the preset down-sampling library, and the pathological image is down-sampled according to the preset down-sampling coefficient to obtain the processing After the thumbnail image.
  • the preset down-sampling library refers to a database specially used for down-sampling processing of pathological images.
  • the preset downsampling factor is a constant set according to the actual needs of the user, and the value range of the constant is 0-9.
  • Down-sampling processing refers to an N*M image, setting the down-sampling coefficient k, and taking one pixel every k pixels in each row and every column of the N*M image to form a new image.
  • the down-sampling coefficient is set to 0-9. When the down-sampling coefficient is 0, it means that the pathological image will not be down-sampled. When the down-sampling coefficient is 9, it means that The pathological image undergoes maximum down-sampling processing.
  • the down-sampling coefficient is set to 1, when the pathological image is down-sampled, for each row and each column of the pathological image, one pixel is taken out as the target pixel at every interval of 1 pixel, and finally according to Each target pixel constitutes a thumbnail image corresponding to the original pathological image.
  • S42 Extract the region of interest from the thumbnail image to obtain an image of the region of interest.
  • the thumbnail image is imported into the preset processing library to extract the region of interest, and the region of interest image after the region of interest extraction is obtained.
  • the preset processing library refers to a processing library specifically used to extract regions of interest from thumbnail images.
  • the thumbnail image is obtained by down-sampling the pathological image in the target format, and the area of interest is extracted from the thumbnail image to obtain the area of interest image.
  • the area of interest image is obtained by down-sampling the pathological image in the target format, and the area of interest is extracted from the thumbnail image to obtain the area of interest image.
  • step S42 the extraction of the region of interest on the thumbnail image to obtain the region of interest image includes the following steps:
  • the preset region of interest parameter refers to the parameter corresponding to the partial region image selected by the user from the thumbnail image.
  • the parameter database refers to a database specially used for storing preset parameters of the region of interest.
  • the preset region of interest parameters include the coordinate points of the region of interest and the corresponding length and width of the region of interest.
  • S422 Generate an image mask according to preset parameters of the region of interest.
  • the image mask refers to the area where the selected image, figure or object, or the image to be processed is shielded to control the image processing. Import the preset region of interest parameters into the preset processing library for image mask generation processing, and obtain the image mask corresponding to the preset region of interest parameters.
  • the preset processing library refers to a database specifically used for image mask generation processing.
  • S423 Perform an AND operation on the image mask and the thumbnail image to obtain an image of the region of interest.
  • the image mask obtained in the root step S422 is multiplied by the pixel value of each pixel in the image mask by the pixel value of the pixel corresponding to the same position in the thumbnail image, and each pixel is obtained according to the multiplication result.
  • the pixel value of each pixel is obtained, and the new image is taken as the image of the region of interest.
  • the corresponding pixels are A(0,0), A1(0,1), A2(0,2), B(1,0), B1 (1,1), B2(1,2), C(2,0), C1(2,1) and C2(2,2)
  • the corresponding pixel values are 90, 0, 23, 90, 50, 22, 23, 255 and 89, where the pixels of the region of interest are A, A1, B, and B1 respectively
  • there are 9 pixels in the same position as the thumbnail image in the image mask, and the coordinates of the corresponding pixels are respectively
  • Q(0,0), Q1(0,1), Q2(0,2), W(1,0), W1(1,1), W2(1,2), E(2,0), E1(2,1) and E2(2,2) since the pixels of the region of interest in the thumbnail image are A, A1, B, and B1 respectively, the corresponding pixels in the same position in the image mask are respectively Q , Q1, W, and W1, so the pixel
  • the pixel value is multiplied by the pixel value of the corresponding pixel at the same position in the thumbnail image to obtain the coordinates (0, 0), (0, 1), (0, 2), (1, 0), (1, 1) ), (1, 2), (2, 0), (2, 1) and (2, 2) correspond to pixel values of 90, 0, 0, 90, 50, 0, 0, 0 and 0, respectively.
  • the image of the pixel value corresponding to this pixel is the region of interest image.
  • the image mask is generated according to preset parameters of the region of interest, and the image mask and the thumbnail image are used for calculation to obtain the region of interest image. In this way, accurate acquisition of the image of the region of interest is realized, and the accuracy of subsequent labeling of the image of the region of interest is ensured.
  • step S422 generating an image mask according to preset region of interest parameters includes the following steps:
  • S4221 Determine the coordinate parameters of the pixel points of the region of interest and the region of non-interest in the thumbnail image according to the preset region of interest parameters, where the thumbnail image includes the region of interest and the region of non-interest.
  • the thumbnail image is composed of pixels, and each pixel point corresponds to a coordinate point. Since the preset region of interest parameters include the coordinate points of the region of interest, the thumbnail image is compared with the preset interest
  • the area composed of coordinate points with the same area parameters is determined as the area of interest of the thumbnail image, and the coordinate points in the area of interest of the thumbnail image are determined, that is, the coordinate parameters of the pixels in the area of interest are determined;
  • the area composed of coordinate points different from the preset area of interest parameters is determined as the non-interest area of the thumbnail image, and the coordinate points in the non-interest area in the thumbnail image are determined, that is, the pixel points of the non-interest area are determined
  • the coordinate parameters is determined.
  • a thumbnail image is composed of 6 pixels A, B, C, D, E, and F, and the coordinate points corresponding to each pixel are (0, 0), (0, 1), (0, 2). ), (1, 0), (1, 1), (1, 2), the coordinate points of the region of interest included in the preset region of interest parameter are (0, 0), (0, 1), (0 , 2), the area composed of the same coordinate points as the preset area of interest parameters in the thumbnail image is determined as the area of interest of the thumbnail image, that is, the area of interest in the thumbnail image is composed of pixel points A, B, C
  • the corresponding coordinate parameters are (0, 0), (0, 1), (0, 2); the area composed of coordinate points different from the preset area of interest parameters in the thumbnail image is determined as the thumbnail image
  • the non-interest area that is, the area of interest in the thumbnail image is composed of pixels D, E, and F, and the corresponding coordinate parameters are (1, 0), (1, 1), (1,2).
  • an image template with the same coordinate parameters is generated through a preset port, and the image template is Pixels with the same coordinate parameters as the region of interest are determined as target pixels, and pixels in the image template with the same coordinate parameters as the non-interest region are determined as ordinary pixels.
  • the preset port refers to a processing port dedicated to generating an image template.
  • S4223 Set the pixel values of the target pixel and the ordinary pixel in the image template to the preset target value and the preset ordinary value, respectively, and use the set image template as the image mask.
  • step S4222 the target pixel and the ordinary pixel in the image template are obtained, the pixel value of the target pixel is set to the preset target value, the pixel value of the ordinary pixel is set to the preset ordinary value, and the image After the pixel values of all target pixels and common pixels in the template are set, the image template is determined as an image mask.
  • the preset target value refers to a value set according to user requirements for highlighting the color of the target pixel with respect to the color of the common pixel, for example, it may be specifically 0.
  • the preset common value refers to a numerical value that is set to highlight the color of the common pixel relative to the color of the target pixel according to user requirements, and may be specifically 255.
  • the pixels in the image mask correspond to the pixels in the thumbnail image.
  • the pixel value of the pixel in the image mask is mainly the preset target value or the preset normal value
  • the pixel value of the pixel in the image mask is mainly used. The value is multiplied by the pixel value of the pixel in the thumbnail image.
  • the corresponding image template is generated according to the coordinate parameters of the thumbnail, and the pixel values of the pixels in the image template are set to obtain the image mask. In this way, accurate acquisition of the image mask is realized, the accuracy of subsequent operations using the image mask is ensured, and the accuracy of the pathological image standard is further guaranteed.
  • the pathological image labeling method further includes the following steps:
  • S71 traverse the pixels in the pathological image, and obtain the RGB component value of each pixel.
  • the pixel points in the pathological image are traversed according to a preset traversal mode to obtain the RGB component value of each pixel point, where R, G, and B represent the colors of the three channels of red, green, and blue, respectively.
  • the preset traversal method can be based on the pixel point of the upper left corner of the pathological image as the starting point, and traverse line by line from top to bottom from left to right, or traverse from the midline position of the pathological image to both sides at the same time. It can also be other traversal methods, and there is no restriction here.
  • x and y are the abscissa and ordinate of each pixel in the pathological image
  • g (x, y) is the gray value of the pixel (x, y) after graying
  • R (x, y) Is the color component of the R channel of the pixel (x, y)
  • the pathological image in order to achieve accurate extraction of the information content in the pathological image, the pathological image needs to be grayed out first.
  • the parameter values of k 1 , k 2 and k 3 can be performed according to actual application needs. Setting is not limited here. By adjusting the value range of k 1 , k 2 , and k 3 , the proportions of R channel, G channel and B channel can be adjusted respectively.
  • the RGB model is a commonly used way of expressing color information. It uses the brightness of the three primary colors of red, green and blue to quantitatively represent colors.
  • This model is also called the additive color mixing model, which is a method in which RGB three-color light is superimposed on each other to achieve color mixing, so it is suitable for display of luminous bodies such as displays.
  • the pathological image is weighted to calculate the gray value by formula (1).
  • the component method, the maximum value method, or the average value method may also be used to gray the pathological image. There are no restrictions here.
  • the pathological image is grayed out using formula (1), In this way, the pixel value range of the pixels in the pathological image is set between 0-255, which further reduces the amount of original data of the pathological image and improves the calculation efficiency in the subsequent processing and calculation.
  • the pathological image labeling method further includes the following steps:
  • the user types are mainly registered users and anonymous users. Before an operating user can annotate pathological images in the client, he or she needs to log in as a registered user or as an anonymous user. Obtain the user type of the operating user directly from the preset user library.
  • the preset user database refers to a database dedicated to storing user types of operating users.
  • S82 Perform permission judgment on the data saving request of the user type, and obtain the target user who has the data saving request permission.
  • a registered user has a special user database for storing data for labeling pathological images, and the pathological image data can be saved in the client.
  • the data annotated by anonymous users in the client is only temporarily saved, and pathological image data cannot be saved in the client.
  • step S81 the user type corresponding to the operating user is obtained. If the user type is a registered user, it means that the user type has the permission to save the data; if the user type is anonymous, it means that the user type does not have the data save request. , And determine the registered user as the target user.
  • registered users include their corresponding id.
  • S83 Receive the data saving request sent by the target user, and save the marking information of the pathological image of the target user in the user database.
  • the id contained in the registered user is used to match the id with the library id in the preset storage library, and the successfully matched storage library is determined as the user database , And save the target user's annotation information of the pathological image in the client to the user database.
  • the preset storage library refers to the storage library and the library id corresponding to the storage library.
  • the permission of the data saving request is judged according to the user type, the operating user who has the permission of the data saving request is determined as the target user, and the marking information of the pathological image of the target user is saved in the user database.
  • the storage and processing of the target user data is realized, the security of the target user's storage of the pathological image annotation data is ensured, and the target user is convenient for the target user to call the pathological image annotation data, thereby improving the work efficiency of the target user.
  • a pathological image labeling device is provided, and the pathological image labeling device corresponds to the pathological image labeling method in the above-mentioned embodiment one to one.
  • the pathological image labeling device includes a first acquisition module 81, a recognition module 82, a conventional format module 83, a target format module 84, a second acquisition module 85 and a labeling module 86.
  • the detailed description of each functional module is as follows:
  • the first acquiring module 81 is configured to acquire pathological images to be labeled from a preset image library
  • the recognition module 82 is used to recognize the image format of the pathological image, and determine whether it is a pathological image in a target format or a pathological image in a conventional format;
  • the conventional format module 83 is used to determine the pathological image corresponding to the conventional format as the region of interest image if the image format is a conventional format;
  • the target format module 84 is configured to, if the image format is the target format, perform down-sampling processing on the pathological image corresponding to the target format, and use the down-sampling processed pathological image as the region of interest image;
  • the second obtaining module 85 is configured to receive the labeling request of the operating user, and obtain the application type corresponding to the labeling request from the preset type library;
  • the annotation module 86 is used to match the application type with the description information in the preset annotation library, and select the annotation strategy corresponding to the successfully matched description information to annotate the image of the region of interest.
  • the preset annotation library contains the description information and The labeling strategy corresponding to the description information.
  • the identification module 82 includes:
  • the third acquisition sub-module is used to acquire the file extension corresponding to the pathological image from the preset type table
  • the comparison sub-module is used to compare the file extension with the preset extension
  • target format module 84 includes:
  • the down-sampling sub-module is used to perform down-sampling processing on the pathological image corresponding to the target format if the image format is the target format to obtain a thumbnail image;
  • the extraction sub-module is used to extract the region of interest from the thumbnail image to obtain the region of interest image.
  • the extraction sub-module includes:
  • the fourth acquiring unit is used to acquire preset parameters of the region of interest
  • An image mask generating unit configured to generate an image mask according to preset parameters of the region of interest
  • the arithmetic unit is used to perform an AND operation between the image mask and the thumbnail image to obtain an image of the region of interest.
  • the generating unit includes:
  • the coordinate determination subunit is used to determine the coordinate parameters of the pixel points of the region of interest and the non-interest region in the thumbnail image according to the preset region of interest parameters, where the thumbnail image includes the region of interest and the non-interest region;
  • the image template generating subunit is used to generate an image template with the same coordinate parameters through a preset port, where the image template contains target pixels of the region of interest and ordinary pixels of the non-interest region;
  • the image mask determining subunit is used to set the pixel values of the target pixel and the ordinary pixel in the image template to the preset target value and the preset ordinary value, respectively, and use the set image template as the image mask.
  • the pathological image labeling device further includes:
  • the traversal module is used to traverse the pixels in the pathological image and obtain the RGB component value of each pixel;
  • the gray-scale calculation module is used to perform gray-scale processing on the pathological image according to the RGB component value of the pixel according to the following formula:
  • x and y are the abscissa and ordinate of each pixel in the pathological image
  • g (x, y) is the gray value of the pixel (x, y) after graying
  • R (x, y) Is the color component of the R channel of the pixel (x,y)
  • G(x,y) is the color component of the G channel of the pixel (x,y)
  • B(x,y) is the pixel (x,y)
  • the color components of the B channel, k 1 , k 2 and k 3 are all constants.
  • the pathological image labeling device further includes:
  • the fifth obtaining module is used to obtain the user type of the operating user from the preset user library
  • the authorization judgment module is used to judge the authorization of the data saving request of the user type, and obtain the target user who has the authorization of the data saving request;
  • the saving module is used to receive the data saving request sent by the target user, and save the marking information of the pathological image of the target user in the user database.
  • FIG. 9 is a block diagram of the basic structure of the computer device 90 in an embodiment of this application.
  • the computer device 90 includes a memory 91, a processor 92, and a network interface 93 that are communicatively connected to each other through a system bus. It should be pointed out that FIG. 9 only shows a computer device 90 with components 91-93, but it should be understood that it is not required to implement all the shown components, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions.
  • Its hardware includes but is not limited to microprocessors, dedicated Integrated Circuit (Application Specific Integrated Circuit, ASIC), Programmable Gate Array (Field-Programmable Gate Array, FPGA), Digital Processor (Digital Signal Processor, DSP), embedded devices, etc.
  • ASIC Application Specific Integrated Circuit
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Processor
  • the computer device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer device can interact with the user through a keyboard, a mouse, a remote control, a touch panel, or a voice control device.
  • the memory 91 includes at least one type of readable storage medium, the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static memory Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disks, optical disks, etc.
  • the memory 91 may be an internal storage unit of the computer device 90, such as a hard disk or memory of the computer device 90.
  • the memory 91 may also be an external storage device of the computer device 90, such as a plug-in hard disk equipped on the computer device 90, a smart memory card (Smart Media Card, SMC), and a secure digital (Secure Digital, SD) card, Flash Card, etc.
  • the memory 91 may also include both the internal storage unit of the computer device 90 and its external storage device.
  • the memory 91 is generally used to store an operating system and various application software installed in the computer device 90, such as computer readable instructions of the pathological image labeling method.
  • the memory 91 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 92 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 92 is generally used to control the overall operation of the computer device 90.
  • the processor 92 is configured to run computer-readable instructions or processed data stored in the memory 91, for example, computer-readable instructions for running the pathological image labeling method.
  • the network interface 93 may include a wireless network interface or a wired network interface, and the network interface 93 is generally used to establish a communication connection between the computer device 90 and other electronic devices.
  • This application also provides another implementation manner, that is, a non-volatile computer-readable storage medium storing pathological image data information entry process, and the pathological
  • the image data information entry process can be executed by at least one processor, so that the at least one processor executes the steps of any one of the pathological image labeling methods described above.
  • the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. ⁇
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes several instructions to enable a computer device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present application.
  • a computer device which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

La présente invention se rapporte au domaine technique de l'intelligence artificielle et concerne un procédé et un dispositif d'annotation d'une image de pathologie, un appareil informatique et un support d'informations. Le procédé d'annotation de l'image de pathologie consiste : à identifier un format d'image d'une image de pathologie acquise et à déterminer si l'image de pathologie a un format cible ou un format classique (S2) ; si le format de l'image est le format classique, à déterminer que l'image de pathologie correspondant au format classique est une image d'une région d'intérêt (S3) ; si le format d'image est le format cible, à effectuer un traitement de sous-échantillonnage sur l'image de pathologie correspondant au format cible et à acquérir une image de la région d'intérêt (S4) ; à recevoir une demande d'annotation d'un utilisateur et à acquérir, à partir d'une bibliothèque de type préconfiguré, un type d'application correspondant à la demande d'annotation (S5) ; et à mettre en correspondance le type d'application avec des informations descriptives dans une bibliothèque d'annotation préconfigurée et à sélectionner une politique d'annotation correspondant aux informations descriptives mises en correspondance avec succès, afin d'annoter l'image de la région d'intérêt (S6). L'invention permet d'annoter une image de pathologie avec précision, ce qui permet d'améliorer l'efficacité d'un utilisateur lors de l'annotation d'une image de pathologie.
PCT/CN2019/116925 2019-08-01 2019-11-10 Procédé et dispositif d'annotation d'une image de pathologie, appareil informatique et support d'informations WO2021017272A1 (fr)

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