Medical imaging data acquisition method, device, electronic device and storage medium

Abstract

The present invention discloses a method, device, electronic device and storage medium for acquiring medical imaging data. The method includes acquiring a first medical imaging data set; performing image processing on the images in the first medical imaging data set to obtain a second medical imaging data set; performing differential recognition on the images in the second medical imaging data set and a preset template, and acquiring a preset number of images with the largest differences as medical imaging data. The solution provided by the present invention can convert medical imaging data in a non-editable format into editable medical imaging data, and then can recognize the editable medical imaging data through recognition means, and based on the recognition results, obtain the medical imaging data that best reflects the patient's condition from a large number of medical imaging data, without the need for medical workers to manually select, thereby reducing the time and energy required by medical workers and ensuring a high recognition accuracy rate.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging technology, and in particular to a method, device, electronic equipment and storage medium for acquiring medical imaging data. Background Art

[0002] Medical imaging data is crucial for hospitals and also represents a crucial privacy concern for patients. Therefore, hospitals must maintain strict confidentiality and security requirements regarding the storage and use of medical imaging data. Medical imaging data is typically generated by high-precision scanning equipment, often in specialized formats. For security reasons, these specialized formats typically cannot be edited or adjusted.

[0003] However, in actual use, medical devices typically generate hundreds to thousands of images per scan, specifically tailored to the patient's condition. Medical professionals must select a small number of images from these hundreds or thousands that best reflect the patient's condition and use them as diagnostic images. This process consumes considerable time and effort, and the accuracy of the selected images cannot be guaranteed. Summary of the Invention

[0004] To solve the technical problem that medical imaging data cannot be edited and requires medical workers to manually select it, which consumes a lot of time and energy, embodiments of the present invention provide a medical imaging data acquisition method, device, electronic device and storage medium.

[0005] The technical solution of the embodiment of the present invention is achieved as follows:

[0006] An embodiment of the present invention provides a method for acquiring medical image data, the method comprising:

[0007] Acquire a first medical imaging dataset; wherein the format of the images in the first medical imaging dataset is a non-editable format;

[0008] Performing image processing on the images in the first medical image dataset to obtain a second medical image dataset; wherein the format of the images in the second medical image dataset is an editable format;

[0009] Perform differential recognition on the images in the second medical image data set and the preset template, and obtain the first preset number of images with the largest differences as medical image data.

[0010] In the above solution, performing image processing on the images in the first medical image dataset to obtain the second medical image dataset includes:

[0011] Converting the images in the first medical image data set into an editable format to obtain third medical image data; and performing special format image scanning on the images in the first medical image data set to obtain fourth medical image data; wherein the formats of the third medical image data and the fourth medical image data are in an editable format;

[0012] Overlapping the third medical image data with the fourth medical image data to obtain fifth medical image data; performing a deduplication operation on the fifth medical image data to obtain sixth medical image data;

[0013] The obtained set of all sixth medical image data is used as a second medical image data set.

[0014] In the above solution, converting the images in the first medical imaging data set into an editable format to obtain third medical imaging data includes:

[0015] Dividing the image in the first medical image dataset into a plurality of regions according to grayscale values, and obtaining the coordinates of each region;

[0016] Call the conversion function to convert the format of each area in turn to obtain the converted area image;

[0017] Match the transformed region image with the coordinates of each region before transformation one by one;

[0018] The converted regional images are synthesized based on the matched coordinates to obtain third medical image data.

[0019] In the above solution, the step of performing special format image scanning on the images in the first medical image data set to obtain the fourth medical image data includes:

[0020] Scanning pixel values ​​of the images in the first medical image dataset row by row along one direction;

[0021] An image is generated line by line based on the pixel value of each pixel point in each scanned line to obtain fourth medical image data.

[0022] In the above solution, the steps of overlapping the third medical image data and the fourth medical image data to obtain the fifth medical image data; and performing a deduplication operation on the fifth medical image data to obtain the sixth medical image data include:

[0023] Dividing the third medical image data into a plurality of first regions according to grayscale values, and obtaining coordinates of each first region; and dividing the fourth medical image data into a plurality of second regions according to grayscale values, and obtaining coordinates of each second region;

[0024] Matching the first area with the second area one by one;

[0025] Aligning the coordinates of the first region with the coordinates of the corresponding second region according to the matching result, so that the matched first region and the corresponding second region overlap;

[0026] After the coordinate alignment, the non-overlapping portion of the first region and the second region is deleted to obtain sixth medical image data.

[0027] In the above solution, performing differential recognition between the images in the second medical image data set and a preset template and obtaining a preset number of images with the largest differences as medical image data includes:

[0028] Use preset templates to train neural network models;

[0029] inputting images in the second medical imaging dataset into the trained neural network model to obtain differentiated results;

[0030] Based on the obtained differentiation results, a preset number of images with the largest differentiation are obtained as medical imaging data.

[0031] In the above solution, the step of obtaining the first medical image data set includes:

[0032] In a local area network mode, receiving a first medical imaging data set sent by a medical device;

[0033] The performing image processing on the images in the first medical image dataset to obtain the second medical image dataset includes:

[0034] After the local area network mode is switched to the Internet mode, image processing is performed on the images in the first medical image dataset to obtain a second medical image dataset.

[0035] An embodiment of the present invention further provides a medical imaging data acquisition device, the medical imaging data acquisition device comprising:

[0036] An acquisition module, configured to acquire a first medical image data set; wherein the format of the images in the first medical image data set is a non-editable format;

[0037] an image processing module, configured to perform image processing on the images in the first medical image dataset to obtain a second medical image dataset; wherein the format of the images in the second medical image dataset is an editable format;

[0038] The differential recognition module is used to perform differential recognition on the images in the second medical image data set and the preset template, and obtain the images with the largest differences among the first preset number as medical image data.

[0039] An embodiment of the present invention further provides an electronic device, comprising: a processor and a memory for storing a computer program that can be run on the processor; wherein,

[0040] The processor is configured to execute the steps of any of the above methods when running the computer program.

[0041] An embodiment of the present invention further provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0042] The medical imaging data acquisition method, device, electronic device and storage medium provided by the embodiments of the present invention acquire a first medical imaging data set; wherein the format of the images in the first medical imaging data set is a non-editable format; image processing is performed on the images in the first medical imaging data set to obtain a second medical imaging data set; wherein the format of the images in the second medical imaging data set is an editable format; the images in the second medical imaging data set are differentially identified with a preset template, and a preset number of images with the largest differences are acquired as medical imaging data. The solution provided by the present invention can convert medical imaging data in a non-editable format into editable medical imaging data, and then can identify the editable medical imaging data through identification means, and obtain the medical imaging data that best reflects the patient's condition from a large number of medical imaging data based on the identification results, without the need for manual selection by medical workers, thereby reducing the time and energy required by medical workers and ensuring a high recognition accuracy rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the process of obtaining medical image data according to an embodiment of the present invention;

[0044] Figure 2 This is a schematic structural diagram of a medical imaging data acquisition device according to an embodiment of the present invention;

[0045] Figure 3 This is a diagram of the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0047] The embodiment of the present invention provides a method for acquiring medical imaging data, such as Figure 1 As shown, the method includes:

[0048] Step 101: Acquire a first medical image dataset; wherein the format of the images in the first medical image dataset is a non-editable format;

[0049] Step 102: performing image processing on the images in the first medical image dataset to obtain a second medical image dataset; wherein the format of the images in the second medical image dataset is an editable format;

[0050] Step 103: Perform differential recognition on the images in the second medical image data set and the preset template, and obtain a preset number of images with the largest differences as medical image data.

[0051] Specifically, this embodiment can be applied to a B-ultrasound imaging data system. A first medical image dataset can be obtained from a color ultrasound B-ultrasound device. In practical applications, the first medical image dataset can be in DICOM format. The DICOM format cannot be edited or adjusted, and therefore, existing image recognition technologies cannot be used to identify the DICOM format and obtain recognition results.

[0052] Based on this, the method of this embodiment can be used to convert and process the uneditable first medical image data to obtain editable second medical image data, so that the editable second medical image data can be identified using recognition means to obtain recognition results, thereby reducing the time and energy spent by medical workers on manual identification due to the inability to edit medical image data, and avoiding errors caused by manual identification, thereby ensuring recognition accuracy.

[0053] In one embodiment, performing image processing on the images in the first medical image dataset to obtain the second medical image dataset includes:

[0054] Converting the images in the first medical image data set into an editable format to obtain third medical image data; and performing special format image scanning on the images in the first medical image data set to obtain fourth medical image data; wherein the formats of the third medical image data and the fourth medical image data are in an editable format;

[0055] Overlapping the third medical image data with the fourth medical image data to obtain fifth medical image data; performing a deduplication operation on the fifth medical image data to obtain sixth medical image data;

[0056] The obtained set of all sixth medical image data is used as a second medical image data set.

[0057] Specifically, in this embodiment, two methods are used to convert the first medical image data to obtain conversion results. Because either conversion method will introduce certain errors, resulting in certain differences between the converted image and the image before conversion, this embodiment, after converting the first medical image data using the two methods, overlaps and removes duplicates of the two conversion results, ensuring that the processed medical image data remains substantially consistent with the first medical image data, greatly reducing the errors caused by the conversion.

[0058] In one embodiment, converting the images in the first medical imaging data set into an editable format to obtain third medical imaging data includes:

[0059] Dividing the image in the first medical image dataset into a plurality of regions according to grayscale values, and obtaining the coordinates of each region;

[0060] Call the conversion function to convert the format of each area in turn to obtain the converted area image;

[0061] Match the transformed region image with the coordinates of each region before transformation one by one;

[0062] The converted regional images are synthesized based on the matched coordinates to obtain third medical image data.

[0063] In practical applications, the images in the first medical imaging dataset often contain a large amount of content and vary in depth. If a conversion function is called to convert the entire image, the resulting image is often chaotic and significantly different from the original image. Therefore, to reduce errors, in this embodiment, the images in the first medical imaging dataset are first segmented into multiple regions based on their grayscale values, and the coordinates of each region are recorded. Each region is then converted using a conversion function, and based on the recorded coordinates of each region, the converted regions are synthesized according to the coordinates to obtain the third medical imaging data.

[0064] Specifically, regions with the same grayscale value may be divided into one region, and then the images in the first medical image dataset may be divided into multiple regions according to the grayscale value.

[0065] In addition, since the grayscale value of each region is the same, when the conversion function is called to perform the conversion, the conversion result obtained has a smaller error than the conversion result obtained by converting an image containing different grayscale values.

[0066] In one embodiment, performing special format image scanning on the images in the first medical image data set to obtain fourth medical image data includes:

[0067] Scanning pixel values ​​of the images in the first medical image dataset row by row along one direction;

[0068] An image is generated line by line based on the pixel value of each pixel point in each scanned line to obtain fourth medical image data.

[0069] Specifically, in this embodiment, a photoelectric scanner can be used to scan the images in the first medical image data set. In practical applications, pixel values ​​can be scanned row by row along a direction, and an image can be generated row by row based on the scan results to obtain the fourth medical image data. Here, pixel values ​​can be scanned row by row from left to right or from top to bottom.

[0070] In one embodiment, the overlapping of the third medical image data and the fourth medical image data to obtain fifth medical image data; and performing a deduplication operation on the fifth medical image data to obtain sixth medical image data include:

[0071] Dividing the third medical image data into a plurality of first regions according to grayscale values, and obtaining coordinates of each first region; and dividing the fourth medical image data into a plurality of second regions according to grayscale values, and obtaining coordinates of each second region;

[0072] Matching the first area with the second area one by one;

[0073] Aligning the coordinates of the first region with the coordinates of the corresponding second region according to the matching result, so that the matched first region and the corresponding second region overlap;

[0074] After the coordinate alignment, the non-overlapping portion of the first region and the second region is deleted to obtain sixth medical image data.

[0075] In actual application, since the third medical image data is spliced ​​together using regional coordinates, directly aligning the third medical image data with the fourth medical image data based on the overall coordinates will result in a significant deviation in the overall overlapped image. Therefore, in this embodiment, the third medical image data and the fourth medical image data are first divided according to grayscale values, and then, based on the coordinates of each region, each region is individually overlapped, and the non-overlapped portions are deleted to obtain the sixth medical image data. Since this application overlaps each region individually rather than overlapping the entire image at once, the accuracy of the acquired image can be guaranteed.

[0076] In one embodiment, performing differential recognition between the images in the second medical image data set and a preset template and obtaining a preset number of images with the largest differences as the medical image data includes:

[0077] Use preset templates to train neural network models;

[0078] inputting images in the second medical imaging dataset into the trained neural network model to obtain differentiated results;

[0079] Based on the obtained differentiation results, a preset number of images with the largest differentiation are obtained as medical imaging data.

[0080] In practical applications, a preset template can be determined in advance, and the required medical image data can be obtained by using the differences between the images in the second medical image dataset and the preset template. Specifically, a neural network can be used to perform recognition and obtain recognition results. After obtaining the editable medical image data, machine learning methods are used to determine the required medical image data. This embodiment can use conventional neural network recognition technology for recognition, which will not be detailed here.

[0081] In one embodiment, obtaining the first medical image dataset includes:

[0082] In a local area network mode, receiving a first medical imaging data set sent by a medical device;

[0083] The performing image processing on the images in the first medical image dataset to obtain the second medical image dataset includes:

[0084] After the local area network mode is switched to the Internet mode, image processing is performed on the images in the first medical image dataset to obtain a second medical image dataset.

[0085] Specifically, to ensure the security of medical devices and medical data, medical devices are often only connected to the hospital's local area network (LAN) or are disconnected from the internet. Image conversion and processing often involve calling functions or external resources. Therefore, to ensure the security of medical devices and medical data, in this embodiment, the first medical image dataset sent by the medical device is received only in LAN mode. Only after the LAN mode is switched to internet mode are the images in the first medical image dataset processed to obtain the second medical image dataset.

[0086] The medical imaging data acquisition method provided by an embodiment of the present invention obtains a first medical imaging data set; wherein the format of the images in the first medical imaging data set is a non-editable format; performs image processing on the images in the first medical imaging data set to obtain a second medical imaging data set; wherein the format of the images in the second medical imaging data set is an editable format; performs differential recognition between the images in the second medical imaging data set and a preset template, and obtains a preset number of images with the largest differences as medical imaging data. The solution provided by the present invention can convert medical imaging data in a non-editable format into editable medical imaging data, and then can recognize the editable medical imaging data through recognition means, and obtain the medical imaging data that best reflects the patient's condition from a large number of medical imaging data based on the recognition results, without the need for medical workers to manually select, thereby reducing the time and energy required by medical workers and ensuring a high recognition accuracy rate.

[0087] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a medical image data acquisition device, such as Figure 2 As shown, the medical image data acquisition device 200 includes: an acquisition module 201, an image processing module 202 and a difference recognition module 203; wherein,

[0088] An acquisition module 201 is configured to acquire a first medical image dataset; wherein the format of the images in the first medical image dataset is a non-editable format;

[0089] An image processing module 202 is configured to perform image processing on the images in the first medical image dataset to obtain a second medical image dataset; wherein the format of the images in the second medical image dataset is an editable format;

[0090] The difference recognition module 203 is configured to perform difference recognition between the images in the second medical image data set and a preset template, and obtain a preset number of images with the largest differences as medical image data.

[0091] In actual application, the acquisition module 201, the image processing module 202 and the difference recognition module 203 can be implemented by a processor in the medical image data acquisition device.

[0092] It should be noted that the above embodiments provide apparatuses that are implemented using the division of the above program modules as an example. In actual applications, the above processing can be distributed to different program modules as needed, that is, the internal structure of the terminal can be divided into different program modules to complete all or part of the above-described processing. In addition, the apparatuses provided in the above embodiments and the above-described method embodiments are based on the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0093] To implement the method of an embodiment of the present invention, an embodiment of the present invention further provides a computer program product. The computer program product includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the above method.

[0094] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides an electronic device (computer device). Specifically, in one embodiment, the computer device can be a terminal, and its internal structure diagram can be as follows: Figure 3 As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the method of any one of the above embodiments is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display screen, and the input device A05 of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0095] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0096] The device provided by an embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method of any one of the above embodiments is implemented.

[0097] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0101] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0102] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0103] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0104] It is understood that the memory of the embodiments of the present invention can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for acquiring medical image data, characterized in that: The method comprises: Acquire a first medical imaging dataset; wherein the format of the images in the first medical imaging dataset is a non-editable format; Performing image processing on the images in the first medical image dataset to obtain a second medical image dataset; wherein the format of the images in the second medical image dataset is an editable format; performing differential recognition between the images in the second medical image dataset and a preset template, and obtaining a preset number of images with the largest differences as medical image data; The method of performing image processing on the images in the first medical image dataset to obtain the second medical image dataset includes: Converting the images in the first medical image data set into an editable format to obtain third medical image data; and performing special format image scanning on the images in the first medical image data set to obtain fourth medical image data; wherein the formats of the third medical image data and the fourth medical image data are in an editable format; Overlapping the third medical image data with the fourth medical image data to obtain fifth medical image data; performing a deduplication operation on the fifth medical image data to obtain sixth medical image data; The obtained set of all sixth medical image data is used as a second medical image data set.

2. The method according to claim 1, characterized in that Converting the images in the first medical imaging data set into an editable format to obtain third medical imaging data includes: Dividing the image in the first medical image dataset into a plurality of regions according to grayscale values, and obtaining the coordinates of each region; Call the conversion function to convert the format of each area in turn to obtain the converted area image; Match the transformed region image with the coordinates of each region before transformation one by one; The converted regional images are synthesized based on the matched coordinates to obtain third medical image data.

3. The method according to claim 1, characterized in that The performing special format image scanning on the images in the first medical image data set to obtain fourth medical image data includes: Scanning pixel values ​​of the images in the first medical image dataset row by row along one direction; An image is generated line by line based on the pixel value of each pixel point in each scanned line to obtain fourth medical image data.

4. The method according to claim 1, wherein The step of overlapping the third medical image data and the fourth medical image data to obtain fifth medical image data; and performing a deduplication operation on the fifth medical image data to obtain sixth medical image data includes: Dividing the third medical image data into a plurality of first regions according to grayscale values, and obtaining coordinates of each first region; and dividing the fourth medical image data into a plurality of second regions according to grayscale values, and obtaining coordinates of each second region; Matching the first area with the second area one by one; Aligning the coordinates of the first region with the coordinates of the corresponding second region according to the matching result, so that the matched first region and the corresponding second region overlap; After the coordinate alignment, the non-overlapping portion of the first region and the second region is deleted to obtain sixth medical image data.

5. The method according to claim 1, wherein The step of performing differential recognition between the images in the second medical image data set and a preset template and obtaining a preset number of images with the largest differences as medical image data includes: Use preset templates to train neural network models; inputting images in the second medical imaging dataset into the trained neural network model to obtain differentiated results; Based on the obtained differentiation results, a preset number of images with the largest differentiation are obtained as medical imaging data.

6. The method according to claim 1, characterized in that The acquiring of the first medical image data set includes: In a local area network mode, receiving a first medical imaging data set sent by a medical device; The performing image processing on the images in the first medical image dataset to obtain the second medical image dataset includes: After the local area network mode is switched to the Internet mode, image processing is performed on the images in the first medical image dataset to obtain a second medical image dataset.

7. A medical imaging data acquisition device, characterized in that: The medical image data acquisition device includes: An acquisition module, configured to acquire a first medical image data set; wherein the format of the images in the first medical image data set is a non-editable format; an image processing module, configured to perform image processing on the images in the first medical image dataset to obtain a second medical image dataset; wherein the format of the images in the second medical image dataset is an editable format; a differential recognition module, configured to perform differential recognition between the images in the second medical image data set and a preset template, and obtain a preset number of images with the greatest differences as medical image data; The image processing module is used to perform image processing on the images in the first medical image dataset to obtain a second medical image dataset, including: Converting the images in the first medical image data set into an editable format to obtain third medical image data; and performing special format image scanning on the images in the first medical image data set to obtain fourth medical image data; wherein the formats of the third medical image data and the fourth medical image data are in an editable format; Overlapping the third medical image data with the fourth medical image data to obtain fifth medical image data; performing a deduplication operation on the fifth medical image data to obtain sixth medical image data; The obtained set of all sixth medical image data is used as a second medical image data set.

8. An electronic device, characterized in that: include: A processor and a memory for storing a computer program capable of running on the processor; wherein, When the processor is used to run the computer program, the processor performs the steps of the method according to any one of claims 1 to 6.

9. A storage medium storing a computer program, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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