Digital pathological section color calibration method and device

Abstract

The invention provides a digital pathological section color calibration method and device, and the method comprises the following steps: obtaining a standard color image and an image to be subjected to color correction, and extracting the feature points of the standard color image and the feature points of the image to be subjected to color correction, matching the standard color image and the image to be subjected to color correction based on the feature points to obtain a standard color matching image and a matching image to be subjected to color correction; performing histogram color correction on the matching image to be subjected to color correction by taking a standard color matching image as a reference to obtain a first color correction image, and performing CCM color correction on the first color correction image by taking the standard color matching image as the reference to obtain a second color correction image, and performing gamma correction on the color of the second color correction image by taking a standard color matching image as a reference to obtain a color correction completed image. According to the scheme, all pixel information of the target image is fully utilized, the color correction process is quantified to eliminate artificial errors, and the efficiency and accuracy of color correction of the digital pathological image can be remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of color correction, and in particular to a method and device for color calibration of digital pathology sections. Background Art

[0002] Digital slide scanners are precision instruments that integrate technologies from multiple disciplines, including optics, mechanics, electronics, and computers. By controlling the microscopic imaging system and slide motion, they capture continuous, high-resolution microscopic images and seamlessly stitch them together to create high-resolution whole-slide images (WSIs). These instruments are widely used in pathological diagnosis, teaching and training, pharmaceutical research, and scientific research. As applications expand and scanner technology continues to advance, a variety of scanning technologies have emerged, placing higher demands on the color accuracy of slide images.

[0003] Tissue staining is a standard method for examining tissue cellular structure in clinical pathology and life science research, but traditional methods require complex sample preparation, specialized laboratory facilities, and highly trained tissue technicians, making them difficult to implement in resource-limited settings. A variety of new technologies are now available for color generation in digital pathology slides. Among them, deep learning technology digitally generates tissue staining images by training neural networks, providing a fast, cost-effective, and accurate alternative to standard chemical staining methods. These methods place higher demands on color standardization, and the importance of color standardization is gradually increasing.

[0004] Different laboratories, equipment or staining methods may cause color differences in pathological sections, which in turn affect the diagnostic results. Color standardization can reduce such differences, improve diagnostic accuracy and consistency, and facilitate image analysis. Therefore, it is urgent to perform quantitative difference analysis on the color of digital pathology images to complete color correction. Summary of the Invention

[0005] The embodiments of the present application provide a method and device for color calibration of digital pathology sections, which quantify the color calibration process to eliminate human errors, significantly improve the efficiency and accuracy of color correction of digital pathology images, automatically provide color calibration parameters and reduce color difference values, and provide a standardized reference for the qualification of digital pathology images.

[0006] In a first aspect, an embodiment of the present application provides a method for color calibration of digital pathology slides, the method comprising: Acquire a standard color image and an image to be calibrated that is in the same field of view as the standard color image, and extract feature points of the standard color image and the image to be calibrated; Matching the standard color image with the image to be color-calibrated based on feature points of the standard color image and feature points of the image to be color-calibrated to obtain a standard color matching image and an image to be color-calibrated, wherein the standard color matching image and the image to be color-calibrated have the same image content; The image to be color-corrected is subjected to histogram color correction based on the standard color matching image to obtain a first color-corrected image, the first color-corrected image is subjected to CCM color correction based on the standard color matching image to obtain a second color-corrected image, and the color of the second color-corrected image is subjected to gamma correction based on the standard color matching image to obtain a color-corrected image.

[0007] In a second aspect, an embodiment of the present application provides a digital pathology slide color calibration device, comprising: An acquisition module is used to acquire a standard color image and an image to be calibrated that is within the same field of view as the standard color image, and to extract feature points of the standard color image and the image to be calibrated; a feature point matching module, which matches the standard color image with the image to be calibrated based on the feature points of the standard color image and the feature points of the image to be calibrated to obtain a standard color matching image and an image to be calibrated, wherein the image content of the standard color matching image and the image to be calibrated are the same; The color correction module is used to perform histogram correction on the image to be color-corrected based on a standard color matching image to obtain a first color-corrected image, perform CCM color correction on the first color-corrected image based on the standard color matching image to obtain a second color-corrected image, and perform gamma correction on the color of the second color-corrected image based on the standard color matching image to obtain a color-corrected image.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform a digital pathology slice color calibration method.

[0009] In a fourth aspect, an embodiment of the present application provides a readable storage medium, wherein the readable storage medium stores a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes a method for color calibration of digital pathology slices.

[0010] The main contributions and innovations of the present invention are as follows: When extracting feature points, the embodiment of the present application can accurately capture key features at different scales through multi-scale space construction and Hessian matrix calculation. Fine details can be obtained at small scales, and macro features can be highlighted at large scales, laying an accurate foundation for image matching. Feature point matching is combined with geometric transformation parameter cropping to ensure that matching images with the same content are obtained, ensuring the consistency of subsequent color calibration objects. Histogram color calibration can quickly reduce the color difference between the image to be calibrated and the standard image through RGB channel separation and cumulative distribution function mapping, paving the way for further color calibration. CCM color calibration optimizes the matrix with the goal of minimizing the sum of squares of color differences and constrains the sum of values ​​in each row to be 1, ensuring that white balance remains unchanged while accurately adjusting colors. Gamma correction further refines color correction through dynamic parameter optimization with the goal of minimizing color difference, ultimately significantly improving the efficiency and accuracy of color correction of digital pathology images, eliminating human errors, and providing a standardized reference for image qualification.

[0011] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a flow chart of a method for color calibration of digital pathology slides according to an embodiment of the present application; Figure 2 is a schematic diagram of a standard color image and an image to be calibrated according to an embodiment of the present application; Figure 3 is a schematic diagram of comparing a standard color matching image and a color calibration completed image according to an embodiment of the present application; Figure 4 is a structural block diagram of a digital pathology slide color calibration device according to an embodiment of the present application; Figure 5 Schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0013] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not intended to represent all implementations consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of this specification, as detailed in the appended claims.

[0014] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than those described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments, and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0015] Example 1 The embodiment of the present application provides a color calibration method for digital pathology slices, which fully utilizes all pixel information of the target image, quantifies the color calibration process to eliminate human errors, can significantly improve the efficiency and accuracy of color correction of digital pathology images, automatically provide color calibration parameters and reduce color difference values, and provide a standardized reference for the eligibility of digital pathology images. Specifically, reference Figure 1 , the method comprising: Acquire a standard color image and an image to be calibrated that is in the same field of view as the standard color image, and extract feature points of the standard color image and the image to be calibrated; Matching the standard color image with the image to be color-calibrated based on feature points of the standard color image and feature points of the image to be color-calibrated to obtain a standard color matching image and an image to be color-calibrated, wherein the standard color matching image and the image to be color-calibrated have the same image content; The image to be color-corrected is subjected to histogram color correction based on the standard color matching image to obtain a first color-corrected image, the first color-corrected image is subjected to CCM color correction based on the standard color matching image to obtain a second color-corrected image, and the color of the second color-corrected image is subjected to gamma correction based on the standard color matching image to obtain a color-corrected image.

[0016] In some specific embodiments, the standard color image and the image to be color-calibrated in this solution are both pathological slice images obtained by digitally scanning slices. The schematic diagram of the standard color image and the image to be color-calibrated is as shown in FIG. Figure 2 As shown, the standard color image is a pathology section image certified by experts in this field, and the standard color image can clearly see the details in the pathology section.

[0017] In some embodiments, a multi-scale space is constructed for a standard color image to obtain a standard multi-scale space, and a Hessian matrix is ​​calculated for each pixel at each scale level in the standard multi-scale space to obtain a Hessian eigenvalue. If the Hessian eigenvalue of the current pixel is the maximum value in the corresponding local area, the current pixel is used as a feature point of the standard color image. The corresponding local area includes a first area and a second area. The first area is centered on the current pixel and includes the current pixel and pixels adjacent to the current pixel. The second area is an area corresponding to the first area at an adjacent scale level of the current scale level.

[0018] Similarly, a multi-scale space is constructed for the image to be calibrated to obtain a multi-scale space to be calibrated, and the Hessian matrix is ​​calculated for each pixel at each scale level in the multi-scale space to be calibrated to obtain the Hessian eigenvalue. If the Hessian eigenvalue of the current pixel is the maximum value in the corresponding local area, the current pixel is used as the feature point of the image to be calibrated. The corresponding local area includes a first area and a second area. The first area is centered on the current pixel, including the current pixel and the pixels adjacent to the current pixel. The second area is the area corresponding to the first area at the adjacent scale level of the current scale level.

[0019] Specifically, this scheme constructs a standard multi-scale space and a multi-scale space to be calibrated by using filters of different sizes.

[0020] Specifically, a Hessian matrix is ​​constructed for each pixel, and the size of the Hessian matrix determinant is used as the Hessian eigenvalue of the pixel. This scheme uses the extreme points of the Hessian eigenvalue in the local area as feature points, which can reflect the curvature changes in the local area of ​​the image. Extreme points at different scales correspond to key features of structures of different sizes. For example, in pathological images, extreme points at small scales may capture fine features in the image such as texture details and small edge intersections; while extreme points at large scales tend to highlight relatively macro features such as object contours and shape changes in larger areas.

[0021] Furthermore, before constructing the multi-scale space for the standard color image and the image to be calibrated, the standard color image and the image to be calibrated are grayscaled, and the features of the standard color image and the image to be calibrated are represented by integral images.

[0022] Specifically, integral image is a feature representation method for digital image processing, which is used to quickly calculate the sum of grayscale values ​​in local areas of an image. This scheme uses integral images to represent the features of the standard color image and the image to be calibrated. Simple addition and subtraction operations can be performed in subsequent image processing to obtain the sum of grayscale values ​​in any area, which facilitates subsequent multi-scale space construction, feature point matching and other operations.

[0023] In some embodiments, feature points of a standard color image and feature points of an image to be calibrated whose vector distance is less than a set threshold are used as matching feature points, and geometric transformation parameters of the standard color image and the image to be calibrated are obtained. Based on the geometric transformation parameters and the matching feature points, the standard color image and the image to be calibrated are cropped to obtain a standard color matching image and a matching image to be calibrated.

[0024] Specifically, this scheme obtains the feature description vector of each feature point by calculating the Haar wavelet response of the area around each feature point in the standard color image and the image to be calibrated, and then calculates the vector distance between each feature description vector in the standard color image and each feature description vector in the image to be calibrated. In addition, the feature description vector includes the position, scale, and direction information of the feature point.

[0025] Specifically, this solution uses a feature descriptor matrix to represent matching feature points. In the feature descriptor matrix, each row includes two indexes, which are two successfully matched feature points. This solution uses cosine similarity or Euclidean distance to match feature points.

[0026] Furthermore, this scheme uses the MSAC algorithm to obtain the geometric transformation parameters of the standard color image and the image to be calibrated. Specifically, taking the standard color image as an example, random average sampling is performed in the standard color image in a random sampling manner to obtain a data set W, and then the minimum data set Q for plane model fitting is randomly selected from the data set W without repetition. The minimum data set Q is fitted to obtain a plane model S using the least squares method, and the distance from all points in the data set W to the plane model S is calculated. The points within the allowable error threshold are marked as inliers, and the remaining points are marked as outliers. The cost Ci of the plane model is calculated, and the cost Ci of the current model is compared with the cost Cb of the previous best model. The smaller one is recorded as the cost of the new best model, and the corresponding inliers and model parameters are recorded. Random average sampling is repeated in the standard color image and the model parameters of the inliers are calculated again until the iteration ends. All inliers are used to fit the plane model using the least squares method to obtain the optimal plane model parameters. Finally, the optimal plane model parameters of the standard color image and the optimal plane model parameters of the image to be calibrated are used to obtain the geometric transformation parameters.

[0027] Specifically, the cost function expression of the MSAC algorithm is as follows:

[0028] Among them, when the distance e from point ρ to the model is less than the threshold T, the point is judged as an internal point and the weight is e; otherwise, it is an external point and the weight is T.

[0029] In some embodiments, the overlapping area of ​​the standard color image and the image to be calibrated is cut out as the standard color matching image and the image to be calibrated. Using CIEDE2000 to evaluate their color difference, it can be obtained that the average color difference between the standard color matching image and the image to be calibrated is 15.10419, which is much larger than the qualified color difference value of 5. Therefore, the image to be calibrated needs to be calibrated.

[0030] Specifically, CIEDE2000 is a formula for evaluating color difference and is an improved color difference evaluation method proposed by the International Commission on Illumination (CIE) in 2000.

[0031] In some embodiments, in the histogram color correction step, the standard color matching image is separated into RGB channels, and the histogram data distribution under each channel is calculated to obtain the standard image cumulative distribution function under each channel. The color matching image to be corrected is separated into RGB channels, and the histogram data distribution under each channel is calculated to obtain the color correction image cumulative distribution function under each channel. The pixel value of any pixel in the standard color matching image is used as the first pixel value. According to the cumulative distribution function of the corresponding channel, the pixel value with the closest cumulative probability to the first pixel value is found in the color matching image to be corrected as the second pixel value, and the first pixel value is used to map the second pixel value. When the color difference between the color matching image to be corrected and the standard matching image is less than the set threshold, the histogram color correction is terminated to obtain the first color correction image.

[0032] Specifically, the cumulative distribution function in this solution represents the probability accumulation value of each pixel value, reflecting the cumulative pixel distribution of the image.

[0033] In some embodiments, in the CCM color correction step, a CCM matrix is ​​constructed, and the RGB channel matrix of the standard color matching image and the first color correction image is used as a data set. The CCM matrix is ​​optimized with the objective function of minimizing the sum of squares of color differences between the standard color matching image and the first color correction image. After the optimization is completed, a second color correction image is obtained, wherein the sum of the values ​​in each row is 1 as a constraint of the CCM matrix.

[0034] Specifically, in the CCM color correction step, this solution completes the CCM color correction by constructing a CCM matrix and adjusting the RGB channel matrix of the first color correction image to minimize the sum of the squares of the color differences between the standard color matching image and the first color correction image.

[0035] Specifically, the purpose of using the sum of the values ​​of each row to be 1 as the constraint of the CCM matrix is ​​to ensure that the white balance of the optimized image remains unchanged.

[0036] In some embodiments, in the gamma correction step, dynamic gamma correction parameters are set, and the second color-calibrated image is gamma-corrected using the dynamic gamma correction parameters to obtain a gamma-calibrated image. The color difference between the standard color matching image and the gamma-calibrated image is used as a cost function. When the cost function is minimized, the gamma calibration is completed, and the current gamma-calibrated image is used as the calibration completion image.

[0037] Specifically, the initial value of the dynamic gamma correction parameter is set to 0.85, the maximum value is 1.15, the optimization interval is 0.05, and during the gamma correction process, the dynamic gamma correction parameter is updated at an interval of 0.01.

[0038] Specifically, the comparison between the standard color matching image and the color calibration completed image is as follows: Figure 3 As shown by Figure 3 It can be clearly seen that there is basically no color difference between the color-calibrated image and the standard color-matching image. According to CIEDE2000, the color difference between the color-calibrated image and the standard color-matching image is calculated, and the color difference between the two is only 3.36373.

[0039] Example 2 Based on the same concept, refer to Figure 4 , this application also proposes a digital pathology slide color calibration device, comprising: An acquisition module is used to acquire a standard color image and an image to be calibrated that is within the same field of view as the standard color image, and to extract feature points of the standard color image and the image to be calibrated; a feature point matching module, which matches the standard color image with the image to be calibrated based on the feature points of the standard color image and the feature points of the image to be calibrated to obtain a standard color matching image and an image to be calibrated, wherein the image content of the standard color matching image and the image to be calibrated are the same; The color correction module is used to perform histogram correction on the image to be color-corrected based on a standard color matching image to obtain a first color-corrected image, perform CCM color correction on the first color-corrected image based on the standard color matching image to obtain a second color-corrected image, and perform gamma correction on the color of the second color-corrected image based on the standard color matching image to obtain a color-corrected image.

[0040] Example 3 This embodiment also provides an electronic device, referring to Figure 4 , includes a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0041] Specifically, the processor 402 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0042] Memory 404 may include a large-capacity memory 404 for data or instructions. By way of example, and not limitation, memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 404 may include removable or non-removable (or fixed) media. Where appropriate, memory 404 may be internal or external to the data processing device. In certain embodiments, memory 404 is non-volatile memory. In certain embodiments, memory 404 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0043] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0044] The processor 402 reads and executes computer program instructions stored in the memory 404 to implement any one of the digital pathology slide color calibration methods in the above embodiments.

[0045] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0046] Transmission device 406 can be used to receive or transmit data via a network. Specific examples of such networks may include wired or wireless networks provided by the electronic device's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0047] The input / output device 408 is used to input or output information. In this embodiment, the input information may be a standard color image, an image to be color-calibrated, etc., and the output information may be a color-calibrated image, etc.

[0048] Optionally, in this embodiment, the processor 402 may be configured to execute the following steps through a computer program: Acquire a standard color image and an image to be calibrated that is in the same field of view as the standard color image, and extract feature points of the standard color image and the image to be calibrated; Matching the standard color image with the image to be color-calibrated based on feature points of the standard color image and feature points of the image to be color-calibrated to obtain a standard color matching image and an image to be color-calibrated, wherein the standard color matching image and the image to be color-calibrated have the same image content; The image to be color-corrected is subjected to histogram color correction based on the standard color matching image to obtain a first color-corrected image, the first color-corrected image is subjected to CCM color correction based on the standard color matching image to obtain a second color-corrected image, and the color of the second color-corrected image is subjected to gamma correction based on the standard color matching image to obtain a color-corrected image.

[0049] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0050] In general, various embodiments may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0051] The embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components that are configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, it should be noted at this point that, for example, Figure 5 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or memory blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0052] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The above embodiments merely illustrate several embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A color calibration method for digital pathology slides, characterized in that: The following steps are involved: Acquire a standard color image and an image to be calibrated that is in the same field of view as the standard color image, and extract feature points of the standard color image and the image to be calibrated; Matching the standard color image with the image to be color-calibrated based on feature points of the standard color image and feature points of the image to be color-calibrated to obtain a standard color matching image and an image to be color-calibrated, wherein the standard color matching image and the image to be color-calibrated have the same image content; The image to be color-corrected is subjected to histogram color correction based on the standard color matching image to obtain a first color-corrected image, the first color-corrected image is subjected to CCM color correction based on the standard color matching image to obtain a second color-corrected image, and the color of the second color-corrected image is subjected to gamma correction based on the standard color matching image to obtain a color-corrected image.

2. A digital pathology slide color calibration method according to claim 1, characterized in that: A multi-scale space is constructed for the standard color image to obtain a standard multi-scale space. The Hessian matrix is ​​calculated for each pixel at each scale level in the standard multi-scale space to obtain the Hessian eigenvalue. If the Hessian eigenvalue of the current pixel is the maximum value in the corresponding local area, the current pixel is used as the feature point of the standard color image. The corresponding local area includes a first area and a second area. The first area is centered on the current pixel and includes the current pixel and pixels adjacent to the current pixel. The second area is the area corresponding to the first area at the adjacent scale level of the current scale level.

3. The color calibration method for digital pathology sections according to claim 1, characterized in that: A multi-scale space is constructed for the image to be calibrated to obtain the multi-scale space to be calibrated, and a Hessian matrix is ​​calculated for each pixel at each scale level in the multi-scale space to be calibrated to obtain a Hessian eigenvalue. If the Hessian eigenvalue of the current pixel is the maximum value in the corresponding local area, the current pixel is used as the feature point of the image to be calibrated. The corresponding local area includes a first area and a second area. The first area is centered on the current pixel and includes the current pixel and pixels adjacent to the current pixel. The second area is an area corresponding to the first area at an adjacent scale level of the current scale level.

4. A digital pathology slide color calibration method according to claim 1, characterized in that: The feature points of the standard color image and the feature points of the image to be calibrated whose vector distance is less than a set threshold are used as matching feature points, and the geometric transformation parameters of the standard color image and the image to be calibrated are obtained. Based on the geometric transformation parameters and the matching feature points, the standard color image and the image to be calibrated are cropped to obtain the standard color matching image and the image to be calibrated.

5. The method for color calibration of digital pathology sections according to claim 1, wherein: In the histogram color correction step, the standard color matching image is separated into RGB channels, and the histogram data distribution under each channel is calculated to obtain the standard image cumulative distribution function under each channel. The color matching image to be corrected is separated into RGB channels, and the histogram data distribution under each channel is calculated to obtain the color correction image cumulative distribution function under each channel. The pixel value of any pixel in the standard color matching image is used as the first pixel value, and the pixel value with the closest cumulative probability to the first pixel value is found in the color matching image to be corrected as the second pixel value according to the cumulative distribution function of the corresponding channel. The first pixel value is used to map the second pixel value. When the color difference between the color matching image to be corrected and the standard matching image is less than the set threshold, the histogram color correction is ended to obtain the first color correction image.

6. A digital pathology slide color calibration method according to claim 1, characterized in that: In the CCM color correction step, a CCM matrix is ​​constructed. The standard color matching image and the RGB channel matrix of the first color correction image are used as data sets. The CCM matrix is ​​optimized with the objective function of minimizing the sum of the squares of the color differences between the standard color matching image and the first color correction image. After the optimization is completed, a second color correction image is obtained. The sum of the values ​​in each row is 1, which is used as a constraint on the CCM matrix.

7. The method for color calibration of digital pathology sections according to claim 1, characterized in that: In the gamma correction step, dynamic gamma correction parameters are set, and the second color-calibrated image is gamma-corrected using the dynamic gamma correction parameters to obtain a gamma-calibrated image. The color difference between the standard color matching image and the gamma-calibrated image is used as a cost function. When the cost function is minimized, the gamma calibration is completed, and the current gamma-calibrated image is used as the calibration completion image.

8. A digital pathology slide color calibration device, characterized in that: include: An acquisition module is used to acquire a standard color image and an image to be calibrated that is within the same field of view as the standard color image, and to extract feature points of the standard color image and the image to be calibrated; a feature point matching module, which matches the standard color image with the image to be calibrated based on the feature points of the standard color image and the feature points of the image to be calibrated to obtain a standard color matching image and an image to be calibrated, wherein the image content of the standard color matching image and the image to be calibrated are the same; The color correction module is used to perform histogram correction on the image to be color-corrected based on a standard color matching image to obtain a first color-corrected image, perform CCM color correction on the first color-corrected image based on the standard color matching image to obtain a second color-corrected image, and perform gamma correction on the color of the second color-corrected image based on the standard color matching image to obtain a color-corrected image.

9. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the digital pathology slide color calibration method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes a digital pathology slide color calibration method according to any one of claims 1 to 7.

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