Induced pluripotent stem cell density detection method, computer equipment and medium

Abstract

The invention relates to the technical field of image processing, in particular to an induced pluripotent stem cell density detection method, computer equipment and a medium, and the method comprises the following steps: acquiring an induced pluripotent stem cell image; presetting a local area of each pixel point; obtaining a local brightness performance value according to the gray level distribution of the pixel points in the local area; obtaining local boundary ambiguity according to the gradient distribution characteristics; obtaining local form regularity according to the uniformity characteristic of gray level distribution in a pixel point local area; obtaining a density comprehensive performance value of each pixel point; clustering the pixel points to obtain a preset number of clusters; and respectively enhancing each cluster by adopting histogram equalization to obtain an enhanced induced pluripotent stem cell image, and carrying out density detection on the enhanced induced pluripotent stem cell image. The invention aims to enhance the induced pluripotent stem cell image and improve the accuracy of the cell density detection result.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method for detecting the density of induced pluripotent stem cells, a computer device and a medium. Background Art

[0002] Density detection of induced pluripotent stem cells (iPSCs) plays an important role in regenerative medicine and cell therapy. With the continuous advancement of iPSC technology, accurate detection and analysis of cell density has become particularly critical because it directly affects the evaluation of cell proliferation rate, optimization of culture conditions, and prediction of cell yield. Traditional manual counting methods have been gradually replaced by automated and semi-automated image analysis technologies, which can quickly and accurately identify and count cells with the help of computer vision and machine learning algorithms, thereby significantly improving detection efficiency and accuracy.

[0003] Currently, image enhancement often uses histogram equalization to improve contrast, but traditional histogram equalization methods are usually based on global processing of the entire image, without considering the local features of the image. This global processing method may cause excessive or insufficient contrast in certain areas, thereby affecting the detail performance of the image. In the process of induced pluripotent stem cell density detection, this problem may cause unclear cell boundaries, affect the resolution between cells, and thus reduce the accuracy of the final density detection. Summary of the invention

[0004] In view of the above, it is necessary to provide an induced pluripotent stem cell density detection method, computer equipment and medium to solve the above problems.

[0005] According to one aspect of the present application, a method for detecting density of induced pluripotent stem cells is provided, the method comprising:

[0006] Acquire images of induced pluripotent stem cells;

[0007] Taking each pixel as the center, a first local area and a second local area of ​​each pixel are preset, wherein the second local area includes the first local area; according to the grayscale distribution of each pixel in the first local area and the second local area, a local brightness expression value of each pixel is obtained; according to the gradient distribution characteristics of each pixel in a preset direction in the second local area, the local boundary fuzziness of each pixel is obtained; according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel, the local morphological regularity of each pixel is obtained;

[0008] According to the local boundary fuzziness and local morphological regularity of each pixel point, combined with the local brightness performance value, the density comprehensive performance value of each pixel point is obtained; based on the density comprehensive performance value, the pixels are clustered to obtain a preset number of clusters; histogram equalization is used to enhance each cluster cluster to obtain an enhanced induced pluripotent stem cell image, and the enhanced induced pluripotent stem cell image is density detected.

[0009] The local brightness value of each pixel is obtained as follows:

[0010] The mean grayscale value of all pixels in the first local area of ​​each pixel is recorded as the first mean; the mean grayscale value of all pixels in the second local area of ​​each pixel is recorded as the second mean; the absolute value of the difference between the first mean and the second mean is calculated, and the ratio of the first mean to the absolute value of the difference is normalized to obtain the local brightness performance value of each pixel.

[0011] The local boundary fuzziness of each pixel is obtained as follows:

[0012] The local boundary fuzziness of the i-th pixel is recorded as W i , its formula form is:

[0013]

[0014] Where L represents the number of preset directions; M i,l represents the number of pixels in the second local area in the lth direction of the i-th pixel; g i,l represents the gradient value set of all pixels in the second local area in the lth direction of the i-th pixel; g i,l,m represents the gradient value of the mth pixel in the second local area in the lth direction of the ith pixel; norm() represents the normalization function; min() represents the minimum function.

[0015] The local morphological regularity of each pixel is obtained as follows:

[0016] Obtaining grayscale uniformity in each direction within the second local region of each pixel point according to the distribution of grayscale differences between adjacent pixel points in each direction within the second local region of each pixel point;

[0017] The local morphological regularity of each pixel is obtained according to the discrete degree of the grayscale uniformity in multiple directions in the second local area of ​​each pixel.

[0018] The grayscale uniformity in each direction of the second local area of ​​each pixel is obtained as follows:

[0019] The standard deviation of the grayscale differences between all adjacent pixels in each direction within the second local area of ​​each pixel; and the negative correlation mapping result of the standard deviation is used as the grayscale uniformity in each direction within the second local area of ​​each pixel.

[0020] The specific formula for obtaining the comprehensive density performance value of each pixel is: R i =Q i ×E i +(1-Q i )×W i ; Among them, R i Indicates the comprehensive density performance value of the i-th pixel, Q i represents the local brightness value of the i-th pixel, E i represents the local morphological regularity of the i-th pixel, W i Represents the local boundary blur of the i-th pixel.

[0021] The clustering of pixels includes:

[0022] The comprehensive density performance value of all pixels is used as the input of the clustering algorithm, the absolute value of the difference between the comprehensive density performance values ​​of the pixels is used as the metric between the pixels in the clustering algorithm, and a preset number of clusters are output.

[0023] The density detection of the enhanced induced pluripotent stem cell image is specifically performed as follows:

[0024] A neural network is trained based on induced pluripotent stem cell images with known cell density, the induced pluripotent stem cell images to be detected are used as input, and the cell density is obtained using the trained neural network.

[0025] According to another aspect of the present invention, there is provided a computer device for detecting density of induced pluripotent stem cells, comprising:

[0026] An induced pluripotent stem cell image acquisition module, used for acquiring induced pluripotent stem cell images;

[0027] The induced pluripotent stem cell image analysis module is used to preset a first local area and a second local area of ​​each pixel point with each pixel point as the center, wherein the second local area includes the first local area; obtain the local brightness expression value of each pixel point according to the grayscale distribution of each pixel point in the first local area and the second local area; obtain the local boundary fuzziness of each pixel point according to the gradient distribution characteristics of each pixel point in a preset direction in the second local area; obtain the local morphological regularity of each pixel point according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel point;

[0028] The induced pluripotent stem cell image density detection module is used to obtain a comprehensive density performance value of each pixel based on the local boundary fuzziness and local morphological regularity of each pixel, combined with the local brightness performance value; cluster the pixels based on the comprehensive density performance value to obtain a preset number of clusters; use histogram equalization to enhance each cluster respectively to obtain an enhanced induced pluripotent stem cell image, and perform density detection on the enhanced induced pluripotent stem cell image.

[0029] According to another aspect of the present application, there is provided an induced pluripotent stem cell density detection medium, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0030] In the above scheme, an induced pluripotent stem cell image is obtained, and a local area is preset with each pixel as the center. The local brightness expression value is obtained by analyzing the grayscale distribution between different local areas to describe the density distribution of the area where the pixel is located. Since the density distribution of induced pluripotent stem cells is different, the boundary characteristics of the corresponding areas are different. Therefore, the local boundary fuzziness is obtained according to the gradient distribution characteristics of the pixel in the preset direction in the local area, which helps to further judge the density of the pixel location according to the fuzziness of the boundary. Since the cell morphology of induced pluripotent stem cells is related to density, the local morphological regularity is obtained according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel, and the uniformity characteristics of the grayscale distribution are used to quantify whether the cell morphology is regular. The comprehensive density expression value of each pixel is obtained to measure the density characteristics of the local area of ​​the pixel. Then, a clustering algorithm is used to obtain different density distribution areas, and each area is enhanced by histogram equalization to avoid the loss of local details and excessive enhancement caused by traditional histogram equalization, which helps to improve the accuracy of subsequent cell density detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flowchart of the steps of a method for detecting the density of induced pluripotent stem cells provided in the present application;

[0032] Figure 2 A schematic diagram for obtaining the comprehensive density performance value provided for this application;

[0033] Figure 3 A block diagram of a computer device for detecting density of induced pluripotent stem cells provided in this application. DETAILED DESCRIPTION

[0034] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0036] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart includes one or more steps for implementing the method. Without departing from the scope of the present application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0037] See also Figure 1 , which shows a flow chart of the steps of a method for detecting the density of induced pluripotent stem cells provided in one embodiment of the present application, the method comprising the following steps:

[0038] Step 1: Acquire images of induced pluripotent stem cells.

[0039] A certain number of cells are collected from the induced pluripotent stem cell culture dish and suspended in an appropriate culture medium. In order to prevent the cells from moving during the detection process, fixation is usually required. A common method is to add a certain concentration of formaldehyde or other fixatives to stabilize the cell structure. Next, the cells are stained with DAPI (4',6-diamidino-2-phenylindole) so that the cell morphology and structure can be observed under a microscope. Finally, the stained cell image is obtained using a microscope and grayed to obtain an induced pluripotent stem cell image.

[0040] Step 2: With each pixel as the center, preset the first local area and the second local area of ​​each pixel, wherein the second local area includes the first local area; obtain the local brightness expression value of each pixel according to the grayscale distribution of each pixel in the first local area and the second local area; obtain the local boundary blur of each pixel according to the gradient distribution characteristics of each pixel in the preset direction in the second local area; obtain the local morphological regularity of each pixel according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel.

[0041] During the culture process of induced pluripotent stem cells, changes in cell growth and development as well as culture conditions will cause cell density in different areas to present different distribution patterns.

[0042] In areas with higher cell density, cells are closely arranged and overlap more, resulting in increased absorption and scattering of light, which ultimately causes these areas to appear lower brightness or darker grayscale values ​​in the image. Conversely, in areas with lower cell density, there are larger gaps between cells, and more light can pass through these areas to reach the image sensor, causing these areas to appear as brighter grayscale values.

[0043] Based on this, with each pixel as the center, the first local area and the second local area of ​​each pixel are preset; in this embodiment, the size of the first local area of ​​the pixel is 5×5; the size of the second local area is 9×9; the second local area includes the first local area; the implementer can adjust the size of the first local area and the second local area according to the actual situation, and this application does not impose any restrictions on this.

[0044] According to the grayscale distribution of pixels in two local areas of each pixel, the local brightness expression value of each pixel is obtained: the mean of the grayscale values ​​of all pixels in the first local area of ​​each pixel is recorded as the first mean; the mean of the grayscale values ​​of all pixels in the second local area of ​​each pixel is recorded as the second mean; the absolute value of the difference between the first mean and the second mean is calculated, and the ratio of the first mean to the absolute value of the difference is normalized to obtain the local brightness expression value of each pixel.

[0045] It should be noted that in order to avoid the situation where the denominator is zero, a preset value needs to be added to the denominator. The preset value in this embodiment is 0.1, and the implementer can adjust it according to the actual situation.

[0046] For each pixel, the closer the grayscale distribution of the pixel in the first local area is to that in the second local area, it means that the pixel is in the core area of ​​a certain cell density area. Conversely, it means that the pixel is in an area where the cell density changes. The higher the overall grayscale value of the pixel in the first local area, the higher the pixel is in an area with lower cell density, and the greater the local brightness performance value.

[0047] For iPS cell regions with different densities, the clarity of cell boundaries will also vary. In high-density regions, cell boundaries may appear blurred due to the close arrangement of cells, while in low-density regions, cell boundaries are usually more obvious and easy to identify due to the larger spacing between cells.

[0048] In high-density areas, the boundaries between cells are not clear, resulting in a lack of sufficient contrast between the cell boundaries and the surrounding background. Specifically, this means that the gradient value between the cell and the background is low, and in these areas, the gradient changes at the cell boundaries are relatively gentle, lacking a clear transition. As a result, the boundaries between cells become blurred, and it is difficult to clearly distinguish between cells and the background around them.

[0049] The Sobel operator is used to obtain the gradient information of each pixel of the induced pluripotent stem cell image. According to the gradient distribution characteristics of each pixel in the preset direction in the second local area, the local boundary fuzziness of each pixel is obtained: the local boundary fuzziness of the i-th pixel is recorded as W i , its formula form is:

[0050]

[0051] Wherein, L represents the number of preset directions. In this embodiment, eight neighborhood directions of the pixel point are used; M i,l represents the number of pixels in the second local area in the lth direction of the i-th pixel; g i,l represents the gradient value set of all pixels in the second local area in the lth direction of the i-th pixel; g i,l,m represents the gradient value of the mth pixel in the second local area in the lth direction of the ith pixel; norm() represents the normalization function, specifically the sigmoid function; min() represents the minimum function.

[0052] By finding the lowest gradient value in each direction of the pixel point, and using it as a benchmark to compare with the gradient values ​​of other pixels in that direction, As a weight, the degree of proximity between the gradient value of the pixel point in the same direction and the minimum gradient value is obtained, that is, This reflects the clarity of the region boundary in each direction. However, since the region boundaries may not completely match in different directions, the weakest visibility in all directions will be selected as a reference. Finally, normalization is performed; since visibility and blur are negatively correlated, The local boundary blurriness index of the local area of ​​the i-th pixel is called the local boundary blurriness. In short, it is to find the most blurred boundary performance by comparing the gradient values ​​in all directions, and then use this value to measure the boundary blurriness of the local area of ​​the i-th pixel.

[0053] Thus, the local boundary fuzziness of each pixel can be determined, which is analyzed based on the characteristics of induced pluripotent stem cells in high-density areas. The higher the value, the more it conforms to the boundary performance of high-density areas, and vice versa, the more it conforms to the boundary performance of low-density areas.

[0054] Cell morphology may vary in different density areas. In high-density areas, cells tend to become flatter or irregular due to the crowding between them, while in low-density areas, cells usually maintain a more natural round or oval shape.

[0055] Therefore, the local grayscale characteristics can be analyzed according to the different cell densities. In high-density areas, the irregular morphology of cells will cause the grayscale distribution to present a chaotic and uneven characteristic; while in low-density areas, the cell morphology is more regular and the grayscale distribution tends to be more orderly and uniform.

[0056] Based on this, according to the distribution of grayscale differences between adjacent pixels in each direction within the second local area of ​​each pixel, the grayscale uniformity in each direction within the second local area of ​​each pixel is obtained. In this embodiment, the distribution of grayscale differences between adjacent pixels is specifically the standard deviation of the grayscale differences between all adjacent pixels in each direction within the second local area of ​​each pixel, and the reciprocal of the standard deviation is used as the grayscale uniformity in each direction within the second local area of ​​each pixel. In order to avoid the situation where the denominator is zero, a preset value needs to be added to the denominator, and the preset value is 0.1.

[0057] Further, the local morphological regularity of each pixel is obtained according to the degree of dispersion of the grayscale uniformity in multiple directions in the second local area of ​​each pixel. In this embodiment, the coefficient of variation is used to measure the dispersion between multiple variables. The implementer may also use other methods to measure the dispersion, such as variance, standard deviation, etc., which is not limited in this application.

[0058] The closer the grayscale uniformity in each direction is, the more uniform the grayscale distribution in the second local area of ​​the corresponding pixel point is, and therefore the greater the possibility that the pixel point is in the low-density area of ​​induced pluripotent stem cells.

[0059] Step three: according to the local boundary fuzziness and local morphological regularity of each pixel point, combined with the local brightness performance value, obtain the comprehensive density performance value of each pixel point; cluster the pixels based on the comprehensive density performance value to obtain a preset number of clusters; use histogram equalization to enhance each cluster cluster to obtain an enhanced induced pluripotent stem cell image, and use a neural network to perform density detection on the induced pluripotent stem cell image.

[0060] Since high-density cell distribution has complex actual characteristics, a single factor cannot accurately determine the cell location. In order to improve the accuracy of the analysis, this application starts with the two characteristics of local boundary fuzziness and local morphological regularity, combined with the local brightness performance value, and conducts a comprehensive analysis of each pixel point to more accurately identify and determine the distribution of induced pluripotent stem cells.

[0061] Taking the comprehensive brightness performance of the local area as the benchmark, when it is larger, more attention is paid to the local morphological regularity; when it is smaller, more attention is paid to the local edge fuzziness; based on this, according to the local boundary fuzziness and local morphological regularity of each pixel, combined with the local brightness performance value, the density comprehensive performance value of each pixel is obtained, and its formula form is: R i =Q i ×E i +(1-Q i )×W i ; Among them, R i Indicates the comprehensive density performance value of the i-th pixel, Q i represents the local brightness value of the i-th pixel, E i represents the local morphological regularity of the i-th pixel, W i Indicates the local boundary fuzziness of the i-th pixel. The larger the comprehensive density performance value, the more likely it is that the pixel is located in a high-density distribution area, and the smaller the value, the more likely it is that the pixel is located in a corresponding low-density distribution area.

[0062] Among them, the schematic diagram of obtaining the comprehensive performance value of density is as follows Figure 2 shown.

[0063] All pixel points are clustered based on the comprehensive density performance value to obtain a preset number of clusters, each cluster representing an area with similar density distribution; histogram equalization is used to enhance each cluster to obtain an enhanced induced pluripotent stem cell image, and a neural network is used to perform density detection on the induced pluripotent stem cell image.

[0064] In this embodiment, the preset number is 8, and the implementer can set it according to the actual situation; in this embodiment, the K-means clustering algorithm is used for clustering, and the absolute value of the difference between the density comprehensive performance values ​​of the pixels is used as the metric between the pixels. It should be noted that the K-means algorithm and histogram equalization are both existing well-known technologies, and this application will not repeat them; through the clustering algorithm, the induced pluripotent stem cell image can be divided into multiple areas with similar density distribution. Histogram equalization is performed on different areas respectively, and the enhancement effect of each area will be different, making the overall grayscale distribution of the image more uniform, thereby enhancing certain detail information, and providing a basis for the accuracy of the density detection results of the induced pluripotent stem cell images using neural networks in the subsequent use.

[0065] In this embodiment, a convolutional neural network (CNN) is trained based on an induced pluripotent stem cell image with known cell density, and the induced pluripotent stem cell image to be detected is used as input, and the cell density is obtained using the trained convolutional neural network CNN. The convolutional neural network CNN is a well-known technology, and this application will not elaborate on it.

[0066] Based on the same concept as the method embodiment of the present application, a computer device for detecting density of induced pluripotent stem cells is proposed, comprising:

[0067] An induced pluripotent stem cell image acquisition module, used for acquiring induced pluripotent stem cell images;

[0068] The induced pluripotent stem cell image analysis module is used to preset a first local area and a second local area of ​​each pixel point with each pixel point as the center, wherein the second local area includes the first local area; obtain the local brightness expression value of each pixel point according to the grayscale distribution of each pixel point in the first local area and the second local area; obtain the local boundary fuzziness of each pixel point according to the gradient distribution characteristics of each pixel point in a preset direction in the second local area; obtain the local morphological regularity of each pixel point according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel point;

[0069] The induced pluripotent stem cell image density detection module is used to obtain a comprehensive density performance value of each pixel based on the local boundary fuzziness and local morphological regularity of each pixel, combined with the local brightness performance value; cluster the pixels based on the comprehensive density performance value to obtain a preset number of clusters; use histogram equalization to enhance each cluster respectively to obtain an enhanced induced pluripotent stem cell image, and perform density detection on the enhanced induced pluripotent stem cell image.

[0070] Among them, a block diagram of a computer device for detecting density of induced pluripotent stem cells, such as Figure 3 shown.

[0071] Based on the same concept as the method embodiment of the present application, an induced pluripotent stem cell density detection medium is proposed, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0072] In summary, an induced pluripotent stem cell image is obtained, and a local area is preset with each pixel as the center. The local brightness expression value is obtained by analyzing the grayscale distribution between different local areas to describe the density distribution of the pixel area. Since the density distribution of induced pluripotent stem cells is different, the boundary characteristics of the corresponding areas are different. Therefore, the local boundary fuzziness is obtained according to the gradient distribution characteristics of the pixel in the preset direction in the local area, which helps to further judge the density of the pixel location according to the fuzziness of the boundary. Since the cell morphology of induced pluripotent stem cells is related to density, the local morphological regularity is obtained according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel, and the uniformity characteristics of the grayscale distribution are used to quantify whether the cell morphology is regular. The comprehensive density expression value of each pixel is obtained to measure the density characteristics of the local area of ​​the pixel. Then, a clustering algorithm is used to obtain different density distribution areas, and each area is enhanced by histogram equalization to avoid the loss of local details and excessive enhancement caused by traditional histogram equalization, which helps to improve the accuracy of subsequent cell density detection results.

[0073] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for detecting the density of induced pluripotent stem cells, characterized in that: The method comprises the following steps: Acquire images of induced pluripotent stem cells; Taking each pixel as the center, a first local area and a second local area of ​​each pixel are preset, wherein the second local area includes the first local area; according to the grayscale distribution of each pixel in the first local area and the second local area, a local brightness expression value of each pixel is obtained; according to the gradient distribution characteristics of each pixel in a preset direction in the second local area, the local boundary fuzziness of each pixel is obtained; according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel, the local morphological regularity of each pixel is obtained; According to the local boundary fuzziness and local morphological regularity of each pixel point, combined with the local brightness performance value, the density comprehensive performance value of each pixel point is obtained; based on the density comprehensive performance value, the pixels are clustered to obtain a preset number of clusters; histogram equalization is used to enhance each cluster cluster to obtain an enhanced induced pluripotent stem cell image, and the enhanced induced pluripotent stem cell image is density detected.

2. The method for detecting the density of induced pluripotent stem cells according to claim 1, characterized in that: The local brightness value of each pixel is obtained as follows: The mean grayscale value of all pixels in the first local area of ​​each pixel is recorded as the first mean; the mean grayscale value of all pixels in the second local area of ​​each pixel is recorded as the second mean; the absolute value of the difference between the first mean and the second mean is calculated, and the ratio of the first mean to the absolute value of the difference is normalized to obtain the local brightness performance value of each pixel.

3. The method for detecting the density of induced pluripotent stem cells according to claim 1, characterized in that: The local boundary fuzziness of each pixel is obtained as follows: The local boundary fuzziness of the i-th pixel is recorded as W i , its formula form is: Where L represents the number of preset directions; M i,l represents the number of pixels in the second local area in the lth direction of the i-th pixel; g i,l represents the gradient value set of all pixels in the second local area in the lth direction of the i-th pixel; g i,l,m represents the gradient value of the mth pixel in the second local area in the lth direction of the ith pixel; norm() represents the normalization function; min() represents the minimum function.

4. The method for detecting the density of induced pluripotent stem cells according to claim 1, characterized in that: The local morphological regularity of each pixel is obtained as follows: Obtaining grayscale uniformity in each direction within the second local region of each pixel point according to the distribution of grayscale differences between adjacent pixel points in each direction within the second local region of each pixel point; The local morphological regularity of each pixel is obtained according to the discrete degree of the grayscale uniformity in multiple directions in the second local area of ​​each pixel.

5. The method for detecting the density of induced pluripotent stem cells according to claim 4, characterized in that: The grayscale uniformity in each direction of the second local area of ​​each pixel is obtained as follows: The standard deviation of the grayscale differences between all adjacent pixels in each direction within the second local area of ​​each pixel; and the negative correlation mapping result of the standard deviation is used as the grayscale uniformity in each direction within the second local area of ​​each pixel.

6. The method for detecting the density of induced pluripotent stem cells according to claim 1, characterized in that: The specific formula for obtaining the comprehensive density performance value of each pixel is: R i =Q i ×E i +(1-Q i )×W i ; Among them, R i Indicates the comprehensive density performance value of the i-th pixel, Q i represents the local brightness value of the i-th pixel, E i represents the local morphological regularity of the i-th pixel, W i Represents the local boundary blur of the i-th pixel.

7. The method for detecting the density of induced pluripotent stem cells according to claim 1, characterized in that: The clustering of pixels comprises: The comprehensive density performance value of all pixels is used as the input of the clustering algorithm, the absolute value of the difference between the comprehensive density performance values ​​of the pixels is used as the metric between the pixels in the clustering algorithm, and a preset number of clusters are output.

8. The method for detecting the density of induced pluripotent stem cells according to claim 1, characterized in that: The density detection of the enhanced induced pluripotent stem cell image is specifically performed as follows: A neural network is trained based on induced pluripotent stem cell images with known cell density, the induced pluripotent stem cell images to be detected are used as input, and the cell density is obtained using the trained neural network.

9. A computer device for detecting density of induced pluripotent stem cells, characterized in that: include: An induced pluripotent stem cell image acquisition module, used for acquiring induced pluripotent stem cell images; The induced pluripotent stem cell image analysis module is used to preset a first local area and a second local area of ​​each pixel point with each pixel point as the center, wherein the second local area includes the first local area; obtain the local brightness expression value of each pixel point according to the grayscale distribution of each pixel point in the first local area and the second local area; obtain the local boundary fuzziness of each pixel point according to the gradient distribution characteristics of each pixel point in a preset direction in the second local area; obtain the local morphological regularity of each pixel point according to the uniformity characteristics of the grayscale distribution in each direction in the second local area of ​​each pixel point; The induced pluripotent stem cell image density detection module is used to obtain a comprehensive density performance value of each pixel based on the local boundary fuzziness and local morphological regularity of each pixel, combined with the local brightness performance value; cluster the pixels based on the comprehensive density performance value to obtain a preset number of clusters; use histogram equalization to enhance each cluster respectively to obtain an enhanced induced pluripotent stem cell image, and perform density detection on the enhanced induced pluripotent stem cell image.

10. An induced pluripotent stem cell density detection medium, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

Images