Image recognition analysis method and system for heart macrophage inflammation regulation and control

Abstract

The invention discloses an image recognition analysis method and system for heart macrophage inflammation regulation and control, and relates to the technical field of macrophage inflammation regulation and control recognition, and the method comprises the following steps: observing and obtaining a cell image through a fluorescence microscope, and preprocessing the cell image to obtain a preprocessed image; constructing a cell recognition deep learning model, and training the cell recognition deep learning model by extracting fluorescence features of macrophages and gray features of inflammatory cells; carrying out statistics on macrophages and inflammatory cells in the inflammation area, and outputting a regulation and control inspection result; the method is used for solving the problem that the judgment of doctors on the condition of cardiac inflammation is easily affected due to insufficient recognition accuracy of macrophages and inflammatory cells in the existing macrophage inflammation regulation and control recognition technology.

Description

Technical Field

[0001] The present invention relates to the technical field of macrophage inflammation regulation and recognition, and in particular to an image recognition and analysis method and system for cardiac macrophage inflammation regulation. Background Art

[0002] Macrophages are important components of the immune system. They have the function of clearing bacteria, viruses and other pathogens. They also play a key role in regulating inflammatory responses and tissue repair. Macrophage inflammation regulation identification technology refers to the technology used to identify, monitor and regulate the role and response of macrophages in the inflammatory process.

[0003] Since the heart is the most important organ in the human body, timely medical treatment is required when inflammation occurs in the heart. Doctors need to examine the inflammation in the heart and make a medical diagnosis based on the examination results. Inflammation in the heart can usually only be examined through image recognition. In image recognition, accurate identification of inflammatory cells is particularly important. At the same time, macrophages are an important factor in the cure of inflammation, so the identification of macrophages is also extremely important. The existing macrophage inflammation regulation and recognition technology is difficult to accurately identify macrophages and inflammatory cells, and the recognition accuracy is insufficient, resulting in errors in the indicators of macrophages and inflammatory cells in the examination results, which in turn affects the doctor's judgment. The existing macrophage inflammation regulation and recognition technology also has insufficient recognition accuracy for macrophages and inflammatory cells, which easily affects the doctor's judgment on the condition of cardiac inflammation. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. Cell images are observed and acquired through a fluorescence microscope, and the cell images are preprocessed to obtain preprocessed images. A cell recognition deep learning model is constructed at the same time. The cell recognition deep learning model is trained through macrophage sample images and through inflammation sample images. The macrophages and inflammatory areas in the preprocessed images are then divided through the cell recognition deep learning model. Finally, the macrophages and inflammatory cells in the inflammatory area are counted and the regulation inspection results are output to solve the problem that the existing macrophage inflammation regulation and recognition technology still has insufficient recognition accuracy for macrophages and inflammatory cells, which easily affects the doctor's judgment on the condition of cardiac inflammation.

[0005] To achieve the above objectives, in a first aspect, the present application provides an image recognition and analysis method for regulating cardiac macrophage inflammation, comprising the following steps: Observe and acquire cell images through a fluorescence microscope, pre-process the cell images, and obtain pre-processed images; Construct a deep learning model for cell recognition, train it by extracting the fluorescence features of macrophages and the grayscale features of inflammatory cells, divide the macrophages and inflammatory areas in the preprocessed images, and divide the functional states of macrophages; The macrophages and inflammatory cells in the inflammatory area are counted and the control test results are output.

[0006] Further, observing and acquiring cell images through a fluorescence microscope, preprocessing the cell images, and obtaining preprocessed images include the following sub-steps: Fluorescently staining macrophages with CD68 and CD206, wherein CD68 and CD206 are markers of M1 macrophages and M2 macrophages, respectively, wherein the functional state of the M1 macrophages is pro-inflammatory, and the functional state of the M2 macrophages is anti-inflammatory; Observe and obtain cell images through fluorescence microscopy; The contrast of the cell image is increased by a first value, and the brightness of the cell image is increased by a first value, to obtain a preprocessed image.

[0007] Furthermore, a cell recognition deep learning model is constructed, and the cell recognition deep learning model is trained by extracting the fluorescence features of macrophages and the grayscale features of inflammatory cells, and the macrophages and inflammatory areas in the preprocessed image are divided into regions, and the functional states of macrophages are divided, including the following sub-steps: Build a deep learning model for cell recognition and train it using macrophage sample images; The deep learning model for cell recognition was trained using inflammation sample images; Macrophages and inflammatory areas in the preprocessed images are segmented using a cell recognition deep learning model.

[0008] Furthermore, a cell recognition deep learning model is constructed, and training of the cell recognition deep learning model includes the following sub-steps: Acquire a macrophage sample image in which macrophages are fluorescently stained with CD68 and CD206, wherein the macrophage sample image is used to train a cell recognition deep learning model; Marking the stained cells in the macrophage sample image as stained cells, performing contour extraction on the macrophage sample image, extracting independent stained cells in the macrophage sample image, and manually marking the functional states of the stained cells, wherein the functional states include pro-inflammatory macrophages and anti-inflammatory macrophages, namely, M1 macrophages and M2 macrophages, which are named M1 cells and M2 cells, respectively; The first number of M1 cells is extracted and named as the first cluster. The second number of M2 cells is extracted and named as the second cluster. The single M1 cell in the first cluster is named as M1 single cell. The single M2 cell in the second cluster is named as M2 single cell. The M1 single cells are labeled and marked by the symbol P. n Indicates that the M2 single cells are labeled by the symbol Q i Indicates, where n∈Z+ and i∈Z+, n is the serial number of P, i is the serial number of Q, and Z+ represents a positive integer; M1 single cell P n Perform image extraction and extract P n The RGB value of each pixel in is marked as M1 color value, and the P n The M1 color value is expressed as C1(n,m), C1(n,m) represents P n The RGB value of the mth pixel in, m∈Z+; for M2 single cell Q i Perform image extraction and extract Q i The RGB value of each pixel is marked as M2 color value, and the Q i The M2 color value is represented by C2(i,j), and C2(i,j) represents Q i The RGB value of the jth pixel in , j∈Z+, where C1(n,m) and C2(i,j) are just the numbers of the RGB values, which are used to distinguish the RGB values ​​of different pixels in different cells. The format of the RGB value is (R,G,B), where R, G and B represent the values ​​of the red, green and blue color channels respectively; Extract the R value, G value and B value in C1(n,m), and mark them as R1(n,m), G1(n,m) and B1(n,m) respectively; extract the R value, G value and B value in C2(i,j), and mark them as R2(i,j), G2(i,j) and B2(i,j) respectively; R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. The single-channel color values ​​are further analyzed to obtain the standard color value range.

[0009] Furthermore, further analysis of the single channel color value includes the following sub-steps: R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. For any single-channel color value, obtain its maximum color value and minimum color value to form a range interval, marked as a color value interval, and denote the maximum color value and the minimum color value as CLmax and CLmin respectively. Calculate (CLmax-CLmin) / 2+CLmin, retain the calculation result as an integer and mark it as the color value moving coverage number; A new interval is set from the minimum color value to the color value movement coverage number, marked as the color value movement interval, the minimum value and the maximum value of the color value movement interval are named the movement minimum value and the movement maximum value respectively, the single-channel color value currently analyzed is marked as the analysis color value, the number of analysis color values ​​in the color value movement interval is obtained, marked as NF, the movement minimum value and the movement maximum value are increased by one and the range number is obtained again, and the execution is repeated until the movement maximum value is equal to the color value maximum value, the number of analysis color values ​​is obtained, marked as NG, NF / NG is calculated, and the calculation result is marked as the color value proportion, and it is determined whether there is a situation in all the calculated color value proportions that the color value proportion is greater than or equal to the first proportion threshold value, if so, a sufficient range signal is output, if not, an insufficient range signal is output; If a sufficient range signal is output, the color value movement coverage number is reduced by one, the color value proportion is re-analyzed, and whether the color value proportion is greater than or equal to the first proportion threshold is re-judged, until an insufficient range signal is output; if an insufficient range signal is output, the color value movement coverage number is increased by one, the color value proportion is re-analyzed, and whether the color value proportion is greater than or equal to the first proportion threshold is re-judged, until an sufficient range signal is output; After the loop is stopped, the moving interval of the output range sufficient signal is used as the standard interval of the color value for analyzing the color value, and all single-channel color values ​​are analyzed to obtain the R1 standard interval, G1 standard interval, B1 standard interval, R2 standard interval, G2 standard interval and B2 standard interval.

[0010] Furthermore, training the cell recognition deep learning model through inflammation sample images includes the following sub-steps: Acquire inflammation sample images, and annotate inflammatory cells in the inflammation sample images using manual standards; The inflammatory sample image is converted into a grayscale image, and the grayscale value of the pixel in the inflammatory cell is extracted and marked as the inflammatory grayscale value; The inflammatory gray values ​​are sorted and numbered in ascending order, and the symbol D h Indicates, where h∈Z+ and h is the serial number of D, with h as the X-axis, D h Establish a plane rectangular coordinate system for the Y axis and name it as the grayscale trend chart; For any inflammatory cell, the inflammatory gray value is calculated according to D h Enter the grayscale trend graph, name the coordinate points in the grayscale trend graph as grayscale trend coordinate points, connect the grayscale trend coordinate points through a smooth curve in the order of h from small to large, name the obtained curve as a grayscale trend line, analyze one inflammatory cell to obtain one grayscale trend line, analyze the inflammatory cells in all inflammatory sample images, and obtain the third number of grayscale trend lines; The area between any two grayscale trend lines is marked as a trend area, and all trend areas are extracted to form a grayscale trend area.

[0011] Furthermore, segmenting macrophages and inflammatory areas in the preprocessed image by a cell recognition deep learning model includes the following sub-steps: The contour of the preprocessed image is extracted by using the contour extraction technology, and the extracted cells are marked as cells to be analyzed; For any cell to be analyzed, extract the R value, G value and B value of the RGB value of the pixel point in the cell to be analyzed, and mark them as AR, AG and AB respectively. The AR of different pixels forms a range interval, which is marked as AR interval. The AG of different pixels forms a range interval, which is marked as AG interval. The AB of different pixels forms a range interval, which is marked as AB interval. The AR interval, the AG interval, and the AB interval are analyzed by analyzing the color value standard interval, and the first proportion threshold in the analyzed color value standard interval is changed to the second proportion threshold, and the intervals obtained by the analysis are named AR interval to be identified, AG interval to be identified, and AB interval to be identified respectively; Respectively judge whether the AR to-be-identified interval is within the R1 standard interval, whether the AG to-be-identified interval is within the G1 standard interval, and whether the AB to-be-identified interval is within the B1 standard interval. If the judgment results are all yes, then output the M1 macrophage signal, otherwise output the M2 judgment signal; If the M2 judgment signal is output, it is judged whether the AR to-be-identified interval is within the R2 standard interval, whether the AG to-be-identified interval is within the G2 standard interval, and whether the AB to-be-identified interval is within the B2 standard interval. If the judgment results are all yes, the M2 macrophage signal is output, otherwise the inflammatory judgment signal is output; If an inflammatory determination signal is output, it is analyzed whether the cell to be analyzed is an inflammatory cell.

[0012] Furthermore, analyzing whether the cells to be analyzed are inflammatory cells includes the following sub-steps: If an inflammatory judgment signal is output, the cells to be analyzed are analyzed by analyzing the grayscale trend line, and the obtained grayscale trend line is marked as the trend line to be analyzed, and the trend line to be analyzed is placed in the grayscale trend chart to determine whether all the trend lines to be analyzed are in the grayscale trend area. If so, an inflammatory signal is output, otherwise a normal signal is output; If an M1 macrophage signal is output, the cells to be analyzed are marked as M1 macrophages; if an M2 macrophage is output, the cells to be analyzed are marked as M2 macrophages; if an inflammatory signal is output, the cells to be analyzed are marked as inflammatory cells; if a normal signal is output, the cells to be analyzed are marked as normal tissue cells; The area surrounded by the outermost inflammatory cells is the inflammatory area.

[0013] Furthermore, counting macrophages and inflammatory cells in the inflammatory area and outputting the control inspection results includes the following sub-steps: The number of M1 macrophages was counted and labeled as M1 number; The number of M2 macrophages was counted and labeled as M2 number; The number of inflammatory cells was counted and labeled as inflammatory number; The M1 count, M2 count and inflammatory count are output as control examination results and uploaded to the patient's examination report.

[0014] In a second aspect, the present application provides an image recognition and analysis system for regulating cardiac macrophage inflammation, including an image preprocessing module, an image recognition module, and an inspection output module; the image preprocessing module and the inspection output module are respectively data-connected to the image recognition module; The image preprocessing module is used to observe and acquire cell images through a fluorescence microscope, and preprocess the cell images to obtain preprocessed images; The image recognition module is used to build a deep learning model for cell recognition, and trains the deep learning model for cell recognition by extracting the fluorescence characteristics of macrophages and the grayscale characteristics of inflammatory cells, and divides the macrophages and inflammatory areas in the preprocessed image and divides the functional states of the macrophages; The inspection output module is used to count macrophages and inflammatory cells in the inflammatory area and output the control inspection results.

[0015] Beneficial effects of the present invention: The present invention constructs a cell recognition deep learning model, then extracts the fluorescence characteristics of macrophages and the grayscale characteristics of inflammatory cells to train the cell recognition deep learning model, and divides the macrophages and inflammatory areas in the preprocessed image and divides the functional states of the macrophages. The advantage is that the size and shape of macrophages vary greatly between different individuals and cannot be measured by a unified standard. Therefore, it is more accurate to judge by color value characteristics. The image characteristics of macrophages are enhanced by fluorescent labeling, and then they are identified. The color value characteristics of macrophages in the state of fluorescent labeling are analyzed to identify macrophages. At the same time, inflammatory cells can be directly analyzed by their grayscale characteristics due to their similar size and shape, thereby improving the accuracy and effectiveness of macrophage and inflammatory cell identification. The present invention observes and acquires cell images through a fluorescence microscope, preprocesses the cell images to obtain preprocessed images, and then divides the macrophages and inflammatory areas in the preprocessed images through a cell recognition deep learning model. Finally, the macrophages and inflammatory cells in the inflammatory area are counted and the regulation and inspection results are output. The advantage is that the M1 number, M2 number and inflammatory number obtained after identifying macrophages and inflammatory cells through the cell recognition deep learning model are more accurate, which can help doctors more accurately judge the severity and direction of the patient's condition in the examination report, further improving the accuracy and effectiveness of cardiac macrophage inflammation regulation and identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a functional block diagram of the system of the present invention; Figure 2 This is the image of the macrophage sample of the present invention; Figure 3 The inflammation sample image of the present invention; Figure 4 A schematic diagram of a grayscale trend line of the present invention; Figure 5 It is a schematic diagram of multiple grayscale trend lines of the present invention; Figure 6 is a schematic diagram of the trend area of ​​the present invention; Figure 7 It is a schematic diagram of the grayscale trend area of ​​the present invention; Figure 8 It is a schematic diagram of placing the trend line to be analyzed into the grayscale trend graph of the present invention; Fig. 9 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, the present application provides an image recognition and analysis system for regulating cardiac macrophage inflammation, including an image preprocessing module, an image recognition module, and an inspection output module; the image preprocessing module and the inspection output module are respectively connected to the image recognition module data; The image preprocessing module is used to observe and obtain cell images through a fluorescence microscope, preprocess the cell images, and obtain preprocessed images; The image preprocessing module is configured with image preprocessing strategies, which include: Macrophages were fluorescently stained with CD68 and CD206. CD68 and CD206 are markers of M1 macrophages and M2 macrophages, respectively. The functional state of M1 macrophages is pro-inflammatory, while the functional state of M2 macrophages is anti-inflammatory. Observe and obtain cell images through fluorescence microscopy; The contrast of the cell image is increased by a first value, and the brightness of the cell image is increased by a first value to obtain a preprocessed image; In practical applications, fluorescent staining uses existing fluorescent staining technology. After fluorescent staining, M1 macrophages and M2 macrophages appear visually blue and orange, respectively. At this time, M1 macrophages and M2 macrophages can be effectively identified by analyzing their RGB values ​​by computer. The first value is set to 20. The first value is used to increase the contrast and brightness of the cell image. In most cases, it is found that when the first value is set to 20, the macrophages and inflammatory areas can be more prominent.

[0019] The image recognition module is used to build a deep learning model for cell recognition. The deep learning model for cell recognition is trained by extracting the fluorescence features of macrophages and the grayscale features of inflammatory cells, and the macrophages and inflammatory areas in the preprocessed image are divided into regions, and the functional states of macrophages are divided. The image recognition module includes a macrophage recognition training unit, an inflammatory cell recognition training unit, and a cell recognition unit. The macrophage recognition training unit is used to build a deep learning model for cell recognition, and train the deep learning model for cell recognition through macrophage sample images; The macrophage recognition training unit is configured with a macrophage recognition training strategy, which includes: See also Figure 2 As shown, a macrophage sample image in which macrophages are fluorescently stained with CD68 and CD206 is obtained, and the macrophage sample image is used to train a cell recognition deep learning model; Mark the stained cells in the macrophage sample image as stained cells, perform contour extraction on the macrophage sample image, extract independent stained cells in the macrophage sample image, and manually mark the functional states of the stained cells, which include pro-inflammatory macrophages and anti-inflammatory macrophages, namely, M1 macrophages and M2 macrophages, which are named M1 cells and M2 cells respectively; The first number of M1 cells is extracted and named as the first cluster. The second number of M2 cells is extracted and named as the second cluster. The single M1 cell in the first cluster is named as M1 single cell. The single M2 cell in the second cluster is named as M2 single cell. The M1 single cells are labeled and marked by the symbol P. n Indicates that the M2 single cells are labeled by the symbol Q i Indicates, where n∈Z+ and i∈Z+, n is the serial number of P, i is the serial number of Q, and Z+ represents a positive integer; In practical applications, Figure 2 The image shown is one of the many macrophage sample images. In this embodiment, it is presented in the form of a grayscale image, but in actual application, it is presented in the form of a color image. Figure 2 Some M1 cells and M2 cells are marked in the image, and the existing contour extraction technology is used to extract the stained cells. There are blocked stained cells in the extracted image, that is, the extracted stained cells are incomplete, but even if they are incomplete, the value range of their RGB values ​​is the same as that of complete stained cells, and they can also be included in the reference range; all macrophage sample images are analyzed, and 17865 M1 cells are obtained, that is, the first number is 17865; all macrophage sample images are analyzed, and 14472 M2 cells are obtained, that is, the second number is 14472, that is, 1≤n≤17865, 1≤i≤14472; M1 single cell P n Perform image extraction and extract P n The RGB value of each pixel in is marked as M1 color value, P n The M1 color value is expressed as C1(n,m), C1(n,m) represents P n The RGB value of the mth pixel in, m∈Z+; for M2 single cell Q i Perform image extraction and extract Q i The RGB value of each pixel in is marked as M2 color value, Qi The M2 color value is represented by C2(i,j), and C2(i,j) represents Q i The RGB value of the jth pixel in , j∈Z+, where C1(n,m) and C2(i,j) are just the numbers of the RGB values, which are used to distinguish the RGB values ​​of different pixels in different cells. The format of the RGB value is (R,G,B), where R, G and B represent the values ​​of the red, green and blue color channels respectively; Extract the R value, G value and B value in C1(n,m), and mark them as R1(n,m), G1(n,m) and B1(n,m) respectively; extract the R value, G value and B value in C2(i,j), and mark them as R2(i,j), G2(i,j) and B2(i,j) respectively; In practical applications, for any M1 single cell P n For example, when n=1, P1 is extracted, and there are 384 pixels in P1, that is, for P1, 1≤m≤384. For example, when m=186, C1(1,186) is extracted as (15,143,231), and when m=242 is extracted, C1(1,242) is (10,56,160). C1(1,186) and C1(1,242) are intended to show the data format of C1(n,m). The format of C2(i,j) is the same as that of C1(n,m). , so it is not specifically shown in this embodiment; taking C1(1,186) as an example, extracting the R value, G value and B value of C1(1,186), respectively obtaining R1(1,186)=15, G1(1,186)=143, B1(1,186)=231, the origin of R2(i,j), G2(i,j) and B2(i,j) is the same as R1(n,m), G1(n,m) and B1(n,m), so it is not specifically shown in this embodiment; R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. The single-channel color values ​​are further analyzed to obtain the standard range of color values. R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. For any single-channel color value, obtain its maximum color value and minimum color value to form a range interval, marked as a color value interval, and denote the maximum color value and the minimum color value as CLmax and CLmin respectively. Calculate (CLmax-CLmin) / 2+CLmin, retain the calculation result as an integer and mark it as the color value moving coverage number; In practical applications, take R1(n,m) as an example. Due to the different values ​​of n and m, there are several color values ​​in R1(n,m). The maximum color value and the minimum color value in R1(n,m) are 19 and 7 respectively, and the color value interval of R1(n,m) is [7,19]. In this embodiment, it is marked as R1 interval. The calculated R1 movement covers (19-7) / 2+7=13 digits. A new interval is set from the minimum color value to the color value movement coverage number, marked as the color value movement interval, the minimum value and the maximum value of the color value movement interval are named the movement minimum value and the movement maximum value respectively, the single-channel color value currently analyzed is marked as the analysis color value, the number of analysis color values ​​in the color value movement interval is obtained, marked as NF, the movement minimum value and the movement maximum value are increased by one and the range number is obtained again, and the execution is repeated until the movement maximum value is equal to the color value maximum value, the number of analysis color values ​​is obtained, marked as NG, NF / NG is calculated, and the calculation result is marked as the color value proportion, and it is determined whether there is a situation in all the calculated color value proportions that the color value proportion is greater than or equal to the first proportion threshold value, if so, a sufficient range signal is output, if not, an insufficient range signal is output; In practical applications, the color value moving interval of R1 is named R1 moving interval, and the R1 moving interval is set to [7,13]. The minimum moving value and the maximum moving value are 7 and 13 respectively. The number of R1(n,m) between [7,13] in all R1(n,m) is NF 2634095. For R1(n,m), NG is the number of R1(n,m), and the NG is 6860140. The color value ratio is 0.38 through calculation, that is, 38% of R1(n,m) is in [7,13]. The minimum moving value and the maximum moving value are increased at the same time. 1, the R1 moving interval is [8,14], and the color value proportion is analyzed again until the maximum value of the moving value is equal to 19. At this time, a total of 7 color value proportions are obtained, and the first proportion threshold is set to 0.8. The first proportion threshold is set to find the range where the single-channel color value is concentrated. Because the stained cells are stained, not all parts can show the most ideal color expression. At this time, it is necessary to find the value range of most of the color value values ​​of all colors to improve the accuracy of judgment; among the 7 color value proportions, there is a color value proportion greater than 0.8, so the output range is sufficient signal; If a sufficient range signal is output, the color value movement coverage number is reduced by one, the color value proportion is re-analyzed, and whether the color value proportion is greater than or equal to the first proportion threshold is re-judged, until an insufficient range signal is output; if an insufficient range signal is output, the color value movement coverage number is increased by one, the color value proportion is re-analyzed, and whether the color value proportion is greater than or equal to the first proportion threshold is re-judged, until an sufficient range signal is output; After the loop is stopped, the moving interval of the output range sufficient signal is used as the color value standard interval for analyzing the color value, and all single-channel color values ​​are analyzed to obtain the R1 standard interval, G1 standard interval, B1 standard interval, R2 standard interval, G2 standard interval and B2 standard interval; In practical applications, if a sufficient range signal is output, the R1 movement coverage number is reduced by one, and the R1 movement coverage number is 12, and the new R1 movement interval is [7,12]. The color value proportion is analyzed again. After the analysis is completed, a sufficient range signal is output. The R1 movement coverage number is reduced by one again and the color value proportion is re-analyzed. At this time, an insufficient range signal is output and the analysis is stopped. The previous analysis outputted a sufficient range signal, which means that the range setting of the R1 movement interval is reasonable, which can make 80% of R1(n,m) within the R1 movement interval. However, this only proves that the R1 movement interval can cover 80% of R1(n,m), and the density of R1(n,m) within the R1 movement interval is not the largest. Ideally, the R1 movement interval with the largest density is to be found. Therefore, it is necessary to reduce the R1 movement coverage number by one and re-analyze. If a sufficient range signal is output next time, it means that there is still room for the R1 movement interval to be reduced, which can make The density of R1(n,m) is greater. If the next output is an insufficient range signal, it means that the R1 moving interval cannot be reduced again. Under the condition that it can cover 80% or more of R1(n,m), its density reaches the maximum. Therefore, the last R1 moving interval with sufficient output range signal is taken as the standard, that is, when the R1 moving coverage number is 11, and when the R1 moving coverage number is equal to 11, a total of 9 color value proportions are analyzed, and the R1 moving interval with the largest color value proportion is taken as the R1 standard interval. In this embodiment, among the 9 color value proportions, the color value proportion is the largest when the R1 moving interval is [11,15], so the R1 standard interval is [11,15]; the analysis process of the G1 standard interval, the B1 standard interval, the R2 standard interval, the G2 standard interval and the B2 standard interval are the same as the analysis process of the R1 standard interval, so they are not specifically displayed in this embodiment; The inflammatory cell recognition training unit is used to train the cell recognition deep learning model through inflammation sample images; The inflammatory cell recognition training unit is configured with an inflammatory cell recognition training strategy, which includes: See also Figure 3 As shown, an inflammation sample image is obtained, and inflammatory cells in the inflammation sample image are annotated using manual standards; The inflammatory sample image is converted into a grayscale image, and the grayscale value of the pixel in the inflammatory cell is extracted and marked as the inflammatory grayscale value; See also Figures 4 to 5As shown, the inflammatory gray values ​​are sorted and numbered in ascending order, and the symbol D h Indicates, where h∈Z+ and h is the serial number of D, with h as the X-axis, D h Establish a plane rectangular coordinate system for the Y axis and name it as the grayscale trend chart; For any inflammatory cell, the inflammatory gray value is calculated according to D h Enter the grayscale trend graph, name the coordinate points in the grayscale trend graph as grayscale trend coordinate points, connect the grayscale trend coordinate points through a smooth curve in the order of h from small to large, name the obtained curve as a grayscale trend line, analyze one inflammatory cell to obtain one grayscale trend line, analyze the inflammatory cells in all inflammatory sample images, and obtain the third number of grayscale trend lines; See also Figure 6 to Figure 7 As shown, the area between any two grayscale trend lines is marked as a trend area, and all trend areas are extracted to form a grayscale trend area; In practical applications, Figure 3 This is one of many inflammatory sample images. The irregular circle is the manually marked mark. When extracting inflammatory cells, the image inside the irregular circle can be contour extracted. For any inflammatory cell, a total of 727 pixels are extracted, that is, a total of 727 inflammatory gray values. By sorting and numbering, D1 to D 727 , construct the grayscale trend diagram as follows Figure 4 As shown, Figure 4 The grayscale trend line of an inflammatory cell is shown in Figure 5 The figure shows multiple grayscale trend lines obtained after the analysis of multiple inflammatory cells. Since the size, shape and color of inflammatory cells are similar, the number of pixels contained in different inflammatory cells is also small. By analyzing the trend of their grayscale values, inflammatory cells can be quickly and accurately identified. For any two grayscale trend lines, connect the beginning of their curves and the end of their curves to obtain a closed area, which is the trend area. The area composed of all trend areas is the grayscale trend area. Figure 6 The trend area between the two grayscale trend lines is shown in Figure 5 For example, Figure 5 The grayscale trend area in Figure 7 As shown in the figure, the grayscale trend area reveals the changing law and trend of the grayscale value of inflammatory cells. If the grayscale trend line obtained by analysis is within the grayscale trend area, it means that the changing law and trend of the grayscale trend line conform to the characteristics of inflammatory cells, and the cells can be identified as inflammatory cells. The cell recognition unit is used to segment macrophages and inflammatory areas in the preprocessed image through a cell recognition deep learning model; The cell recognition unit is configured with a cell recognition strategy, which includes: The contour of the preprocessed image is extracted by using the contour extraction technology, and the extracted cells are marked as cells to be analyzed; For any cell to be analyzed, extract the R value, G value and B value of the RGB value of the pixel point in the cell to be analyzed, and mark them as AR, AG and AB respectively. The AR of different pixels forms a range interval, which is marked as AR interval. The AG of different pixels forms a range interval, which is marked as AG interval. The AB of different pixels forms a range interval, which is marked as AB interval. In practical applications, taking a cell to be analyzed as an example, 426 pixels are obtained in the cell to be analyzed, and each pixel is extracted to obtain 426 ARs, 426 AGs, and 426 ABs. The minimum and maximum values ​​of the 426 ARs form a range interval, which is the AR interval. The definitions of the AG interval and the AB interval are the same as those of the AR interval. The AR interval, the AG interval, and the AB interval are analyzed by analyzing the color value standard interval, and the first proportion threshold in the analyzed color value standard interval is changed to the second proportion threshold, and the intervals obtained by the analysis are named AR interval to be identified, AG interval to be identified, and AB interval to be identified respectively; In practical applications, the second proportion threshold is set to 0.5. The second proportion threshold is set to prevent individual differences and staining deviations from causing errors in the analysis results. The significance of setting it to 0.5 is to ensure that the color values ​​of more than 50% of the areas of the cells to be analyzed are within the standard range, so as to determine whether the cells to be analyzed belong to M1 macrophages or M2 macrophages; the AR interval, AG interval, and AB interval are analyzed by analyzing the color value standard interval, and the color value proportion is calculated and compared with the second proportion threshold to output the judgment result. At this time, the standard intervals analyzed by analyzing the color value standard interval are AR to be identified interval, AG to be identified interval, and AB to be identified interval. Respectively judge whether the AR to-be-identified interval is within the R1 standard interval, whether the AG to-be-identified interval is within the G1 standard interval, and whether the AB to-be-identified interval is within the B1 standard interval. If the judgment results are all yes, then output the M1 macrophage signal, otherwise output the M2 judgment signal; If the M2 judgment signal is output, it is judged whether the AR to-be-identified interval is within the R2 standard interval, whether the AG to-be-identified interval is within the G2 standard interval, and whether the AB to-be-identified interval is within the B2 standard interval. If the judgment results are all yes, the M2 macrophage signal is output, otherwise the inflammatory judgment signal is output; In practical applications, the R1 standard interval, the G1 standard interval, and the B1 standard interval belong to M1 macrophages. It is judged whether the AR interval to be identified is within the R1 standard interval, whether the AG interval to be identified is within the G1 standard interval, and whether the AB interval to be identified is within the B1 standard interval. If the judgment results are all yes, it means that the color values ​​of more than 50% of the pixels in the cells to be analyzed meet the fluorescence characteristics of M1 macrophages, indicating that the cells to be analyzed belong to M1 macrophages. The analysis process in the M2 judgment signal is the same as the analysis process for judging M1 macrophages. When it meets the fluorescence characteristics of M2 macrophages, the cells to be analyzed are M2 macrophages. If the cells to be analyzed are neither M1 macrophages nor M2 macrophages, it is necessary to judge whether the cells to be analyzed are inflammatory cells. If an inflammatory judgment signal is output, then analyzing whether the cell to be analyzed is an inflammatory cell; See also Figure 8 As shown, if an inflammatory judgment signal is output, the cells to be analyzed are analyzed by analyzing the grayscale trend line, and the obtained grayscale trend line is marked as the trend line to be analyzed, and the trend line to be analyzed is placed in the grayscale trend chart to determine whether all the trend lines to be analyzed are in the grayscale trend area. If so, an inflammatory signal is output, otherwise a normal signal is output; If an M1 macrophage signal is output, the cells to be analyzed are marked as M1 macrophages; if an M2 macrophage is output, the cells to be analyzed are marked as M2 macrophages; if an inflammatory signal is output, the cells to be analyzed are marked as inflammatory cells; if a normal signal is output, the cells to be analyzed are marked as normal tissue cells; The area surrounded by the outermost inflammatory cells is the inflammatory area; In practical applications, the grayscale trend area reveals the changing law and trend of the grayscale value of inflammatory cells. If the grayscale trend line obtained by analysis is within the grayscale trend area, it means that the changing law and trend of the grayscale trend line conform to the characteristics of inflammatory cells, and it can be determined as an inflammatory cell. Figure 8 As shown, Figure 8 This is a schematic diagram of placing the trend line to be analyzed into the grayscale trend chart, in which the gray area is the grayscale trend area. After the judgment of all cells to be analyzed is completed, a number of inflammatory cells are obtained. Inflammatory cells are usually regional, that is, they gather in a certain area in the human tissue. A closed range can be obtained by connecting the outermost inflammatory cells, which is the inflammation range. It can be marked in the cell image to help doctors better judge the condition.

[0020] The inspection output module is used to count macrophages and inflammatory cells in the inflammatory area and output the control inspection results; The inspection output module is configured with inspection output strategies, which include: The number of M1 macrophages was counted and labeled as M1 number; The number of M2 macrophages was counted and labeled as M2 number; The number of inflammatory cells was counted and labeled as inflammatory number; Output the M1 count, M2 count, and inflammatory count as the results of the regulatory examination and upload them to the patient's examination report; In practical applications, the role of the cell recognition deep learning model is to identify the number of macrophages and inflammatory cells in different functional states in inflamed tissues in the heart through image recognition, so as to provide a basis for doctors to evaluate the patient's condition and treatment plan. Macrophages are the most important factor in inflammatory response. Existing detection technologies for cardiac inflammation make it difficult to accurately identify the number of macrophages and inflammatory cells. Therefore, there are certain deviations in the choice of condition and treatment methods, which will lead to a longer treatment cycle.

[0021] Example 2, please refer to Fig. 9 As shown, the present application provides an image recognition and analysis method for regulating cardiac macrophage inflammation, comprising the following steps: Step S1, observing and acquiring cell images through a fluorescence microscope, preprocessing the cell images to obtain preprocessed images; Step S1 includes the following sub-steps: Step S101, fluorescently staining macrophages using CD68 and CD206, CD68 and CD206 are markers of M1 macrophages and M2 macrophages, respectively, the functional state of M1 macrophages is pro-inflammatory, and the functional state of M2 macrophages is anti-inflammatory; Step S102, observing and acquiring cell images through a fluorescence microscope; Step S103, increasing the contrast of the cell image by a first value, and increasing the brightness of the cell image by a first value, to obtain a preprocessed image; Step S2, constructing a deep learning model for cell recognition, training the deep learning model for cell recognition by extracting the fluorescence features of macrophages and the grayscale features of inflammatory cells, and dividing the macrophages and inflammatory areas in the preprocessed image and dividing the functional states of the macrophages; Step S2 includes the following sub-steps: Step S201, constructing a cell recognition deep learning model, and training the cell recognition deep learning model through macrophage sample images; Step S201 includes the following sub-steps: Step S201.1, obtaining a macrophage sample image in which macrophages are fluorescently stained with CD68 and CD206, wherein the macrophage sample image is used to train a cell recognition deep learning model; Step S201.2, marking the stained cells in the macrophage sample image as stained cells, performing contour extraction on the macrophage sample image, extracting independent stained cells in the macrophage sample image, and manually marking the functional states of the stained cells, which include pro-inflammatory macrophages and anti-inflammatory macrophages, namely, M1 macrophages and M2 macrophages, which are named M1 cells and M2 cells respectively; Step S201.3, extract a cluster consisting of a first number of M1 cells, named the first cluster, extract a cluster consisting of a second number of M2 cells, named the second cluster, name the single M1 cell in the first cluster as M1 single cell, name the single M2 cell in the second cluster as M2 single cell, label the M1 single cells, and use the symbol P n Indicates that the M2 single cells are labeled by the symbol Q i Indicates, where n∈Z+ and i∈Z+, n is the serial number of P, i is the serial number of Q, and Z+ represents a positive integer; Step S201.4, for M1 single cell P n Perform image extraction and extract P n The RGB value of each pixel in is marked as M1 color value, P n The M1 color value is expressed as C1(n,m), C1(n,m) represents P n The RGB value of the mth pixel in, m∈Z+; for M2 single cell Q i Perform image extraction and extract Q i The RGB value of each pixel in is marked as M2 color value, Q i The M2 color value is represented by C2(i,j), and C2(i,j) represents Q i The RGB value of the jth pixel in , j∈Z+, where C1(n,m) and C2(i,j) are just the numbers of the RGB values, which are used to distinguish the RGB values ​​of different pixels in different cells. The format of the RGB value is (R,G,B), where R, G and B represent the values ​​of the red, green and blue color channels respectively; Step S201.5, extract the R value, G value and B value in C1(n,m), and mark them as R1(n,m), G1(n,m) and B1(n,m) respectively; extract the R value, G value and B value in C2(i,j), and mark them as R2(i,j), G2(i,j) and B2(i,j) respectively; Step S201.6, R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values, and the single-channel color values ​​are further analyzed to obtain a color value standard range; Step S201.6 includes the following sub-steps: Step S201.6.a, R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. For any single-channel color value, obtain its maximum color value and minimum color value to form a range interval, marked as a color value interval, and denote the maximum color value and the minimum color value as CLmax and CLmin respectively. Calculate (CLmax-CLmin) / 2+CLmin, retain the calculation result as an integer and mark it as the color value moving coverage number; Step S201.6.b, set a new interval from the minimum color value to the color value movement coverage number, marked as the color value movement interval, name the minimum value and the maximum value of the color value movement interval as the moving minimum value and the moving maximum value respectively, mark the single-channel color value currently analyzed as the analysis color value, obtain the number of analysis color values ​​in the color value movement interval, marked as NF, increase the moving minimum value and the moving maximum value by one and obtain the range number again, repeat the process until the moving maximum value is equal to the color value maximum value, obtain the number of analysis color values, marked as NG, calculate NF / NG, mark the calculation result as the color value proportion, determine whether there is a situation in all the calculated color value proportions where the color value proportion is greater than or equal to the first proportion threshold, if so, output a sufficient range signal, if not, output an insufficient range signal; Step S201.6.c, if the output range is sufficient, the color value movement coverage number is reduced by one, the color value proportion is re-analyzed and it is re-determined whether the color value proportion is greater than or equal to the first proportion threshold value, until the output range is insufficient. If the output range is insufficient, the color value movement coverage number is increased by one, the color value proportion is re-analyzed and it is re-determined whether the color value proportion is greater than or equal to the first proportion threshold value, until the output range is sufficient. Step S201.6.d, after the loop stops, the moving interval of the output range sufficient signal is used as the color value standard interval for analyzing the color value, and all single-channel color values ​​are analyzed to obtain the R1 standard interval, G1 standard interval, B1 standard interval, R2 standard interval, G2 standard interval and B2 standard interval; Step S202, training a cell recognition deep learning model using inflammation sample images; Step S202 includes the following sub-steps: Step S202.1, obtaining an inflammation sample image, and labeling inflammatory cells in the inflammation sample image using manual standards; Step S202.2, converting the inflammation sample image into a grayscale image, extracting the grayscale values ​​of the pixels in the inflammatory cells, and marking them as inflammatory grayscale values; Step S202.3, sort and number the inflammatory gray values ​​in ascending order, using the symbol D h Indicates, where h∈Z+ and h is the serial number of D, with h as the X-axis, D h Establish a plane rectangular coordinate system for the Y axis and name it as the grayscale trend chart; Step S202.4: for any inflammatory cell, the inflammatory gray value is calculated according to D h Enter the grayscale trend graph, name the coordinate points in the grayscale trend graph as grayscale trend coordinate points, connect the grayscale trend coordinate points through a smooth curve in the order of h from small to large, name the obtained curve as a grayscale trend line, analyze one inflammatory cell to obtain one grayscale trend line, analyze the inflammatory cells in all inflammatory sample images, and obtain the third number of grayscale trend lines; Step S202.5, marking the area between any two grayscale trend lines as a trend area, and extracting all trend areas to form a grayscale trend area; Step S203, dividing the macrophages and inflammation areas in the preprocessed image by using a cell recognition deep learning model; Step S203 includes the following sub-steps: Step S203.1, performing contour extraction on the preprocessed image using a contour extraction technique, and marking the extracted cells as cells to be analyzed; Step S203.2, for any cell to be analyzed, extract the R value, G value and B value of the RGB value of the pixel point in the cell to be analyzed, and mark them as AR, AG and AB respectively, and the AR of different pixels forms a range interval, which is marked as AR interval, the AG of different pixels forms a range interval, which is marked as AG interval, and the AB of different pixels forms a range interval, which is marked as AB interval; Step S203.3, analyzing the AR interval, the AG interval, and the AB interval by analyzing the color value standard interval, and changing the first proportion threshold in the analyzed color value standard interval to the second proportion threshold, and naming the analyzed intervals as the AR interval to be identified, the AG interval to be identified, and the AB interval to be identified, respectively; Step S203.4, respectively judging whether the AR to-be-identified interval is within the R1 standard interval, whether the AG to-be-identified interval is within the G1 standard interval, and whether the AB to-be-identified interval is within the B1 standard interval. If the judgment results are all yes, then outputting an M1 macrophage signal, otherwise outputting an M2 judgment signal; Step S203.5, if the M2 determination signal is output, then determine whether the AR interval to be identified is within the R2 standard interval, whether the AG interval to be identified is within the G2 standard interval, and whether the AB interval to be identified is within the B2 standard interval. If the determination results are all yes, then output the M2 macrophage signal, otherwise output the inflammatory determination signal; Step S203.6, if an inflammatory determination signal is output, analyzing whether the cell to be analyzed is an inflammatory cell; Step S203.6 includes the following sub-steps: Step S203.6.a, if an inflammatory judgment signal is output, the cells to be analyzed are analyzed by analyzing the grayscale trend line, the obtained grayscale trend line is marked as the trend line to be analyzed, the trend line to be analyzed is placed in the grayscale trend chart, and it is determined whether all the trend lines to be analyzed are in the grayscale trend area. If so, an inflammatory signal is output, otherwise a normal signal is output; Step S203.6.b, if an M1 macrophage signal is output, the cells to be analyzed are marked as M1 macrophages; if an M2 macrophage signal is output, the cells to be analyzed are marked as M2 macrophages; if an inflammatory signal is output, the cells to be analyzed are marked as inflammatory cells; if a normal signal is output, the cells to be analyzed are marked as normal tissue cells; Step S203.6.c, the area surrounded by the outermost inflammatory cells is the inflammation area; Step S3, counting macrophages and inflammatory cells in the inflammatory area, and outputting the control inspection results; Step S3 includes the following sub-steps: Step S301, counting the number of M1 macrophages, marked as M1 number; Step S302, counting the number of M2 macrophages, marked as M2 number; Step S303, counting the number of inflammatory cells and marking them as inflammatory number; Step S304, outputting the M1 quantity, M2 quantity and inflammatory quantity as the control examination results and uploading them to the patient's examination report.

[0022] Embodiment 3, the present application provides an electronic device, which may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the image recognition and analysis method for regulating cardiac macrophage inflammation are executed to achieve the following functions: observe and obtain cell images through a fluorescence microscope, pre-process the cell images, and obtain pre-processed images; construct a cell recognition deep learning model, and train the cell recognition deep learning model by extracting the fluorescence characteristics of macrophages and the grayscale characteristics of inflammatory cells; count the macrophages and inflammatory cells in the inflammatory area, and output the regulation inspection results.

[0023] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0024] Example 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the image recognition and analysis method for regulating cardiac macrophage inflammation are executed to achieve the following functions: observe and acquire cell images through a fluorescence microscope, preprocess the cell images, and obtain preprocessed images; construct a cell recognition deep learning model, and train the cell recognition deep learning model by extracting the fluorescence characteristics of macrophages and the grayscale characteristics of inflammatory cells; count the macrophages and inflammatory cells in the inflammatory area, and output the regulation inspection results.

[0025] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0026] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0027] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. 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. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An image recognition and analysis method for regulating cardiac macrophage inflammation, characterized in that: The steps include: Observe and acquire cell images through a fluorescence microscope, pre-process the cell images, and obtain pre-processed images; Construct a deep learning model for cell recognition, train it by extracting the fluorescence features of macrophages and the grayscale features of inflammatory cells, divide the macrophages and inflammatory areas in the preprocessed images, and divide the functional states of macrophages; The macrophages and inflammatory cells in the inflammatory area are counted and the control test results are output.

2. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 1, characterized in that: Observe and obtain cell images through a fluorescence microscope, preprocess the cell images, and obtain preprocessed images, including the following sub-steps: Fluorescently staining macrophages with CD68 and CD206, wherein CD68 and CD206 are markers of M1 macrophages and M2 macrophages, respectively, wherein the functional state of the M1 macrophages is pro-inflammatory, and the functional state of the M2 macrophages is anti-inflammatory; Observe and obtain cell images through fluorescence microscopy; The contrast of the cell image is increased by a first value, and the brightness of the cell image is increased by a first value, to obtain a preprocessed image.

3. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 2, characterized in that: Constructing a deep learning model for cell recognition, training the deep learning model by extracting the fluorescence features of macrophages and the grayscale features of inflammatory cells, and dividing the macrophages and inflammatory areas in the preprocessed image and dividing the functional states of macrophages includes the following sub-steps: Build a deep learning model for cell recognition and train it using macrophage sample images; The deep learning model for cell recognition was trained using inflammation sample images; Macrophages and inflammatory areas in the preprocessed images are segmented using a cell recognition deep learning model.

4. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 3, characterized in that: Constructing a deep learning model for cell recognition. Training the deep learning model for cell recognition includes the following sub-steps: Acquire a macrophage sample image in which macrophages are fluorescently stained with CD68 and CD206, wherein the macrophage sample image is used to train a cell recognition deep learning model; Marking the stained cells in the macrophage sample image as stained cells, performing contour extraction on the macrophage sample image, extracting independent stained cells in the macrophage sample image, and manually marking the functional states of the stained cells, wherein the functional states include pro-inflammatory macrophages and anti-inflammatory macrophages, namely, M1 macrophages and M2 macrophages, which are named M1 cells and M2 cells, respectively; The first number of M1 cells is extracted and named as the first cluster. The second number of M2 cells is extracted and named as the second cluster. The single M1 cell in the first cluster is named as M1 single cell. The single M2 cell in the second cluster is named as M2 single cell. The M1 single cells are labeled and marked by the symbol P. n Indicates that the M2 single cells are labeled by the symbol Q i Indicates, where n∈Z+ and i∈Z+, n is the serial number of P, i is the serial number of Q, and Z+ represents a positive integer; M1 single cell P n Perform image extraction and extract P n The RGB value of each pixel in is marked as M1 color value, and the P n The M1 color value is expressed as C1(n,m), C1(n,m) represents P n The RGB value of the mth pixel in, m∈Z+; for M2 single cell Q i Perform image extraction and extract Q i The RGB value of each pixel is marked as M2 color value, and the Q i The M2 color value is represented by C2(i,j), and C2(i,j) represents Q i The RGB value of the jth pixel in , j∈Z+, where C1(n,m) and C2(i,j) are just the numbers of the RGB values, which are used to distinguish the RGB values ​​of different pixels in different cells. The format of the RGB value is (R,G,B), where R, G and B represent the values ​​of the red, green and blue color channels respectively; Extract the R value, G value and B value in C1(n,m), and mark them as R1(n,m), G1(n,m) and B1(n,m) respectively; extract the R value, G value and B value in C2(i,j), and mark them as R2(i,j), G2(i,j) and B2(i,j) respectively; R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. The single-channel color values ​​are further analyzed to obtain the standard color value range.

5. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 4, characterized in that: Further analysis of single channel color values ​​includes the following sub-steps: R1(n,m), G1(n,m), B1(n,m), R2(i,j), G2(i,j) and B2(i,j) are collectively referred to as single-channel color values. For any single-channel color value, obtain its maximum color value and minimum color value to form a range interval, marked as a color value interval, and denote the maximum color value and the minimum color value as CLmax and CLmin respectively. Calculate (CLmax-CLmin) / 2+CLmin, retain the calculation result as an integer and mark it as the color value moving coverage number; A new interval is set from the minimum color value to the color value movement coverage number, marked as the color value movement interval, the minimum value and the maximum value of the color value movement interval are named the movement minimum value and the movement maximum value respectively, the single-channel color value currently analyzed is marked as the analysis color value, the number of analysis color values ​​in the color value movement interval is obtained, marked as NF, the movement minimum value and the movement maximum value are increased by one and the range number is obtained again, and the execution is repeated until the movement maximum value is equal to the color value maximum value, the number of analysis color values ​​is obtained, marked as NG, NF / NG is calculated, and the calculation result is marked as the color value proportion, and it is determined whether there is a situation in all the calculated color value proportions that the color value proportion is greater than or equal to the first proportion threshold value, if so, a sufficient range signal is output, if not, an insufficient range signal is output; If a sufficient range signal is output, the color value movement coverage number is reduced by one, the color value proportion is re-analyzed, and whether the color value proportion is greater than or equal to the first proportion threshold is re-judged, until an insufficient range signal is output; if an insufficient range signal is output, the color value movement coverage number is increased by one, the color value proportion is re-analyzed, and whether the color value proportion is greater than or equal to the first proportion threshold is re-judged, until an sufficient range signal is output; After the loop is stopped, the moving interval of the output range sufficient signal is used as the standard interval of the color value for analyzing the color value, and all single-channel color values ​​are analyzed to obtain the R1 standard interval, G1 standard interval, B1 standard interval, R2 standard interval, G2 standard interval and B2 standard interval.

6. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 5, characterized in that: Training the deep learning model for cell recognition using inflammation sample images includes the following sub-steps: Acquire inflammation sample images, and annotate inflammatory cells in the inflammation sample images using manual standards; The inflammatory sample image is converted into a grayscale image, and the grayscale value of the pixel in the inflammatory cell is extracted and marked as the inflammatory grayscale value; The inflammatory gray values ​​are sorted and numbered in ascending order, and the symbol D h Indicates, where h∈Z+ and h is the serial number of D, with h as the X-axis, D h Establish a plane rectangular coordinate system for the Y axis and name it as the grayscale trend chart; For any inflammatory cell, the inflammatory gray value is calculated according to D h Enter the grayscale trend graph, name the coordinate points in the grayscale trend graph as grayscale trend coordinate points, connect the grayscale trend coordinate points through a smooth curve in the order of h from small to large, name the obtained curve as a grayscale trend line, analyze one inflammatory cell to obtain one grayscale trend line, analyze the inflammatory cells in all inflammatory sample images, and obtain the third number of grayscale trend lines; The area between any two grayscale trend lines is marked as a trend area, and all trend areas are extracted to form a grayscale trend area.

7. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 6, characterized in that: The segmentation of macrophages and inflammatory areas in the preprocessed image using the cell recognition deep learning model includes the following sub-steps: The contour of the preprocessed image is extracted by using the contour extraction technology, and the extracted cells are marked as cells to be analyzed; For any cell to be analyzed, extract the R value, G value and B value of the RGB value of the pixel point in the cell to be analyzed, and mark them as AR, AG and AB respectively. The AR of different pixels forms a range interval, which is marked as AR interval. The AG of different pixels forms a range interval, which is marked as AG interval. The AB of different pixels forms a range interval, which is marked as AB interval. The AR interval, the AG interval, and the AB interval are analyzed by analyzing the color value standard interval, and the first proportion threshold in the analyzed color value standard interval is changed to the second proportion threshold, and the intervals obtained by the analysis are named AR interval to be identified, AG interval to be identified, and AB interval to be identified respectively; Respectively judge whether the AR to-be-identified interval is within the R1 standard interval, whether the AG to-be-identified interval is within the G1 standard interval, and whether the AB to-be-identified interval is within the B1 standard interval. If the judgment results are all yes, then output the M1 macrophage signal, otherwise output the M2 judgment signal; If the M2 judgment signal is output, it is judged whether the AR to-be-identified interval is within the R2 standard interval, whether the AG to-be-identified interval is within the G2 standard interval, and whether the AB to-be-identified interval is within the B2 standard interval. If the judgment results are all yes, the M2 macrophage signal is output, otherwise the inflammatory judgment signal is output; If an inflammatory determination signal is output, it is analyzed whether the cell to be analyzed is an inflammatory cell.

8. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 7, characterized in that: Analyzing whether the cells to be analyzed are inflammatory cells includes the following sub-steps: If an inflammatory judgment signal is output, the cells to be analyzed are analyzed by analyzing the grayscale trend line, and the obtained grayscale trend line is marked as the trend line to be analyzed, and the trend line to be analyzed is placed in the grayscale trend chart to determine whether all the trend lines to be analyzed are in the grayscale trend area. If so, an inflammatory signal is output, otherwise a normal signal is output; If an M1 macrophage signal is output, the cells to be analyzed are marked as M1 macrophages; if an M2 macrophage is output, the cells to be analyzed are marked as M2 macrophages; if an inflammatory signal is output, the cells to be analyzed are marked as inflammatory cells; if a normal signal is output, the cells to be analyzed are marked as normal tissue cells; The area surrounded by the outermost inflammatory cells is the inflammatory area.

9. The image recognition and analysis method for regulating cardiac macrophage inflammation according to claim 8, characterized in that: Counting macrophages and inflammatory cells in the inflammatory area and outputting the control inspection results include the following sub-steps: The number of M1 macrophages was counted and labeled as M1 number; The number of M2 macrophages was counted and labeled as M2 number; The number of inflammatory cells was counted and labeled as inflammatory number; The M1 count, M2 count and inflammatory count are output as control examination results and uploaded to the patient's examination report.

10. An image recognition and analysis system for regulating cardiac macrophage inflammation, used to implement the image recognition and analysis method for regulating cardiac macrophage inflammation according to any one of claims 1 to 9, characterized in that: It includes an image preprocessing module, an image recognition module and an inspection output module; the image preprocessing module and the inspection output module are respectively connected to the image recognition module; The image preprocessing module is used to observe and acquire cell images through a fluorescence microscope, and preprocess the cell images to obtain preprocessed images; The image recognition module is used to build a deep learning model for cell recognition, and trains the deep learning model for cell recognition by extracting the fluorescence characteristics of macrophages and the grayscale characteristics of inflammatory cells, and divides the macrophages and inflammatory areas in the preprocessed image and divides the functional states of the macrophages; The inspection output module is used to count macrophages and inflammatory cells in the inflammatory area and output the control inspection results.

Images