Streaming image processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to a streaming image processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first streaming graph and a plurality of second streaming graphs which are output by a flow cytometer and correspond to a target sample; performing grouping and gate setting on the plurality of cells in the first flow chart to obtain five cell populations; performing cross gate setting on each second streaming graph to obtain a cross gate corresponding to each second streaming graph; according to cross gates respectively corresponding to the plurality of second flow diagrams, determining expression conditions corresponding to each cell population in each CD molecule; if the expression condition corresponding to each CD molecule of each cell population meets the preset expression condition, a cell population analysis result corresponding to the first flow chart is output, and the cell population analysis result comprises the positions, the convenience, the number and the ratio of the total number of the five cell populations. According to the flow image processing method and device, the electronic equipment and the storage medium, the accuracy of cell analysis and cell population classification is improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method, device, electronic device and storage medium for processing streaming images. Background Art

[0002] With the in-depth development of medical testing technology, the detection and analysis of cells in blood samples can help with subsequent hematology-related analysis. At present, flow cytometry is widely used in hematology. Analysts can use a flow cytometer to process blood samples, and then set gates based on the flow graphs generated by the flow cytometer to achieve classification and analysis of each cell contained in the blood sample to obtain cell population analysis results. Since most of the current electronic equipment used for analysis still requires doctors to manually set gates and perform classification operations, classification errors caused by improper manual operation may occur. Therefore, there are problems such as low efficiency of cell analysis and poor classification accuracy. Summary of the Invention

[0003] The embodiments of the present application disclose a flow image processing method, device, electronic device and storage medium, which improve the efficiency of cell analysis of target samples and the accuracy of cell group classification.

[0004] The present application discloses a method for processing streaming images, including:

[0005] Obtaining a first flow cytometer graph and multiple second flow cytometer graphs corresponding to a target sample, output by a flow cytometer; the first flow cytometer graph includes the expression of the leukocyte common antigen CD45 by multiple cells in the target sample, as well as cell physical parameters of the multiple cells; the second flow cytometer graph includes the expression of two different CD molecules by multiple cells in the target sample;

[0006] Grouping and gating the multiple cells in the first flow cytometry plot to obtain five cell populations;

[0007] Performing cross-gating on each of the second flow cytometry graphs to obtain a cross-gate corresponding to each of the second flow cytometry graphs;

[0008] determining the expression of each CD molecule in each cell population according to the cross gates corresponding to the plurality of second flow cytometry graphs;

[0009] If the expression of each CD molecule in each cell population meets the preset expression conditions, the cell population analysis results corresponding to the first flow cytometry graph are output, and the cell population analysis results include the position, boundary, number and total proportion of the five cell populations.

[0010] As an optional embodiment, the grouping and gating of multiple cells in the first flow cytometry graph to obtain five cell groups includes: grouping multiple cells in the first flow cytometry graph to obtain five cell groups; gating the first flow cytometry graph based on the five cell groups to obtain gates corresponding to the five cell groups; and classifying multiple cells in the first flow cytometry graph based on the gates corresponding to the five cell groups to obtain five cell groups.

[0011] As an optional embodiment, the multiple cells in the first flow cytometer are grouped to obtain five cell groups, including: clustering the multiple cells in the first flow cytometer to obtain five cluster centers; determining the cell types corresponding to the five cluster centers according to the positions corresponding to the five cluster centers in the first flow cytometer; determining the cluster center and the corresponding cell type to which each cell belongs according to the distance between the position of each cell in the first flow cytometer and the position of each cluster center, so as to obtain five cell groups.

[0012] As an optional embodiment, after cross-gating each of the second flow cytometry graphs to obtain the cross-gate corresponding to each of the second flow cytometry graphs, the method further includes: adjusting the cross-gate of each of the second flow cytometry graphs according to the expression of the five cell groups in each of the second flow cytometry graphs; determining the expression of each of the cell groups corresponding to each CD molecule according to the cross-gates corresponding to the multiple second flow cytometry graphs, including: determining the expression of each of the cell groups corresponding to each CD molecule according to the adjusted cross-gates corresponding to the multiple second flow cytometry graphs.

[0013] As an optional embodiment, before adjusting the cross gate of each of the second flow cytometry graphs according to the expression of the five cell groups in each of the second flow cytometry graphs, the method also includes: in the first flow cytometry graph, color-labeling the five cell groups respectively to obtain color-labeling results; according to the color-labeling results, color-labeling each cell in the multiple second flow cytometry graphs to obtain multiple color-labeled second flow cytometry graphs; and determining the expression of the five cell groups in each of the second flow cytometry graphs according to the color of each cell label in each color-labeled second flow cytometry graph.

[0014] As an optional implementation, the adjusting the cross gate of each of the second flow cytometry images according to the expression of the five cell groups in each of the second flow cytometry images comprises: dividing the target second flow cytometry image into four image regions based on the cross gate corresponding to the target second flow cytometry image; the target second flow cytometry image is any second flow cytometry image; determining the first cell type corresponding to each of the image regions according to the first CD molecule and the second CD molecule corresponding to the target second flow cytometry image; the first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytometry image; determining the cell group corresponding to each of the image regions according to the second cell type corresponding to the five cell groups and the first cell type corresponding to each of the image regions; calculating the cell proportion of the cell number of the cell group corresponding to the target image region in the total number of cells in the target image region in the target image region; the target image region is any image region of the target second flow cytometry image; and moving the horizontal side and / or the vertical side of the cross gate corresponding to the target second flow cytometry image according to the cell proportion of the cell group corresponding to each of the image regions to obtain the adjusted cross gate of the target second flow cytometry image.

[0015] As an optional implementation, the moving the horizontal side and / or the vertical side of the cross gate corresponding to the target second flow cytometry image according to the cell proportion of the cell group corresponding to each of the image regions comprises: determining the cell proportion range corresponding to each of the image regions according to the first CD molecule, the second CD molecule and the first cell type corresponding to each of the image regions; and moving the horizontal side and / or the vertical side of the cross gate corresponding to the target second flow cytometry image according to the cell proportion of the cell group corresponding to each of the image regions and the cell proportion range, until the cell proportion of the cell group corresponding to each of the image regions is in the cell proportion range corresponding to each of the image regions.

[0016] Embodiments of the present application disclose a flow cytometry image processing device, comprising:

[0017] An image acquisition module is configured to acquire a first flow cytometry image and a plurality of second flow cytometry images corresponding to a target sample output by a flow cytometer; the first flow cytometry image comprises expression of a plurality of cells in the target sample corresponding to a common antigen CD45 of white blood cells and cell physical parameters of the plurality of cells; and the second flow cytometry image comprises expression of the plurality of cells in the target sample corresponding to two different CD molecules.

[0018] A cell grouping module is configured to group and gate the plurality of cells in the first flow cytometry image to obtain five cell groups.

[0019] a gating module, configured to perform cross-gating on each of the second flow cytometry graphs to obtain a cross-gate corresponding to each of the second flow cytometry graphs;

[0020] an expression analysis module, configured to determine the expression of each CD molecule in each cell population based on the cross gates corresponding to the plurality of second flow cytometry images;

[0021] A result output module is used to output the cell population analysis results corresponding to the first flow cytometry graph if the expression of each CD molecule in each cell population meets the preset expression conditions, and the cell population analysis results include the position, boundary, number and total proportion of the five cell populations.

[0022] An embodiment of the present application discloses an electronic device, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor implements the method described in any of the above embodiments.

[0023] An embodiment of the present application discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0024] An embodiment of the present application discloses a computer program product, including a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0025] The embodiments of the present application disclose a flow image processing method, device, electronic device and storage medium, wherein the electronic device obtains a first flow cytometer and multiple second flow cytometers corresponding to a target sample output by a flow cytometer; the first flow cytometer includes the expression of the common leukocyte antigen CD45 of multiple cells in the target sample, as well as the cell physical parameters of the multiple cells; the second flow cytometer includes the expression of two different CD molecules of multiple cells in the target sample; the multiple cells in the first flow cytometer are grouped and gated to obtain five cell groups; a cross-gating is performed on each second flow cytometer to obtain a cross-gate corresponding to each second flow cytometer; the expression of each CD molecule of each cell group is determined based on the cross-gates corresponding to the multiple second flow cytometers; if the expression of each CD molecule of each cell group meets the preset expression, the cell group analysis results corresponding to the first flow cytometer are output, and the cell group analysis results include the position, boundary, number and total proportion of the five cell groups. In an embodiment of the present application, the electronic device obtains five cell populations by accurately clustering and gating the cells in the first flow cytometry graph, and further classifies these cells into five cell populations. Combined with the cross-gates of multiple second flow cytometry graphs, it can analyze in detail the expression of each cell population corresponding to different CD molecules, thereby achieving further verification of the five cell populations to improve the accuracy and rationality of cell population classification; and, automatically setting gates for the five clusters of the first flow cytometry graph and cross-gating for multiple second flow cytometry graphs is achieved, which simplifies the manual gating process in the traditional method and can also reduce classification errors caused by improper manual operation, thereby improving the cell analysis efficiency and classification accuracy of the target sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 Schematic diagram of a scenario of a method for processing streaming images in one embodiment;

[0028] Figure 2A is a flowchart of a method for processing streaming images in one embodiment;

[0029] Figure 2B is a schematic diagram of a first flow cytometer in one embodiment;

[0030] Figure 2C is a schematic diagram of a second flow chart in one embodiment;

[0031] Figure 3A flow chart for obtaining five cell populations in one embodiment;

[0032] Figure 4 A flow chart for obtaining five cell populations in one embodiment;

[0033] Figure 5 is a flowchart of a method for processing streaming images in another embodiment;

[0034] Figure 6 A flowchart of adjusting a cross gate of each second flow chart in one embodiment;

[0035] Figure 7 is a flowchart of a method for processing streaming images in another embodiment;

[0036] Figure 8 is a block diagram of a streaming image processing device according to an embodiment;

[0037] Figure 9 FIG. 4 is a structural block diagram of an electronic device in an embodiment. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0039] It should be noted that the terms "including," "having," and any variations thereof in the embodiments and drawings of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0040] It is understood that the terms "first," "second," and the like used herein may be used to describe various elements herein, but these elements are not limited by these terms. These terms are used solely to distinguish a first element from another element. For example, a first flow chart may be referred to as a second flow chart, and similarly, a second flow chart may be referred to as a first flow chart, without departing from the scope of this application.

[0041] The first flow graph and the second flow graph are both flow graphs, but they are different flow graphs.

[0042] In the related art, in the case of analyzing a blood sample, after an analysis personnel obtains a flow cytometry diagram of a flow cytometer, the analysis personnel needs to manually classify each cell contained in the flow cytometry diagram and distinguish different cell types using different colors. After the cells are classified, the analysis personnel also needs to switch back and forth between multiple flow cytometry diagrams and manually perform a gate setting operation, which usually takes a certain amount of time. For a flow cytometry diagram in which cell adhesion is serious, the time taken is even longer, resulting in low analysis efficiency. Moreover, after the flow cytometry diagram is set, each cell in each sample may need to be analyzed one by one manually to check the expression of the cell, and the position of a cross gate is manually adjusted according to the expression. After the above series of manual operations, the analysis personnel completes the analysis and classification of each cell in the sample. Because the human concentration of attention is limited, during the manual operation, not only is the speed of CD molecule expression analysis of the cell slow, but also problems such as inaccurate classification, five-group gate, and inaccurate position of the cross gate due to subjective errors may occur, resulting in errors in subsequent judgments about whether a user corresponding to the blood sample has a blood disease or analysis of the disease condition of the blood disease.

[0043] Embodiments of the present application disclose a flow image processing method and device, an electronic device, and a storage medium, which improve the cell analysis efficiency of a target sample and the classification accuracy of a cell group.

[0044] Some terms related to the present application are introduced as follows:

[0045] Flow cytometry refers to a technology for counting and sorting micro-particles suspended in a fluid. Flow cytometry can realize high-speed, one-by-one quantitative analysis and sorting of cells by detecting labeled fluorescent signals. Flow cytometry can be realized by a flow cytometer.

[0046] A flow cytometer is a device for performing multi-parameter, rapid quantitative analysis and sorting of single cells or other biological micro-particles suspended in a sample. The core technology of the flow cytometer is flow cytometry. The working principle of the flow cytometer can be summarized as follows: the sample passes through a specific fluid dynamics device to form a single cell laminar flow, so that the cells pass through a detection area at a certain speed. In the detection area, the cells are irradiated by a laser beam to excite fluorescent signals or scattered light signals. These signals are received by a photomultiplier tube or other optical detectors and converted into electrical signals. By processing and analyzing these electrical signals by a computer, various biophysical and biochemical parameters of each cell contained in the sample can be obtained, such as cell size, shape, internal particle size, surface marker expression, etc. The flow cytometer can be used to detect various CD molecules, receptors, and other markers on the surface of cells. In some embodiments, the flow cytometer can be used to perform flow detection on a target sample, and the detection data can be analyzed and processed to obtain a flow cytometry diagram.

[0047] CD45 is a transmembrane protein on the surface of white blood cells, also known as Leukocyte Common Antigen (LCA). CD45 is universally expressed in all hematopoietic cell lines (including lymphocytes, myeloid cells, erythroid cells, megakaryocytes / platelets, and mast cells), but the expression intensity and molecular form of CD45 vary among different cell types.

[0048] Cell physical parameters refer to quantitative indicators used to describe the physical properties of cells. Cell physical parameters include scattered light parameters, such as FSC-A (Forward Scatter-Area) and SSC-A (Side Scatter-Area). SSC-A is the area of ​​side scattered light detected by the flow cytometer when the laser beam is irradiated onto the cell and the particles inside the cell (such as organelles, cell nuclei, protein aggregates, etc.) scatter the laser beam. SSC-A can reflect the granularity of the cell and analyze the structural characteristics of the cell. The larger the SSC-A value in the flow cytometer, the more complex the internal structure of the cell or the more granular it is. Cells of different cell types have different SSC-A values. FSC-A is the area of ​​the scattered light signal generated by the cell's forward scattering of the laser beam. FSC-A reflects the size of the cell; the larger the cell, the higher the FSC-A value.

[0049] A flow cytometer plot is a visualization of data generated by a flow cytometer, used to display the expression of multiple parameters for each cell in a sample. Flow cytometer plots may include, but are not limited to, scatter plots, histograms, contour plots, and pseudocolor plots. In a scatter plot, a dot can represent one or more cells. Flow cytometer analysis can reveal different cell characteristics based on different parameters. For example, a first flow cytometer plot may be a CD45 / SSC-A flow cytometer plot, which displays the expression of CD45 and the area of ​​side scattered light from multiple cells in a target sample. In a CD45 / SSC-A flow cytometer plot, the horizontal axis represents the intensity of CD45 expression, and the vertical axis represents the granularity of the cells. A second flow cytometer plot displays the expression of two different CD molecules for multiple cells in the target sample. These two CD molecules can be any two CD molecules, for example, CD3 and CD7. In a second CD3 / CD7 flow cytometer plot, the horizontal axis represents the intensity of CD3 expression, and the vertical axis represents the intensity of CD7 expression.

[0050] Gating is a crucial step in flow cytometry, used to define one or more boundaries or regions on a flow cytometer to separate cell populations with distinct characteristics. Gating methods include, but are not limited to, threshold gating, scattered light gating, and fluorescence gating.

[0051] A gate refers to a boundary line or regional edge line used to select a cell population with a certain characteristic. The shape of the gate can be arbitrary, including but not limited to linear gates, rectangular gates, circular gates, elliptical gates, polygonal gates, and four-quadrant gates. Among them, the five-cluster gate refers to the boundary line set on the flow cytometer based on the scattered light and fluorescence characteristics of the cells. The five-cluster gate contains gates corresponding to the five cell populations, which are used to distinguish and identify the five cell populations; the cross gate, also known as the four-quadrant gate, refers to the cross-shaped boundary line that divides the flow cytometer into four quadrants, which is used to analyze the intensity of two fluorescent markers.

[0052] The five major cell populations refer to the five types of cells that are classified based on their size and granularity during flow cytometry analysis. For example, based on the CD45 and SSA-C channels, cells in a target sample can be divided into five cell populations: granulocytes, CD45 weakly positive cells, CD45 negative cells, monocytes, and lymphocytes.

[0053] The flow image processing and analysis method provided in this application can be applied to Figure 1 In the electronic device 100 shown. Figure 1 As shown, the electronic device 100 can be connected to a flow cytometer 110. The flow cytometer 110 is used to perform flow cytometry on a target sample 120. The electronic device 100 may include, but is not limited to, a smart terminal device, a tablet computer, a PC (Personal Computer), a computer, a laptop computer, etc. A communication connection can be established between the electronic device 100 and the flow cytometer 110. Available connection methods include, but are not limited to, data cable connection, wireless local area network (WLAN) connection, WiFi connection, and other wired or wireless connection methods.

[0054] When it is necessary to analyze each cell contained in the target sample 120, the flow cytometer 110 can be used to perform flow cytometric detection on the target sample 120 based on CD45 and cell physical parameters to obtain a first flow cytometric graph, and the target sample 120 can be detected based on any two different CD molecular parameters to obtain multiple second flow cytometric graphs, and the first flow cytometric graph and the multiple second flow cytometric graphs can be sent to the electronic device 100. After the electronic device 100 receives the first flow cytometer and multiple second flow cytometers corresponding to the target sample 120 transmitted by the flow cytometer 110, the electronic device 100 clusters and sets gates on the multiple cells in the first flow cytometer to obtain five cell populations. The electronic device 100 also cross-gates each of the second flow cytometers to obtain cross-gates corresponding to each of the second flow cytometers. The electronic device 100 then determines the expression of each CD molecule for each cell population based on the cross-gates corresponding to the multiple second flow cytometers. If the expression of each CD molecule for each cell population meets the preset expression conditions, the electronic device 100 outputs the cell population analysis results corresponding to the first flow cytometer to obtain the accurate positions, boundaries, numbers, and total percentages of the five cell populations. The electronic device 100 can automatically set gates for the five clusters in the first flow cytometer and cross-gates for the multiple second flow cytometers to further inspect and classify the five cell populations, thereby reducing classification errors caused by improper manual operation, thereby improving the efficiency of cell analysis of the target sample 120 and the accuracy of cell population classification.

[0055] like Figure 2A As shown, in one embodiment, a method for processing streaming images is provided, which can be applied to the above electronic device. The method may include the following steps 210 to 250.

[0056] Step 210 : Acquire a first flow cytometer image and a plurality of second flow cytometer images corresponding to the target sample outputted by the flow cytometer.

[0057] In some embodiments, an analyst places a collected target sample into a flow cytometer for flow cytometric analysis. The flow cytometer can generate a first flow cytogram and multiple second flow cytograms corresponding to the target sample based on multiple CD molecules set by the analyst, and send the first flow cytogram and multiple second flow cytograms to an electronic device. After obtaining the first flow cytogram and multiple second flow cytograms, the electronic device can analyze the first flow cytogram and multiple second flow cytograms to analyze and classify the multiple cells contained in the target sample and obtain a cell population analysis result.

[0058] The first flow cytometer graph includes the expression of the common leukocyte antigen CD45 corresponding to multiple cells in the target sample, as well as the cell physical parameters of the multiple cells; for example, the first flow cytometer graph may be a CD45 / SSC-A flow cytometer graph, a CD45 / FSC-A flow cytometer graph, etc.

[0059] Because CD45 is ubiquitously expressed in all cell lines, and different cell types express CD45 with varying intensities and molecular forms, and because scattered light parameters can reflect the granularity of individual cells, the electronic device can more accurately group or classify individual cells contained in the target sample using the first flow cytometry plot.

[0060] For example, a point in a CD45 / SSC-A flow cytometry plot may represent one or more cells; the value of the abscissa of a point may represent the expression intensity of CD45 in the cells corresponding to the point, and the value of the ordinate may represent the granularity of the cells corresponding to the point.

[0061] The second flow cytometric graph includes the expression of two different CD molecules in a plurality of cells in the target sample. The two CD molecules in the second flow cytometric graph can be any two CD molecules in a CD (Cluster of Differentiation) such as CD58, CD38, 7AAD, CD66c, CD13+33, CD34, CD10, CD19, CD200, or CD45. For example, the value of the horizontal axis in a CD19 / CD10 flow cytometric graph or other flow cytometric graphs can represent the expression intensity of the cell in CD19, and the value of the vertical axis can represent the expression intensity of the cell in CD10.

[0062] In some embodiments, since the names of the various flow CD molecules in the flow cytometer may be different from the names of the CD molecules stored in the electronic device, in order to avoid the electronic device being unable to recognize the CD molecules in the first flow graph or the multiple second flow graphs, the electronic device can modify the names of the various flow CD molecules in the first flow graph and the multiple second flow graphs to the corresponding CD molecule names in the electronic device after receiving the first flow graph and the multiple second flow graphs sent by the flow cytometer.

[0063] Optionally, the electronic device can obtain the names and corresponding descriptions of each flow CD molecule sent by the flow cytometer; then semantically match the flow description information corresponding to each flow CD molecule name with the description information corresponding to multiple stored CD molecule names to obtain the degree of match between each flow description information and the multiple description information; and in the first flow graph and multiple second flow graphs, modify each flow CD molecule name to the CD molecule name corresponding to the description information with the greatest degree of match. For example, if the electronic device detects that the flow description information corresponding to flow CD molecule name a has the greatest degree of match with the description information corresponding to CD molecule name A, the flow CD molecule name a in both the first flow graph and the multiple second flow graphs will be modified to CD molecule name A. This effectively eliminates inconsistencies in CD molecule names and prevents the electronic device from being able to analyze the expression of the CD molecule in the cell.

[0064] In step 220 , the multiple cells in the first flow cytometry graph are grouped and gated to obtain five cell populations.

[0065] In some embodiments, the electronic device may perform feature preprocessing on multiple cells in the first flow cytometry plot to obtain biometric features of each cell; calculate the correlation between the biometric features of each cell, and classify cells with correlations greater than a correlation threshold into the same cell population, thereby obtaining five cell populations. Cell biometric features may include, but are not limited to, cell size, shape, internal structure, and surface molecule expression.

[0066] In some embodiments, the electronic device may draw a five-group gate (e.g., Figure 2B As shown, the ordinate SS INT LIN represents the linear signal of side scattered light, the abscissa CD45BV510 represents the fluorescence intensity of CD45 stained with BV510 fluorescent dye), and the five-group gate includes gates corresponding to five cell groups, which are used to divide the multiple cells in the first flow cytometry graph into five cell groups, and one gate includes multiple cells of one cell group.

[0067] Step 230 , cross-gating each second flow graph to obtain a cross-gate corresponding to each second flow graph.

[0068] In some embodiments, each second flow cytometry graph is divided into regions according to the expression of two different CD molecules in each cell in each second flow cytometry graph, thereby obtaining a cross gate corresponding to each second flow cytometry graph.

[0069] The cross gate is formed by the intersection of two vertical and horizontal lines. The vertical line divides the expression of one CD molecule in the second flow cytometric profile into high and low thresholds, while the horizontal line divides the expression of another CD molecule in the second flow cytometric profile into high and low thresholds. The cross gate divides the second flow cytometric profile into four quadrants: Lower left quadrant: represents a cell population in which the expression of both CD molecules is below the set threshold. Lower right quadrant: represents a cell population in which the expression of the first CD molecule is above the set threshold, while the expression of the second CD molecule is below the set threshold. Upper left quadrant: represents a cell population in which the expression of the first CD molecule is below the set threshold, while the expression of the second CD molecule is above the set threshold. Upper right quadrant: represents a cell population in which the expression of both CD molecules is above the set threshold.

[0070] For example, assuming that a second flow cytometry graph is a CD58 / CD19 flow cytometry graph, the cross gate can divide the second flow cytometry graph into four quadrants (e.g. Figure 2CAs shown in the figure, "APC" and "FITC" are two different fluorescent dyes); in the second flow chart, the CD molecule on the horizontal axis is CD58 and the CD molecule on the vertical axis is CD19. Then the electronic device can obtain the upper left quadrant (CD58-CD19+, indicating a cell population that does not express CD58 and highly expresses CD19), the lower left quadrant (CD58-CD19-, indicating a cell population that does not express both CD58 and CD19), the upper right quadrant (CD58+CD19-, indicating a cell population that highly expresses CD58 and does not express CD19), and the lower right quadrant (CD58+CD19+, indicating a cell population that highly expresses both CD58 and CD19). Among them, it can be considered that high expression of CD58 is equivalent to "positive" expression of CD58; low expression of CD58 is equivalent to "negative" expression of CD58.

[0071] In some embodiments, the electronic device may perform a cross-gating on each second flow cytometer according to the FlowDensity algorithm to obtain a cross-gate corresponding to each second flow cytometer; wherein the FlowDensity algorithm is used to set gates in complex cell populations. Specifically, the electronic device may use the FlowDensity algorithm or kernel density estimation method to perform density estimation on the fluorescence signals corresponding to the two different CD molecules in the second flow cytometer, and visualize the density estimation results, typically using a two-dimensional density map or contour map to display the joint distribution of the fluorescence signals corresponding to the two different CD molecules; the electronic device identifies the density peaks of the fluorescence signals corresponding to the two different CD molecules in the density distribution map, these peaks typically corresponding to the centers of the quadrant areas; the electronic device then calculates a preliminary threshold for segmenting the quadrant areas based on the distribution and relative positions of the density peaks, using density peak detection methods, clustering algorithms, or empirical rules; and preliminarily sets a cross-gate on the second flow cytometer according to the calculated preliminary threshold.

[0072] In some embodiments, after the electronic device draws a cross gate in the second flow cytometry graph, it iteratively optimizes the position of the cross gate by adjusting the threshold and using an adaptive boundary setting function based on the aggregation characteristics and distribution morphology of multiple cells in the second flow cytometry graph to ensure that the position of the cross gate can accurately segment the second flow cytometry graph.

[0073] Step 240 : determining the expression of each CD molecule in each cell population based on the cross gates corresponding to the plurality of second flow cytometry images.

[0074] The expression of each CD molecule in each cell may include but is not limited to the number of cells expressing "positive / negative" for each CD molecule in each cell population, the type of CD molecule corresponding to the "positive / negative" CD molecules expressed in each cell population, the proportion of cells highly expressing various CD molecules in each cell population to the total cells, the location distribution of each cell or cell population, the difference between different cell populations, the average expression level (average fluorescence intensity) of each CD molecule in each cell population, and the like.

[0075] Step 250: If the expression of each CD molecule in each cell population meets the preset expression conditions, the cell population analysis result corresponding to the first flow cytometry graph is output.

[0076] The preset expression conditions include the preset expression conditions corresponding to each CD molecule in each cell population. The preset expression conditions may include the preset number of "positive / negative" cells, the preset CD molecule types corresponding to the CD molecules expressing "positive / negative", etc.

[0077] The cell population analysis results include the location, boundaries, number, and total percentage of the five cell populations. They can also include multiple second flow cytometry images after cross-gate marking, the first flow cytometry image after five-group gate marking, and the first flow cytometry image after grouping.

[0078] In some embodiments, after the electronic device determines the expression status of each cell population corresponding to each CD molecule, it can match the expression status of each cell population corresponding to each CD molecule with the corresponding preset expression status; if the expression status of each cell population corresponding to each CD molecule matches the corresponding preset expression status, it means that the expression status of each cell population corresponding to each CD molecule meets the preset expression status, that is, the electronic device accurately analyzes and classifies the cells of the target sample, and the electronic device can output the cell population analysis results corresponding to the first flow cytometry graph.

[0079] Optionally, if the electronic device detects that the expression of each CD molecule in any cell population does not match the corresponding preset expression, it means that the expression of each CD molecule in at least one cell population does not meet the preset expression, that is, the electronic device is inaccurate in the cell analysis and classification of the target sample. The electronic device can then re-execute the step of grouping the multiple cells in the first flow cytometry graph to obtain five new cell populations, to obtain new five-group gates, and thus to obtain five new cell populations, and then determine whether they meet the preset expression conditions, through an iterative method, until accurate and reliable cell population analysis results are obtained.

[0080] In some embodiments, the preset expression conditions may include multiple preset CD molecule species expressed positively by each cell population; the electronic device may determine the multiple CD molecule species expressed positively by each cell population based on the expression conditions corresponding to each CD molecule of each cell population; if it is detected that the multiple CD molecule species expressed positively by each cell population are exactly the same as the multiple preset CD molecule species corresponding to each cell population, the electronic device may deem that the expression conditions corresponding to each CD molecule of each cell population meet the preset expression conditions and output the cell population analysis results corresponding to the first flow cytometry graph.

[0081] For example, assume that in the target sample, a certain cell population expresses 8 CD molecule species positively, and it is known that the cell population normally expresses 8 preset CD molecule species positively; if the electronic device detects that the 8 CD molecule species are exactly the same as the 8 preset CD molecule species, then the electronic device can deem that the expression of each CD molecule in the cell population meets the preset expression.

[0082] In some embodiments, when outputting the cell population analysis results corresponding to the first flow cytometry plot, the electronic device may further calculate, based on multiple cell populations expressing different CD molecules, the cell ratio of the cell number of each cell population to the total cell number. If the electronic device detects that the cell ratio corresponding to the first cell population is not within a preset ratio range corresponding to the cell type corresponding to the first cell population, the electronic device may output a warning prompt corresponding to the first cell population, where the first cell population is any cell population, and the warning prompt indicates that the cell ratio corresponding to the first cell population is abnormal.

[0083] Specifically, assuming that the target sample is a bone marrow sample, the normal lymphocyte range is 15%-25%; assuming that the cell category corresponding to the first cell population is lymphocytes, the preset ratio range is 15%-25%. Therefore, if the electronic device detects that the cell ratio corresponding to the first cell population is 14%, the electronic device outputs a warning prompt corresponding to the first cell population.

[0084] The early warning prompt may also include a prompt that the cell ratio corresponding to the first cell population is lower than or exceeds a preset ratio range; for example, if the cell ratio corresponding to the first cell population is lower than the preset ratio range, the early warning prompt may be described as a relative decrease in the ratio; if the cell ratio corresponding to the first cell population is higher than the preset ratio range, the early warning prompt may be described as a relative increase in the ratio. Using the early warning prompt, the analyst can be quickly informed that the cell ratio of the first cell population is abnormal, so that corresponding solutions can be taken in time to assist the analyst in conducting disease diagnosis and treatment analysis of the target sample; and the early warning prompt may include a prompt that the cell ratio is lower than or exceeds the preset ratio range, which can help the analyst directly make corresponding adjustment measures without reconfirming the cell ratio of the first cell population, thereby improving the analyst's work efficiency.

[0085] In an embodiment of the present application, the electronic device obtains five cell populations by accurately clustering and gating the cells in the first flow cytometry graph, and further classifies these cells into five cell populations. Combined with the cross-gates of multiple second flow cytometry graphs, it can analyze in detail the expression of each cell population corresponding to different CD molecules, thereby achieving further verification of the five cell populations to improve the accuracy and rationality of cell population classification; and, automatically setting gates for the five clusters of the first flow cytometry graph and cross-gating for multiple second flow cytometry graphs is achieved, which simplifies the manual gating process in the traditional method and can also reduce classification errors caused by improper manual operation, thereby improving the cell analysis efficiency and classification accuracy of the target sample.

[0086] In some embodiments, as Figure 3 As shown, the step of grouping and gating the multiple cells in the first flow cytometry graph to obtain five cell groups may include steps 302 to 306.

[0087] Step 302: Group the multiple cells in the first flow cytometry graph to obtain five cell populations.

[0088] In some embodiments, the electronic device may cluster the multiple cells in the first flow cytometry graph according to a K-means algorithm to obtain five cell populations. Specifically, the electronic device may first randomly select five cells from the multiple cells in the first flow cytometry graph as central cells, and iteratively update the positions of the central cells and the division of the cell populations until all cells in the first flow cytometry graph are clustered into five clusters, thereby obtaining five cell populations, such that cells within each cell population are as similar as possible, while cells between different cell populations are as different as possible.

[0089] In some embodiments, as Figure 4 As shown, the electronic device groups the multiple cells in the first flow cytometry graph to obtain five cell populations, which may include steps 402 to 406.

[0090] Step 402: Cluster the multiple cells in the first flow cytometry graph to obtain five cluster centers.

[0091] In some embodiments, the electronic device may cluster the multiple cells in the first flow cytometry graph based on DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) to obtain five cluster centers.

[0092] Specifically, the electronic device can cluster the multiple cells in the first flow cytometry graph according to the preset ε (neighborhood radius) and MinPts (minimum number of samples) parameters, divide the multiple cells in the first flow cytometry graph into five different clusters and noise points, remove the cells corresponding to the noise points, and determine the cluster label corresponding to each cluster. Cells with the same cluster label belong to the same cluster. The electronic device then obtains five cluster centers by calculating the geometric center of each cluster (such as the mean) or based on the density distribution of cells in the cluster.

[0093] Step 404 : determining the cell types corresponding to the five cluster centers respectively according to their respective corresponding positions in the first flow cytometer graph.

[0094] In some embodiments, the CD molecule expression level values ​​and scattered light parameter values ​​of the five cluster centers in CD45 are determined based on the positions corresponding to the five cluster centers in the first flow cytometry graph; based on the preset expression ranges corresponding to the five cell types in CD45 and the preset scattered light ranges corresponding to the five cell types, the preset expression ranges to which the CD molecule expression level values ​​belong and the preset scattered light ranges to which the scattered light parameter values ​​belong are determined to respectively determine the cell types corresponding to the five cluster centers.

[0095] For example, assuming that the CD molecule expression level value of cluster center B in CD45 belongs to the preset expression range corresponding to lymphocytes in CD45, and the scattered light parameter value of cluster center B belongs to the preset scattered light range corresponding to lymphocytes, the electronic device believes that the cell type corresponding to cluster center B is lymphocytes.

[0096] Step 406 : Determine the cluster center to which each cell belongs and the corresponding cell type based on the distance between the position of each cell and the position of each cluster center in the first flow cytometry graph, so as to obtain five cell populations.

[0097] In some embodiments, the electronic device may use a K-Nearest Neighbors (KNN) algorithm to classify each cell, determine the cluster center to which each cell belongs and the corresponding cell type, to obtain five cell populations, with one cluster center corresponding to one cell population.

[0098] Specifically, the electronic device can calculate the distance between each cell in the first flow graph and the five cluster centers in the first flow graph. The method of calculating distance that the electronic device can use includes but is not limited to Euclidean distance, Manhattan distance or other suitable distance measurement methods. The electronic device can assign each cell to the cluster center containing the largest number of neighbors among the K neighbors and the corresponding cell type based on the cluster center to which the K neighbors of each cell in the first flow graph belong, that is, the cluster center to which each cell belongs is the same as the cluster center to which most of the K neighbors of each cell belong; the cell type corresponding to the cluster center to which the cell belongs is the cell type corresponding to the cell.

[0099] Alternatively, if multiple cluster centers contain the same number of neighbors, the electronic device may employ other strategies (e.g., random selection, distance weighting, etc.) to determine to which cluster center the cell ultimately belongs. Specifically, the electronic device may group cells belonging to the same cluster center into one cell population based on the cell types corresponding to the cells in the first flow cytometry plot, resulting in five cell populations, each corresponding to a cluster center and a specific cell type.

[0100] In an embodiment of the present application, the electronic device clusters multiple cells, first determines five cluster centers, and then determines the cluster center to which each cell belongs one by one based on the distance between the cluster center and the cells to determine the cell population, rather than directly obtaining the cell population by distance. This can reduce the noise and error caused by direct clustering, thereby improving the accuracy of cell clustering.

[0101] Step 304 : Setting gates on the first flow cytometry graph based on the five cell populations to obtain gates corresponding to the five cell populations.

[0102] In some embodiments, the electronic device can determine the boundary outlines of each of the five cell populations based on their respective distribution positions in the first flow cytometer plot; based on the boundary outlines of each cell population, five gates are drawn on the first flow cytometer plot to obtain five clustering gates, each corresponding to a cell population. Commonly used gate shapes in flow cytometry can be linear, rectangular, circular, elliptical, polygonal, etc. In this embodiment, polygons are used to accurately capture the boundary outlines of the cell populations.

[0103] Step 306 : Classify the multiple cells in the first flow cytometry graph according to the gates corresponding to the five cell populations to obtain five cell populations.

[0104] In some embodiments, the electronic device can determine the expression intensity range of CD45 and SSC-A of each cell population in the first flow cytometry graph based on the gates corresponding to the five cell populations; determine the cell types corresponding to the cells in the gates corresponding to the five cell populations based on the preset expression intensity ranges corresponding to CD45 and SSC-A of each cell type, and obtain five cell populations. Among them, the expression intensity refers to the amount of CD45 expressed on the cell surface of each cell and the fluorescence intensity of SSC-A; the preset expression intensity ranges corresponding to various cell types in CD45 and SSC-A can be determined based on a large amount of experimental data and clinical studies. For example, the preset expression intensity interval of nucleated red blood cells for CD45 and CD45Dim cells can be 10 1 -10 2 The preset expression intensity interval of CD45 between CD45Dim cells and lymphocytes can be 10 2 -10 3 Fluorescence intensity, etc.

[0105] In an embodiment of the present application, the electronic device first clusters multiple cells in the first flow cytometry graph to determine five cell populations, and then sets gates on these five cell populations, so that the cells can be more accurately classified according to the five gates, effectively distinguishing the characteristics of different cell populations, and improving the accuracy and reliability of cell analysis. Through the clustering strategy of the DBSCAN and K-means algorithms and the gating method for the boundary contours of the cell population, it is ensured that the cell characteristics within each cell population are highly similar, while the cell characteristics between different subpopulations are significantly different.

[0106] like Figure 5 As shown, in some embodiments, a method for processing streaming images is provided, which can be applied to the above electronic device. The method may include the following steps 502 to 512.

[0107] Step 502 : Acquire a first flow cytometer image and a plurality of second flow cytometer images corresponding to the target sample outputted by the flow cytometer.

[0108] Step 504 : Group the multiple cells in the first flow cytometry graph to obtain five cell groups.

[0109] Step 506 , cross-gating each second flow graph to obtain a cross-gate corresponding to each second flow graph.

[0110] The description of steps 502 to 506 can be found in the description of steps 210 to 230 in the above embodiment, and will not be repeated here.

[0111] Step 508 : Adjust the cross gate of each second flow cytometry graph according to the expression of the five cell populations in each second flow cytometry graph.

[0112] In order to further optimize the division of cell populations in complex samples or in the presence of certain noise, ensure more accurate identification of various cell populations, and ensure the accuracy and consistency of cell analysis, the electronic device can adjust the position of the cross gate in each second flow cytometry graph according to the expression of these five cell populations in each second flow cytometry graph after obtaining the cross gate corresponding to the second flow cytometry graph, so as to improve the accuracy of cell analysis.

[0113] Optionally, the electronic device may adjust the cross-gates of each second flow cytometry graph based on the distribution position, expression intensity, density, distribution shape, etc. of the five cell populations in the second flow cytometry graph. For example, a cell population with high expression intensity may require a more precise cross-gate (a smaller area encircled by the cross-gate) to distinguish, while a cell population with low expression intensity may require a larger cross-gate (a larger area encircled by the cross-gate) to include all cells.

[0114] In some specific embodiments, Figure 6 As shown, the electronic device adjusts the cross gate of each second flow cytometry graph according to the expression of the five cell populations in each second flow cytometry graph, which may include steps 602 to 610.

[0115] Step 602 : Divide the target second flow chart into four image regions based on the cross gate corresponding to the target second flow chart.

[0116] The target second flow cytometer graph is any second flow cytometer graph.

[0117] Since the cross gate can divide the second flow cytometry graph into four quadrants, the four quadrants of the cross gate can correspond to four image regions, respectively. The cells in each image region have different expression characteristics of specific CD molecules.

[0118] Step 604 : Determine the first cell type corresponding to each image region based on the first CD molecule and the second CD molecule corresponding to the target second flow cytometer.

[0119] The first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytometric profile.

[0120] Since different CD molecules are expressed by different cell types, and the CD molecules that can produce an immune response reaction are also different, the electronic device can determine the first cell type corresponding to each image region according to the first CD molecule and the second CD molecule corresponding to the target second flow cytometry based on the preset correspondence between the CD molecule and the cell type. For example, assuming that the target second flow cytometry is a CD10 / CD19 flow cytometry, according to the cell characteristics of different cell types, in the upper right region of the CD10 / CD19 flow cytometry, the cells in the upper right region are represented as double positive (CD10+CD19+, indicating high expression of CD10 and CD19), and then according to the CD10, CD19 and cell specificity of various cell types, the electronic device can obtain that under the CD10 and CD19 channels, lymphocytes are usually highly expressed, and it can be considered that the first cell type corresponding to the upper right region of the CD10 / CD19 flow cytometry is a lymphocyte.

[0121] In step 606, the electronic device determines the cell group corresponding to each image region according to the second cell type corresponding to the five cell groups and the first cell type corresponding to each image region.

[0122] In some embodiments, the electronic device can match the second cell type corresponding to the five cell groups with the first cell type corresponding to each image region, and take the cell group corresponding to the second cell type matched with the first cell type corresponding to each image region as the cell group corresponding to each image region.

[0123] For example, assuming that the second cell type corresponding to the cell group A is a lymphocyte, and the first cell type corresponding to the image region W is also a lymphocyte, the image region W corresponds to the cell group A.

[0124] In step 608, the electronic device calculates the cell proportion of the cell group corresponding to the target image region in the total number of cells in the target image region.

[0125] The target image region is any image region of the target second flow cytometry.

[0126] For example, assuming that the image region W contains a total of 100 events, and the cell group A corresponding to the image region W is distributed in 40 events of the image region W, then the cell proportion of the cell group A in the image region W in the total number of cells in the image region W is 40%.

[0127] In step 610, the electronic device moves the horizontal side and / or the vertical side of the cross gate corresponding to the target second flow cytometry according to the cell proportion of the cell group corresponding to each image region to obtain an adjusted cross gate of the target second flow cytometry.

[0128] In some embodiments, the electronic device can determine the cell ratio range corresponding to each image area based on the first CD molecule, the second CD molecule and the first cell type corresponding to each image area; and according to the cell ratio and cell ratio range of the cell population corresponding to each image area, move the horizontal and / or vertical edges of the cross gate corresponding to the target second flow cytometry graph until the cell ratio of the cell population corresponding to each image area is within the cell ratio range corresponding to each image area.

[0129] The cell proportion range corresponding to each image area refers to the proportion range of the number of cells of the first cell type to the total number of cells in the image area under normal circumstances and within the CD molecule expression range corresponding to each image area. That is, if the proportion value of the number of cells of the first cell type to the total number of cells in the image area is within the cell proportion range corresponding to the image area, it means that the cell classification of the image area is accurate.

[0130] For example, assuming that the target second flow cytometer is a CD10 / CD19 flow cytometer, based on the cell characteristics of different cell types, the first cell type corresponding to the upper right region of the CD10 / CD19 flow cytometer (CD10+CD19+, indicating high expression of both CD10 and CD19) is lymphocytes, with a cell proportion range of >5%; and the first cell type corresponding to the upper left region of the CD10 / CD19 flow cytometer (CD10-CD19+, indicating low expression of CD10 and high expression of CD19) is monocytes, with a cell proportion range of <1%. If the electronic device detects that the cell proportion of lymphocytes in the upper right region of the CD10 / CD19 flow cytometer is less than 5%, the electronic device moves the horizontal and / or vertical sides of the cross-gate to increase the number of lymphocytes, that is, the horizontal and / or vertical sides of the cross-gate may be moved toward the lymphocyte population. If the electronic device detects that the cell ratio of monocytes is higher than 1% in the upper left area of ​​the CD10 / CD19 flow cytometer, the horizontal and / or vertical edges of the cross gate are moved to reduce the cell number of monocytes, that is, the horizontal and / or vertical edges of the cross gate can be moved away from the monocyte population.

[0131] In one embodiment, the electronic device can determine the target edge in the cross-gate corresponding to the target flow cytometer based on the first CD molecule and the second CD molecule, and the target edge is the edge that is moved first; if the cell ratio of the cell population corresponding to the current image area is not within the cell ratio range corresponding to the current image area, the target edge is moved so that the cell ratio of the cell population corresponding to the current image area is within the cell ratio range corresponding to the current image area; when the cell ratio of the target cell population corresponding to each image area is within the cell ratio range corresponding to each image area, the current cross-gate is used as the adjusted cross-gate corresponding to the target flow cytometer.

[0132] Since the target second flow cytometry graph is divided into four image areas by the cross gate, the lower left area indicates that the cells weakly express both CD molecules in the target second flow cytometry graph. Therefore, when adjusting the cross gate according to the cell ratio of the cell population corresponding to each image area, the electronic device can usually ignore the lower left area, and the other three areas can be the first target image area, the second target image area, and the third target image area, respectively.

[0133] Specifically, assuming that the current image area is the first target image area, if the electronic device detects that the cell proportion of the cell group corresponding to the first target image area is not within the cell proportion range corresponding to the first target image area, the target edge is moved so that the cell proportion of the cell group corresponding to the first target image area is within the cell proportion range corresponding to the first target image area.

[0134] Next, the electronic device can detect the second target image area. If the cell proportion of the cell group corresponding to the second target image area is not within the cell proportion range corresponding to the second target image area, the target edge is first moved so that the cell proportion of the cell group corresponding to the first target image area is within the cell proportion range corresponding to the first target image area, and the cell proportion of the cell group corresponding to the second target image area is also within the cell proportion range corresponding to the second target image area. If moving the target edge cannot meet the conditions that the cell proportion of the cell group corresponding to the first target image area is within the cell proportion range corresponding to the first target image area, and the cell proportion of the cell group corresponding to the second target image area is also within the cell proportion range corresponding to the second target image area, then the edges in the cross gate except the target edge are moved.

[0135] Finally, the electronic device can detect the third target image area. If the cell ratio of the cell population corresponding to the third target image area is within the cell ratio range corresponding to the third target image area, the current cross gate is used as the adjusted cross gate corresponding to the target flow cytometer; if the cell ratio of the cell population corresponding to the third target image area is not within the cell ratio range corresponding to the third target image area, the target edge is moved first, and then the edges in the cross gate except the target edge are moved, so that the cell ratio of the cell population corresponding to the first target image area is within the cell ratio range corresponding to the first target image area, and the cell ratio of the cell population corresponding to the second target image area is also within the cell ratio range corresponding to the second target image area, and the cell ratio of the cell population corresponding to the third target image area is within the cell ratio range corresponding to the third target image area.

[0136] Step 510 : Determine the expression of each CD molecule in each cell population based on the adjusted cross gates corresponding to the plurality of second flow cytometry images.

[0137] Step 512: If the expression of each CD molecule in each cell population meets the preset expression conditions, the cell population analysis result corresponding to the first flow cytometry graph is output.

[0138] The description of steps 510 to 512 can be found in the description of steps 240 to 250 in the above embodiment, and will not be repeated here.

[0139] In an embodiment of the present application, by adjusting the cross gate corresponding to each second flow cytometry graph, automatic gating of the five groups of the first flow cytometry graph and cross gating of multiple second flow cytometry graphs are achieved, which simplifies the manual gating process in the traditional method and can also reduce classification errors caused by improper manual operation, thereby improving the cell analysis efficiency and classification accuracy of the target sample.

[0140] like Figure 7 As shown, in some embodiments, a method for processing streaming images is provided, which can be applied to the above electronic device. The method may include the following steps 702 to 706.

[0141] Step 702 : In the first flow cytometry graph, color-label each cell contained in the five cell populations to obtain a color labeling result.

[0142] The color labeling result includes the colors corresponding to the five cell populations included in the first flow cytometry graph. The electronic device can pre-set the correspondence between the cell populations and the colors.

[0143] Color-labeling each cell included in the first flow cytometry graph refers to associating each cell included in the first flow cytometry graph with a color corresponding to the cell population to which it belongs, thereby associating each cell included in the target sample with a corresponding color. For example, the electronic device may preset the color corresponding to cell population M to be green, and then the cells belonging to cell population M in the first flow cytometry graph may be labeled green; if the color corresponding to cell population P is blue, then the cells belonging to cell population P in the first flow cytometry graph may be labeled blue, etc., but the present invention is not limited thereto.

[0144] In some embodiments, cells belonging to the same cell group have the same color, and cells not belonging to the same cell group have different colors. Therefore, the cell group to which each cell included in the first flow cytometry graph belongs can be intuitively determined by color.

[0145] Step 704 : color-label each cell in the plurality of second flow cytometry images according to the color-labeling result, to obtain a plurality of color-labeled second flow cytometry images.

[0146] In some embodiments, the electronic device associates each cell included in the multiple second flow cytometry graphs with each cell included in the first flow cytometry graph based on the color labeling results in the first flow cytometry graph, determines the cell populations of each cell included in the multiple second flow cytometry graphs, and based on the color labeling results, color labels each cell included in the multiple second flow cytometry graphs according to the colors corresponding to the cell populations included in the first flow cytometry graph.

[0147] Specifically, assuming that the color labeling results include that the color corresponding to the cell group M is green and the color corresponding to the cell group P is blue, the first flow cytometry graph includes events 1, 2, 3, 4 and 5, among which events 1, 3 and 4 belong to the cell group M, and events 2 and 5 belong to the cell group P. The electronic device determines, based on the color labeling results, that events A, B, C, D and E contained in each second flow cytometry graph are associated with events 1, 2, 3, 4 and 5 contained in the first flow cytometry graph, respectively. Event A, C and D are cell group M, and event B and event E are cell group P. Event A, C and D contained in multiple second flow cytometry graphs are all labeled green, and event B and event E are all labeled blue to obtain a second flow cytometry graph after color labeling. The events mentioned above refer to objects detected in the teacher graph, which may include cells, viruses, pathogens, etc.

[0148] Step 706 : In each color-labeled second flow cytometry graph, the expression status of the five cell populations in each second flow cytometry graph is determined according to the color labeled for each cell.

[0149] In some embodiments, after determining the expression of the five cell populations in each second flow cytometry graph, the electronic device can adjust the cross gate of each second flow cytometry graph according to the expression of the five cell populations in each second flow cytometry graph, thereby more accurately adjusting the cross gate by obtaining more accurate CD molecule expression.

[0150] Optionally, the electronic device can determine the expression pattern of each cell population on different CD molecule channels based on the color and fluorescence intensity of each cell label. The expression pattern can include high expression, low expression, no expression, or expression at a specific ratio.

[0151] In an embodiment of the present application, since the first flow cytometry graph and the multiple second flow cytometry graphs all correspond to the target sample, the cells contained in the first flow cytometry graph and the multiple second flow cytometry graphs have an associated correspondence. Color-labeling the multiple second flow cytometry graphs based on the color labeling results of the first flow cytometry graph can maintain the consistency of the cells, so that the individual cells contained in each second flow cytometry graph can be directly color-labeled according to the correspondence with the first flow cytometry graph, which can reduce the error of classifying and color-labeling each cell using only the second flow cytometry graph, thereby improving the accuracy of the expression of the five cell populations obtained subsequently in each second flow cytometry graph; at the same time, ensuring the consistency and integrity of the cell analysis and cell population classification process of the entire target sample.

[0152] like Figure 8 As shown, in one embodiment, a flow cytometry image processing device 800 is provided, which can be applied to the above-mentioned electronic device. The flow cytometry image processing device 800 can include an image acquisition module 810, a cell clustering module 820, a gating module 830, an expression analysis module 840, and a result output module 850.

[0153] The image acquisition module 810 is used to obtain a first flow cytometer graph and multiple second flow cytometer graphs corresponding to the target sample; the first flow cytometer graph includes the expression of the common leukocyte antigen CD45 and the cell physical parameters of the multiple cells in the target sample; the second flow cytometer graph includes the expression of two different CD molecules in the multiple cells in the target sample.

[0154] The cell clustering module 820 is used to cluster and set gates for the multiple cells in the first flow cytometry graph to obtain five cell clusters.

[0155] The gating module 830 is used to perform cross-gating on each second flow graph to obtain a cross gate corresponding to each second flow graph.

[0156] The expression analysis module 840 is used to determine the expression status of each cell population for each CD molecule based on the cross gates corresponding to the plurality of second flow cytometry images.

[0157] The result output module 850 is used to output the cell population analysis results corresponding to the first flow cytometry graph if the expression of each CD molecule in each cell population meets the preset expression conditions. The cell population analysis results include the location, boundaries, number and total percentage of the five cell populations.

[0158] In some embodiments, the cell clustering module 820 is further used to cluster multiple cells in the first flow cytometry graph to obtain five cell populations; set gates on the first flow cytometry graph based on the five cell populations to obtain gates corresponding to the five cell populations; and classify multiple cells in the first flow cytometry graph based on the gates corresponding to the five cell populations to obtain five cell populations.

[0159] Optionally, the cell clustering module 820 is also used to cluster multiple cells in the first flow cytometry graph to obtain five cluster centers; determine the cell types corresponding to the five cluster centers according to the positions corresponding to the five cluster centers in the first flow cytometry graph; determine the cluster center and the corresponding cell type to which each cell belongs according to the distance between the position of each cell in the first flow cytometry graph and the position of each cluster center, so as to obtain five cell populations.

[0160] In some embodiments, the streaming image processing apparatus 800 may further include an adjustment module and a color labeling module.

[0161] The adjustment module is used to adjust the cross gate of each second flow cytometry graph according to the expression of the five cell populations in each second flow cytometry graph.

[0162] Optionally, the expression analysis module 840 is configured to determine the expression status of each cell population for each CD molecule based on the adjusted cross gates corresponding to the plurality of second flow cytometry images.

[0163] The color labeling module is used to color-label the five cell populations in the first flow cytometry graph to obtain color labeling results; based on the color labeling results, color-label each cell in multiple second flow cytometry graphs to obtain multiple color-labeled second flow cytometry graphs; and based on the color of each cell label in each color-labeled second flow cytometry graph, determine the expression of the five cell populations in each second flow cytometry graph.

[0164] In some embodiments, the adjustment module is further used to divide the target second flow cytometry graph into four image areas based on the cross-gate corresponding to the target second flow cytometry graph; the target second flow cytometry graph is any second flow cytometry graph; the first cell type corresponding to each image area is determined according to the first CD molecule and the second CD molecule corresponding to the target second flow cytometry graph; the first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytometry graph; the cell population corresponding to each image area is determined according to the second cell types corresponding to the five cell populations and the first cell type corresponding to each image area; in the target image area, the cell ratio of the number of cells of the cell population corresponding to the target image area to the total number of cells in the target image area is calculated; the target image area is any image area of ​​the target second flow cytometry graph; according to the cell ratio of the cell population corresponding to each image area, the horizontal and / or vertical edges of the cross-gate corresponding to the target second flow cytometry graph are moved to obtain the adjusted cross-gate of the target second flow cytometry graph.

[0165] Optionally, the adjustment module is further used to determine the cell proportion range corresponding to each image area based on the first CD molecule, the second CD molecule and the first cell type corresponding to each image area; and to move the horizontal and / or vertical edges of the cross gate corresponding to the target second flow cytometry graph according to the cell proportion and cell proportion range of the cell population corresponding to each image area until the cell proportion of the cell population corresponding to each image area is within the cell proportion range corresponding to each image area.

[0166] In an embodiment of the present application, the electronic device obtains five cell populations by accurately clustering and gating the cells in the first flow cytometry graph, and further classifies these cells into five cell populations. Combined with the cross-gates of multiple second flow cytometry graphs, it can analyze in detail the expression of each cell population corresponding to different CD molecules, thereby achieving further verification of the five cell populations to improve the accuracy and rationality of cell population classification; and, automatically setting gates for the five clusters of the first flow cytometry graph and cross-gating for multiple second flow cytometry graphs is achieved, which simplifies the manual gating process in the traditional method and can also reduce classification errors caused by improper manual operation, thereby improving the cell analysis efficiency and classification accuracy of the target sample.

[0167] Figure 9 FIG. 1 is a block diagram of an electronic device in one embodiment. The electronic device may be a mobile phone, a tablet computer, a smart wearable device, or the like. Figure 9 As shown, the electronic device 900 may include one or more of the following components: a processor 910, a memory 920 coupled to the processor 910, wherein the memory 920 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the above embodiments when executed by one or more processors 910.

[0168] The processor 910 may include one or more processing cores. The processor 910 utilizes various interfaces and circuits to connect various components within the electronic device 900. It executes instructions, programs, code sets, or instruction sets stored in the memory 920, and accesses data stored in the memory 920 to perform various functions and process data within the electronic device 900. Optionally, the processor 910 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 910 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 910 and may be implemented separately via a communications chip.

[0169] The memory 920 may include a random access memory (RAM) or a read-only memory (ROM). The memory 920 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 920 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electronic device 900 during use.

[0170] It can be understood that the electronic device 900 may include more or fewer structural elements than those in the above structural block diagram, for example, including a power supply, input buttons, a camera, a speaker, a screen, an RF (Radio Frequency) circuit, a Wi-Fi (Wireless Fidelity) module, a Bluetooth module, a sensor, etc., and is not limited here.

[0171] An embodiment of the present application discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the methods described in the above embodiments when executed by a processor.

[0172] The embodiments of the present application disclose a computer program product, including a computer program, and the computer program can be executed by a processor to implement the methods described in the above embodiments.

[0173] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or the like.

[0174] As used herein, any reference to memory, storage, database, or other medium may include nonvolatile and / or volatile memory. Suitable nonvolatile memory may include ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus DRAM (RDRAM), and direct Rambus DRAM (DRDRAM).

[0175] It should be understood that the references to "one embodiment" or "an embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "in one embodiment" or "in an embodiment" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for the present application.

[0176] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the above-mentioned processes does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0177] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of this embodiment.

[0179] In addition, the functional units in the embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0180] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory and includes several requests for a computer device (which can be a personal computer, server or network device, etc., specifically a processor in a computer device) to execute some or all of the steps of the above-mentioned methods of various embodiments of the present application.

[0181] The above describes in detail a method, device, electronic device, and storage medium for processing streaming images disclosed in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only intended to help understand the method and core concept of the present application. At the same time, for those skilled in the art, based on the concept of the present application, there may be changes in the specific implementation methods and application scope. In summary, the contents of this specification should not be understood as limiting the present application.

Claims

1. A method for processing streaming images, characterized in that: The method comprises: Obtaining a first flow cytometer graph and multiple second flow cytometer graphs corresponding to a target sample, output by a flow cytometer; the first flow cytometer graph includes the expression of the leukocyte common antigen CD45 by multiple cells in the target sample, as well as cell physical parameters of the multiple cells; the second flow cytometer graph includes the expression of two different CD molecules by multiple cells in the target sample; Grouping and gating the multiple cells in the first flow cytometry plot to obtain five cell populations; Performing cross-gating on each of the second flow cytometry graphs to obtain a cross-gate corresponding to each of the second flow cytometry graphs; determining the expression of each CD molecule in each cell population according to the cross gates corresponding to the plurality of second flow cytometry graphs; If the expression of each CD molecule in each cell population meets the preset expression conditions, the cell population analysis results corresponding to the first flow cytometry graph are output, and the cell population analysis results include the position, boundary, number and total proportion of the five cell populations.

2. The method according to claim 1, characterized in that The multiple cells in the first flow cytometry graph are grouped and gated to obtain five cell populations, including: Grouping the multiple cells in the first flow cytometry graph to obtain five cell populations; According to the five cell populations, gates are set on the first flow cytometry graph to obtain gates corresponding to the five cell populations respectively; The multiple cells in the first flow cytometry graph are classified according to the gates corresponding to the five cell populations to obtain five cell populations.

3. The method according to claim 2, characterized in that The plurality of cells in the first flow cytometry graph are grouped to obtain five cell populations, including: Clustering the multiple cells in the first flow cytometry graph to obtain five cluster centers; Determining the cell types corresponding to the five cluster centers respectively according to the positions corresponding to the five cluster centers in the first flow cytometry graph; According to the distance between the position of each cell in the first flow cytometry graph and the position of each cluster center, the cluster center to which each cell belongs and the corresponding cell type are determined to obtain five cell populations.

4. The method according to claim 1, wherein After performing cross-gating on each of the second flow graphs to obtain a cross-gate corresponding to each of the second flow graphs, the method further includes: Adjusting the cross gate of each of the second flow cytometry graphs according to the expression of the five cell populations in each of the second flow cytometry graphs; Determining the expression of each CD molecule in each cell population according to the cross gates corresponding to the plurality of second flow cytometry images includes: The expression of each CD molecule in each cell population is determined based on the adjusted cross gates corresponding to the plurality of second flow cytometry images.

5. The method according to claim 4, characterized in that Before adjusting the cross gate of each of the second flow cytometry graphs according to the expression of the five cell populations in each of the second flow cytometry graphs, the method further includes: In the first flow cytometry graph, the five cell populations are respectively color-coded to obtain color-coded results; According to the color labeling results, color labeling is performed on each cell in the plurality of second flow cytometry images to obtain a plurality of color-labeled second flow cytometry images; The expression status of the five cell populations in each of the second flow cytometry graphs after color marking is determined according to the color marked for each cell in the second flow cytometry graphs.

6. The method according to claim 4, characterized in that The step of adjusting the cross gate of each of the second flow cytometry graphs according to the expression of the five cell populations in each of the second flow cytometry graphs comprises: Based on a cross gate corresponding to a target second flow cytometer graph, the target second flow cytometer graph is divided into four image regions; the target second flow cytometer graph is any second flow cytometer graph; determining a first cell type corresponding to each of the image regions according to a first CD molecule and a second CD molecule corresponding to the target second flow cytometric graph, wherein the first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytometric graph; Determining the cell population corresponding to each image region according to the second cell types corresponding to the five cell populations and the first cell type corresponding to each image region; In a target image region, calculating the cell ratio of the number of cells of the cell population corresponding to the target image region to the total number of cells in the target image region; the target image region is any image region of the target second flow cytometer; According to the cell ratio of the cell population corresponding to each of the image regions, the horizontal side and / or the vertical side of the cross gate corresponding to the target second flow cytometry graph is moved to obtain an adjusted cross gate of the target second flow cytometry graph.

7. The method according to claim 6, characterized in that The step of moving the horizontal side and / or vertical side of the cross gate corresponding to the target second flow cytometry graph according to the cell ratio of the cell population corresponding to each of the image regions comprises: determining a cell proportion range corresponding to each of the image regions according to the first CD molecule, the second CD molecule, and the first cell type corresponding to each of the image regions; According to the cell ratio and cell ratio range of the cell population corresponding to each of the image areas, the horizontal and / or vertical edges of the cross gate corresponding to the target second flow cytometry graph are moved until the cell ratio of the cell population corresponding to each of the image areas is within the cell ratio range corresponding to each of the image areas.

8. A streaming image processing device, characterized in that: The device comprises: an image acquisition module, configured to acquire a first flow cytometer graph and a plurality of second flow cytometer graphs corresponding to a target sample, the first flow cytometer graph comprising the expression of the leukocyte common antigen CD45 and the cell physical parameters of the plurality of cells in the target sample; and a second flow cytometer graph comprising the expression of two different CD molecules in the plurality of cells in the target sample. a cell clustering module, configured to cluster and gate the plurality of cells in the first flow cytometry plot to obtain five cell clusters; a gating module, configured to perform cross-gating on each of the second flow cytometry graphs to obtain a cross-gate corresponding to each of the second flow cytometry graphs; an expression analysis module, configured to determine the expression of each CD molecule in each cell population based on the cross gates corresponding to the plurality of second flow cytometry images; A result output module is used to output the cell population analysis results corresponding to the first flow cytometry graph if the expression of each CD molecule in each cell population meets the preset expression conditions, and the cell population analysis results include the position, boundary, number and total proportion of the five cell populations.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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