Method and apparatus for processing streaming image, electronic device and storage medium

Abstract

Embodiments of the present application relate to a flow image processing method and device, electronic equipment and storage medium, the method comprising: acquiring a first flow graph and a plurality of second flow graphs corresponding to a target sample output by a flow cytometer; clustering and gating a plurality of cells in the first flow graph to obtain five cell groups; cross-gating each second flow graph to obtain a cross gate corresponding to each second flow graph; determining the expression of each cell group corresponding to each CD molecule according to the cross gate corresponding to each second flow graph; if the expression of each cell group corresponding to each CD molecule meets the preset expression condition, outputting a cell group analysis result corresponding to the first flow graph, the cell group analysis result including the position, convenience, quantity and total proportion of the five cell groups. The flow image processing method, device, electronic equipment and storage medium improve the accuracy of cell analysis and cell group classification.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and specifically to a method, apparatus, electronic device, and storage medium for processing streaming images. Background Technology

[0002] With the advancement of medical testing technology, the detection and analysis of cells in blood samples can facilitate subsequent hematological analyses. Currently, flow cytometry is widely used in hematology. Analysts can use flow cytometers to process blood samples, and then use the flow cytometry data to perform gating, classifying and analyzing the individual cells in the blood sample to obtain cell population analysis results. However, because most current electronic analysis devices still require manual gating and classification by physicians, classification errors due to improper manual operation can occur, resulting in low efficiency and poor classification accuracy in cell analysis. Summary of the Invention

[0003] This application discloses a method, apparatus, electronic device, and storage medium for processing streaming images, which improves the efficiency of cell analysis of target samples and the accuracy of cell population classification.

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

[0005] The flow cytometer outputs a first flow cytogram and multiple second flow cytograms corresponding to the target sample. The first flow cytogram includes the expression of multiple cells in the target sample corresponding to the leukocyte common antigen CD45, as well as the cell physical parameters of the multiple cells. The second flow cytogram includes the expression of multiple cells in the target sample corresponding to two different CD molecules.

[0006] Multiple cells in the first flow cytogram are grouped and gated to obtain five cell populations;

[0007] Perform cross-gate design on each of the second flow cytometry graphs to obtain the cross gate corresponding to each of the second flow cytometry graphs;

[0008] Based on the cross-gates corresponding to the multiple second flow cytometry plots, the expression status of each cell population for each CD molecule is determined.

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

[0010] As an optional implementation, the step of grouping and gatening multiple cells in the first flow cytometry to obtain five cell populations includes: grouping multiple cells in the first flow cytometry to obtain five cell populations; gatening the first flow cytometry according to the five cell populations to obtain gates corresponding to the five cell populations respectively; and classifying the multiple cells in the first flow cytometry according to the gates corresponding to the five cell populations respectively to obtain five cell populations.

[0011] As an optional implementation, the step of grouping multiple cells in the first flow cytometry to obtain five cell populations includes: clustering multiple cells in the first flow cytometry to obtain five cluster centers; determining the cell types corresponding to the five cluster centers based on their respective positions in the first flow cytometry; and determining the cluster center and corresponding cell type of each cell based on the distance between the position of each cell in the first flow cytometry and the position of each cluster center, thereby obtaining five cell populations.

[0012] As an optional implementation, after performing cross-gating on each of the second flow cytometry plots to obtain the cross-gates corresponding to each of the second flow cytometry plots, the method further includes: adjusting the cross-gates of each of the second flow cytometry plots according to the expression of the five cell populations in each of the second flow cytometry plots; determining the expression of each cell population in each CD molecule according to the cross-gates corresponding to the multiple second flow cytometry plots includes: determining the expression of each cell population in each CD molecule according to the adjusted cross-gates corresponding to the multiple second flow cytometry plots.

[0013] As an optional implementation, before adjusting the cross-gate of each of the second flow cytometry plots based on the expression of the five cell populations in each of the second flow cytometry plots, the method further includes: color-coding the five cell populations in the first flow cytometry plot to obtain color-coding results; color-coding each cell in the multiple second flow cytometry plots based on the color-coding results to obtain multiple color-coded second flow cytometry plots; and determining the expression of the five cell populations in each of the second flow cytometry plots based on the color of each cell in each color-coded second flow cytometry plot.

[0014] As an optional implementation, adjusting the gates of each second flow cytogram based on the expression of the five cell populations in each second flow cytogram includes: dividing the target second flow cytogram into four image regions based on the gates corresponding to the target second flow cytogram; the target second flow cytogram is any second flow cytogram; determining 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 cytogram; the first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytogram; determining the cell population corresponding to each image region based on the second cell types corresponding to the five cell populations and the first cell type corresponding to each image region; calculating the cell proportion of the cell population corresponding to the target image region to the total number of cells in the target image region within the target image region; the target image region is any image region of the target second flow cytogram; and moving the horizontal and / or vertical sides of the gates corresponding to the target second flow cytogram based on the cell proportion of the cell population corresponding to each image region to obtain the adjusted gates of the target second flow cytogram.

[0015] As an optional implementation, the step of moving the horizontal and / or vertical edges of the cross gate corresponding to the target second flow cytometry map according to the cell proportion of the cell population corresponding to each image region includes: determining the cell proportion range corresponding to each image region based on the first CD molecule, the second CD molecule, and the first cell type corresponding to each image region; and moving the horizontal and / or vertical edges of the cross gate corresponding to the target second flow cytometry map according to the cell proportion of the cell population corresponding to each image region and the cell proportion range, until the cell proportion of the cell population corresponding to each image region is within the cell proportion range corresponding to each image region.

[0016] This application discloses a streaming image processing apparatus, comprising:

[0017] The image acquisition module is used to acquire a first flow cytometer and multiple second flow cytometers output by the flow cytometer, corresponding to the target sample; the first flow cytometer includes the expression status of multiple cells in the target sample corresponding to the leukocyte common antigen CD45, as well as the cell physical parameters of the multiple cells; the second flow cytometer includes the expression status of multiple cells in the target sample corresponding to two different CD molecules respectively;

[0018] The cell clustering module is used to cluster and gate multiple cells in the first flow cytogram to obtain five cell clusters.

[0019] A gate module is used to perform cross-shaped gates on each of the second flow graphs to obtain the cross-shaped gates corresponding to each of the second flow graphs;

[0020] The expression analysis module is used to determine the expression status of each cell population for each CD molecule based on the cross-gates corresponding to the multiple second flow cytometry plots.

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

[0022] This application discloses an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to implement the method described in any of the above embodiments.

[0023] This application discloses a computer-readable storage medium that stores a computer program, which, when executed by a processor, implements the methods described in any of the above embodiments.

[0024] This application discloses a computer program product, including a computer program, which, when executed by a processor, implements the method described in any of the above embodiments.

[0025] The present application discloses a flow cytometry image processing method, apparatus, electronic device, and storage medium. The electronic device acquires a first flow cytometry image and multiple second flow cytometry images output by a flow cytometer, corresponding to a target sample. The first flow cytometry image includes the expression status of multiple cells in the target sample corresponding to the leukocyte common antigen CD45, as well as the cell physical parameters of the multiple cells. The second flow cytometry image includes the expression status of multiple cells in the target sample corresponding to two different CD molecules. The multiple cells in the first flow cytometry image are grouped and gated to obtain five cell populations. Cross-gating is performed on each second flow cytometry image to obtain a cross gate corresponding to each second flow cytometry image. Based on the cross gates corresponding to the multiple second flow cytometry images, the expression status of each cell population corresponding to each CD molecule is determined. If the expression status of each cell population corresponding to each CD molecule meets the preset expression status, the cell population analysis result corresponding to the first flow cytometry image is output. The cell population analysis result includes the position, boundary, number, and total percentage of the five cell populations. In this embodiment, the electronic device accurately groups and gates the cells in the first flow cytometry graph to obtain five cell populations. These cells are then further classified into five cell groups. Combined with the cross-gating of multiple second flow cytometry graphs, the expression of each cell group at different CD molecules can be analyzed in detail. This allows for further verification of the five cell groups, improving the accuracy and rationality of cell group classification. Furthermore, the device automatically gates the five groups in the first flow cytometry graph and the cross-gating of multiple second flow cytometry graphs, simplifying the manual gate setting process in traditional methods and reducing classification errors caused by improper manual operation. This improves the efficiency and accuracy of cell analysis of the target sample. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of a streaming image processing method in one embodiment;

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

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

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

[0031] Figure 3A flowchart illustrating five cell populations obtained in one embodiment;

[0032] Figure 4 This is a flowchart illustrating the five cell populations obtained in one embodiment;

[0033] Figure 5 A flowchart of a streaming image processing method in another embodiment;

[0034] Figure 6 A flowchart for adjusting the cross gates of each second flow chart in one embodiment;

[0035] Figure 7 A flowchart of a streaming image processing method in another embodiment;

[0036] Figure 8 This is a block diagram of a streaming image processing apparatus in one embodiment;

[0037] Figure 9 This is a structural block diagram of an electronic device in one embodiment. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

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

[0040] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of this application, 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.

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

[0042] In related technologies, when analyzing blood samples, after obtaining the flow cytometry graph, analysts need to manually classify the cells in the graph and use different colors to distinguish different cell types. After classifying the cells, they need to switch between multiple flow cytometry graphs and manually perform gating operations, which usually takes a considerable amount of time. For flow cytometry graphs with severe cell adhesion, the time required is even longer, leading to low analytical efficiency. Furthermore, after gating the flow cytometry graph, it may be necessary to manually analyze each cell in each sample individually, check the cell expression, and manually adjust the position of the cross gates based on the expression. Only after all these manual operations can the analyst complete the analysis and classification of each cell in the sample. Due to the limited concentration of human personnel, the manual operation process not only results in slow analysis of CD molecule expression in cells but also may lead to inaccurate classification, inaccurate placement of the five-group gates, and incorrect cross gate positions due to subjective errors. This can cause subsequent errors in determining whether the user corresponding to the blood sample has a blood disease or in analyzing the severity of blood diseases.

[0043] This application discloses a method, apparatus, electronic device, and storage medium for processing streaming images, which improves the efficiency of cell analysis of target samples and the accuracy of cell population classification.

[0044] The following is an introduction to some of the terms used in this application:

[0045] Flow cytometry is a technique used to count and sort tiny particles suspended in a fluid. It enables high-speed, individual quantitative analysis and sorting of cells by detecting labeled fluorescent signals. Flow cytometry can be performed using a flow cytometer.

[0046] Flow cytometry is a device for the rapid, multi-parameter quantitative analysis and sorting of single cells or other biological particles suspended in a sample. Its core technology is flow cytometry. The working principle of flow cytometry can be summarized as follows: the sample is passed through a specific fluid dynamics device to form a single-cell laminar flow, allowing cells to pass through a detection zone at a certain speed. In the detection zone, the cells are illuminated by a laser beam, emitting fluorescence or scattered light signals. These signals are received by photomultiplier tubes or other optical detectors and converted into electrical signals. Computer processing and analysis of these electrical signals allow for the acquisition of various biophysical and biochemical parameters of each cell in the sample, such as cell size, shape, internal particle size, and expression levels of surface markers. Flow cytometry can be used to detect various CD molecules, receptors, and other markers on the cell surface. In some embodiments, flow cytometry can be used to perform flow cytometry detection on target samples, and the detection data can be analyzed to obtain flow cytometry plots.

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

[0048] Cellular physical parameters refer to quantitative indicators used to describe the physical properties of cells. Cellular 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 flow cytometry when a laser beam irradiates a cell, caused by particles within the cell (such as organelles, the nucleus, and protein aggregates). SSC-A reflects cell granularity and analyzes cell structural characteristics; a higher SSC-A value in the flow cytometry plot indicates a more complex internal cell structure or greater granularity. Different cell types have different SSC-A values. FSC-A is the area of ​​the scattered light signal generated by the forward scattering of a laser beam by the cell; FSC-A reflects cell size information; larger cells have higher FSC-A values.

[0049] Flow cytometry is a data visualization method generated by flow cytometry, used to show the expression of individual cells in a sample across multiple parameters. Flow cytometry plots can include, but are not limited to, scatter plots, histograms, contour plots, and pseudo-color plots; in a scatter plot, one point can represent one or more cells. Different cell characteristics can be analyzed based on different parameters using flow cytometry. For example, the first flow cytometry plot can be a CD45 / SSC-A flow cytometry plot, including the expression of multiple cells in the target sample at CD45 and the area of ​​lateral scattering light from the cells. In the CD45 / SSC-A flow cytometry plot, the horizontal axis represents the expression intensity of cells at CD45, and the vertical axis represents the granularity of the cells. The second flow cytometry plot includes the expression of multiple cells in the target sample at two different CD molecules. These two CD molecules can be any two CD molecules; for example, if the two CD molecules are CD3 and CD7, then the horizontal axis of the second flow cytometry plot of CD3 / CD7 represents the expression intensity of cells at CD3, and the vertical axis represents the expression intensity of cells at CD7.

[0050] Gating is an important step in flow cytometry, used to draw one or more boundaries or regions on the flow cytogram to separate cell populations with different characteristics. Gating methods include, but are not limited to, threshold gating, light scattering gating, and fluorescence gating.

[0051] A gate is a boundary line or region edge used to select a cell population with a specific characteristic. The shape of a gate can be arbitrary, including but not limited to linear gates, rectangular gates, circular gates, elliptical gates, polygonal gates, and four-quadrant gates. Specifically, a quintic gate refers to a boundary line set on a flow cytometry plot based on the scattered light and fluorescence characteristics of cells. A quintic gate contains gates corresponding to five different cell populations, used to distinguish and identify these five cell populations. A cross gate, also known as a four-quadrant gate, refers to a cross-shaped boundary line dividing the flow cytometry plot into four quadrants, used to analyze the intensity of two fluorescent labels.

[0052] The five cell populations refer to five cell types classified based on cell size and granularity during flow cytometry analysis. For example, based on 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 streaming image processing and analysis method provided in this application can be applied to, for example... Figure 1 In the electronic device 100 shown. For example... Figure 1 As shown, the electronic device 100 can be connected to the flow cytometer 110. The flow cytometer 110 is used to perform flow cytometry detection on the target sample 120. The electronic device 100 may include, but is not limited to, smart terminal devices, tablet computers, PCs (Personal Computers), computers, laptops, 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, wired or wireless connection methods such as data cable connection, Wireless Local Area Network (WLAN) connection, and WiFi connection.

[0054] When it is necessary to analyze each cell contained in the target sample 120, a flow cytometer 110 can be used to perform flow cytometry detection on the target sample 120 based on CD45 and cell physical parameters to obtain a first flow cytogram, and to detect the target sample 120 based on any two different CD molecular parameters to obtain multiple second flow cytograms, and send the first flow cytogram and multiple second flow cytograms to the electronic device 100. After receiving the first flow cytometry plot and multiple second flow cytometry plots corresponding to the target sample 120 transmitted by the flow cytometer 110, the electronic device 100 performs cell clustering and gatening on multiple cells in the first flow cytometry plot, resulting in five cell populations. The electronic device 100 also performs cross-gating on each of the second flow cytometry plots, obtaining cross-gates corresponding to each second flow cytometry plot. Based on the cross-gates corresponding to the multiple second flow cytometry plots, the expression status of each cell population for each CD molecule is determined. If the expression status of each cell population for each CD molecule conforms to the preset expression status, the electronic device 100 outputs the cell population analysis results corresponding to the first flow cytometry plot, obtaining accurate positions, boundaries, quantities, and total percentages of the five cell populations. The electronic device 100 can automatically perform gatening on the five clusters of the first flow cytometry plot and cross-gating on the multiple second flow cytometry plots, enabling further verification and classification of the five cell populations, reducing classification errors caused by improper manual operation, thereby improving the cell analysis efficiency and cell population classification accuracy of the target sample 120.

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

[0056] Step 210: Obtain the first flow cytometry plot and multiple second flow cytometry plots output by the flow cytometer, corresponding to the target sample.

[0057] In some embodiments, analysts place the collected target sample into a flow cytometer for flow cytometry analysis. The flow cytometer generates a first flow cytometry plot and multiple second flow cytometry plots corresponding to the target sample based on multiple CD molecules set by the analyst, and sends the first flow cytometry plot and multiple second flow cytometry plots to an electronic device. After acquiring the first flow cytometry plot and multiple second flow cytometry plots, the electronic device can analyze the first flow cytometry plot and multiple second flow cytometry plots to analyze and classify the multiple cells contained in the target sample, thereby obtaining cell population analysis results.

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

[0059] Because CD45 is universally expressed in all cell lines, and the expression intensity and molecular form of CD45 vary among different cell types, and because scattered light parameters can reflect the granularity of individual cells, electronic devices can more accurately group or classify the individual cells in a target sample using a first flow cytometry.

[0060] For example, a point in a CD45 / SSC-A flow cytometry plot can represent one or more cells; the x-axis value of a point can represent the expression intensity of CD45 in the cell corresponding to that point, and the y-axis value can represent the granularity of the cell corresponding to that point.

[0061] The second flow cytometry plot shows the expression of multiple cells in the target sample at two different CD molecules. The two CD molecules in the second flow cytometry plot can be any two CD molecules from CD58, CD38, 7AAD, CD66c, CD13+33, CD34, CD10, CD19, CD200, or CD45 (Cluster of Differentiation). For example, in a CD19 / CD10 flow cytometry plot, the horizontal axis value represents the expression intensity of CD19, and the vertical axis value represents the expression intensity of CD10.

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

[0063] Optionally, the electronic device can acquire the names and corresponding descriptive information of each flow cytometry CD molecule sent by the flow cytometer; then, it performs semantic matching between the flow cytometry descriptive information corresponding to each flow cytometry CD molecule name and the stored descriptive information corresponding to multiple CD molecule names to obtain the matching degree between each flow cytometry descriptive information and the multiple descriptive information; in the first flow cytometry plot and multiple second flow cytometry plots, it modifies each flow cytometry CD molecule name to the CD molecule name corresponding to the descriptive information with the highest matching degree. For example, if the electronic device detects that the flow cytometry descriptive information corresponding to flow cytometry CD molecule name 'a' has the highest matching degree with the descriptive information corresponding to CD molecule name 'A', then it modifies the flow cytometry CD molecule name 'a' in the first flow cytometry plot and multiple second flow cytometry plots to CD molecule name 'A'. This can effectively eliminate inconsistencies in CD molecule names and prevent the electronic device from being unable to analyze the expression of the CD molecule in cells.

[0064] Step 220: Group and gate the multiple cells in the first flow cytometry graph to obtain five cell groups.

[0065] In some embodiments, the electronic device may perform feature preprocessing on multiple cells in the first flow cytogram to obtain the biological characteristics of each cell; calculate the correlation between the biological characteristics of each cell; and group cells with a correlation greater than a correlation threshold into the same cell population to obtain five cell populations. The biological characteristics of the cells may include, but are not limited to, cell size, shape, internal structure, and surface molecular expression.

[0066] In some embodiments, the electronic device may plot a five-group gate (e.g., ...) on the first streaming graph. Figure 2B As shown, the vertical axis SS INT LIN represents the linear signal of the side-scattered light, and the horizontal axis CD45BV510 represents the CD45 fluorescence intensity stained with BV510 fluorescent dye. The five-group gate contains gates corresponding to five cell populations, which are used to divide multiple cells in the first flow cytogram into five cell populations. Each gate contains multiple cells of one cell population.

[0067] Step 230: Perform cross gates on each second flow chart to obtain the cross gates corresponding to each second flow chart.

[0068] In some embodiments, based on the expression of each cell in each of the two different CD molecules in each second flow cytogram, each second flow cytogram is divided into regions to obtain the cross-gate corresponding to each second flow cytogram.

[0069] The cross-shaped 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 cytometry graph into high and low thresholds, and the horizontal line divides the expression of another CD molecule into high and low thresholds. The cross-shaped gate divides the second flow cytometry graph into four quadrants: Lower left quadrant: Represents cell populations where the expression of both CD molecules is below the set threshold. Lower right quadrant: Represents cell populations where 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 cell populations where 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 cell populations where the expression of both CD molecules is above the set threshold.

[0070] For example, assuming a second streaming plot is a CD58 / CD19 streaming plot, the cross gate can divide the second streaming plot into four quadrants (e.g., Figure 2CAs shown, "APC" and "FITC" are two different fluorescent dyes. In the second flow cytogram, the CD molecules on the horizontal axis are CD58, and the CD molecules on the vertical axis are CD19. The electronic device can obtain the following quadrants: upper left (CD58-CD19+, representing a cell population that does not express CD58 but highly expresses CD19), lower left (CD58-CD19-, representing a cell population that does not express either CD58 or CD19), upper right (CD58+CD19-, representing a cell population that highly expresses CD58 but does not express CD19), and lower right (CD58+CD19+, representing a cell population that highly expresses both CD58 and CD19). High expression of CD58 can be considered equivalent to "positive" CD58 expression; low expression of CD58 can be considered equivalent to "negative" CD58 expression.

[0071] In some embodiments, the electronic device can perform cross-gating on each second flow cytometry plot according to the FlowDensity algorithm to obtain the cross-gate corresponding to each second flow cytometry plot; wherein, the FlowDensity algorithm is used to set gates in complex cell populations. Specifically, the electronic device can use the FlowDensity algorithm or methods such as nuclear density estimation to estimate the density of the fluorescence signals corresponding to two different CD molecules in the second flow cytometry plot, and visualize the density estimation results, usually using a two-dimensional density map or contour map to show 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, and these peaks usually correspond to the center of the quadrant region; the electronic device then calculates the preliminary threshold for segmenting the quadrant region according to the distribution and relative position of the density peaks, using methods such as density peak detection methods, clustering algorithms, or empirical rules; and preliminarily sets the cross-gate on the second flow cytometry plot according to the calculated preliminary threshold.

[0072] In some embodiments, after drawing a cross gate in the second flow cytometry graph, the electronic device 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 patterns 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: Based on the cross-gates corresponding to the multiple second flow cytometry plots, determine the expression status of each cell population for each CD molecule.

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

[0075] Step 250: If the expression of each cell population for each CD molecule meets the preset expression criteria, then output the cell population analysis results corresponding to the first flow cytogram.

[0076] The preset expression profile includes the preset expression profile of each cell population for each CD molecule. The preset expression profile may include the preset number of "positive / negative" cells, the preset CD molecule type corresponding to the "positive / negative" CD molecule, etc.

[0077] The cell population analysis results include the location, boundaries, number, and percentage of the total number of the five cell populations. It may also include multiple second flow cytometry plots after the cross gate is labeled, first flow cytometry plots after the five-group gate is labeled, and first flow cytometry plots after grouping.

[0078] In some embodiments, after the electronic device determines the expression status of each cell population for each CD molecule, it can match the expression status of each cell population for each CD molecule with the corresponding preset expression status. If the expression status of each cell population for each CD molecule matches the corresponding preset expression status, it means that the expression status of each cell population for each CD molecule conforms to the preset expression status, that is, the electronic device's cell analysis and classification of the target sample are accurate, and the electronic device can output the cell population analysis results corresponding to the first flow cytometry.

[0079] Optionally, if the electronic device detects that the expression of any cell population for each CD molecule does not match the corresponding preset expression, it indicates that at least one cell population does not conform to the preset expression for each CD molecule. In other words, the electronic device's cell analysis and classification of the target sample is inaccurate. In this case, the electronic device can re-execute the step of grouping multiple cells in the first flow cytometry to obtain five new cell populations, thereby obtaining five new grouping gates and five new cell populations. Then, it can re-judge whether they conform to the preset expression. Through iterative methods, it can continue until an accurate and reliable cell population analysis result is obtained.

[0080] In some embodiments, the preset expression profile may include multiple preset CD molecule types that are expressed positively in each cell population; the electronic device may determine the multiple CD molecule types that are expressed positively in each cell population based on the expression profile of each CD molecule corresponding to each cell population; if the multiple CD molecule types that are expressed positively in each cell population are found to be exactly the same as the multiple preset CD molecule types corresponding to each cell population, the electronic device may consider that the expression profile of each cell population corresponding to each CD molecule conforms to the preset expression profile, and output the cell population analysis results corresponding to the first flow cytogram.

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

[0082] In some embodiments, when the electronic device outputs the cell population analysis results corresponding to the first flow cytogram, it can also calculate the proportion of cells in each cell population expressing different CD molecules to the total number of cells. If the electronic device detects that the proportion of cells corresponding to the first cell population is not within the preset proportion range corresponding to the cell category of the first cell population, it outputs an early warning message for the first cell population, where the first cell population can be any cell population, and the early warning message is used to indicate that the proportion of cells corresponding to the first cell population is abnormal.

[0083] Specifically, assuming the target sample is a bone marrow sample, the normal lymphocyte range is 15%-25%; assuming the cell type corresponding to the first cell group is lymphocytes, the preset proportion range is 15%-25%. Therefore, if the electronic device detects that the proportion of cells corresponding to the first cell group is 14%, the electronic device will output a warning prompt corresponding to the first cell group.

[0084] The alert notification can also include a prompt indicating that the proportion of cells corresponding to the first cell population is lower or higher than a preset range. For example, if the proportion of cells corresponding to the first cell population is lower than the preset range, the alert notification can describe it as a relative decrease; if the proportion of cells corresponding to the first cell population is higher than the preset range, the alert notification can describe it as a relative increase. Using alert notifications, analysts can be quickly informed that the proportion of cells in the first cell population is abnormal, allowing for timely intervention and assisting analysts in conducting disease diagnosis and treatment analysis of target samples. Furthermore, the inclusion of a prompt indicating that the proportion of cells is lower or higher than the preset range eliminates the need to reconfirm the proportion of cells in the first cell population, enabling analysts to make direct adjustments and improving their work efficiency.

[0085] In this embodiment, the electronic device accurately groups and gates the cells in the first flow cytometry graph to obtain five cell populations. These cells are then further classified into five cell groups. Combined with the cross-gating of multiple second flow cytometry graphs, the expression of each cell group at different CD molecules can be analyzed in detail. This allows for further verification of the five cell groups, improving the accuracy and rationality of cell group classification. Furthermore, the device automatically gates the five groups in the first flow cytometry graph and the cross-gating of multiple second flow cytometry graphs, simplifying the manual gate setting process in traditional methods and reducing classification errors caused by improper manual operation. This improves the efficiency and accuracy of cell analysis of the target sample.

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

[0087] Step 302: The multiple cells in the first flow cytometry plot are divided into five cell populations.

[0088] In some embodiments, the electronic device may group multiple cells in the first flow cytometry graph according to the K-means algorithm to obtain five cell populations. Specifically, the electronic device may first randomly select five cells as center cells from the multiple cells in the first flow cytometry graph, and iteratively update the position of the center cells and the division of the cell populations until all cells in the first flow cytometry graph are grouped into five clusters, resulting in five cell populations, such that the cells within each cell population are as similar as possible, while the differences between cells in different cell populations are as large as possible.

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

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

[0091] In some embodiments, the electronic device may cluster multiple cells in the first streaming 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 multiple cells in the first flow cytometry graph according to preset parameters ε (neighborhood radius) and MinPts (minimum number of samples), dividing the multiple cells in the first flow cytometry graph into five different clusters and noise points, removing the cells corresponding to the noise points, determining the cluster label corresponding to each cluster, and cells with the same cluster label belong to the same cluster. The electronic device then obtains five cluster centers by calculating the geometric center (such as the mean) of each cluster or according to the density distribution of cells within the cluster.

[0093] Step 404: Determine the cell types corresponding to the five cluster centers based on their respective positions in the first flow cytometry plot.

[0094] In some embodiments, based on the positions of the five cluster centers in the first flow cytometry, the expression level of CD molecules and the scattered light parameter value of the five cluster centers in CD45 are determined; 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 range to which the expression level of CD molecules belongs and the preset scattered light range to which the scattered light parameter value belongs are determined, so as to determine the cell types corresponding to the five cluster centers respectively.

[0095] For example, assuming that the expression level of CD molecules in CD45 of cluster center B belongs to the preset expression range of lymphocytes in CD45, and the scattering light parameter value of cluster center B belongs to the preset scattering light range of lymphocytes, then the electronic device considers the cell type corresponding to cluster center B to be lymphocytes.

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

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

[0098] Specifically, the electronic device can calculate the distances between each cell in the first streaming graph and its five cluster centers. The methods used by the electronic device to calculate these distances include, but are not limited to, Euclidean distance, Manhattan distance, or other suitable distance metrics. Based on the cluster centers to which the K neighbors of each cell belong in the first streaming graph, the electronic device assigns each cell to the cluster center containing the most of its K neighbors and its corresponding cell type; that is, the cluster center to which each cell belongs is the same as the cluster center to which the majority of its K neighbors belong; the cell type corresponding to the cluster center to which the cell belongs is the cell type corresponding to the cell.

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

[0100] In this embodiment of the application, the electronic device clusters multiple cells, first determining five cluster centers, and then determining the cluster center to which each cell belongs based on the distance between the cluster centers and the cells, thereby determining the cell population, rather than directly obtaining the cell population through distance. This reduces noise and errors caused by direct clustering, thereby improving the accuracy of cell grouping.

[0101] Step 304: Based on the five cell populations, gate the first flow cytometry graph to obtain the gates corresponding to the five cell populations respectively.

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

[0103] Step 306: Classify the multiple cells in the first flow cytometry graph according to the phyla 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 in the first flow cytometry for each of the five cell populations based on the gates corresponding to them; and determine the cell types corresponding to the cells within the gates corresponding to the five cell populations based on the preset expression intensity ranges of CD45 and SSC-A for each cell type, thus obtaining five cell populations. Here, 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 of CD45 and SSC-A for each cell type can be determined based on extensive experimental data and clinical studies. For example, the preset expression intensity range for CD45 and CD45Dim cells in nucleated erythrocytes can be 10. 1 -10 2 The preset expression intensity range of CD45 in CD45Dim cells and lymphocytes can be 10. 2 -10 3 Fluorescence intensity, etc.

[0105] In this embodiment, the electronic device first groups multiple cells in the first flow cytometry graph to determine five cell populations, and then gates these five cell populations. This allows for more accurate cell classification based on the five gates, effectively distinguishing the characteristics of different cell populations and improving the accuracy and reliability of cell analysis. The grouping strategy using DBSCAN and K-means algorithms, along with the gate method targeting the boundary contours of cell populations, ensures high similarity of cell features within each cell population, while significant differences in cell features exist between different subpopulations.

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

[0107] Step 502: Obtain the first flow cytometry plot and multiple second flow cytometry plots output by the flow cytometer, corresponding to the target sample.

[0108] Step 504: The multiple cells in the first flow cytometry plot are divided into five cell populations.

[0109] Step 506: Perform cross gates on each second flow chart to obtain the cross gates corresponding to each second flow chart.

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

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

[0112] To further optimize cell population segmentation in complex samples or under noisy conditions, and to ensure more accurate identification of various cell populations, thereby ensuring the accuracy and consistency of cell analysis, the electronic device, after obtaining the cross gates corresponding to the second flow cytogram, can adjust the positions of the cross gates in each second flow cytogram based on the expression of these five cell populations in each second flow cytogram, thereby improving the accuracy of cell analysis.

[0113] Optionally, the electronic device can adjust the gates of each second flow cytogram based on the distribution location, expression intensity, density, and distribution shape of the five cell populations in the second flow cytogram. For example, cell populations with high expression intensity may require more precise gates (with smaller areas enclosed by the gates) to distinguish them, while cell populations with low expression intensity may require larger gates (with larger areas enclosed by the gates) to include all cells.

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

[0115] Step 602: Based on the cross gate corresponding to the target second flow graph, divide the target second flow graph into four image regions.

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

[0117] Since the cross-gate can divide the second flow cytogram into four quadrants, these four quadrants of the cross-gate can correspond to four image regions, and 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 cytometry.

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

[0120] Since different cell types express different CD molecules, and therefore different CD molecules can elicit an immune response, the electronic device can determine the first cell type corresponding to each image region based on a preset correspondence between CD molecules and cell types, according to the first and second CD molecules corresponding to the target second flow cytometry. For example, assuming the target second flow cytometry is a CD10 / CD19 flow cytometry, based on the cell characteristics of different cell types, cells in the upper right region of the CD10 / CD19 flow cytometry are represented as double-positive (CD10+CD19+, indicating high expression of both CD10 and CD19). Furthermore, based on the cell specificity of CD10, CD19, and various cell types, the electronic device can determine that lymphocytes are typically highly expressed in the CD10 and CD19 channels. Therefore, the first cell type corresponding to the upper right region of the CD10 / CD19 flow cytometry can be considered to be lymphocytes.

[0121] Step 606: Determine the cell population corresponding to each image region based on the second cell type corresponding to each of the five cell populations and the first cell type corresponding to each image region.

[0122] In some embodiments, the electronic device may match the second cell type corresponding to each of 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 that matches the first cell type corresponding to each image region as the cell group corresponding to each image region.

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

[0124] Step 608: In the target image region, calculate the proportion of the number of cells in the cell population corresponding to the target image region to the total number of cells in the target image region.

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

[0126] For example, suppose that there are a total of 100 events in image region W, and the cell group A corresponding to image region W has 40 events in image region W. Then the number of cells in cell group A in image region W accounts for 40% of the total number of cells in image region W.

[0127] Step 610: Based on the cell proportion of the cell population corresponding to each image region, move the horizontal and / or vertical sides of the cross gate corresponding to the target second flow cytometry to obtain the cross gate of the target second flow cytometry.

[0128] In some embodiments, the electronic device may determine the cell proportion range corresponding to each image region based on the first CD molecule, the second CD molecule, and the first cell type corresponding to each image region; and move the horizontal and / or vertical sides of the cross gate corresponding to the target second flow cytogram according to the cell proportion of the cell population corresponding to each image region and the cell proportion range, until the cell proportion of the cell population corresponding to each image region is within the cell proportion range corresponding to each image region.

[0129] The cell proportion range corresponding to each image region refers to the proportion of the number of cells of the first cell type to the total number of cells in the image region under normal conditions within the CD molecule expression range corresponding to each image region. In other words, if the proportion of the number of cells of the first cell type to the total number of cells in the image region is within the cell proportion range corresponding to the image region, it indicates that the cell classification of the image region is accurate.

[0130] For example, assuming the target second flow cytometry plot is a CD10 / CD19 flow cytometry plot, based on the cell characteristics of different cell types, the first cell type corresponding to the upper right region of the CD10 / CD19 flow cytometry plot (CD10+CD19+, indicating high expression of both CD10 and CD19) is lymphocytes, with a cell proportion range of >5%; the first cell type corresponding to the upper left region of the CD10 / CD19 flow cytometry plot (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 a lymphocyte cell proportion of less than 5% in the upper right region of the CD10 / CD19 flow cytometry plot, it moves the horizontal and / or vertical sides of the cross gate to increase the number of lymphocytes, i.e., the horizontal and / or vertical sides of the cross gate can move towards the lymphocyte population. If the electronic device detects a monocyte cell proportion higher than 1% in the upper left region of the CD10 / CD19 flow cytometry, it moves the horizontal and / or vertical sides of the cross gate to reduce the number of monocyte cells. That is, the horizontal and / or vertical sides 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 cytometry graph 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 region is not within the range of the cell ratio corresponding to the current image region, the target edge is moved so that the cell ratio of the cell population corresponding to the current image region is within the range of the cell ratio corresponding to the current image region; if the cell ratio of the target cell population corresponding to each image region is within the range of the cell ratio corresponding to each image region, the current cross gate is taken as the adjusted cross gate corresponding to the target flow cytometry graph.

[0132] Since the target second flow cytogram is divided into four image regions by a cross gate, the lower left region indicates that the cells in the target second flow cytogram have weak expression of both CD molecules. Therefore, when adjusting the cross gate according to the cell proportion of the cell population corresponding to each image region, the electronic device can usually ignore the lower left region. The other three regions can be the first target image region, the second target image region, and the third target image region, respectively.

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

[0134] Next, the electronic device can detect the second target image region. If the cell ratio of the cell group corresponding to the second target image region is not within the range of the cell ratio corresponding to the second target image region, the target edge is moved first so that the cell ratio of the cell group corresponding to the first target image region is within the range of the cell ratio corresponding to the first target image region, and the cell ratio of the cell group corresponding to the second target image region is also within the range of the cell ratio corresponding to the second target image region. If moving the target edge cannot satisfy the condition that the cell ratio of the cell group corresponding to the first target image region is within the range of the cell ratio corresponding to the first target image region, and the cell ratio of the cell group corresponding to the second target image region is also within the range of the cell ratio corresponding to the second target image region, then the edges in the cross gate other than the target edge are moved.

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

[0136] Step 510: Based on the adjusted crossgates corresponding to multiple second flow cytometry plots, determine the expression status of each cell population for each CD molecule.

[0137] Step 512: If the expression of each cell population in each CD molecule meets the preset expression conditions, then output the cell population analysis results corresponding to the first flow cytogram.

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

[0139] In this embodiment, by adjusting the cross gates corresponding to each second flow cytometry plot, automatic gating of the five groups of the first flow cytometry plot and cross gates of multiple second flow cytometry plots are achieved, simplifying the manual gating process in traditional methods and reducing classification errors caused by improper manual operation, thereby improving the efficiency and classification accuracy of cell analysis of target samples.

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

[0141] Step 702: In the first flow cytometry plot, color-code each cell contained in the five cell populations to obtain the color-coding results.

[0142] The color-coding results include the colors corresponding to the five cell populations contained in the first flow cytometry plot. The electronic device can pre-set the correspondence between cell populations and colors.

[0143] Color labeling of individual cells in the first flow cytometry plot refers to associating each cell in the plot with the color corresponding to its cell population, thereby linking each cell in the target sample to its corresponding color. For example, an electronic device can preset the color corresponding to cell population M to green, so cells belonging to cell population M in the first flow cytometry plot can be labeled green; if the color corresponding to cell population P is blue, then cells belonging to cell population P in the first flow cytometry plot can be labeled blue, and so on, but is not limited to this.

[0144] In some embodiments, cells belonging to the same cell group are associated with the same color, while cells not belonging to the same cell group are associated with different colors. Therefore, the cell group to which each cell in the first flow cytogram belongs can be intuitively determined by color.

[0145] Step 704: Based on the color labeling results, color label each cell in the multiple second flow cytometry plots to obtain multiple color-labeled second flow cytometry plots.

[0146] In some embodiments, the electronic device associates each cell contained in multiple second flow cytometry plots with each cell contained in the first flow cytometry plot based on the color labeling results in the first flow cytometry plot, determines the cell population of each cell contained in the multiple second flow cytometry plots, and colors each cell contained in the multiple second flow cytometry plots according to the color corresponding to each cell population contained in the first flow cytometry plot based on the color labeling results.

[0147] Specifically, assuming the color-coding results include green for cell population M and blue for cell population P, the first flow cytometry graph contains events 1, 2, 3, 4, and 5. Events 1, 3, and 4 belong to cell population M, and events 2 and 5 belong to cell population P. Based on the color-coding results, the electronic device determines that events A, B, C, D, and E in each second flow cytometry graph are associated with events 1, 2, 3, 4, and 5 in the first flow cytometry graph. Events A, C, and D belong to cell population M, and events B and E belong to cell population P. Events A, C, and D in multiple second flow cytometry graphs are labeled green, and events B and E are labeled blue, resulting in a color-coded second flow cytometry graph. The events mentioned above refer to objects detected in the graph, which may include cells, viruses, pathogens, etc.

[0148] Step 706: In each of the second flow cytometry plots after color annotation, determine the expression of the five cell populations in each of the second flow cytometry plots based on the color of each cell.

[0149] In some embodiments, after determining the expression of the five cell populations in each of the second flow cytometry plots, the electronic device can adjust the cross gates of each second flow cytometry plot based on the expression of the five cell populations in each second flow cytometry plot, thereby obtaining more accurate CD molecule expression and thus more accurately adjusting the cross gates.

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

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

[0152] like Figure 8 As shown, in one embodiment, a streaming image processing apparatus 800 is provided, which can be applied to the above-mentioned electronic device. The streaming image processing apparatus 800 may 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 acquire the first flow cytometry plot and multiple second flow cytometry plots output by the flow cytometer corresponding to the target sample; the first flow cytometry plot includes the expression of multiple cells in the target sample corresponding to the leukocyte common antigen CD45, as well as the cell physical parameters of multiple cells; the second flow cytometry plot includes the expression of multiple cells in the target sample corresponding to two different CD molecules respectively.

[0154] The cell clustering module 820 is used to cluster and gate multiple cells in the first flow cytogram to obtain five cell populations.

[0155] A gate module 830 is used to perform cross-shaped gates on each second flow chart to obtain the cross-shaped gates corresponding to each second flow chart.

[0156] The expression analysis module 840 is used to determine the expression of each cell population for each CD molecule based on the cross-gate corresponding to multiple second flow cytometry plots.

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

[0158] In some embodiments, the cell clustering module 820 is further configured to cluster multiple cells in the first flow cytometry graph to obtain five cell populations; to gate the first flow cytometry graph according to the five cell populations to obtain gates corresponding to the five cell populations respectively; and to classify multiple cells in the first flow cytometry graph according to the gates corresponding to the five cell populations respectively to obtain five cell groups.

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

[0161] The adjustment module is used to adjust the cross gates of each second flow cytogram based on the expression of the five cell populations in each second flow cytogram.

[0162] Optionally, the expression analysis module 840 is used to determine the expression of each cell population for each CD molecule based on the adjusted crossgate corresponding to each of the multiple second flow cytometry plots.

[0163] The color annotation module is used to color-annotate the five cell populations in the first flow cytometry plot to obtain the color annotation results; based on the color annotation results, it color-annotates each cell in multiple second flow cytometry plots to obtain multiple color-annotated second flow cytometry plots; based on the color of each cell in each color-annotated second flow cytometry plot, it determines the expression of the five cell populations in each second flow cytometry plot.

[0164] In some embodiments, the adjustment module is further configured to divide the target second flow cytometry graph into four image regions 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; 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 graph; the first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytometry graph; determine 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; calculate the cell ratio of the cell number of the cell group corresponding to the target image region to 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 graph; move the horizontal and / or vertical sides of the cross gate corresponding to the target second flow cytometry graph according to the cell ratio of the cell group corresponding to each image region to obtain the cross gate of the target second flow cytometry graph after adjustment.

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

[0166] In this embodiment, the electronic device accurately groups and gates the cells in the first flow cytometry graph to obtain five cell populations. These cells are then further classified into five cell groups. Combined with the cross-gating of multiple second flow cytometry graphs, the expression of each cell group at different CD molecules can be analyzed in detail. This allows for further verification of the five cell groups, improving the accuracy and rationality of cell group classification. Furthermore, the device automatically gates the five groups in the first flow cytometry graph and the cross-gating of multiple second flow cytometry graphs, simplifying the manual gate setting process in traditional methods and reducing classification errors caused by improper manual operation. This improves the efficiency and accuracy of cell analysis of the target sample.

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

[0168] Processor 910 may include one or more processing cores. Processor 910 connects to various parts within the electronic device 900 using various interfaces and lines, and performs various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets, or instruction sets stored in memory 920, and by calling data stored in memory 920. Optionally, processor 910 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 910 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 910 and may be implemented separately using a communication chip.

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

[0170] Understandably, the electronic device 900 may include more or fewer structural elements than those shown in the block diagram above, such as power supply, input buttons, camera, speaker, screen, RF (Radio Frequency) circuit, Wi-Fi (Wireless Fidelity) module, Bluetooth module, sensor, etc., and may not be limited herein.

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

[0172] This application discloses a computer program product, including a computer program, which, when executed by a processor, implements the methods described in the above embodiments.

[0173] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), etc.

[0174] Any references to memory, storage, databases, or other media used herein may include non-volatile and / or volatile memory. Suitable non-volatile 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 is used as an external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus DRAM (RDRAM), and Direct Rambus DRAM (DRDRAM).

[0175] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0176] In the various embodiments of this application, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order 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 this application.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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. The components shown as units may or may not be physical units; they can be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0179] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0180] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-accessible memory. Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of this application.

[0181] The foregoing has provided a detailed description of a streaming image processing method, apparatus, electronic device, and storage medium disclosed in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for processing streaming images, characterized in that, The method includes: The flow cytometer outputs a first flow cytogram and multiple second flow cytograms corresponding to the target sample. The first flow cytogram includes the expression of multiple cells in the target sample corresponding to the leukocyte common antigen CD45, as well as the cell physical parameters of the multiple cells. The second flow cytogram includes the expression of multiple cells in the target sample corresponding to two different CD molecules. Multiple cells in the first flow cytogram are grouped and gated to obtain five cell populations; Perform cross-gating on each of the second flow cytometry graphs to obtain the corresponding cross-gating gates for each second flow cytometry graph, including: Based on the expression of the five cell populations in each of the second flow cytometry plots, the cross gates of each of the second flow cytometry plots are adjusted. Based on the cross-gates corresponding to the multiple second flow cytometry plots, the expression status of each cell population for each CD molecule is determined, including: Based on the adjusted crossgates corresponding to the multiple second flow cytometry plots, the expression status of each cell population for each CD molecule is determined. If the expression of each cell population in each CD molecule meets the preset expression conditions, the cell population analysis results corresponding to the first flow cytogram are output. The cell population analysis results include the position, boundary, number, and percentage of the total number of the five cell populations.

2. The method according to claim 1, characterized in that, The process of grouping and gatening multiple cells in the first flow cytogram yields five cell populations, including: Multiple cells in the first flow cytogram were divided into five cell populations. Based on the five cell populations, the first flow cytometry is gated to obtain the gates corresponding to the five cell populations respectively. Based on the phyla corresponding to the five cell populations, the multiple cells in the first flow cytometry are classified to obtain five cell populations.

3. The method according to claim 2, characterized in that, The process of grouping multiple cells in the first flow cytogram yields five cell populations, including: Clustering of multiple cells in the first flow cytogram yields five cluster centers; Based on the positions of the five cluster centers in the first flow cytometry plot, the cell types corresponding to the five cluster centers are determined. Based on the distance between the position of each cell in the first flow cytometry 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, characterized in that, Before adjusting the gates of each of the second flow cytometry plots based on the expression of the five cell populations in each plot, the method further includes: In the first flow cytogram, the five cell populations are color-labeled to obtain the color-labeling results; Based on the color annotation results, each cell in the multiple second flow cytometry plots is color-annotated to obtain multiple color-annotated second flow cytometry plots; Based on the color of each cell in each of the second flow cytometry plots after color annotation, the expression of the five cell populations in each of the second flow cytometry plots is determined.

5. The method according to claim 1, characterized in that, The step of adjusting the gates of each of the second flow cytometry plots based on the expression of the five cell populations in each plot includes: Based on the cross gate corresponding to the target second flow map, the target second flow map is divided into four image regions; the target second flow map can be any second flow map. Based on the first CD molecule and the second CD molecule corresponding to the target second flow cytometry, the first cell type corresponding to each image region is determined; the first CD molecule and the second CD molecule are two different CD molecules in the target second flow cytometry; Based on the second cell type corresponding to each of the five cell groups and the first cell type corresponding to each image region, the cell group corresponding to each image region is determined. In the target image region, calculate the proportion of cells in 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 cytometry. Based on the cell proportion of the cell population corresponding to each image region, the horizontal and / or vertical sides of the cross gate corresponding to the target second flow cytometry are moved to obtain the cross gate of the target second flow cytometry.

6. The method according to claim 5, characterized in that, The step of moving the horizontal and / or vertical edges of the cross gate corresponding to the target second flow cytometry map according to the cell proportion of the cell population corresponding to each image region includes: Based on the first CD molecule, the second CD molecule, and the first cell type corresponding to each image region, determine the cell ratio range corresponding to each image region; Based on the cell proportion and cell proportion range of the cell population corresponding to each of the image regions, the horizontal and / or vertical sides of the cross gate corresponding to the target second flow cytogram are moved until the cell proportion of the cell population corresponding to each of the image regions is within the cell proportion range corresponding to each of the image regions.

7. A streaming image processing apparatus, characterized in that, The device includes: The image acquisition module is used to acquire a first flow cytometer and multiple second flow cytometers output by the flow cytometer, corresponding to the target sample; the first flow cytometer includes the expression status of multiple cells in the target sample corresponding to the leukocyte common antigen CD45, as well as the cell physical parameters of the multiple cells; the second flow cytometer includes the expression status of multiple cells in the target sample corresponding to two different CD molecules respectively; The cell clustering module is used to cluster and gate multiple cells in the first flow cytogram to obtain five cell clusters. The gate module is used to perform cross-gate design on each of the second flow graphs to obtain the corresponding cross gate for each second flow graph, including: Based on the expression of the five cell populations in each of the second flow cytometry plots, the cross gates of each of the second flow cytometry plots are adjusted. The expression analysis module is used to determine the expression status of each cell population for each CD molecule based on the cross-gates corresponding to the multiple second flow cytometry plots, including: Based on the adjusted crossgates corresponding to the multiple second flow cytometry plots, the expression status of each cell population for each CD molecule is determined. The result output module is used to output the cell population analysis results corresponding to the first flow cytogram if the expression of each cell population in each CD molecule meets the preset expression conditions. The cell population analysis results include the position, boundary, number and total percentage of the five cell populations.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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