A blood cell analyzer and a blood cell analysis method

Abstract

This application discloses a blood cell analyzer and a blood cell analysis method. The blood cell analyzer obtains the detection results of reticulocytes based on the projection data of red blood cell particles in the extension direction of reticulocyte particles. Since the overlap between reticulocytes and other red blood cell particles is small when projecting the red blood cell particles in the extension direction, the accuracy of the detection results can be effectively improved. Furthermore, the blood cell analyzer of this application can automatically adjust the projection direction according to the distribution data of red blood cell particles, making the blood cell analyzer applicable to the detection of blood cells of different morphologies and improving the compatibility of the blood cell analyzer in multi-species detection.

Description

Technical Field

[0001] This application relates to the field of blood analysis technology, and in particular to a blood cell analyzer and a blood cell analysis method. Background Technology

[0002] Reticulocytes are transitional cells between late erythroblasts and mature erythrocytes. They are immature erythrocytes, slightly larger than mature erythrocytes, and their cytoplasm exhibits a reticular structure after staining. Reticulocytes are an indicator of bone marrow erythropoiesis function. Current blood cell analyzers use lasers to illuminate flowing blood cell particles to obtain their optical signals, and then separate reticulocytes from other blood cells based on the differences in their optical signals.

[0003] Existing blood cell analyzers typically use preset segmentation thresholds or fixed detection areas to separate reticulocytes from general blood cells. However, when blood cell analyzers are used to detect samples from multiple species, the morphological differences between blood cells in different species mean that using segmentation thresholds or fixed detection areas can lead to inaccurate detection results for reticulocytes, resulting in low detection accuracy of the blood cell analyzer. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a blood cell analyzer and a blood cell analysis method.

[0005] To address the aforementioned problems, this application provides a blood cell analyzer, including a detection module and a processor. The detection module includes a light emitter, a flow chamber, and a light receiver. The flow chamber is used to circulate a sample stream formed by the sample to be tested. The light emitter emits a laser beam to the sample stream, and the light receiver receives optical signals generated by the sample stream under laser irradiation. The processor is connected to the detection module and is used to perform the following: acquiring at least two optical signals generated by the sample to be tested at different receiving angles; generating a first scatter plot based on the at least two optical signals and acquiring the distribution area of ​​erythrocyte particles in the first scatter plot; acquiring the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area; and acquiring the projection data of the erythrocyte particles in the extension direction to obtain the detection result of reticulocytes in the sample to be tested based on the projection data.

[0006] Optionally, the processor is configured to perform: calculating the covariance matrix corresponding to the distribution data of the distribution region to obtain a first feature direction and a second feature direction of the covariance matrix; identifying a first region and a second region in the distribution region, wherein the particle distribution density of the first region is less than the particle distribution density of the second region; and selecting the extension direction from the first feature direction and the second feature direction according to the overlap ratio of the first region and the second region.

[0007] Optionally, the processor is configured to: obtain a first overlap ratio of the first region and the second region in the first scatter plot in the first feature direction, and obtain a second overlap ratio of the first region and the second region in the first scatter plot in the second feature direction; in response to the first overlap ratio being greater than the second overlap ratio, use the second feature direction as the extension direction; in response to the first overlap ratio being less than the second overlap ratio, use the first feature direction as the extension direction.

[0008] Optionally, the processor is configured to perform: obtaining a first matrix based on the distribution data of the red blood cell particles in the distribution area; obtaining the mean of the distribution data of the first matrix, performing a decentering process on the first matrix to obtain a second matrix; calculating the covariance matrix of the second matrix, and determining the first feature direction and the second feature direction based on the eigenvectors of the covariance matrix.

[0009] Optionally, the processor is configured to perform: obtaining a first matrix based on the coordinate data of the red blood cell particles; obtaining the projection data based on the product of the first matrix and the extension direction; generating a histogram of the projection data to calculate a segmentation threshold based on the peak data of the histogram; and outputting the detection result of the reticulocytes based on the number of red blood cell particles other than the segmentation threshold of the histogram.

[0010] Optionally, the first scatter plot also includes red blood cell particles from a second region; the processor is configured to perform: calculating the centroid position of the red blood cell particles based on the distribution data; obtaining the third characteristic direction of the red blood cell particles based on the centroid position and the origin position of the first scatter plot; and using the normal vector corresponding to the third characteristic direction as the extension direction.

[0011] Optionally, the optical signal of the sample to be tested includes at least two of a forward scattering signal, a first side scattering signal, a second side scattering signal, and a fluorescence pulse signal; the processor is configured to perform the following: generating a plurality of third scatter plots based on at least two of the forward scattering signal, the first side scattering signal, the second side scattering signal, and the fluorescence pulse signal, wherein the plurality of third scatter plots include the first region; obtaining the region range of the first region in the plurality of third scatter plots; and using the third scatter plot with the largest region range as the first scatter plot.

[0012] Optionally, the processor is configured to execute: using the third scatter plot with the largest area of ​​the first region in the plurality of third scatter plots as the first scatter plot, or using the third scatter plot with the largest distance between red blood cell particles in the first region in the plurality of third scatter plots as the first scatter plot.

[0013] To address the aforementioned problems, this application provides a blood cell analyzer for animal blood cell detection, comprising a detection module and a processor. The detection module includes a light emitter, a flow chamber, and a light receiver. The flow chamber is used to circulate a sample stream formed by the sample to be tested. The light emitter emits laser light into the sample stream, and the light receiver receives optical signals generated by the sample stream under laser irradiation. The processor is connected to the detection module and is used to perform the following: acquiring at least two optical signals generated by the sample to be tested at different receiving angles; generating a first scatter plot based on the at least two optical signals and acquiring the distribution area of ​​erythrocyte particles in the first scatter plot; acquiring the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area; acquiring the projection data of the erythrocyte particles in the extension direction to obtain the detection result of reticulocytes in the sample to be tested based on the projection data; wherein, the extension direction corresponding to the distribution area is different when the species information of the sample to be tested is different.

[0014] To address the aforementioned problems, this application provides a blood cell analysis method, comprising: acquiring at least two optical signals generated by a sample to be tested at different receiving angles; generating a first scatter plot based on the at least two optical signals, and acquiring the distribution area of ​​erythrocyte particles in the first scatter plot; acquiring the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area; acquiring the projection data of the erythrocyte particles in the extension direction, so as to obtain the detection result of reticulocyte particles in the sample to be tested based on the projection data.

[0015] This application provides a blood cell analyzer and a blood cell analysis method. The blood cell analyzer obtains the detection results of reticulocytes based on the projection data of red blood cell particles in the extension direction of reticulocyte particles. Since the overlap between reticulocytes and other red blood cell particles is small when projecting the red blood cell particles in the extension direction, the accuracy of the detection results can be effectively improved. Furthermore, the blood cell analyzer of this application can automatically adjust the projection direction according to the distribution data of red blood cell particles, making the blood cell analyzer applicable to the detection of blood cells of different morphologies and improving the compatibility of the blood cell analyzer in multi-species detection. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of 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. Wherein:

[0017] Figure 1 This is a schematic diagram of the structure of an embodiment of the blood cell analyzer provided in this application;

[0018] Figure 2 yes Figure 1 A schematic diagram of the operation of the first embodiment of the first scatter plot;

[0019] Figure 3 yes Figure 1 A schematic diagram illustrating the operation of the second embodiment of the first scatter plot;

[0020] Figure 4 yes Figure 1 A schematic diagram illustrating the operation of an embodiment of a central histogram;

[0021] Figure 5 yes Figure 1 A schematic diagram of the operation of the third embodiment of the first scatter plot;

[0022] Figure 6 yes Figure 1 A schematic diagram of the operation of the fourth embodiment of the first scatter plot;

[0023] Figure 7 This is an operational schematic diagram of an embodiment of the second scatter plot provided in this application;

[0024] Figure 8 This is an operational schematic diagram of another embodiment of the second scatter plot provided in this application;

[0025] Figure 9 This is a schematic flowchart of an embodiment of the blood cell analysis method provided in this application;

[0026] Figure 10 This is a schematic diagram of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0027] 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.

[0028] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0029] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0030] This application provides a sample analyzer and a sample detection method thereof.

[0031] Please see Figure 1-2 , Figure 1 This is a schematic diagram of the structure of an embodiment of the blood cell analyzer provided in this application. Figure 2 yes Figure 1 A schematic diagram illustrating the operation of the first embodiment of the first scatter plot. (See diagram below.) Figure 1 As shown, the blood cell analyzer in this embodiment includes a detection module 20 and a processor 10. The detection module 20 is used to test the sample to be tested.

[0032] The detection module 20 includes a light emitter, a flow chamber, and a light receiver. The flow chamber is used to circulate a sample stream formed by the sample to be tested. The light emitter is used to emit laser light to the sample stream, and the light receiver is used to receive the optical signals generated by the sample stream under laser irradiation. The processor 10, connected to the detection module 20, is used to perform the following: acquiring at least two optical signals generated by the sample to be tested at different receiving angles; generating a first scatter plot based on the at least two optical signals, and acquiring the distribution area of ​​red blood cell particles in the first scatter plot; acquiring the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area; and acquiring the projection data of red blood cell particles in the extension direction to obtain the detection result of reticulocytes in the sample to be tested based on the projection data.

[0033] Specifically, the detection module 20 includes a flow chamber for the flow of the sample stream formed by the sample to be tested. The detection module 20 is equipped with corresponding light emitters and light receivers at different angles. The light emitters emit laser light into the sample stream, and the light receivers collect the optical signals generated by the blood cell particles of the sample under laser irradiation. Depending on the receiving angle, the optical signals may include forward scattering signals, first side scattering signals, second side scattering signals, or fluorescence pulse signals.

[0034] When a laser beam illuminates the sample in the flow chamber, the blood cell particles in the sample generate scattered light. Based on the direction of the light, this can be categorized into forward scattered light signals (LS) collected in the forward direction, first lateral scattered light signals (MS) collected at a mid-angle (7-20°) side of the scattered light, and second lateral scattered light signals (HS) collected at a high-angle (45-58°) side of the scattered light. Alternatively, when the sample is treated with a fluorescent dye, the blood cell particles emit a fluorescent signal under laser irradiation. These scattered and / or fluorescent signals can reflect the volume, structure, particle properties, and function of the blood cell particles. By analyzing the difference between the scattered and fluorescent signals, the blood cell analyzer can analyze the blood cell particles in the sample to obtain the detection results of reticulocytes based on the signal differences.

[0035] Specifically, a first scatter plot is established using at least two optical signals generated by blood cell particles under laser irradiation as coordinate axes. Understandably, the first scatter plot is at least a two-dimensional image. When the first scatter plot is generated from three optical signals, it can also be a three-dimensional image. For example, the first scatter plot can be a scatter plot established from the perspective of a first lateral scattering signal and a second lateral scattering signal, or it can be a scatter plot established from the perspective of a forward scattering signal, a first lateral scattering signal, and a second lateral scattering signal.

[0036] Depending on the type of blood cells, different regions of particle clusters will be distributed in the first scatter plot. The blood cell analyzer in this embodiment obtains the distribution area of ​​red blood cell particles in the first scatter plot, and obtains the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area. This extension direction is used to indicate the extension direction of reticulocyte particles in the distribution area or the extension direction of the distribution shape of reticulocyte particles.

[0037] For example, such as Figure 2 As shown, since reticulocytes (Ret) are a transitional immature erythrocyte between late erythroblasts and mature erythrocytes, the particle clusters of reticulocytes are usually close to the regions of other erythrocyte particles. In order to facilitate the subsequent differentiation of reticulocytes from ordinary erythrocytes, the blood cell analyzer in this embodiment needs to obtain the extension direction of the reticulocyte particles distributed in the first scatter plot. The blood cell analyzer projects the erythrocyte particles in this extension direction, so that the number of overlaps between reticulocytes and other erythrocyte particles in this direction is small, and the detection results of the blood cell analyzer are more accurate.

[0038] Furthermore, the projection data is the distribution data of red blood cell particles obtained by projecting red blood cell particles onto the extension direction of reticulocyte particles. After acquiring the projection data, the blood cell analyzer converts the projection data into corresponding histogram distribution data, and divides the histogram data according to the segmentation threshold to obtain the detection results of reticulocytes.

[0039] In this embodiment, the hematology analyzer obtains the detection results of reticulocytes based on the projection data of red blood cell particles onto the extension direction of reticulocyte particles. Since the overlap between reticulocytes and other red blood cell particles is small when projecting the red blood cell particles into their extension direction, the accuracy of the detection results can be effectively improved. Furthermore, the hematology analyzer of this application can automatically adjust the projection direction according to the distribution data of red blood cell particles, making it applicable to the detection of different morphologies of blood cells and improving the compatibility of the hematology analyzer in multi-species detection. Moreover, the hematology analyzer does not require the design of corresponding segmentation thresholds or detection regions for test samples of different species, making the method simple and easy to implement, greatly reducing the research and development cost and complexity of the hematology analyzer.

[0040] Optionally, the first scatter plot also includes red blood cell particles in a second region 102, which is located to one side of the first region 101. The distribution density of red blood cell particles in the second region 102 is greater than that in the first region 101.

[0041] Specifically, the processor 10 is used to perform: calculating the covariance matrix corresponding to the distribution data of the distribution area to obtain the first feature direction and the second feature direction of the covariance matrix; identifying the first region 101 and the second region 102 in the distribution area, wherein the particle distribution density of the first region 101 is less than the particle distribution density of the second region 102; and selecting an extension direction from the first feature direction and the second feature direction according to the overlap ratio of the first region 101 and the second region 102.

[0042] like Figure 2 As shown, the particle clusters in the first region 101 and the second region 102 are both erythrocyte particles. Based on their distribution density, shape, and / or area, the erythrocyte particles clustered in the first region 101 can be preliminarily classified as reticulocyte particles, and the erythrocyte particles clustered in the second region 102 can be preliminarily classified as ordinary erythrocyte particles. Specifically, the distribution area of ​​reticulocytes is usually adjacent to that of ordinary erythrocyte particles. The first region 101 of the first scatter plot is obtained based on the distribution characteristics of reticulocytes, such as their small area and low particle density.

[0043] Specifically, after acquiring the distribution data of red blood cell particles in the distribution area, the covariance matrix of all red blood cell particles is calculated based on the distribution data. This covariance matrix is ​​used to represent the variation relationship of red blood cell particles in different dimensions, thereby obtaining the first characteristic direction and the second characteristic direction of the covariance matrix. The first characteristic direction and the second characteristic direction point to the direction with the largest variance of the distribution data, and the first characteristic direction and the second characteristic direction are perpendicular. After obtaining the first characteristic direction and the second characteristic direction, the first region 101 and the second region 102 in the distribution area are identified. In this embodiment, the processor 10 can first identify the first region 101 in the distribution area, and then obtain the second region 102 according to the relative relationship between the first region 101 and the second region 102 (usually the second region 102 is located on one side of the first region 101 and the particle distribution density of the first region 101 is less than the particle distribution density of the second region 102). Then, based on the overlap ratio of the first region 101 and the second region 102, one of the first characteristic direction and the second characteristic direction is selected as the extension direction.

[0044] In this embodiment, when the extension direction is selected based on the overlap ratio of the first region 101 and the second region 102, the overlap ratio of the first region 101 and the second region 102 in the extension direction is relatively low, so that the number of reticulocyte particles overlapping with other red blood cell particles in this direction is less, and the detection results of the blood cell analyzer are more accurate.

[0045] Optionally, please see Figure 3 , Figure 3 yes Figure 1A schematic diagram of the operation of a second embodiment of the first scatter plot. The processor 10 in this embodiment is configured to: obtain a first overlap ratio of a first region 101 and a second region 102 in a first feature direction in the first scatter plot; and obtain a second overlap ratio of the first region 101 and the second region 102 in a second feature direction in the first scatter plot; in response to the first overlap ratio being greater than the second overlap ratio, using the second feature direction as the extension direction; and in response to the first overlap ratio being less than the second overlap ratio, using the first feature direction as the extension direction.

[0046] Specifically, since the first scatter plot includes red blood cell particles in the first region 101 and the second region 102, after obtaining the first and second characteristic directions of the red blood cell particles, the first and second characteristic directions can be added to the first scatter plot to obtain the first overlap ratio of the first region 101 and the second region 102 in the first scatter plot in the first characteristic direction, and to obtain the second overlap ratio of the first region 101 and the second region 102 in the first scatter plot in the second characteristic direction. For example, Figure 3 As shown, the overlap ratio of the first region 101 and the second region 102 in the first scatter plot can be determined based on the number of overlapping red blood cell particles in the first region 101 and the second region 102 in the first characteristic direction or the second characteristic direction. For example, the number of overlapping particles when the red blood cell particles in the first region 101 and the red blood cell particles in the second region 102 are projected in the first characteristic direction is obtained, so as to calculate the first overlap ratio based on the total number of red blood cell particles; the number of overlapping particles when the red blood cell particles in the first region 101 and the red blood cell particles in the second region 102 are projected in the second characteristic direction is obtained, so as to calculate the second overlap ratio based on the total number of red blood cell particles.

[0047] In this embodiment, based on the overlap ratio of red blood cell particles in the first region 101 and the second region 102, a first feature direction or a second feature direction is selected as the extension direction of reticulocyte particles. This allows the processor 10 to automatically adjust the projection direction of red blood cell particles according to the morphology of the first region 101. This is applicable to the detection of scatter plots generated by optical signals from different receiving angles and blood cells of different morphologies, improving the compatibility of the blood cell analyzer in multi-species detection and thus improving the accuracy of reticulocyte detection results.

[0048] In another embodiment, the processor 10 can directly obtain the number of overlapping particles when the first region 101 and the second region 102 are projected in the first feature direction or the second feature direction. The processor 10 is used to select the first feature direction or the second feature direction as the extension direction according to the relationship between the number of overlapping particles in the first feature direction and the number of overlapping particles in the second feature direction.

[0049] In other embodiments, the processor 10 may also store the correspondence between a preset angle range of the extension direction and the optical signal. That is, the processor 10 stores the corresponding preset angle range under the preset angle of the preset optical signal. After obtaining the first feature direction and the second feature direction, the processor 10 selects the corresponding preset angle range according to the view of the first scatter plot, so as to select the direction within the preset angle range from the first feature direction and the second feature direction as the extension direction.

[0050] Optionally, the processor 10 in this embodiment is configured to perform: obtaining a first matrix based on the distribution data of red blood cell particles in the distribution area, wherein the first matrix includes at least coordinate data of a first dimension and a second dimension; obtaining the mean of the distribution data of the first matrix, performing a decentering process on the first matrix to obtain a second matrix; calculating the covariance matrix of the second matrix, and determining a first feature direction and a second feature direction based on the eigenvectors of the covariance matrix; wherein the first feature direction and the second feature direction are perpendicular.

[0051] Specifically, after establishing the first scatter plot, the blood cell analyzer also obtains a first matrix based on the distribution data of red blood cell particles in the first scatter plot. The distribution data includes the coordinate data of multiple red blood cell particles in the coordinate axes of the first scatter plot. Understandably, when the first scatter plot is a two-dimensional image, the first matrix is ​​also a two-dimensional matrix. For example, when the first scatter plot is an image established from the perspective of a first lateral scattering signal and a second lateral scattering signal, the first matrix is ​​a coordinate matrix in two dimensions: the first dimension can be one of the first lateral scattering signal and the second lateral scattering signal, and the second dimension can be the other. The dimensions of the matrix can be set according to the characteristics of reticulocytes in different animal types. For example, when the first region 101 of the reticulocytes of a particular animal type is more prominent in a two-dimensional image, a two-dimensional first scatter plot can be obtained.

[0052] For example, when the first matrix is ​​a coordinate matrix in two dimensions of the first and second side-scattered signals, the first matrix can be represented as:

[0053]

[0054] Among them, X n Y n The nth two-dimensional coordinate data of the red blood cell particles is composed of the first and second side scattering signals as coordinate axes.

[0055] By obtaining the mean of the distribution data of the first matrix in each dimension, the first matrix can be decentered to obtain the second matrix. Specifically, the first average value of the distribution data of the red blood cell particles in the first lateral scattering signal is calculated. And the second average value of the distribution data of the second lateral scattering signal The first matrix can be centered to obtain the second matrix. The second matrix is ​​essentially the first matrix with zero mean. The formula for the second matrix is ​​as follows:

[0056]

[0057] After obtaining the second matrix, the blood cell analyzer in this embodiment further calculates the covariance matrix of the second matrix to obtain the first characteristic direction of the covariance matrix in the first dimension and the second characteristic direction of the covariance matrix in the second dimension. Specifically, the covariance matrix is ​​a mathematical matrix used to represent the relationship between variables in each dimension. Because the second matrix has been decentered to remove the close relationships between the variables in the covariance matrix, the characteristic directions of the covariance matrix are uncorrelated. The formula for the covariance matrix can be as follows:

[0058]

[0059] Where C is the covariance matrix, for Y is

[0060] Understandably, the eigendirection of the covariance matrix is ​​used to represent the transformation direction of the covariance matrix, and the eigendirection of the covariance matrix can be obtained by orthogonal decomposition of the covariance matrix. In an optional embodiment, since the distribution density of red blood cell particles in the second region 102 is much greater than that in the first region 101, when the shape of the second region 102 is close to an ellipse, the eigendirection of the covariance matrix is ​​close to the direction vector of the red blood cell particles in the second region 102. For example, if the shape of the second region 102 is close to an ellipse, the eigendirection of the covariance matrix may include a first eigendirection corresponding to the major axis of the second region 102 and a second eigendirection corresponding to the minor axis of the second region 102.

[0061] In this embodiment, the processor 10 selects a first feature direction or a second feature direction as the extension direction based on the relative positional relationship between the red blood cell particles in the first region 101 and the red blood cell particles in the second region 102. The selected extension direction needs to result in a large number of segments of the projection data of the reticulocyte particles. For example, such as Figure 3As shown, the first feature direction is V1 and the second feature direction is V2. Since the red blood cell particles in the second region 102 and the red blood cell particles in the first region 101 are arranged sequentially in the second feature direction, the red blood cell particles in the first region 101 overlap with the red blood cell particles in the second region 102 when projected in the second feature direction. Therefore, the second feature direction can be selected as the extension direction of the reticulocyte particles to improve the accuracy of the detection results of reticulocytes.

[0062] Optionally, please see Figure 4 , Figure 4 yes Figure 1 A schematic diagram illustrating the operation of an embodiment of the histogram. The processor 10 in this embodiment is used to perform the following actions: obtaining a first matrix based on the distribution data of red blood cell particles; obtaining projection data based on the product of the first matrix and the extension direction; generating a histogram of the projection data to calculate a segmentation threshold based on the peak data of the histogram; and outputting the detection result of reticulocytes based on the number of red blood cell particles outside the segmentation threshold of the histogram.

[0063] Specifically, the projection data of red blood cell particles can represent the projected lengths of all red blood cell particles in the extension direction of the first matrix. The projection data can be obtained by multiplying the first matrix with either the first or second characteristic direction. Understandably, since the first or second characteristic direction is a feature vector of the first matrix in a certain dimension (i.e., the first and second characteristic directions are one-dimensional vector data), the projection data obtained by the processor 10 is also one-dimensional vector data. Therefore, after obtaining the projection data, the processor 10 can perform histogram statistics on the projection data to generate the aforementioned histogram, such as... Figure 4 As shown.

[0064] After acquiring the histogram of the projection data, the processor 10 can calculate a segmentation threshold based on the peak data of the histogram. Based on the number of red blood cell particles outside the segmentation threshold, the processor 10 can obtain the detection result of reticulocytes. The detection result can be the detection result of reticulocytes or the ratio of reticulocytes to red blood cell particles; no specific limitation is made here. The segmentation threshold can be calculated by at least one method, such as calculating the half-maximum width of the histogram, calculating the valley between the main peak of red blood cell particles in the second region 102 and the tail peak of red blood cell particles in the first region 101, or using Gaussian fitting to compare the main peak of red blood cell particles in the second region 102 with the tail peak of red blood cell particles in the first region 101; no specific limitation is made here. After acquiring the segmentation threshold, the processor 10 can determine red blood cell particles outside the segmentation threshold on the histogram as reticulocyte particles for detection and analysis of reticulocytes in the sample to be tested. For example, as... Figure 4As shown, when the segmentation threshold is 1.98, the peaks in the histogram before the dashed line can be defined as the main peaks of ordinary red blood cell particles, and the peaks after the dashed line can be defined as the tail peaks of reticulocytes. The detection results of reticulocytes can be obtained based on the number of cell particles in the tail portion.

[0065] In this embodiment, the processor 10 calculates the histogram segmentation threshold based on the projection data of red blood cell particles in the extension direction of reticulocyte particles, so as to obtain the detection result based on the number of red blood cell particles outside the histogram segmentation threshold. This enables the blood cell analyzer to be applicable to the detection of blood cells of different morphologies, improves the compatibility of the blood cell analyzer in multi-species detection, and thus improves the accuracy of reticulocyte detection results.

[0066] Optionally, please see Figure 5 , Figure 5 yes Figure 1 A schematic diagram illustrating the operation of the third embodiment of the first scatter plot. (See diagram below.) Figure 5 As shown, in one embodiment, the processor 10 is used to perform: calculating the centroid positions of a plurality of red blood cell particles based on distribution data; obtaining the third characteristic direction of the red blood cell particles based on the centroid positions and the origin position of the first scatter plot; and using the normal vector corresponding to the third characteristic direction as the extension direction.

[0067] Specifically, the blood cell analyzer of this embodiment obtains the extension direction of reticulocyte particles by calculating the centroid position of the red blood cell particles. For example, the centroid position can be obtained by calculating the average coordinates of the red blood cell particles under the current optical signal; alternatively, the center position can be obtained by acquiring the projection histograms of the red blood cell particles under various optical signals and calculating the peak position of each projection histogram. Users can choose the method of calculating the centroid position according to the application scenario, computing resources, etc., and no specific limitations are made here. Figure 5 As shown, when the calculated centroid position is [CX, CY], the third characteristic direction is... Figure 5 As shown, the direction indicates that the third feature direction corresponds to the extension direction of the particle cluster formed by the red blood cell particles in the second region 102. Since the red blood cell particles in the first region 101 are located on one side of the second region 102, when the red blood cell particles are projected onto the third feature direction, the red blood cell particles in the first region 101 are easily obscured by the red blood cell particles in the second region 102, resulting in inaccurate identification of reticulocyte particles. Therefore, in this embodiment, after obtaining the third feature direction, the normal vector of the third feature direction is calculated, and the normal vector is used as the extension direction, so that the distribution line of the red blood cell particles in the second region 102 under this extension direction is shorter, further reducing the possibility of reticulocyte particles being obscured and improving the accuracy of the detection results of reticulocytes.

[0068] In another embodiment, since the processor 10 determines the third feature direction of the red blood cell particles based on the position of the center of gravity and the origin of the first scatter plot, and the third feature direction coincides with the extension direction of the red blood cell particles in the second region 102, after obtaining the third feature direction, the processor 10 can also directly use the third feature direction as the extension direction, so that when the red blood cell particles are projected in the third feature direction, the number of overlaps between reticulocytes and other red blood cell particles is small, thereby improving the accuracy of the detection results of reticulocytes.

[0069] For other implementation methods, please refer to Figure 6 , Figure 6 yes Figure 1 A schematic diagram illustrating the operation of the fourth embodiment of the first scatter plot. (See diagram below.) Figure 6 As shown, the processor 10 is used to perform: obtaining the third overlap ratio of the first region 101 and the second region 102 in the third feature direction; in response to the third overlap ratio being less than a preset value, using the third feature direction as the extension direction; in response to the third overlap ratio being greater than or equal to the preset value, using the normal vector corresponding to the third feature direction as the extension direction.

[0070] Specifically, the third overlap ratio of the first region 101 and the second region 102 in the third feature direction is obtained. The third overlap ratio is the proportion of overlapping particles when the red blood cell particles of the first region 101 and the second region 102 are projected in the third feature direction. When the third overlap ratio is less than a preset value, that is, the proportion of occlusion of reticulocytes is low, the detection result of reticulocytes is more accurate, so the third feature direction can be directly used as the extension direction. When the third overlap ratio is greater than or equal to the preset value, the proportion of occlusion of reticulocytes is large, and the segmentation threshold of reticulocytes is more ambiguous. Therefore, in order to ensure the accuracy of the detection result, the normal vector of the third feature direction can be used as the extension direction to reduce the number of occlusions between reticulocytes and other red blood cells in the projection direction, so as to analyze reticulocytes based on the projection data of red blood cell particles projected in the fourth feature direction. Figure 6 As shown, when the calculated centroid position is [CX, CY], the normal vector of the third characteristic direction is... Figure 6 As shown in the V direction, it can be observed that when projecting onto the normal vector of the third feature direction, the number of reticulocyte particles obscured by the red blood cell particles in the second region 102 is less than that when projecting onto the third feature direction.

[0071] In this embodiment, the processor 10 selects the third feature direction or the normal vector of the third feature direction as the extension direction according to the relationship between the third overlap ratio and the preset value. This enables the processor 10 to automatically adjust the projection direction of red blood cell particles according to the shape of the first region 101, making the blood cell analyzer suitable for detecting blood cells of different shapes, improving the compatibility of the blood cell analyzer in multi-species detection, and improving the accuracy of reticulocyte detection results.

[0072] Optionally, the blood cell analyzer of this embodiment can also obtain the extension direction of reticulocyte particles by fitting the second region 102. For example, since the distribution density of red blood cell particles in the second region 102 is much greater than that in the first region 101, the extension direction of reticulocyte particles is usually limited by the characteristic direction of red blood cell particles in the second region 102. Therefore, when the shape of the second region 102 is close to elliptical, the characteristic direction of red blood cell particles can be obtained by fitting the second region 102 to an ellipse, so as to obtain the extension direction of reticulocyte particles. No specific limitation is made here.

[0073] Optionally, the optical signal of the sample to be tested includes at least two of the following: forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal. The processor 10 is configured to perform the following: acquire the optical signals generated by the sample to be tested at different receiving angles; acquire a second scatter plot based on at least two of the forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal; and remove the blood shadow region of the second scatter plot according to a preset optical region or optical threshold, so as to segment the region where the red blood cell particles are located in the second scatter plot. The region where the red blood cell particles are located segmented in the second scatter plot can be understood as the distribution region of the red blood cell particles in the above embodiment, and the distribution data of this distribution region serves as the basis data for the aforementioned calculation of the extension direction, projection data, etc.

[0074] Specifically, please see Figure 7-8 , Figure 7 This is an operational schematic diagram of an embodiment of the second scatter plot provided in this application. Figure 8 This is an operational schematic diagram of another embodiment of the second scatter plot provided in this application. For example... Figure 7 and 8 As shown, Figure 7 The second scatter plot before removing the blood shadow area. Figure 8 This is the second scatter plot after removing the blood shadow area; Figure 7The blood sample contains several non-uniformly distributed discrete data points, excluding the concentrated red blood cell particle regions, located in various directions outside these regions. These discrete data points are generally considered to be blood cell contents leaking out after processing, i.e., blood shadows. To ensure the accuracy of blood cell analysis, the blood cell analyzer in this embodiment needs to filter the distribution data of red blood cell particles under various optical signals when analyzing the sample to remove blood shadow data and ensure the accuracy of the distribution data. The first scatter plot and the second scatter plot can be generated under the same optical signal or under different optical signals; no specific limitation is made here.

[0075] In an optional implementation, the distribution data of red blood cell particles can be filtered by setting empirical regions or optical threshold combinations to remove blood shadow data. For example, the processor 10 can acquire the species information of the sample to be tested and acquire several pre-set empirical regions or optical threshold combinations corresponding to the species information. For instance, when the sample to be tested is a cat blood sample, the processor 10 acquires a first empirical region or a first optical threshold combination when the sample species information is cat, to remove blood shadow data of the sample to be tested according to the first empirical region or the first optical threshold combination; or, when the sample to be tested is a human blood sample, the processor 10 acquires a second empirical region or a second optical threshold combination when the sample species information is human, to remove blood shadow data of the sample to be tested according to the second empirical region or the second optical threshold combination. In other implementations, the processor 10 can acquire a first scatter plot composed of the optical signals after removing the blood shadow data.

[0076] In this embodiment, the processor 10 obtains a second scatter plot based on at least two of the forward scatter signal, the first side scatter signal, the second side scatter signal, and the fluorescence pulse signal; and removes the blood shadow region of the second scatter plot according to a preset optical region or optical threshold, so that the processor 10 can segment the region where the red blood cell particles are located based on the distribution data of the blood cells and obtain the distribution data of the red blood cell particles, thereby improving the accuracy of blood cell analysis.

[0077] Optionally, the optical signal of the sample to be tested includes at least two of the following: forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal. The processor 10 is configured to perform the following: generating a plurality of third scatter plots based on at least two of the forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal, wherein the plurality of third scatter plots includes a first region 101; obtaining the region range of the first region 101 of the plurality of third scatter plots; and selecting the third scatter plot with the largest region range as the first scatter plot.

[0078] Specifically, the third scatter plot is a plurality of scatter images obtained by arranging and combining at least two of the following signals: forward scatter signal, first side scatter signal, second side scatter signal, and fluorescence pulse signal. For example, the third scatter plot may include scatter images composed of the perspectives of the forward scatter signal and the first side scatter signal, scatter images composed of the perspectives of the first side scatter signal and the second side scatter signal, scatter images composed of the perspectives of the forward scatter signal and the second side scatter signal, scatter images composed of the forward scatter signal and the fluorescence pulse signal, and so on. No specific limitation is made here.

[0079] After obtaining several third scatter plots, since optical signals from different receiving angles are typically used to characterize different properties of blood cells, reticulocytes differ significantly from ordinary red blood cells under certain optical signals. This results in variations in the morphology and size of the first region 101 in the third scatter plots formed under different optical signals. Therefore, using third scatter plots with different optical signals as the first scatter plot may lead to discrepancies in the detection results of reticulocytes, resulting in decreased reliability of the detection results. For example, because human reticulocytes contain ribonucleic acid (RNA) while ordinary red blood cells do not, the intensity of reticulocytes is higher than that of ordinary red blood cells in the dimensions of fluorescence pulse signals or side-scatter signals. This makes the analysis of reticulocytes more accurate when using third scatter plots of fluorescence pulse signals and side-scatter signals as the first scatter plot when analyzing human blood samples. Therefore, when a blood cell analyzer is used for multi-species detection, the morphology, volume, etc. of blood cells from different sample species may be different. In this embodiment, the blood cell analyzer selects the scatter plot with the largest area of ​​the first region 101 from several third scatter plots as the first scatter plot, which makes the blood cell analyzer more accurate in analyzing reticulocytes.

[0080] For example, such as Figure 2 and Figure 8 As shown, Figure 8 This is a scatter plot obtained from the perspectives of the forward-scattered light signal and the first side-scattered light signal. Figure 2 This is a scatter plot obtained from the perspectives of the first and second side-scattered signals. Figure 2 and Figure 8It is known that the distribution pattern of the first region 101 in the scatter plot obtained from different perspectives is different. In order to obtain the distribution data of reticulocytes to the maximum extent, when analyzing reticulocytes, the processor 10 of this embodiment selects the scatter plot with the largest region range of the first region 101 as the first scatter plot, that is, the scatter plot obtained from the perspective of the first lateral scatter signal and the second lateral scatter signal is used as the first scatter plot, so as to make the subsequent reticulocyte analysis more accurate. The region range can be determined according to one or more parameters such as region area, distance between cell particles in the region, and maximum width of the region.

[0081] Furthermore, in one embodiment, the first scatter plot can be selected from the third scatter plot based on the area of ​​the first region 101, that is, the area range of the first region 101 can be determined based on the area of ​​the first region 101. In another embodiment, the area range of the first region 101 can also be determined based on the ratio of the area of ​​the first region 101 to the area of ​​the second region 102.

[0082] In other embodiments, the third scatter plot with the largest distance between red blood cell particles in the first region 101 can be selected from several third scatter plots as the first scatter plot. Specifically, the two red blood cell particles with the largest relative distance are selected from the first region 101, and the first scatter plot is selected from several third scatter plots based on the distance between the two red blood cell particles, so that the area range of the first region 101 is maximized when the distance between the red blood cell particles is maximized.

[0083] In this embodiment, the processor 10 selects a scatter plot of the corresponding viewpoint based on the area or distance of the first region 101 to perform reticulocyte analysis, making the analysis of the extension direction of reticulocyte particles more accurate. Since the blood cell analyzer of this embodiment can automatically select the corresponding analysis viewpoint based on the morphology of red blood cells, there is no need for optical calibration. It can realize the sharing of system configuration or parameters of multiple species channels of the blood cell analyzer, thereby improving the practicality and reliability of the blood cell analyzer.

[0084] Optionally, after acquiring at least two optical signals, the processor 10 performs data enhancement on the at least two optical signals to improve the effectiveness of the processor 10 in reticulocyte analysis.

[0085] Specifically, such as Figure 2 As shown, Figure 2 The scatter plot is obtained from the perspectives of the first and second side-scattered signals. From this perspective, ordinary red blood cell particles are concentrated in the second region 102. To improve the visibility of the scatter plot, the processor 10 of this embodiment can perform a stretching transformation on the optical signal, making the optical signal data more dispersed, such as... Figure 3 As shown, Figure 3 This is a scatter plot obtained from the perspective of the enhanced first and second side-scattered signals, distinct from... Figure 2 The enhanced optical signal is more clearly presented in the scatter plot, which helps the processor 10 to segment the first region 101 and the second region 102 in the first scatter plot, making the analysis of the extension direction of reticulocyte particles more accurate. For example, the formula for the stretching transformation can be as follows:

[0086]

[0087] AHS = k*HS;

[0088] Wherein, AMS is the enhanced signal obtained by stretching the first side-scattered signal, AHS is the enhanced signal obtained by stretching the second side-scattered signal, MS is the first side-scattered signal, and HS is the second side-scattered signal; a, b, c, and k are enhancement coefficients, which can be determined experimentally.

[0089] This embodiment also proposes a blood cell analyzer for use in animal blood cell detection. Specifically, the blood cell analyzer includes a detection module 20 and a processor 10; the detection module 20 includes a light emitter, a flow chamber, and a light receiver. The flow chamber is used to flow a sample stream formed by the sample to be tested, the light emitter is used to emit laser light to the sample stream, and the light receiver is used to receive the optical signal generated by the sample stream under laser irradiation.

[0090] The processor 10 is connected to the detection module 20 and is used to perform the following: acquiring at least two optical signals generated by the sample under test at different receiving angles; generating a first scatter plot based on the at least two optical signals and acquiring the distribution area of ​​red blood cell particles in the first scatter plot; acquiring the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area; acquiring the projection data of red blood cell particles in the extension direction, so as to obtain the detection result of reticulocytes in the sample under test based on the projection data; wherein, the extension direction of the distribution area is different when the species information of the sample under test is different.

[0091] Specifically, when the species information of the test samples is different, the blood cell morphology of the test samples is different, resulting in differences in the distribution area of ​​red blood cell particles in the first scatter plot generated by at least two optical signals. For example, when the species information is different, the relative positional relationship and distribution density relationship of the first region 101 and the second region 102 are different, so the extension direction of the distribution area is different.

[0092] In this embodiment, the detection results of reticulocytes are obtained based on the projection data of red blood cell particles in the extension direction. In this extension direction, the number of overlaps between reticulocytes and other red blood cell particles is small, which allows the blood cell analyzer to automatically adjust the projection direction according to the distribution characteristics of the first region 101. This makes the blood cell analyzer applicable to the detection of blood cells of different species, improves the compatibility of the blood cell analyzer in multi-species detection, and improves the detection accuracy of the blood cell analyzer.

[0093] Please see Figure 9 , Figure 9 This is a schematic flowchart of an embodiment of the blood cell analysis method provided in this application. Figure 9 As shown in the embodiments of this application, a blood cell analysis method is also proposed, which includes the following steps:

[0094] Step S11: Acquire at least two optical signals generated by the sample under test at different receiving angles.

[0095] Specifically, after the sample analysis is started, the sample to be tested in the control detection module 20 circulates in the flow chamber, and the detection laser emitted by the light emitter irradiates the blood cell particles of the sample to be tested, so that the light receiver of the detection module 20 receives the optical signal generated by the laser irradiation of the blood cell particles. At different receiving angles, at least two optical signals of the sample to be tested can be obtained.

[0096] Step S12: Generate a first scatter plot based on at least two optical signals, and obtain the distribution area of ​​red blood cell particles in the first scatter plot.

[0097] After acquiring at least two optical signals, a first scatter plot is generated based on the at least two optical signals. It can be understood that when the number of optical signals is three or more, the first scatter plot is a three-dimensional or higher image, but no specific limitation is made here.

[0098] Step S13: Obtain the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area.

[0099] In this process, several red blood cell particles form a distribution area on the first scatter plot. The distribution data of this area can be the coordinate data of the optical signal generated by each red blood cell particle after laser irradiation in the current coordinate axis. For example, the first scatter plot is a scatter plot from the perspective of the forward scattered light signal and the first side scattered light signal, that is, the distribution data is the coordinate data composed of the forward scattered light signal and the first side scattered light signal of each red blood cell particle. The extension direction of the reticulocyte particles is obtained based on the distribution data. The extension direction can be understood as the extension direction or extension direction of the reticulocyte particles in the distribution area or distribution shape.

[0100] Step S14: Obtain projection data of red blood cell particles in the extension direction, so as to obtain the detection results of reticulocytes in the sample to be tested based on the projection data.

[0101] After obtaining the extension direction of reticulocyte particles, all erythrocyte particles in the first scatter plot are projected onto that extension direction to obtain projection data. After obtaining the projection data, the blood cell analyzer converts the projection data into corresponding histogram distribution data to obtain the reticulocyte detection results. The histogram distribution data can be understood as the distribution data formed after the projection data has been statistically analyzed using a histogram; for example, it may include data such as the peak value and peak width of the histogram, which are not specifically limited here.

[0102] In this embodiment, the blood cell analysis method obtains the detection results of reticulocytes based on the projection data of red blood cell particles in the extension direction of reticulocyte particles. Since the red blood cell particles in the first region 101 are more likely to be reticulocytes, the number of overlaps between reticulocytes and other red blood cell particles is small when projecting the red blood cell particles in the extension direction, thus improving the accuracy of the detection results of reticulocytes. Furthermore, the blood cell analysis method can automatically adjust the projection direction according to the morphology of the first region 101, making the blood cell analyzer applicable to the detection of blood cells of different morphologies and improving the compatibility of the blood cell analyzer in multi-species detection.

[0103] Optionally, step S13 includes the following steps: calculating the covariance matrix corresponding to the distribution data of the distribution area to obtain the first characteristic direction and the second characteristic direction of the covariance matrix; identifying the first region 101 and the second region 102 in the distribution area, wherein the particle distribution density of the first region 101 is less than the particle distribution density of the second region 102; and selecting an extension direction from the first characteristic direction and the second characteristic direction according to the overlap ratio of the first region 101 and the second region 102.

[0104] Furthermore, the step of selecting the extension direction from the first feature direction and the second feature direction based on the overlap ratio of the first region 101 and the second region 102 may further include the following steps: obtaining the first overlap ratio of the first region 101 and the second region 102 in the first scatter plot in the first feature direction, and obtaining the second overlap ratio of the first region 101 and the second region 102 in the first scatter plot in the second feature direction; in response to the first overlap ratio being greater than the second overlap ratio, using the second feature direction as the extension direction; in response to the first overlap ratio being less than the second overlap ratio, using the first feature direction as the extension direction.

[0105] Optionally, the step of calculating the covariance matrix corresponding to the distribution data of the distribution area to obtain the first and second characteristic directions of the covariance matrix may further include the following steps: obtaining a first matrix based on the distribution data of red blood cell particles in the distribution area; obtaining the mean of the distribution data of the first matrix, and performing a decentering process on the first matrix to obtain a second matrix; calculating the covariance matrix of the second matrix, and determining the first and second characteristic directions based on the eigenvectors of the covariance matrix.

[0106] Optionally, step S14 includes the following steps: obtaining a first matrix based on the distribution data of red blood cell particles; obtaining projection data based on the product of the first matrix and the extension direction; generating a histogram of the projection data to calculate a segmentation threshold based on the peak data of the histogram; and outputting the detection result of reticulocytes based on the number of red blood cell particles outside the segmentation threshold of the histogram.

[0107] Optionally, step S13 includes the following steps: calculating the centroid positions of several red blood cell particles based on the distribution data; obtaining the third characteristic direction of the red blood cell particles based on the centroid positions and the origin position of the first scatter plot; and using the normal vector corresponding to the third characteristic direction as the extension direction.

[0108] Optionally, the optical signal of the sample to be tested includes at least two of the following: forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal; step S12 may include the following steps: generating a plurality of third scatter plots based on at least two of the forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal, wherein the plurality of third scatter plots includes a first region 101; obtaining the region range of the first region 101 of the plurality of third scatter plots; and taking the third scatter plot with the largest region range as the first scatter plot.

[0109] Furthermore, the region range may include the region area, cell particle distance, region width, etc.; the above-mentioned step of using the third scatter plot with the largest region range as the first scatter plot may also include: selecting the third scatter plot with the largest region area of ​​the first region 101 from several third scatter plots as the first scatter plot, or selecting the third scatter plot with the largest distance between red blood cell particles in the first region 101 from several third scatter plots as the first scatter plot.

[0110] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium provided in this application. Figure 10 As shown, the computer-readable storage medium 110 stores program instructions 111 capable of implementing all of the above methods.

[0111] If the integrated units of the various functional units in the various embodiments of this application are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium 110. 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. The computer-readable storage medium 110 includes several instructions in a program instruction 111 to cause a computer device (which may be a personal computer, system server, or network device, etc.), an electronic device (e.g., MP3, MP4, etc., or a mobile terminal such as a mobile phone, tablet, or wearable device, or a desktop computer, etc.) or a processor 10 to execute all or part of the steps of the methods of the various embodiments of this application.

[0112] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media 110 (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] This application is described with flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by a computer-readable storage medium 110. These computer-readable storage media 110 can be provided to a processor 10 of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that program instructions 111, executable by the processor 10 of the computer or other programmable data processing apparatus, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A blood cell analyzer, characterized in that, Applications in animal blood cell detection include: The detection module includes a light emitter, a flow chamber, and a light receiver. The flow chamber is used to flow a sample stream formed by the sample to be tested. The light emitter is used to emit laser light to the sample stream. The light receiver is used to receive the optical signal generated by the sample stream under the laser irradiation. The processor, connected to the detection module, is used to execute: Acquire at least two optical signals generated by the sample under test at different receiving angles; A first scatter plot is generated based on the at least two optical signals, and the distribution area of ​​red blood cell particles in the first scatter plot is obtained. The extension direction of reticulocyte particles in the distribution area is obtained based on the distribution data of the distribution area. When the species information of the test sample is different, the extension direction corresponding to the distribution area is different. The projection data of the red blood cell particles in the extension direction is obtained, so as to obtain the detection result of reticulocytes in the sample to be tested based on the projection data; The step of obtaining the extension direction of reticulocyte particles in the distribution area based on the distribution data of the distribution area includes: Calculate the covariance matrix corresponding to the distribution data of the distribution area to obtain the first feature direction and the second feature direction of the covariance matrix, wherein the first feature direction and the second feature direction are perpendicular; Identify a first region and a second region within the distribution region, wherein the particle distribution density of the first region is less than the particle distribution density of the second region; Obtain the first overlap ratio of the first region and the second region in the first scatter plot in the first feature direction, and obtain the second overlap ratio of the first region and the second region in the first scatter plot in the second feature direction; In response to the first overlap ratio being greater than the second overlap ratio, the second feature direction is taken as the extension direction so that the number of overlaps between the reticulocyte particles and other erythrocyte particles in the extension direction is small; In response to the first overlap ratio being less than the second overlap ratio, the first feature direction is taken as the extension direction so that the number of overlaps between the reticulocyte particles and other erythrocyte particles in the extension direction is small.

2. The blood cell analyzer according to claim 1, characterized in that, The processor is used to execute: Based on the distribution data of the red blood cell particles in the distribution area, a first matrix is ​​obtained; Obtain the mean of the distribution data of the first matrix, and perform a centering process on the first matrix to obtain the second matrix; Calculate the covariance matrix of the second matrix, and determine the first feature direction and the second feature direction based on the eigenvectors of the covariance matrix.

3. The blood cell analyzer according to claim 1, characterized in that, The processor is used to execute: The centroid position of the red blood cell particles is calculated based on the distribution data; The third characteristic direction of the red blood cell particles is obtained based on the centroid position and the origin of the first scatter plot. The normal vector corresponding to the third feature direction is taken as the extension direction.

4. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is used to execute: The first matrix is ​​obtained based on the distribution data of the red blood cell particles; The projection data is obtained based on the product of the first matrix and the extension direction; Generate a histogram of the projected data, and calculate a segmentation threshold based on the peak data of the histogram; Based on the number of red blood cell particles outside the segmentation threshold of the histogram, the detection result of the reticulocytes is output.

5. The blood cell analyzer according to claim 1, characterized in that, The optical signal of the sample under test includes at least two of the following: forward scattering signal, first side scattering signal, second side scattering signal, and fluorescence pulse signal; the processor is used to execute: Based on at least two of the forward scattering signal, the first side scattering signal, the second side scattering signal, and the fluorescence pulse signal, a plurality of third scatter plots are generated, wherein the plurality of third scatter plots include the first region; Obtain the region range of the first region in several third scatter plots, and use the third scatter plot with the largest region range as the first scatter plot.

6. The blood cell analyzer according to claim 5, characterized in that, The processor is used to execute: The first scatter plot is the third scatter plot with the largest area of ​​the first region in the plurality of third scatter plots, or the third scatter plot with the largest distance between red blood cell particles in the first region in the plurality of third scatter plots is the first scatter plot.

7. A method for blood cell analysis, characterized in that, Applied to the hematology analyzer as described in any one of claims 1-6, comprising: Acquire at least two optical signals generated by the sample under test at different receiving angles; A first scatter plot is generated based on the at least two optical signals, and the distribution area of ​​red blood cell particles in the first scatter plot is obtained. The extension direction of reticulocyte particles in the distribution area is obtained based on the distribution data of the distribution area. The projection data of the red blood cell particles in the extension direction is obtained, so as to obtain the detection result of reticulocytes in the sample to be tested based on the projection data.

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