CN114544469A - Particle classification method and blood cell analyzer - Google Patents
Particle classification method and blood cell analyzer Download PDFInfo
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- CN114544469A CN114544469A CN202210439178.0A CN202210439178A CN114544469A CN 114544469 A CN114544469 A CN 114544469A CN 202210439178 A CN202210439178 A CN 202210439178A CN 114544469 A CN114544469 A CN 114544469A
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N2015/1006—Investigating individual particles for cytology
Abstract
The application discloses a particle classification method and a blood cell analyzer, wherein the particle classification method comprises the following steps: obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample, and respectively obtaining preset peak coefficients of the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; obtaining an enhancement signal based on the preset peak coefficients of two pulse signals of the pre-dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal; constructing an enhanced scattergram based on the preset peak coefficients of the pre-scattered pulse signal, the side scattered pulse signal and the remaining one of the fluorescence pulse signals, and the enhancement signal; and obtaining an enhanced particle classification result based on the enhanced scatter diagram. Through the mode, the accuracy of the particle classification result can be improved.
Description
Technical Field
The application belongs to the technical field of medical instruments, and particularly relates to a particle classification method and a blood cell analyzer.
Background
A blood cell analyzer is an instrument capable of detecting cells in blood, and can count and classify cells such as white blood cells, red blood cells, platelets, nucleated red blood cells, reticulocytes, and the like.
Blood cell analyzers generally classify and count blood cells by illuminating cell particles flowing through a detection area with light, collecting light signals reflected or scattered by each type of particle, and then processing and analyzing the light signals. Wherein the collected optical signals may include pre-scattered pulse signals, side scattered pulse signals, and fluorescence pulse signals; the pre-dispersion pulse signal can reflect the size information of the cell, the side dispersion pulse signal can reflect the complexity of the internal structure of the cell, and the fluorescence pulse signal can reflect the content of substances which can be dyed by fluorescent dye, such as DNA, RNA and the like in the cell.
At present, in the process of classifying and counting blood cells by constructing a scatter diagram by using the multiple optical signals, when some blood samples have abnormality, the superposition degree between adjacent particle clusters on the scatter diagram is increased, and the accuracy of a classification result is further reduced.
Disclosure of Invention
The application provides a particle classification method and a blood cell analyzer, which are used for improving the accuracy of a particle classification result.
In order to solve the technical problem, the application adopts a technical scheme that: there is provided a method of particle classification comprising: obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample, and respectively obtaining preset peak coefficients of the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; obtaining an enhancement signal based on the preset peak coefficients of two pulse signals of the pre-dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal; constructing an enhanced scattergram based on the preset peak coefficients of the pre-scattered pulse signal, the side scattered pulse signal and the remaining one of the fluorescence pulse signals, and the enhancement signal; and obtaining an enhanced particle classification result based on the enhanced scatter diagram.
In order to solve the technical problem, the application adopts a technical scheme that: provided is a blood cell analyzer including: an optical detector group for obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample; a memory for storing program data; a controller, coupled to the set of optical detectors and the memory, for executing the program data to implement the particle sorting method described in any of the above embodiments.
Being different from the prior art situation, the beneficial effect of this application is: according to the particle classification method, enhancement signals are obtained based on preset peak coefficients of two pulse signals in a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal, and an enhancement scatter diagram is constructed based on the preset peak coefficients of the remaining one pulse signal in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal and the enhancement signals; and finally, obtaining an enhanced particle classification result based on the enhanced scatter diagram. In the design mode, the information of the two dimensions is comprehensively calculated to obtain the enhanced signals, so that different types of particle clusters (cell clusters) in the finally obtained enhanced scatter diagram can be further separated, the superposition between the particle clusters is less, and the final classification result is more accurate.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a schematic structural view of an embodiment of a blood cell analyzer according to the present application;
FIG. 2 is a schematic flow chart diagram illustrating an embodiment of a particle classification method according to the present application;
FIG. 3 is a schematic flow chart diagram illustrating another embodiment of the particle classification method of the present application;
FIG. 4 is a schematic structural diagram of an embodiment of an original scattergram corresponding to a current blood sample;
FIG. 5 is a schematic structural diagram of an embodiment of an enhanced scattergram corresponding to the original scattergram in FIG. 4.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the present application, the blood sample may be a sample obtained from a human or animal body and reacted with a reagent, wherein the reagent includes a fluorescent reagent or the like. Blood samples include a variety of cells, such as red blood cells, white blood cells, platelets, and the like. Erythrocytes can be classified from the viewpoint of their degree of development, and are specifically subdivided into mature erythrocytes, reticulocytes, and the like, wherein reticulocytes refer to erythrocytes that have not yet fully matured. The scattergram referred to in this application is a two-dimensional scattergram created from the pre-scattered pulse signal, the side scattered pulse signal, and the fluorescence pulse signal of the blood sample.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an embodiment of a blood cell analyzer according to the present application, which may be a fluorescence flow cytometer, including a plurality of detection channels, such as a nucleated red blood cell (WNR) detection channel, a Reticulocyte (RET) detection channel, a white blood cell classification (WDF) channel, an abnormal lymphocyte (WPC) detection channel, and the like. The blood cell analyzer specifically includes a sheath flow assembly device 10, a drive device 12, a semiconductor laser 14, an optical detector group 16, a memory 18, and a controller 11.
The sheath flow assembly device 10 and the driving device 12 include all the components for collecting, mixing, reacting, and staining the sample, so as to allow the blood cells to sequentially and individually pass through the position where the optical detection is performed in the optical detector set 16.
The semiconductor laser 14 is used for emitting a laser signal with stable power to excite a fluorescence signal of the stained cell.
The optical detector set 16 includes optical sensors, refractive optics, detectors, and the like. The set of optical detectors 16 may include a forward scatter light receiving assembly, a side scatter light receiving assembly, and a side fluorescence receiving assembly for obtaining a forward scatter pulse signal, a side scatter pulse signal, and a fluorescence pulse signal, respectively, of the same blood sample. The optical detector set 16 may also be used with a fluorescent staining technique, in which an optical filter is added to the detector and the fluorescence signal is received through the optical filter. Optionally, the 1-5 ° signal collected in the optical detector set 16 is a front scattered pulse signal, and the 10-90 ° signal is a side scattered pulse signal.
The memory 18 is used for storing program data; of course, the memory 18 may also be used to store all the pulse signals received by the optical detector group 16, scatter diagrams obtained by subsequent processing, particle classification results, calculation results, and the like.
The controller 11 is coupled to the set of optical detector elements 16 and the memory 18 for executing program data to implement the particle sorting method as described in the embodiments below. In addition, the controller 11 is also used for regulating and controlling various components; for example, at the start of the detection, mechanical driver components such as motors, valves, pumps, etc. in the driving device 12 are controlled to stain and load blood cells into the particle guide of the sheath flow assembly device 10 to push out each blood cell in order.
In addition, with continued reference to fig. 1, the blood cell analyzer may further include a display 13 and an AD conversion module 15. Wherein, the display 13 may be coupled to the controller 11 for displaying the scatter diagram and the particle classification result. The AD conversion module 15 is coupled between the controller 11 and the optical detector set 16, and is used for converting the electrical signals collected by the optical detector set 16 into digital signals that can be processed by the controller 11.
The particle classification method that can be realized by the controller 11 will be described in detail below. Referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of a particle classification method according to the present application, the particle classification method specifically includes:
s101: and obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample, and respectively obtaining preset peak coefficients of the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal.
Specifically, the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal can be respectively formed by a plurality of sampling points, and the abscissa of each sampling point is sampling time, and the unit can be second and the like; the ordinate of the sampling point may be the sampling data, for example, the light intensity value, etc. And corresponding sampling data can be respectively arranged in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal aiming at the same sampling time.
Alternatively, the preset peak coefficient may be any one of a peak value, a peak area, a peak value and a peak width, a peak width and a peak area, a peak value and a peak width, and a peak area of each of the pre-dispersion pulse signal, the side-dispersion pulse signal, and the fluorescence pulse signal. Namely, the subsequent particle classification method is carried out based on the peak values of each pulse in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; or the subsequent particle classification method is carried out based on the peak areas of each pulse in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; or the subsequent particle classification method is carried out based on the peak value and the peak area of each pulse in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; or the subsequent particle classification method is carried out based on the peak value and the peak width of each pulse in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; or the subsequent particle classification method is carried out based on the peak width and the peak area of each pulse in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; or the subsequent particle classification method is performed based on the peak value, peak width and peak area of each pulse in the pre-scattered pulse signal, the side-scattered pulse signal and the fluorescence pulse signal. The design mode can reduce the computational complexity of the whole particle classification method.
S102: and obtaining an enhanced signal based on preset peak coefficients of two pulse signals in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal.
Specifically, the preset peak coefficients of the two pulse signals for constructing the enhancement signal are positively correlated with the enhancement signal. The design mode can superpose the information corresponding to the same particle cluster in the two pulse signals, so that different particle clusters can be better distinguished.
Optionally, the specific implementation process of step S102 may be:
A. and obtaining the root mean square average value of the preset peak coefficients of the two pulse signals in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal.
Specifically, the pre-pulse signal, the side pulse signal, and the fluorescence pulse signal generally include a plurality of pulses, respectively.
In an application scenario, the specific implementation process of the step a may be: obtaining two pulses at the same sampling time from the selected two pulse signals respectively to form a plurality of pulse pairs; aiming at each pulse pair, obtaining the root-mean-square average value of preset peak coefficients of two pulses in the same pulse pair; expressed as:
wherein, X is a root mean square average value, a and B are respectively preset peak coefficients of two pulse signals, and a and B can be considered as the preset peak coefficients of two pulses in the same pulse pair.
In another application scenario, the specific implementation process of step a may be: 1) two pulse signals selected from the front dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal are defined as a first pulse signal and a second pulse signal, respectively, and the remaining one of the front dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal is defined as a third pulse signal. 2) The first scatter diagram is constructed based on the preset peak coefficients of the first pulse signal and the third pulse signal, and the second scatter diagram is constructed based on the preset peak coefficients of the second pulse signal and the third pulse signal, and the third pulse signal in the first scatter diagram and the third scatter diagram is located on the same coordinate axis, for example, both located on the X axis or the Y axis. 3) And obtaining a plurality of coordinate point pairs from the first scatter diagram and the second scatter diagram, wherein each coordinate point pair comprises a first coordinate point obtained from the first scatter diagram and a second coordinate point obtained from the second scatter diagram, and the preset peak coefficients of the third pulse signals corresponding to the first coordinate point and the second coordinate point are the same. 4) And obtaining the root-mean-square average value of the preset peak coefficients corresponding to the first pulse signal and the second pulse signal in the same coordinate point pair.
B. The enhancement signal is obtained based on a binomial function and a root mean square average.
Specifically, for each pulse pair or coordinate point pair, a corresponding enhancement value is obtained based on a binomial function and a root mean square average value to which each pulse pair or each coordinate point pair corresponds, the plurality of enhancement values forming an enhancement signal.
In the present embodiment, the specific configuration of the binomial function is not limited. For example, the binomial function is formulated as follows:
wherein F (X) is an enhancement signal, X is a root mean square average, K1、K2、K3Are all coefficients, and K1、K2、K3The value of (A) can be set according to the actual situation; for example, the coefficient corresponding to the enhancement signal composed of the front scattered pulse signal and the side scattered pulse signal is different from the coefficient corresponding to the enhancement signal composed of the side scattered pulse signal and the fluorescence pulse signal. For another example, the coefficients of the enhanced signals for the nucleated red blood cell (WNR) detection channel, the Reticulocyte (RET) detection channel, the white blood cell classification (WDF) channel, and the abnormal lymphocyte (WPC) detection channel are different.
In one specific application scenario, K is used when the enhanced signal is formed by the side-scattered pulse signal and the front-scattered pulse signal corresponding to the Reticulocyte (RET) detection channel1May be 1.589 × e-7、K2May be-9.766 × e-4，K3May be 2.333.
Of course, in other embodiments, the enhancement signal may also be obtained based on the root mean square average and other non-linear functions, for example, other non-linear functions may be log functions, etc. The function or rule involved in calculating the enhancement signal is not limited herein, and may be set and changed according to actual requirements.
S103: and constructing an enhanced scatter diagram based on the preset peak coefficient of the front scattered pulse signal, the side scattered pulse signal and the rest one of the fluorescence pulse signals and the enhanced signal.
Specifically, the enhanced signal may be used as an abscissa in the enhanced scattergram, and the preset peak coefficient of the remaining one pulse signal may be used as an ordinate to construct the enhanced scattergram; or, the enhanced signal in the enhanced scattergram can be used as the ordinate, and the preset peak coefficient of the remaining pulse signal can be used as the abscissa to construct the enhanced scattergram.
S104: and obtaining an enhanced particle classification result based on the enhanced scatter diagram.
Specifically, the specific implementation process of step S104 may be: classifying the particles at different positions of the enhanced scatter diagram according to the characteristics of different blood cells, and cutting out regions of various particle clusters, namely the particle classification process; for example, a watershed algorithm, a clustering algorithm, a contour method, a gradient method, etc. may be used to obtain the boundary of each particle group with the rest of the particle groups from the enhanced scatter diagram (i.e. to find the edge position of each particle group); and counting the particle groups to obtain an enhanced particle classification result.
In the design mode, the information of the two dimensions is comprehensively calculated to obtain the enhanced signals, so that different types of particle clusters (cell clusters) in the finally obtained enhanced scatter diagram can be further separated, the superposition between the particle clusters is less, and the final classification result is more accurate.
Referring to fig. 3, fig. 3 is a schematic flow chart of another embodiment of the particle classification method of the present application. The particle classification method specifically comprises the following steps:
s201: and obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample, and respectively obtaining preset peak coefficients of the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal.
Specifically, this step is the same as step S101 described above, and is not described herein again.
S202: and constructing an original scatter diagram based on preset peak coefficients of two pulse signals in the pre-scattering pulse signal, the side scattering pulse signal and the fluorescence pulse signal.
Specifically, the preset peak coefficient may be any one of a peak value, a peak area, a peak value and a peak width, a peak width and a peak area, a peak value and a peak width, and a peak area of each pulse in the pre-dispersion pulse signal, the side-dispersion pulse signal, and the fluorescence pulse signal. Wherein the peak area ≈ peak width. The step S202 specifically includes: constructing an original scatter diagram based on peak values of two pulse signals in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; or, an original scattergram is constructed based on peak areas of two pulse signals among the pre-scattering pulse signal, the side scattering pulse signal and the fluorescence pulse signal.
S203: and obtaining a primary particle classification result based on the primary scatter diagram.
Specifically, the specific implementation process of step S203 may be: classifying particles at different positions of an original scatter diagram according to the characteristics of different blood cells, and cutting out regions of various particle clusters, namely the particle classification process; for example, a watershed algorithm, a clustering algorithm, a contour method, a gradient method, etc. may be used to obtain the boundary of each particle group with the rest of the particle groups from the enhanced scatter diagram (i.e. to find the edge position of each particle group); the individual clusters are counted to obtain the raw particle classification results.
S204: judging whether the superposition coefficient of any two particle clusters in the original scatter diagram meets the preset requirement or not based on the original particle classification result; wherein, the larger the particle contact ratio between the two particle clusters is, the larger the superposition coefficient is.
Specifically, in an embodiment, the specific implementation process of the step S204 may be: A. obtaining edge positions of the particle clusters based on the original particle classification result; for example, the edge positions of each cluster can be obtained from the original scattergram using a watershed algorithm, a clustering algorithm, a contour method, a gradient method, or the like. B. The concentration of particles at the edge position of each cluster is obtained and taken as the superposition coefficient of the corresponding cluster. C. And judging whether each superposition coefficient is smaller than or equal to the corresponding threshold value. Alternatively, the threshold values may be different for different clusters. The process of judging whether the superposition coefficient meets the preset requirement is simple and easy to realize.
In another embodiment, the specific implementation process of step S204 may be: A. obtaining edge positions of the particle clusters based on the original particle classification result; for example, the edge positions of each cluster can be obtained from the original scattergram using a watershed algorithm, a clustering algorithm, a contour method, a gradient method, or the like. B. And obtaining the concentration of the particles between the edge positions of any two adjacent particle clusters, and taking the concentration as a superposition coefficient. Alternatively, there may be identifiable interfering particles between the edge positions of any two adjacent particle clusters, and at this time, only the concentration of interfering particles between the edge positions of two particle clusters may be calculated and used as the superposition coefficient. C. And judging whether each superposition coefficient is smaller than or equal to the corresponding threshold value. Alternatively, the threshold value corresponding to each superposition coefficient may be different. The process of judging whether the superposition coefficient meets the preset requirement is simple and easy to realize.
S205: if yes, outputting the classification result of the original particles.
S206: if not, obtaining an enhanced signal based on preset peak coefficients of two pulse signals in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal; constructing an enhanced scatter diagram based on the preset peak coefficient of the front scattered pulse signal, the side scattered pulse signal and the rest one of the fluorescence pulse signals and the enhanced signal; and obtaining an enhanced particle classification result based on the enhanced scatter diagram.
Specifically, if the superposition coefficient of the two clusters is large, it indicates that the particle contact ratio between the two clusters is large, the two clusters cannot be well distinguished, and the reliability of the counting result of each cluster in the original particle classification result is reduced. Therefore, at this time, in order to ensure the accuracy of the classification result, the enhanced counting process of the above-described step S206 may be performed. The specific implementation process of step S206 is the same as that of steps S102 to S104, and is not described herein again.
In addition, it should be noted that, in the above steps S202-S206, one pulse signal in the original scattergram is constructed for constructing the enhanced signal, and the other pulse signal in the original scattergram is constructed for constructing the enhanced scattergram together with the enhanced signal. That is, in step S206, one coordinate in the original scatter diagram may be kept unchanged, and the other coordinate and the residual pulse signal may be subjected to enhancement processing to reduce the complexity of data processing. And specifically selecting which coordinate in the original scatter diagram is kept unchanged, and selecting according to actual conditions, so long as the discrimination between any two particle clusters in the subsequent enhanced scatter diagram is better. In other embodiments, two pulse signals for constructing the original scatter diagram can be selected to construct the enhanced signal, and the remaining one of the three pulse signals is used for constructing the enhanced scatter diagram together with the enhanced signal.
In the design mode, an original particle classification result corresponding to the original scatter diagram is obtained firstly; and only when the two adjacent particle clusters in the original scatter diagram cannot be well distinguished, the subsequent enhancement processing process is carried out, so that the two particle clusters are better distinguished, and the accuracy of the particle classification result is improved. The design mode can reduce the modification degree of the existing computing program.
In addition, after obtaining the enhanced particle classification result in step S206, outputting a prompt message for obtaining the enhanced particle classification result by using the enhanced signal may be further included. I.e. to remind the operator that the particle classification result is obtained based on the enhancement mode.
The particle classification method provided herein is further described below in the context of a specific application of Reticulocyte (RET) detection channels.
Reticulocytes (RET) are not fully mature red blood cells, and the value in the peripheral blood reflects the production function of bone marrow erythrocytes, thus being of great importance for diagnosis of hematological diseases and observation of therapeutic responses. When the Reticulocyte (RET) detection channel is used for treating cells, the process of dissolving the cells is not carried out, and the fluorescent dye is used for staining nucleic acids of red blood cells, platelets and white blood cells treated by a diluent; the reticulocyte and leukocyte have more nucleic acid content, stronger fluorescence and larger volume of the erythrocyte, the corresponding front scattered pulse signal and side scattered pulse signal are stronger than the platelet, and various cells in the reticulocyte channel can be classified according to the volume and fluorescence intensity information. The specific classification process may be:
A. obtaining a front dispersion pulse signal (FSC), a side dispersion pulse signal (SSC) and a fluorescence pulse Signal (SFL) of the blood sample, and obtaining preset peak coefficients of the front dispersion pulse signal (FSC), the side dispersion pulse signal (SSC) and the fluorescence pulse Signal (SFL), respectively.
B. And constructing an original scatter diagram corresponding to the reticulocyte detection channel based on preset peak coefficients in the pre-scatter pulse signal (FSC) and the fluorescence pulse Signal (SFL). For example, as shown in fig. 4, fig. 4 is a schematic structural diagram of an embodiment of an original scattergram corresponding to a current blood sample. In the original scattergram, the preset peak coefficient in the fluorescence pulse Signal (SFL) is the X-axis, and the preset peak coefficient of the pre-scattered pulse signal (FSC) is the Y-axis.
C. Red Blood Cell (RBCO) clusters, Platelet (PLTO) clusters, White Blood Cell (WBCO) clusters, and Reticulocyte (RET) clusters were classified and calculated based on the raw scatter plots, and the regions of particle distribution are shown in fig. 4.
D. Calculating a superposition coefficient between any two adjacent particle clusters; for example, taking the adjacent Red Blood Cell (RBCO) and Platelet (PLTO) clusters as an example, the concentration of red blood cell debris can be used as a superposition coefficient.
E. The process of creating an enhanced scatter plot is performed in response to the concentration of red blood cell debris being greater than or equal to 2%. Referring to fig. 5, fig. 5 is a schematic structural diagram of an embodiment of an enhanced scattergram corresponding to the original scattergram in fig. 4. In the present embodiment, the enhancement signal may be formed using information of the front dispersion pulse signal (FSC) and the side dispersion pulse signal (SSC), and as Y-axis data, the X-axis data still uses data of the fluorescence pulse Signal (SFL). As is apparent from fig. 5, the enhanced scattergram has an increased degree of discrimination between red blood cell clusters (RBCO) and platelet clusters (PLTO), and the enhanced scattergram also has an expanded diploid red blood cell cluster which reflects the degree of overlap of the current red blood cells as they pass through the sheath flow.
F. And obtaining and outputting an enhanced classification result based on the enhanced scatter diagram.
In summary, in the original reticulocyte channel RET scattergram, the red blood cell debris interferes with the counting of platelet clumps (PLTO) and red blood cell clumps (RBCO), but by enhancing the scattergram, the interference degree of the red blood cell debris can be obviously reduced, so as to effectively improve the accuracy of the counting of the platelet clumps (PLTO) and the red blood cell clumps (RBCO). Especially, when some blood samples are anemia samples, the phenomena of increase of red blood cell fragments and lower average red blood cell volume MCV (blood cell volume) in the anemia samples occur, and the red blood cell fragments in the original scattergram corresponding to the reticulocyte channels interfere with the red blood cell cluster (RBCO) counting and the platelet cluster (PLTO) counting, so that the boundaries of the platelet cluster (PLTO) and the red blood cell cluster (RBCO) are seriously overlapped, and therefore, the problem can be optimized by adopting the enhanced signal scattergram.
Further, as shown in table 1 below, table 1 is a table of calculation results of the original scattergram and the enhanced scattergram. The correlation in table 1 below is a measure of the trend correlation between the number of particles calculated from the original scattergram or the enhanced scattergram and the preset reference value of the device, and the accuracy may be a measure of the difference between the number of particles calculated from the original scattergram or the enhanced scattergram and the true value. As can be seen from table 1, the correlation and accuracy calculated by the enhanced scattergram are superior to those of the original scattergram.
Table 1: original scatter diagram and enhanced scatter diagram calculation result table
| Correlation of raw scatter plot calculations | Enhancing correlation of scatter plot calculations | |
| RBCO | 0.994 | 0.996 |
| PLTO | 0.966 | 0.992 |
| Accuracy of raw scatter plot calculations | Enhancing accuracy of scatter plot calculations | |
| RBCO | 99% | 99% |
| PLTO | 90% | 97% |
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (10)
1. A method of classifying particles, comprising:
obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample, and respectively obtaining preset peak coefficients of the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal;
obtaining an enhancement signal based on the preset peak coefficients of two pulse signals of the pre-dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal;
constructing an enhanced scattergram based on the preset peak coefficients of the pre-scattered pulse signal, the side scattered pulse signal and the remaining one of the fluorescence pulse signals, and the enhancement signal;
and obtaining an enhanced particle classification result based on the enhanced scatter diagram.
2. The particle classification method according to claim 1, wherein the step of obtaining an enhancement signal based on preset peak coefficients of two of the pre-dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal is preceded by:
constructing an original scatter diagram based on the preset peak coefficients of two pulse signals of the pre-scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal;
obtaining an original particle classification result based on the original scatter diagram;
judging whether the superposition coefficient of any two particle clusters in the original scatter diagram meets the preset requirement or not based on the original particle classification result; wherein the larger the particle contact ratio between the two particle clusters is, the larger the superposition coefficient is;
if yes, outputting the original particle classification result; and if not, entering a step of obtaining an enhancement signal based on preset peak coefficients of two pulse signals in the front dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal.
3. The particle classification method according to claim 2,
constructing one of the pulse signals in the original scattergram for constructing the enhancement signal and constructing another of the pulse signals in the original scattergram for constructing the enhancement scattergram with the enhancement signal.
4. The particle classification method according to claim 2, wherein the step of determining whether the superposition coefficient of any two clusters in the original scattergram meets a preset requirement based on the original particle classification result comprises:
obtaining edge positions of the particle clusters based on the original particle classification result;
obtaining the concentration of particles at the edge position of each particle cluster, and taking the concentration as the superposition coefficient corresponding to the particle cluster;
and judging whether each superposition coefficient is smaller than or equal to a corresponding threshold value.
5. The particle classification method according to claim 2, wherein the step of determining whether the superposition coefficient of any two clusters in the original scattergram meets a preset requirement based on the original particle classification result comprises:
obtaining edge positions of the particle clusters based on the original particle classification result;
obtaining the concentration of particles between the edge positions of any two adjacent particle clusters, and taking the concentration as the superposition coefficient;
and judging whether each superposition coefficient is smaller than or equal to a corresponding threshold value.
6. The particle classification method according to any one of claims 1 to 5,
the preset peak coefficients of the two pulse signals constructing the enhancement signal are positively correlated with the enhancement signal.
7. The particle classification method according to claim 6, wherein the step of obtaining an enhancement signal based on preset peak coefficients of two pulse signals of the pre-dispersion pulse signal, the side dispersion pulse signal and the fluorescence pulse signal comprises:
obtaining a root mean square average value of preset peak coefficients of two pulse signals in the front scattered pulse signal, the side scattered pulse signal and the fluorescence pulse signal;
the enhancement signal is obtained based on a binomial function and the root mean square average.
8. The particle sorting method according to claim 1,
the preset peak coefficient comprises any one of a peak value, a peak area, a peak value and a peak width, a peak width and a peak area, a peak value and a peak width and a peak area.
9. The particle classification method according to claim 1, further comprising:
and outputting reminding information for obtaining the classification result of the enhanced particles by using the enhanced signal.
10. A blood cell analyzer, comprising:
an optical detector group for obtaining a front scattered pulse signal, a side scattered pulse signal and a fluorescence pulse signal of the same blood sample;
a memory for storing program data;
a controller coupled to the set of optical detectors and the memory for executing the program data to implement the particle sorting method of any one of claims 1-9.
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