CN114047109B - Sample analyzer and counting method thereof - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 32
- 239000002245 particle Substances 0.000 claims abstract description 144
- 238000001514 detection method Methods 0.000 claims abstract description 102
- 210000003651 basophil Anatomy 0.000 claims abstract description 62
- 210000000265 leukocyte Anatomy 0.000 claims abstract description 40
- 238000005259 measurement Methods 0.000 claims abstract description 37
- 210000000440 neutrophil Anatomy 0.000 claims description 48
- 239000011159 matrix material Substances 0.000 claims description 27
- 210000001616 monocyte Anatomy 0.000 claims description 17
- 210000003979 eosinophil Anatomy 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 13
- 210000004698 lymphocyte Anatomy 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 7
- 239000008188 pellet Substances 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 210000003677 hemocyte Anatomy 0.000 description 3
- 229940000351 hemocyte Drugs 0.000 description 3
- 102000001554 Hemoglobins Human genes 0.000 description 2
- 108010054147 Hemoglobins Proteins 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000000601 blood cell Anatomy 0.000 description 2
- 210000003743 erythrocyte Anatomy 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 210000001995 reticulocyte Anatomy 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004159 blood analysis Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 210000003714 granulocyte Anatomy 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/1012—Calibrating particle analysers; References therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/1031—Investigating individual particles by measuring electrical or magnetic effects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N2015/1006—Investigating individual particles for cytology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
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Abstract
The application discloses sample analyzer and counting method thereof, the sample analyzer includes a control module, a first detection module and a second detection module, the control module is respectively connected with the first detection module and the second detection module, and the counting method includes: the control module generates a first histogram based on first measurement data of the first detection module; the control module obtains an area ratio based on a ratio of the first area to a total area of the first histogram; the control module calculates the ratio of the number of discrete particles to the number of leukocytes of the second detection module; the control module obtains a positive probability value of the sample based on the plurality of area ratios and the number ratio; and the control module obtains the proportion of basophils in the white blood cells based on the positive probability value. By the method, the positive misjudgment rate of the sample can be reduced, the positive detection rate of the sample is increased, and the reliability of the count value of basophils can be improved.
Description
Technical Field
The present application relates to the field of sample analysis technologies, and in particular, to a sample analyzer and a counting method thereof.
Background
The hemocyte analyzer is used for classifying and counting blood cells, and generally, the hemocyte analyzer can classify and count leukocytes into five groups of lymphocytes, monocytes, neutrophils, eosinophils, and basophils.
The prior art hematology analyzer measures basophil count of a sample through a single optical detection channel, wherein basophils are overlapped with neutrophils and eosinophils in size and shape, and the basophil count is wrong under the condition that other abnormal particles appear in a basophil area of the sample.
Or, the hematology analyzer of the prior art measures the basophil count of the sample through a single impedance detection channel, and is easily affected by the blockage of immature granulocytes, lipid particles or micropores of the channel, so that the basophil count is erroneous.
Therefore, the related art hemocyte analyzers derive the count value of basophil through a single channel, which has a problem of unreliability.
Disclosure of Invention
In order to solve the above problems, the present application provides a sample analyzer and a counting method thereof, which can reduce a positive false positive rate of a sample and improve the reliability of a count value of basophils.
The technical scheme adopted by the application is as follows: there is provided a calculation method of a sample analyzer, the sample analyzer including a control module, a first detection module and a second detection module, the control module being connected to the first detection module and the second detection module, respectively, the first detection module being configured to measure a basophil count of a sample, the second detection module being configured to classify leukocytes of the sample, the counting method including:
the control module generates a first histogram based on first measurement data of the first detection module, and sets at least one first threshold line in the first histogram;
the control module acquires a first area of the first histogram after the first threshold line, and obtains an area ratio based on a ratio of the first area to a total area of the first histogram;
the control module obtains four types of particle clusters based on second measurement data of the second detection module, selects at least one particle cluster from the four types of particle clusters, obtains discrete particles in a preset area from the selected particle cluster, and calculates the quantity ratio of the number of the discrete particles in the total number of the white blood cells of the second detection module;
the control module obtains a positive probability value of the sample based on the area ratio and the number ratio;
the control module obtains the proportion of the basophils in the white blood cells based on the positive probability value.
Another technical scheme adopted by the application is as follows: providing a sample analyzer comprising a control module, a first detection module and a second detection module, the control module being connected to the first detection module and the second detection module, respectively, the first detection module being configured to measure a basophil count of a sample, the second detection module being configured to classify leukocytes of the sample, wherein:
the control module is used for generating a first histogram based on first measurement data of the first detection module and setting at least one first threshold line in the first histogram;
the control module is used for acquiring a first area of the first histogram after the first threshold line, and obtaining an area ratio based on the ratio of the first area to the total area of the first histogram;
the control module is used for obtaining four types of particle clusters based on second measurement data of the second detection module, selecting at least one particle cluster from the four types of particle clusters, obtaining discrete particles in a preset area from the selected particle cluster, and calculating the quantity ratio of the quantity of the discrete particles in the total quantity of the white blood cells of the second detection module;
the control module is used for obtaining a positive probability value of the sample based on the area ratio and the number ratio;
the control module is used for obtaining the proportion of the basophils in the white blood cells based on the positive probability value.
The first detection module of the sample analyzer is used for measuring basophils of a sample, and the second detection module is used for measuring leucocytes of the sample; the control module obtains a first area of the first histogram after the first threshold line, and obtains an area ratio based on a ratio of each first area to a total area of the first histogram; the control module obtains four types of particle clusters based on second measurement data of the second detection module, selects at least one particle cluster from the four types of particle clusters, obtains discrete particles in a preset region from the selected particle cluster, and calculates the quantity ratio of the quantity of the discrete particles in the total quantity of the white blood cells of the second detection module; the control module is used for obtaining a positive probability value of the sample based on the area ratio and the number ratio; the control module is used for obtaining the proportion of basophils in the white blood cells based on the positive probability value. Through the mode, the sample analyzer is combined with the first measurement data of the first detection module and the second measurement data of the second detection module to perform comprehensive analysis to obtain the positive probability value of the sample, so that the positive misjudgment rate of the sample can be reduced, and the positive detection rate of the sample is increased; the count value of the basophils is further obtained based on the proportion of the basophils in the leukocytes of the positive probability value, and the reliability of the count value of the basophils can be improved.
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, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
FIG. 1 is a schematic block diagram of a first embodiment of a sample analyzer of the present application;
FIG. 2 is a schematic flow diagram of a first embodiment of a counting method of the sample analyzer of FIG. 1;
FIG. 3 is a schematic view of a first embodiment of a first schematic of the present application;
FIG. 4 is a schematic diagram of a first embodiment of a projection of second measurement data of the present application into a multi-dimensional space;
FIG. 5 is a schematic flow chart of a first embodiment of step S201 in FIG. 2;
FIG. 6 is a schematic flow chart of a first embodiment of step S203 in FIG. 2;
FIG. 7 is a schematic illustration of a first embodiment of the neutrophil particle mass of FIG. 4;
FIG. 8 is a schematic representation of another perspective of the neutrophil particle mass of FIG. 7;
FIG. 9 is a diagram illustrating a second histogram of step S605 in FIG. 6;
fig. 10 is a schematic flowchart of a first embodiment of step S205 in fig. 2.
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. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures. 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.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The application of the sample analyzer is applied to the field of medical treatment or blood analysis, and is used for detecting various tiny particles in a sample so as to realize particle counting, and the more common sample analyzer can be a blood cell analyzer.
The sample analyzer of the present application may be used for performing Blood routine detection on a sample, and the Blood routine detection includes WBC (White Blood Cell) detection, HGB (Hemoglobin) detection, RBC (red Blood Cell) detection, DIFF (diffenential, leukocyte penta-classification) detection, or RET (reticulocyte) detection.
Referring to fig. 1-3, fig. 1 is a schematic structural diagram of a first embodiment of a sample analyzer of the present application, fig. 2 is a schematic flow chart of the first embodiment of a counting method of the sample analyzer of fig. 1, and fig. 3 is a schematic diagram of the first embodiment of a first schematic diagram of the present application.
The sample analyzer 10 of the present embodiment includes a control module 11, a first detection module 12, and a second detection module 13, and the control module 11 is connected to the first detection module 12 and the second detection module 13, respectively.
Wherein the first detection module 12 is used for measuring basophil counts of the sample, and the second detection module 13 is used for leukocyte classification of the sample. The sample analyzer 10 of the present application detects particles of the sample as leukocytes, wherein the leukocyte classification includes Lymphocytes (LYM), Monocytes (MON), Neutrophils (NEU), Eosinophils (EOS), and Basophils (BASO).
The first detection module 12 may also be referred to as a BASO channel, that is, the first detection module 12 measures basophils of the sample by an impedance method to obtain first measurement data, and has the characteristics of simplicity, rapidness, and low cost. For example, the first detection module 12 is used to detect conductivity changes caused by particles of the sample passing through the detection channel to distinguish basophils from lymphocytes, monocytes, neutrophils and eosinophils.
The second detection module 13 may also be referred to as a DIFF channel, that is, the second detection module 13 acquires the second measurement data by collecting an optical signal of each cell (such as lymphocyte, monocyte, neutrophil, eosinophil, and basophil) flowing through the detection area. The second detection module 13 is more sensitive to the detection of basophils than the first detection module 12.
Specifically, the counting method of the sample analyzer 10 includes the steps of:
s201: the control module 11 generates a first histogram based on the first measurement data of the first detection module 12, and sets at least one first threshold line in the first histogram.
The first detection module 12 measures basophils of the sample by an impedance method, for example, the first detection module 12 is provided with a micro-via, lymphocytes, monocytes, neutrophils, eosinophils, and basophils in the sample obtain corresponding electric pulse signals (first measurement data) through the micro-via, and the control module 11 generates a first histogram according to the size (volume of particles) of each pulse in the electric pulse signals and the frequency of occurrence thereof (number of particles corresponding to the volume of particles). The abscissa of the first histogram is the volume of the particles, and the ordinate is the corresponding number of particles, as shown in fig. 3.
Wherein the control module 11 sets at least one first threshold line on the first histogram. The control module 11 of the present embodiment sets a first threshold line 31 on the first histogram.
S202: the control module 11 obtains a first area a1 of the first histogram after the first threshold line 31, and obtains an area ratio a1 based on a ratio of the first area a1 to a total area of the first histogram.
The control module 11 obtains a first area a1 of the first histogram after the first threshold line 31, and obtains an area ratio a1 based on a ratio of the first area a1 and a total area of the first histogram.
As shown in fig. 3, the control module 11 is provided with a dividing line 35 before the first histogram, the dividing line 35 is also called a ghost dividing line, and before the dividing line 35, a ghost part is formed, so that the total area of the first histogram is the area of the first histogram located behind the dividing line 35.
The control module 11 obtains a first area a1 of the first histogram after the first threshold line 31, and divides the first area a1 by the total area of the first histogram to obtain an area ratio A1.
Optionally, the control module 11 sets a plurality of first threshold lines on the first histogram. For example, the control module 11 sets four first threshold lines, namely a first threshold line 31, a first threshold line 32, a first threshold line 33 and a first threshold line 34, in the first histogram.
The control module 11 obtains a first area of the first histogram after each first threshold line to obtain a plurality of first areas; the control module 11 derives a plurality of area ratios based on a ratio of each first area to a total area of the first histogram. Specifically, the control module 11 may set a dividing line 35 at the first valley bottom on the left side of the first histogram, and the first threshold lines 31, 32, 33, and 34 are all set after the dividing line 35.
For example, the control module 11 further obtains first areas a1, a2, A3, a4 of the first histogram after the first threshold lines 31, 32, 33, 34, respectively, and the control module 11 obtains area ratios a1, a2, A3, a4 by using ratios of the first areas a1, a2, A3, a4 to the total area of the first histogram.
S203: the control module 11 obtains four types of particle clusters based on the second measurement data of the second detection module 13, selects at least one particle cluster from the four types of particle clusters, obtains discrete particles in the preset region from the selected particle cluster, and calculates a quantity ratio PB of the number of the discrete particles in the total number of the white blood cells of the second detection module 13.
The control module 11 obtains four types of particle clusters, which are the lymphocyte particle cluster 41, the monocyte particle cluster 42, the neutrophil particle cluster 43, and the eosinophil particle cluster 44, based on the second measurement data of the second detection module 13.
The control module 11 obtains the discrete particles in the preset region from the selected particle group, and calculates a quantity ratio PB of the quantity of the discrete particles in the total number of the white blood cells of the second detection module 13. That is, the control module 11 acquires discrete particles within the preset region from the neutrophil particle mass 43 and calculates a quantity ratio PB of the number of discrete particles in the total number of white blood cells of the second detection module 13.
In one embodiment, control module 11 projects the second measurement data into a multidimensional space (e.g., a two-dimensional space or a three-dimensional space) with the data source as the axis to obtain lymphocyte particle cluster 41, monocyte particle cluster 42, neutrophil particle cluster 43, and eosinophil particle cluster 44, as shown in FIG. 4. The data source may be a forward scattered light signal LS, a medium angle side-scattering light signal MS, or a high angle side-scattering light signal HS, where the forward, medium, and high angles respectively refer to angles at which the light signal is received, the forward angle is smaller than the medium angle, and the medium angle is smaller than the high angle.
The control module 11 acquires the neutrophil pellet 43 as a selected pellet from the lymphocyte pellet 41, the monocyte pellet 42, the neutrophil pellet 43, and the eosinophil pellet 44. The control module 11 obtains discrete particles in a preset area from the selected particle mass, that is, the control module 11 obtains discrete particles 45 between the neutrophil particle mass 43 and the monocyte particle mass 42, and calculates a quantity ratio PB of the quantity of the discrete particles 45 in the total number of the white blood cells of the second detection module 13.
Alternatively, the control module 11 may project the second measurement data into a two-dimensional or three-dimensional space according to the data source of the second detection module 13, and classify the lymphocyte particle cluster 41, the monocyte particle cluster 42, the neutrophil particle cluster 43, and the eosinophil particle cluster 44 by a classification method such as existing clustering, gating, or projection.
S204: the control module 11 obtains a positive probability value P of the sample based on the area ratio a1 and the numerical ratio PB.
When the first histogram sets a first threshold line 31, the control module 11 obtains a positive probability value P of the sample based on the area ratio a1 and the numerical ratio PB. When the first threshold lines 31, 32, 33, and 34 are set in the first histogram, the control module 11 obtains the positive probability value P of the sample based on the area ratio values a1, a2, A3, a4, and the numerical ratio value PB.
S205: the control module 11 obtains the proportion of basophils in the white blood cells based on the positive probability value P.
The control module 11 obtains the proportion of basophils in the leukocytes based on the positive probability value, and obtains the count value of basophils based on the proportion and the count value of leukocytes.
The sample analyzer 10 of this embodiment performs comprehensive analysis by combining the first measurement data of the first detection module 12 and the second measurement data of the second detection module 13 through the control module 11 to obtain a positive probability value P of the sample, so that the positive false-positive rate of the sample can be reduced, and the positive detection rate of the sample can be increased; the control module 11 obtains the proportion of basophils in leukocytes based on the positive probability value P, and further obtains the count value of the basophils, so that the reliability of the count value of the basophils can be improved.
Optionally, step S204 includes: the control module 11 also obtains characteristic information C of the neutrophil particle mass 43, wherein the characteristic information C includes a ratio of a major axis and a minor axis to which an ellipse of the neutrophil particle mass 43 is fitted, a density of the neutrophil particle mass 43, or a Hotelling's T-squared distribution of the neutrophil particle mass 43. Step S205 includes: the control module 11 obtains a positive probability value P of the sample based on the area ratio a1, the number ratio PB, and the feature information C.
Referring to fig. 5, fig. 5 is a schematic flowchart of the first embodiment of step S201 in fig. 2. Wherein, step S201 includes the following steps:
s501: the control module 11 performs a smoothing filtering process on the first histogram.
The control module 11 performs a smoothing filtering process on the first histogram to avoid the noise causing the fluctuation of the first histogram. In other embodiments, the control module 11 sets a reasonable bin distance (bin) to the first histogram to avoid noise from causing fluctuations in the first histogram.
S502: the control module 11 selects a valley of the first histogram or a middle position of the preset range within the preset range located behind the boundary 35 of the first histogram to set the first threshold lines 31, 32, 33, 34.
Wherein the control module 11 is provided with a predetermined range, which is located behind the dividing line 35 of the first histogram. The control module 11 selects a valley bottom within a preset range or sets a first threshold line 31, 32, 33, 34 at a middle position of the preset range.
For example, the preset range set by the control module 11 may be 50-60, 60-70, 70-80, 80-90; the control module 11 selects a valley position within 50-60 to set a first threshold line 31, or selects a 55 position to set the first threshold line 31; the control module 11 selects a valley position within 60-70 to set the first threshold line 32, or selects a 65 position to set the first threshold line 32; the control module 11 selects a valley value position to set a first threshold value line 33 in 70-80, or selects a 75 position to set the first threshold value line 33; the control module 11 selects the valley position to set the first threshold line 34 or selects the 65 position to set the first threshold line 34 within 80-90. In other embodiments, the first threshold lines 31, 32, 33, 34 may be set in other ways by those skilled in the art.
The control module 11 of this embodiment performs a smoothing filtering process on the first histogram, so as to avoid the fluctuation of the first histogram caused by noise; the control module 11 selects a valley bottom or a middle position of the preset range in the preset range to set the first threshold lines 31, 32, 33 and 34, and can quickly set the first threshold lines 31, 32, 33 and 34.
Referring to fig. 6, fig. 6 is a schematic flowchart of the first embodiment of step S203 in fig. 2. Wherein, step S203 includes the following steps:
s601: the control module 11 derives a first matrix based on the coordinate position of the neutrophils in the multi-dimensional space.
Wherein the control module 11 derives the first matrix based on the coordinate position of the neutrophils in the three-dimensional space. For example, the control module 11 obtains the coordinate position of each neutrophil particle in the neutrophil particle mass 43, forming a first matrix. In other embodiments, the control module 11 derives the first matrix, e.g., a two-dimensional space, based on the coordinate locations of the neutrophils in other spaces.
S602: the control module 11 performs data centralization on the first matrix to obtain a second matrix, and obtains covariance of the second matrix to obtain a third matrix.
The control module 11 performs data centralization on the first matrix to form a second matrix. The control module 11 further calculates the covariance of the second matrix to obtain a third matrix.
S603: the control module 11 calculates the eigenvalue and the eigenvector of the third matrix, and selects the eigenvector corresponding to the largest eigenvalue.
The control module 11 calculates eigenvalues and eigenvectors of the third matrix, selects a maximum eigenvalue from the plurality of eigenvalues of the third matrix, obtains an eigenvector corresponding to the maximum eigenvalue, and obtains the selected eigenvector.
Wherein, the control module 11 calculates the selected feature vector to represent the maximum distribution direction, the next largest distribution direction or the consistent vector of the neutrophil particle group 43; in this embodiment, the selected feature vector is taken as an example of the horizontal line 71 in fig. 7-8, and the horizontal line 71 is the maximum distribution direction of the neutrophil particle mass 43, i.e. the major axis of the neutrophil particle mass 43 ellipse fitting.
S604: the control module 11 multiplies the first matrix by the selected eigenvector to obtain projection data of the second measurement data.
The control module 11 multiplies the first matrix by the selected eigenvector, which is equivalent to the control module 11 projecting the data of the first matrix to the space where the selected eigenvector is located, thereby obtaining the projection data of the second measurement data.
For example, the control module 11 acquires three-dimensional space data (i.e., a first matrix) having the signal source as coordinates, and multiplies the data by the selected feature vector, which corresponds to transforming the data into coordinate space data (i.e., projection data) corresponding to the longest axis or the next longest axis of the neutrophil cluster 43. As shown in fig. 7 to 8, the control module 11 projects the neutrophil particle mass 43 on the coordinate axis that is most closely fitted to the shape of the neutrophil particle mass 43, by projecting the second measurement data on the coordinate space with the longest axis (i.e., the horizontal line 71) of the neutrophil particle mass 43 as a coordinate.
S605: the control module 11 generates a second histogram based on the projection data, and sets a second threshold line 91 in the second histogram.
The control module 11 generates a second histogram based on the projection data, as shown in fig. 9. The control module 11 sets a second threshold line 91 in the second histogram, wherein the control module 11 may set a fixed threshold by the second histogram fixed direction or find a specific feature on the difference map of the second histogram to implement the setting of the second threshold line 91 in the second histogram.
S606: the control module 11 sets the particles corresponding to the projection data located outside the second threshold line 91 as the discrete particles 45.
The control module 11 sets the particles corresponding to the projection data located outside the second threshold line 91 of the second histogram as the discrete particles 45.
S607: the control module 11 calculates a quantity ratio PB of the discrete particles 45 in the second measurement data.
The control module 11 calculates a ratio PB of the number of white blood cells of the discrete particles 45 in the second measurement data.
In the above manner, the control module 11 of the present embodiment projects the neutrophil particle mass 43 onto the coordinate axis most closely fitting the shape of the neutrophil particle mass 43 to obtain projection data; the control module 11 generates a second histogram based on the projection data, and sets the particle corresponding to the projection data located outside the second threshold line 91 as the discrete particle 45, thereby improving the accuracy of acquiring the discrete particle 45.
Referring to fig. 10, fig. 10 is a schematic flowchart of the first embodiment of step S205 in fig. 2. Step S204 includes: the control module 11 trains the neural network with the area ratio a1 and the quantity ratio PB so that the neural network outputs a positive probability P. Namely, the neural network receives the area ratio A1 and the numerical ratio PB, and the positive probability P is output after the neural network is trained.
Optionally, the control module 11 trains the neural network with a plurality of area ratio values a1, a2, A3, a4 and quantity ratio values PB, so that the neural network outputs a positive probability P. Alternatively, the control module 11 trains the neural network with a plurality of area ratios a1, a2, A3, a4, quantity ratios PB, and feature information C so that the neural network outputs the positive probability P.
In other embodiments, one skilled in the art may replace the neural network with a boost cascade classifier, a SVM (support vector machine), a Bayesian classification (Bayes), a decision tree, and the like. In addition, the person skilled in the art can also manually obtain the positive probability P based on the plurality of area ratios a1, a2, A3, a4, the quantity ratio PB and the feature information C.
Step S205 includes the steps of:
s101: the control module 11 determines whether the positive probability P is greater than a preset probability threshold.
The control module 11 sets a probability threshold in advance, and determines whether the positive probability P is greater than the probability threshold; if yes, go to step S102; if not, the process proceeds to step S103.
S102: the control module 11 fits the area ratio a1 and the quantity ratio PB to obtain a linear or nonlinear equation, and further obtains the proportion of basophils.
When a first threshold line 31 is set on the first histogram, the control module 11 fits the area ratio a1 and the quantity ratio PB to obtain a linear equation, and further obtains the proportion of basophils; when the first threshold lines 31, 32, 33, and 34 are set in the first histogram, the control module 11 fits the area ratios a1, a2, A3, a4, and the quantity ratio PB to obtain a nonlinear equation, and further obtains the ratio of basophils.
S103: the control module 11 compares the area ratio a1 and the quantity ratio PB, and obtains the proportion of basophils based on the smaller value of the comparison result.
When the first histogram sets a first threshold line 31, the control module 11 compares the area ratio a1 with the quantity ratio PB, and obtains the proportion of basophils based on the smaller value of the comparison result. For example, if the control module 11 determines that the number ratio PB is smaller than the area ratio a1, the proportion of basophils is obtained based on the number ratio PB; alternatively, if the control module 11 determines that the quantity ratio PB is greater than the area ratio a1, the proportion of basophils is obtained based on the area ratio a 1; when the control module 11 determines that the ratio PB is equal to the area ratio a1, the ratio of basophils is obtained based on the area ratio a1 or the ratio PB.
When the first threshold lines 31, 32, 33, and 34 are set in the first histogram, the control module 11 selects a value of a reasonable interval from the area ratios a1, a2, A3, and a4 and compares the value with the quantity ratio PB to obtain a comparison result. The control module 11 obtains the proportion of basophils based on the smaller value of the comparison result. For example, the control module 11 selects the area ratio A3 from the plurality of area ratios a1, a2, A3, a4, and compares the area ratio A3 with the quantity ratio PB; if the quantity ratio PB is smaller than the area ratio a3, the control module 11 obtains a count value of basophils based on the quantity ratio PB; if the quantity ratio PB is greater than the area ratio A3, the control module 11 obtains the basophil fraction based on the area ratio A3.
Through the manner, the proportion of basophils is obtained by comparing the positive probability with the preset probability threshold, and the reliability of the count value of the basophils is improved.
The present application further provides a sample analyzer 10, as shown in fig. 1, the sample analyzer 10 includes a control module 11, a first detection module 12 and a second detection module 13, the control module 11 is respectively connected to the first detection module 12 and the second detection module 13, wherein the first detection module 12 is used for measuring basophil count of a sample, and the second detection module 13 is used for classifying leukocytes of the sample. The sample analyzer 10 may further include a sampling module or a reagent module, which is not described herein again.
The control module 11 is configured to generate a first histogram based on the first measurement data of the first detection module 12, and set at least one first threshold line 31 in the first histogram. The control module 11 is configured to obtain a first area a1 of the first histogram after the first threshold line 31, and obtain an area ratio a1 based on a ratio of the first area a1 to a total area of the first histogram. The control module 11 is configured to obtain four types of particle clusters based on the second measurement data of the second detection module 13, select at least one particle cluster from the four types of particle clusters, obtain discrete particles in the preset region from the selected particle cluster, and calculate a quantity ratio PB of the number of the discrete particles in the total number of white blood cells of the second detection module 13. The control module 11 is configured to obtain a positive probability value P of the sample based on the area ratio a1 and the quantity ratio PB. The control module 11 is used for obtaining the proportion of basophils in the white blood cells based on the positive probability value P.
To sum up, the control module 11 of the present application generates a first histogram based on the first measurement data of the first detection module 12, and sets at least one first threshold line 31 in the first histogram; acquiring a first area a1 of the first histogram after the first threshold line 31, and obtaining an area ratio A1 based on the ratio of the first area a1 to the total area of the first histogram; obtaining four types of particle clusters based on the second measurement data of the second detection module 13, selecting at least one particle cluster from the four types of particle clusters, obtaining discrete particles in the preset region from the selected particle cluster, and calculating a quantity ratio PB of the number of the discrete particles in the total number of the white blood cells of the second detection module 13; obtaining a positive probability value P of the sample based on the area ratio A1 and the numerical ratio PB; and obtaining the proportion of basophils in the white blood cells based on the positive probability value P. Therefore, the positive misjudgment rate of the sample can be reduced, the positive detection rate of the sample can be increased, and the counting value reliability of basophils can be improved.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above description is only for the purpose of illustrating embodiments 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 according to the content of the present specification and the accompanying drawings, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (8)
1. A counting method of a sample analyzer, wherein the sample analyzer comprises a control module, a first detection module and a second detection module, the control module is respectively connected with the first detection module and the second detection module, the first detection module is used for counting basophils of a sample, the second detection module is used for classifying leukocytes of the sample, and the counting method comprises:
the control module generates a first histogram based on first measurement data of the first detection module, and sets at least one first threshold line in the first histogram;
the control module acquires a first area of the first histogram after the first threshold line, and obtains an area ratio based on a ratio of the first area to a total area of the first histogram;
the control module obtains four types of particle clusters based on second measurement data of the second detection module, selects at least one particle cluster from the four types of particle clusters, obtains discrete particles in a preset area from the selected particle cluster, and calculates the quantity ratio of the number of the discrete particles in the total number of the white blood cells of the second detection module;
the control module obtains a positive probability value of the sample based on the area ratio and the number ratio;
the control module obtains the proportion of the basophils in the white blood cells based on the positive probability value;
the four types of particle clusters are lymphocyte particle clusters, monocyte particle clusters, neutrophil particle clusters and eosinophil particle clusters, and the control module selects the neutrophil particle clusters as the selected particle clusters;
the step of acquiring discrete particles in a preset area from the selected particle cluster by the control module comprises the following steps: the control module acquires discrete particles located between the neutrophil particle mass and the monocyte particle mass;
the step of obtaining a positive probability value of the sample based on the area ratio and the number ratio comprises:
the control module trains a neural network by the area ratio and the quantity ratio so that the neural network outputs the positive probability;
the step of the control module obtaining the proportion of the basophil in the white blood cells based on the positive probability value comprises the following steps:
the control module judges whether the positive probability is greater than a preset probability threshold value;
if so, the control module fits the area ratio and the quantity ratio to obtain a linear or nonlinear equation, and further obtains the proportion of the basophils;
if not, the control module compares the area ratio with the quantity ratio, and obtains the proportion of the basophils based on a smaller value of a comparison result.
2. The counting method according to claim 1, wherein the control module sets a boundary line on the first histogram, and the step of setting at least one first threshold line on the first histogram comprises:
the control module carries out smooth filtering processing on the first histogram;
and the control module selects the valley bottom of the first square map or the middle position of the preset range to set the first threshold line in the preset range behind the boundary of the first square map.
3. The counting method according to claim 1, wherein the control module sets a plurality of first threshold lines in the first histogram;
the step of obtaining a first area of the first histogram after the first threshold line comprises:
the control module obtains the area of the first histogram after each first threshold line to obtain a plurality of first areas;
the step of deriving an area ratio based on a ratio of the first area to a total area of the first histogram includes:
the control module obtains a plurality of area ratios based on a ratio of each of the first areas to a total area of the first histogram.
4. The counting method according to claim 1, wherein the white blood cell classification includes lymphocytes, monocytes, neutrophils and eosinophils, and the step of the control module deriving four types of clusters based on the second measurement data of the second detection module comprises:
and the control module projects the second measurement data to a multidimensional space formed by taking a data source as a coordinate axis to obtain the four types of particle clusters.
5. The counting method according to claim 4, wherein the step of the control module acquiring discrete particles between the neutrophil particle mass and the monocyte particle mass comprises:
the control module obtains a first matrix based on the coordinate position of the neutrophils in the multi-dimensional space;
the control module performs data centralization on the first matrix to obtain a second matrix, and obtains covariance of the second matrix to obtain a third matrix;
the control module calculates the eigenvalue and the eigenvector of the third matrix and selects the eigenvector corresponding to the largest eigenvalue;
and the control module multiplies the first matrix by the selected eigenvector to obtain the projection data of the second measurement data.
6. The counting method of claim 5, wherein after the step of multiplying the first matrix by the selected eigenvector by the control module to obtain the projection data of the second measurement data, the counting method further comprises:
the control module generates a second histogram based on the projection data, and sets a second threshold line on the second histogram;
the control module takes the particles corresponding to the projection data which are positioned outside the second threshold value line as the discrete particles;
the control module calculates a quantity ratio of the discrete particles in the second measurement data.
7. The counting method according to claim 4, wherein the control module further obtains characteristic information of the neutrophil particle mass, the characteristic information including a ratio of a major axis and a minor axis of the neutrophil particle mass ellipse fitting, a compactness of the neutrophil particle mass, or a Hotelling T-square distribution of the neutrophil particle mass.
8. A sample analyzer, comprising a control module, a first detection module and a second detection module, wherein the control module is connected to the first detection module and the second detection module respectively, the first detection module is used for counting basophils of a sample, and the second detection module is used for classifying leukocytes of the sample, wherein:
the control module is used for generating a first histogram based on first measurement data of the first detection module and setting at least one first threshold line in the first histogram;
the control module is used for acquiring a first area of the first histogram after the first threshold line, and obtaining an area ratio based on the ratio of the first area to the total area of the first histogram;
the control module is used for obtaining four types of particle clusters based on second measurement data of the second detection module, selecting at least one particle cluster from the four types of particle clusters, obtaining discrete particles in a preset area from the selected particle cluster, and calculating the quantity ratio of the quantity of the discrete particles in the total quantity of the white blood cells of the second detection module;
the control module is used for obtaining a positive probability value of the sample based on the area ratio and the number ratio;
the control module is used for obtaining the proportion of the basophils in the white blood cells based on the positive probability value;
the control module selects the neutrophil particle group as the selected particle group, and acquires discrete particles between the neutrophil particle group and the monocyte particle group;
the control module trains a neural network by the area ratio and the quantity ratio so that the neural network outputs the positive probability; the control module judges whether the positive probability is greater than a preset probability threshold value;
if so, the control module fits the area ratio and the quantity ratio to obtain a linear or nonlinear equation, and further obtains the proportion of the basophils;
if not, the control module compares the area ratio with the quantity ratio, and obtains the proportion of the basophils based on a smaller value of a comparison result.
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