WO2022105141A1 - 血液细胞分析仪的plt粒子检测方法和装置 - Google Patents

血液细胞分析仪的plt粒子检测方法和装置 Download PDF

Info

Publication number
WO2022105141A1
WO2022105141A1 PCT/CN2021/092161 CN2021092161W WO2022105141A1 WO 2022105141 A1 WO2022105141 A1 WO 2022105141A1 CN 2021092161 W CN2021092161 W CN 2021092161W WO 2022105141 A1 WO2022105141 A1 WO 2022105141A1
Authority
WO
WIPO (PCT)
Prior art keywords
particles
classification
histogram
particle
type
Prior art date
Application number
PCT/CN2021/092161
Other languages
English (en)
French (fr)
Inventor
王兴红
邹海涛
Original Assignee
深圳市科曼医疗设备有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳市科曼医疗设备有限公司 filed Critical 深圳市科曼医疗设备有限公司
Publication of WO2022105141A1 publication Critical patent/WO2022105141A1/zh

Links

Images

Classifications

    • 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
    • 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
    • G01N15/02—Investigating particle size or size distribution
    • G01N15/0266—Investigating particle size or size distribution with electrical classification
    • 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
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/011—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells with lysing, e.g. of erythrocytes
    • 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
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/018—Platelets
    • 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
    • G01N2015/1029—Particle size

Definitions

  • the invention relates to the technical field of blood cell analyzers, in particular to a PLT particle detection method and device of a blood cell analyzer.
  • a blood cell analyzer is an instrument that can detect cells in the blood, and can count and classify cells such as white blood cells, red blood cells, nucleated red blood cells, and reticulocytes.
  • the blood sample is diluted and mixed in a conductive diluent, and then the diluent flows through the holes with electrodes at both ends.
  • the voltage across the small hole will generate a voltage pulse due to the change.
  • the larger the volume of the cell particle the larger the voltage pulse.
  • the voltage pulse value of the voltage pulse is counted, and the histogram of the voltage pulse value-pulse number can be obtained.
  • Figure 1 shows the voltage pulse value of a normal blood sample-
  • the histogram of the number of pulses can classify platelets and red blood cells, thereby detecting the number of platelets.
  • FIG. 1 is the histogram of the voltage pulse value-pulse number of the abnormal blood sample.
  • PLT-RBC dividing line is difficult. Accurately distinguishing platelet aggregation particles (PLT particles) and red blood cell particles (RBC particles) greatly affects the detection accuracy of platelets.
  • the technical problem mainly solved by the present invention is how to improve the detection accuracy of platelets in blood samples.
  • an embodiment provides a PLT particle detection method for blood cell analysis, comprising:
  • the particles at least including the first type of particles and the second type of particles;
  • the reference dividing line is modified in each reference sub-category histogram, and the dividing line in each reference sub-category histogram is obtained, that is, a preset number of dividing lines are obtained;
  • each classification histogram into a preset number of sub-classification histograms based on the dividing line;
  • the display histogram function is used to represent the correspondence between the volume of the first type of particles and the number of the first type of particles;
  • a first dividing line is determined, and the first dividing line is used to distinguish the particles of the first type from the particles of the second type in the classification histogram.
  • an embodiment provides a PLT particle detection device of a blood cell analyzer, comprising:
  • a pulse signal acquisition unit configured to acquire pulse signals generated when various particles in the blood sample in the detection area pass through the electric field, the particles at least including the first type of particles and the second type of particles;
  • a classification unit configured to classify the pulse signal according to a plurality of classification accuracies, to obtain a plurality of classification histograms, where the classification histograms are used to represent the corresponding relationship between particle volume and particle number;
  • a dividing line determining unit used for correcting the reference dividing line in each reference sub-class histogram, and obtaining the dividing line in each reference sub-classifying histogram, that is, obtaining a preset number of dividing lines;
  • a sub-category histogram determination unit for dividing each category histogram into a preset number of sub-category histograms based on the dividing line;
  • a unit for determining the number of particles of the first type configured to determine the number of particles of the first type based on the corresponding sub-classification histograms in all the classification histograms;
  • a display histogram function acquisition unit configured to acquire a preset display histogram function, the display histogram function being used to characterize the correspondence between the volume of the first type of particles and the number of the first type of particles;
  • the first dividing line determining unit is configured to determine a first dividing line based on a preset display histogram function and the number of the first type of particles, and the first dividing line is used to distinguish the first type of particles from the first type of particles in the classification histogram. particles of the second type.
  • an embodiment provides a computer-readable storage medium, including a program that can be executed by a processor to implement the method described in the foregoing embodiment.
  • the pulse signal is classified according to a plurality of classification accuracies to obtain a plurality of classification histograms of the classification accuracies, and then the classification histograms of each classification accuracies are obtained.
  • the reference dividing line it is divided into a preset number of reference sub-category histograms, and the reference dividing line is corrected in each reference sub-category histogram to obtain an accurate sub-category histogram.
  • Histogram determine the number of the first type of particles, and then determine the accurate first dividing line according to the preset display histogram function and the number of the first type of particles, so that the first type can be more accurately distinguished in the classification histogram particles and particles of the second type.
  • Fig. 1 is a histogram of the voltage pulse value-pulse number of a normal blood sample
  • FIG. 2 is a histogram of voltage pulse value-pulse number of abnormal blood samples
  • FIG. 3 is a schematic structural diagram of a platelet and red blood cell detection channel in a cell analyzer according to an embodiment
  • Fig. 4 is a flow chart of the PLT particle detection method of an embodiment
  • FIG. 5 is a schematic structural diagram of a PLT particle detection device according to an embodiment
  • FIG. 6 is a schematic diagram of the position of a first dividing line (PLT-RBC dividing line) in a classification histogram according to an embodiment.
  • connection and “connection” mentioned in this application, unless otherwise specified, include both direct and indirect connections (connections).
  • the method of dynamic clustering is adopted, and the data without obvious features in the classification histogram of single classification accuracy is found by the method of dynamic clustering to find the characteristics of the data, so as to realize platelet aggregation particles (PLT) and red blood cells.
  • PLT platelet aggregation particles
  • RBC Classification of particles
  • FIG. 3 is a schematic structural diagram of a platelet and red blood cell detection channel in a cell analyzer according to an embodiment.
  • the platelet and red blood cell detection channel includes a sample cup 101 , a detector 102 , a positive electrode 103 , and a negative electrode 104 , power supply 105 , sensor 106 and aperture 107 .
  • the sample cup 101 is used to hold the blood sample diluted with the diluent, the positive electrode 103 is electrically connected to the positive electrode of the power source 105 , the negative electrode 104 is electrically connected to the negative electrode of the power source 105 , and the detector 102 is arranged in the sample cup 101 A small hole 107 is provided on the cup wall of the detector 102, the positive electrode 103 is set in the detector 102 at the position facing the small hole 107, the negative electrode 104 is set in the sample cup 101 at the position facing the small hole 107, and the positive The electrode 103 and the negative electrode 104 form an electric field at the small hole 107.
  • a voltage pulse is generated, and the voltage pulse is detected by the sensor 106, and the voltage can be obtained as the detection time passes. Pulse signal.
  • FIG. 4 is a flowchart of a PLT particle detection method according to an embodiment. The method includes steps 201 to 208 , which will be described in detail below.
  • Step 201 Acquire pulse signals generated when various particles in the blood sample in the detection area pass through the electric field, and the particles include at least the first type of particles and the second type of particles.
  • the first type of particles are platelet aggregation particles (PLT particles)
  • the second type of particles are red blood cell particles (RBC).
  • the detection area in this embodiment is the small hole 107 of the detector 102 in the platelet and red blood cell detection channel.
  • the voltage changes, thereby generating a voltage pulse.
  • the generated voltage pulses form a voltage pulse signal f(t), t being the detection time, t ⁇ 0, the voltage pulse signal f(t) being a function of the detection time t.
  • Step 202 Classify the pulse signal according to multiple classification accuracies to obtain multiple classification histograms, where the classification histograms are used to represent the corresponding relationship between particle volume and particle number.
  • the classification accuracy i is a natural number from 1 to N
  • the voltage pulse signal f(t) is converted into a histogram of N voltage pulse values-particle number, and the horizontal axis of the histogram represents the size of the pulse, The vertical axis represents the number of particles. Since the larger the particle volume is, the larger the voltage pulse value is, so the voltage pulse value can be equivalent to the particle volume, that is, N classification histograms are obtained, and each classification histogram corresponds to a classification accuracy.
  • Hist(i) is the classification histogram (i ⁇ N) when the classification accuracy is i, there are:
  • the classification histogram Hist(i) is actually a volume-frequency histogram, that is, a volume-frequency related function, so the above formula can be written as:
  • i the classification accuracy
  • V the volume of the particle (the size of the pulse value)
  • t the detection time
  • Step 203 Obtain a preset number of reference boundary lines, and divide each classification histogram into a preset number of reference sub-classification histograms by using the reference boundary lines, and the reference boundary lines are used to represent the volume of reference boundary particles.
  • the maximum theoretical volume MaxV calculated by PLT cells is set as a configuration parameter, and the position of MaxV on the horizontal axis corresponding to the classification histogram corresponding to each classification accuracy is determined, and then the position of MaxV in the classification histogram corresponding to each classification accuracy is determined.
  • each classification histogram is divided into 5 areas by these 5 reference dividing lines, that is, 5 Reference sub-category histograms, namely the first reference sub-category histogram from 1/8MaxV to MaxV on the horizontal axis, the second reference sub-category histogram from 2/8MaxV to MaxV on the horizontal axis, and 3/8MaxV to MaxV on the horizontal axis
  • the third reference sub-category histogram, 4/8 MaxV to MaxV on the horizontal axis are the fourth reference sub-category histogram, and 5/8 MaxV to MaxV on the horizontal axis are the fifth reference sub-category histogram.
  • Step 204 modifying the reference boundary lines in each reference sub-category histogram to obtain the boundary lines in each reference sub-category histogram, that is, to obtain a preset number of boundary lines.
  • the reference dividing line is modified in each reference sub-category histogram to obtain the dividing line in each reference sub-category histogram, including:
  • the particle volume corresponding to the minimum particle number is used as the dividing line of the reference classification histogram.
  • the largest particle volume among the corresponding multiple particle volumes is used as the boundary line of the reference classification histogram.
  • the minimum value of the vertical axis in the histogram of the first reference sub-category is searched from 1/8MAXV to MAXV. If there are multiple minimum values, the rightmost (that is, the largest particle volume) is used as the dividing line 1; Find the minimum value of the vertical axis in the second reference sub-category histogram from 8MAXV to MAXV. If there are multiple minimum values, take the rightmost (that is, the largest particle volume) as the dividing line 2; similarly, in 3/8MAXV to MAXV Demarcation line 3 is obtained, demarcation line 4 is obtained in 4/8MAXV to MAXV, and demarcation line 5 is obtained in 5/8MAXV to MAXV. In summary, 5 demarcation lines can be obtained.
  • Step 205 Divide each classification histogram into a preset number of sub-classification histograms based on the dividing line.
  • the classification histogram corresponding to each classification accuracy can be divided into five sub-classification histograms, that is, the dividing line 1 to MaxV on the horizontal axis is the sub-classification histogram 1, and the dividing line 2 to MaxV on the horizontal axis is the sub-classification histogram.
  • MaxV is the sub-category histogram 2
  • the dividing line 3 to MaxV on the horizontal axis is the sub-category histogram 3
  • the dividing line 4 to MaxV on the horizontal axis is the sub-category histogram 4
  • the dividing line 5 to MaxV on the horizontal axis is the sub-category histogram Figure 5.
  • Step 206 Determine the number of particles of the first type based on the corresponding sub-classification histograms in all the classification histograms.
  • the number of particles of the first type is determined based on the corresponding sub-classification histograms in all classification histograms, including:
  • the particles before each dividing line are counted in each classification histogram to obtain a first set of particles with a preset number in each classification histogram.
  • the sum of the particles before the statistical boundary 1 is denoted as Num1; the sum of the number of particles before the statistical boundary 2 is denoted as Num2; the statistics of the particles before the boundary 3
  • the sum of the numbers is denoted as Num3; the sum of the number of particles before the statistical dividing line 4 is denoted as Num4; the sum of the number of particles before the statistical demarcation line 5 is denoted as Num5.
  • the Num1 in all the classification histograms are taken out to form a set and recorded as S1; the Num2 in all the classification histograms are taken out to form a set and recorded as S2; the Num3 in all the classification histograms are taken out to form a set and recorded as S3 ; Take out Num4 in all classification histograms to form a set and mark it as S4; take out Num5 in all classification histograms to form a set and mark it as S5.
  • the particles in each second particle set are sorted by particle volume.
  • the elements in the set S1 are sorted and the median value is denoted as M1; the elements in the set S2 are sorted and the median value is denoted as M2; the elements in the set S3 are sorted and the median value is denoted as M3;
  • the median value of the elements in S4 after sorting is recorded as M4; the median value of the elements in the set S5 after sorting is recorded as M5.
  • the number of particles of the first type is obtained.
  • Step 207 Acquire a preset display histogram function, wherein the display histogram function is used to represent the corresponding relationship between the volume of the first type of particles and the number of the first type of particles.
  • Step 208 Determine a first dividing line based on the preset display histogram function and the number of the first type of particles, where the first dividing line is used to distinguish the first type of particles from the second type of particles in the classification histogram.
  • the preset display histogram function is F(v), where v represents the particle volume and v ⁇ [0,127];
  • FIG. 5 is a schematic structural diagram of a PLT particle detection device according to an embodiment.
  • the PLT particle detection device includes a pulse signal acquisition unit 301, a classification unit 302, a reference sub-classification histogram acquisition unit 302, and a boundary line A determination unit 304 , a sub-classification histogram determination unit 305 , a first type particle number determination unit 306 , a display histogram function acquisition unit 307 , and a first boundary line determination unit 308 .
  • the pulse signal acquisition unit 301 is configured to acquire pulse signals generated when various particles in the blood sample in the detection area pass through the electric field, wherein the particles include at least the first type of particles and the second type of particles.
  • the first type of particles are platelet aggregation particles (PLT particles)
  • the second type of particles are red blood cell particles (RBC).
  • the detection area in this embodiment is the small hole 107 of the detector 102 in the platelet and red blood cell detection channel.
  • the voltage changes, thereby generating a voltage pulse.
  • the generated voltage pulses form a voltage pulse signal f(t), t being the detection time, t ⁇ 0, the voltage pulse signal f(t) being a function of the detection time t.
  • the classification unit 302 is configured to classify the pulse signals according to a plurality of classification accuracies to obtain a plurality of classification histograms, wherein the classification histograms are used to represent the corresponding relationship between particle volume and particle number.
  • the classification accuracy i is a natural number from 1 to N
  • the voltage pulse signal f(t) is converted into a histogram of N voltage pulse values-particle number, and the horizontal axis of the histogram represents the size of the pulse, The vertical axis represents the number of particles. Since the larger the particle volume is, the larger the voltage pulse value is, so the voltage pulse value can be equivalent to the particle volume, that is, N classification histograms are obtained, and each classification histogram corresponds to a classification accuracy.
  • Hist(i) is the classification histogram (i ⁇ N) when the classification accuracy is i, there are:
  • the classification histogram Hist(i) is actually a volume-frequency histogram, that is, a volume-frequency related function, so the above formula can be written as:
  • i the classification accuracy
  • V the volume of the particle (the size of the pulse value)
  • t the detection time
  • the obtaining reference sub-classification histogram unit 303 is used to obtain a preset number of reference dividing lines, and dividing each classification histogram into a preset number of reference sub-classifying histograms through the reference dividing line, wherein the reference dividing line is used to represent the reference dividing line particle volume.
  • the maximum theoretical volume MaxV calculated by PLT cells is set as a configuration parameter, and the position of MaxV on the horizontal axis corresponding to the classification histogram corresponding to each classification accuracy is determined, and then the position of MaxV in the classification histogram corresponding to each classification accuracy is determined.
  • each classification histogram is divided into 5 areas by these 5 reference dividing lines, that is, 5 Reference sub-category histograms, namely the first reference sub-category histogram from 1/8MaxV to MaxV on the horizontal axis, the second reference sub-category histogram from 2/8MaxV to MaxV on the horizontal axis, and 3/8MaxV to MaxV on the horizontal axis
  • the third reference sub-category histogram, 4/8 MaxV to MaxV on the horizontal axis are the fourth reference sub-category histogram, and 5/8 MaxV to MaxV on the horizontal axis are the fifth reference sub-category histogram.
  • the boundary line determining unit 304 is configured to correct the reference boundary lines in each reference sub-category histogram to obtain the boundary lines in each reference sub-category histogram, that is, to obtain a preset number of boundary lines.
  • the reference dividing line is modified in each reference sub-category histogram to obtain the dividing line in each reference sub-category histogram, including:
  • the particle volume corresponding to the minimum particle number is used as the dividing line of the reference classification histogram.
  • the largest particle volume among the corresponding multiple particle volumes is used as the boundary line of the reference classification histogram.
  • the minimum value of the vertical axis in the histogram of the first reference sub-category is searched from 1/8 to 1/8. If there are multiple minimum values, the rightmost (that is, the largest particle volume) is used as the dividing line 1; Go to the middle to find the minimum value of the vertical axis in the second reference sub-category histogram. If there are multiple minimum values, take the rightmost (that is, the largest particle volume) as the dividing line 2; similarly, the score can be obtained in 3/8 to middle. Demarcation line 3, demarcation line 4 is obtained in 4/8 to medium, and demarcation line 5 is obtained in 5/8 to medium. In summary, 5 demarcation lines can be obtained.
  • the sub-category histogram determining unit 305 is configured to divide each category histogram into a preset number of sub-category histograms based on the dividing line.
  • the classification histogram corresponding to each classification accuracy can be divided into five sub-classification histograms, that is, the dividing line 1 to MaxV on the horizontal axis is the sub-classification histogram 1, and the dividing line 2 to MaxV on the horizontal axis is the sub-classification histogram.
  • MaxV is the sub-category histogram 2
  • the dividing line 3 to MaxV on the horizontal axis is the sub-category histogram 3
  • the dividing line 4 to MaxV on the horizontal axis is the sub-category histogram 4
  • the dividing line 5 to MaxV on the horizontal axis is the sub-category histogram Figure 5.
  • the first-type particle quantity determining unit 306 is configured to determine the number of the first-type particles based on the corresponding sub-classification histograms in all the classification histograms.
  • the number of particles of the first type is determined based on the corresponding sub-classification histograms in all classification histograms, including:
  • the particles before each dividing line are counted in each classification histogram to obtain a first set of particles with a preset number in each classification histogram.
  • the sum of the particles before the statistical boundary 1 is recorded as Num1; the sum of the number of particles before the statistical boundary 2 is recorded as Num2; the statistics of the particles before the boundary 3
  • the sum of the numbers is denoted as Num3; the sum of the number of particles before the statistical dividing line 4 is denoted as Num4; the sum of the number of particles before the statistical demarcation line 5 is denoted as Num5.
  • the Num1 in all the classification histograms are taken out to form a set and recorded as S1; the Num2 in all the classification histograms are taken out to form a set and recorded as S2; the Num3 in all the classification histograms are taken out to form a set and recorded as S3 ; Take out Num4 in all classification histograms to form a set and mark it as S4; take out Num5 in all classification histograms to form a set and mark it as S5.
  • the particles in each second particle set are sorted by particle volume.
  • the elements in the set S1 are sorted and the median value is denoted as M1; the elements in the set S2 are sorted and the median value is denoted as M2; the elements in the set S3 are sorted and the median value is denoted as M3;
  • the median value of the elements in S4 after sorting is recorded as M4; the median value of the elements in the set S5 after sorting is recorded as M5.
  • the number of particles of the first type is obtained.
  • the display histogram function acquiring unit 307 is configured to acquire a preset display histogram function, wherein the display histogram function is used to represent the corresponding relationship between the volume of the first type of particles and the number of the first type of particles.
  • the first dividing line determining unit 308 is configured to determine a first dividing line based on a preset display histogram function and the number of particles of the first type, wherein the first dividing line is used to distinguish the particles of the first type from the particles of the first type in the classification histogram. Class II particles.
  • the preset display histogram function is F(v), where v represents the particle volume and v ⁇ [0,127];
  • FIG. 6 is a schematic diagram of the position of the first dividing line (PLT-RBC dividing line) in the classification histogram of an embodiment. Compared with FIG. 2 in which the first dividing line is obtained by using the prior art, the Accurately identify and detect platelet aggregation particles (PLT particles) and red blood cell particles (RBC particles).
  • PLT particles platelet aggregation particles
  • RBC particles red blood cell particles
  • the program can also be stored in a server, another computer, a magnetic disk, an optical disk, a flash disk or a mobile hard disk and other storage media, and saved by downloading or copying All or part of the functions in the above embodiments can be implemented when the program in the memory is executed by the processor.

Landscapes

  • Chemical & Material Sciences (AREA)
  • Dispersion Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

一种血液细胞分析仪的PLT粒子检测方法和装置，通过对脉冲信号按照多个分类精度分别进行分类，得到多个分类精度的分类直方图，再将每个分类精度的分类直方图按照参考分界线分割为预设数量的参考子分类直方图，在每个参考子分类直方图中对参考分界线进行修正，得到准确的子分类直方图，基于所有分类直方图中对应的子分类直方图，确定血小板聚集粒子的数量，再根据预设的显示直方图函数和血小板聚集粒子的数量，确定准确的第一分界线，使得更为准确地在分类直方图中区分血小板聚集粒子和红细胞粒子。

Description

血液细胞分析仪的PLT粒子检测方法和装置 技术领域
本发明涉及血液细胞分析仪技术领域，具体涉及一种血液细胞分析仪的PLT粒子检测方法和装置。
背景技术
血液细胞分析仪是一种可检测血液中细胞的仪器，可以对白细胞、红细胞、有核红细胞、网织红细胞等细胞进行计数及分类。
血液细胞分析仪实现血小板检测最常见的一种方法为阻抗检测法，将血液样本在具有导电性的稀释液中稀释混匀，然后使稀释液流过两端有电极的小孔，稀释液中的细胞粒子在通过小孔时，小孔两端的电压因变化会产生电压脉冲，细胞粒子的体积越大，电压脉冲就越大，通过记录每个细胞粒子的电压脉冲值，将具有相同电压脉冲值的电压脉冲进行统计，可得到电压脉冲值-脉冲个数的直方图。由于红细胞和血小板的体积、数量均相差很大，正常血液样本的电压脉冲值-脉冲个数的直方图会有很明显的特征，请参考图1，图1为正常血液样本的电压脉冲值-脉冲个数的直方图，根据这个明显特征即可对血小板和红细胞进行分类，从而检测到血小板的数量。
然而，部分血液样本为包含小红细胞的样本或者存在红细胞碎片的样本，小红细胞和红细胞碎片由于体积与血小板相近，很容易被检测为血小板，而巨大的血小板也容易被误是被为红细胞，此时血液样本的电压脉冲值-脉冲个数的直方图没有很明显的特征，请参考图2，图2为异常血液样本的电压脉冲值-脉冲个数的直方图，PLT-RBC分界线很难准确的对血小板聚集粒子(PLT粒子)和红细胞粒子(RBC粒子)进行区分，使得血小板的检测准确性受到很大影响。
发明内容
本发明主要解决的技术问题是如何提高血液样本中血小板的检测准确性。
根据第一方面，一种实施例中提供一种血液细胞分析的PLT粒子检测方法，包括：
获取检测区域中的血液样本中的各种粒子经过电场时产生的脉冲信号，所述粒子至少包括第一类粒子和第二类粒子；
对脉冲信号按照多个分类精度分别进行分类，得到多个分类直方图，所述分类直方图用于表征粒子体积与粒子数量的对应关系；
获取预设数量的参考分界线，通过所述参考分界线将每个分类直方图分割为预设数量的参考子分类直方图，所述参考分界线用于表征参考分界粒子体积；
在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，即得到预设数量的分界线；
基于所述分界线将每个分类直方图分割为预设数量的子分类直方图；
基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量；
获取预设的显示直方图函数，所述显示直方图函数用于表征第一类粒子的体积与第一类粒子的数量的对应关系；
基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，所述第一分界线用于在分类直方图中区分第一类粒子与第二类粒子。
根据第二方面，一种实施例中提供一种血液细胞分析仪的PLT粒子检测装置，包括：
脉冲信号获取单元，用于获取检测区域中的血液样本中的各种粒子经过电场时产生的脉冲信号，所述粒子至少包括第一类粒子和第二类粒子；
分类单元，用于对脉冲信号按照多个分类精度分别进行分类，得到多个分类直方图，所述分类直方图用于表征粒子体积与粒子数量的对应关系；
获取参考子分类直方图单元，用于获取预设数量的参考分界线，通过所述参考分界线将每个分类直方图分割为预设数量的参考子分类直方图，所述参考分界线用于表征参考分界粒子体积；
分界线确定单元，用于在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，即得到预设数量的分界线；
子分类直方图确定单元，用于基于所述分界线将每个分类直方图分割为预设数量的子分类直方图；
第一类粒子数量确定单元，用于基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量；
显示直方图函数获取单元，用于获取预设的显示直方图函数，所述显示直方图函数用于表征第一类粒子的体积与第一类粒子的数量的对应关系；
第一分界线确定单元，用于基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，所述第一分界线用于在分类直方图中区分第一类粒子与第二类粒子。
根据第三方面，一种实施例中提供一种计算机可读存储介质，包括程序，所述程序能够被处理器执行以实现上述实施例所述的方法。
依据上述实施例的血液细胞分析仪的PLT粒子检测方法和装置，通过对脉 冲信号按照多个分类精度分别进行分类，得到多个分类精度的分类直方图，再将每个分类精度的分类直方图按照参考分界线分割为预设数量的参考子分类直方图，在每个参考子分类直方图中对参考分界线进行修正，得到准确的子分类直方图，基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量，再根据预设的显示直方图函数和第一类粒子的数量，确定准确的第一分界线，使得更为准确地在分类直方图中区分第一类粒子和第二类粒子。
附图说明
图1为正常血液样本的电压脉冲值-脉冲个数的直方图；
图2为异常血液样本的电压脉冲值-脉冲个数的直方图；
图3为一种实施例的细胞分析仪中血小板和红细胞检测通道的结构示意图；
图4为一种实施例的PLT粒子检测方法流程图；
图5为一种实施例的PLT粒子检测装置的结构示意图；
图6为一种实施例的分类直方图中第一分界线(PLT-RBC分界线)的位置示意图。
具体实施方式
下面通过具体实施方式结合附图对本发明作进一步详细说明。其中不同实施方式中类似元件采用了相关联的类似的元件标号。在以下的实施方式中，很多细节描述是为了使得本申请能被更好的理解。然而，本领域技术人员可以毫不费力的认识到，其中部分特征在不同情况下是可以省略的，或者可以由其他元件、材料、方法所替代。在某些情况下，本申请相关的一些操作并没有在说明书中显示或者描述，这是为了避免本申请的核心部分被过多的描述所淹没，而对于本领域技术人员而言，详细描述这些相关操作并不是必要的，他们根据说明书中的描述以及本领域的一般技术知识即可完整了解相关操作。
另外，说明书中所描述的特点、操作或者特征可以以任意适当的方式结合形成各种实施方式。同时，方法描述中的各步骤或者动作也可以按照本领域技术人员所能显而易见的方式进行顺序调换或调整。因此，说明书和附图中的各种顺序只是为了清楚描述某一个实施例，并不意味着是必须的顺序，除非另有说明其中某个顺序是必须遵循的。
本文中为部件所编序号本身，例如“第一”、“第二”等，仅用于区分所描述的对象，不具有任何顺序或技术含义。而本申请所说“连接”、“联接”，如无特别说明，均包括直接和间接连接(联接)。
在本发明实施例中，采用动态聚类的方式，将单分类精度的分类直方图中没有明显特征的数据，通过动态聚类的方式找到数据的特征，实现了血小板聚集粒子(PLT)和红细胞粒子(RBC)的分类。
请参考图3，图3为一种实施例的细胞分析仪中血小板和红细胞检测通道的结构示意图，所述的血小板和红细胞检测通道包括样品杯101、检测器102、正电极103、负电极104、电源105、传感器106和小孔107。其中，样品杯101中用于盛放用稀释液进行稀释后的血液样本，正电极103与电源105的正极电连接，负电极104与电源105的负极电连接，检测器102设置在样品杯101中，检测器102的杯壁上设有一小孔107，正电极103设置在检测器102内正对小孔107的位置，负电极104设置在样品杯101内正对小孔107的位置，正电极103和负电极104在小孔107处形成电场，当样品杯101中的各种细胞粒子通过小孔107时产生电压脉冲，通过传感器106检测到电压脉冲，随着检测时间的推移可得到电压脉冲信号。
请参考图4，图4为一种实施例的PLT粒子检测方法流程图，所述的方法包括步骤201至步骤208，下面具体说明。
步骤201，获取检测区域中的血液样本中的各种粒子经过电场时产生的脉冲信号，所述粒子至少包括第一类粒子和第二类粒子。其中，第一类粒子为血小板聚集粒子(PLT粒子)，第二类粒子为红细胞粒子(RBC)。
本实施例中的检测区域为血小板和红细胞检测通道中检测器102的小孔107，当血液样本中的各种粒子依次经过小孔107时引起电压的变化，从而产生电压脉冲，在检测时间内所产生的电压脉冲形成电压脉冲信号f(t)，t为检测时间，t≥0，电压脉冲信号f(t)为关于检测时间t的函数。
步骤202，对脉冲信号按照多个分类精度分别进行分类，得到多个分类直方图，分类直方图用于表征粒子体积与粒子数量的对应关系。
在本实施例中，分类精度i为从1到N的自然数，将电压脉冲信号f(t)转换为N个电压脉冲值-粒子数的直方图，该直方图的横轴表示脉冲的大小，纵轴表示粒子的数量，由于粒子的体积越大，电压脉冲值越大，因此电压脉冲值可等价为粒子的体积，即得到N个分类直方图，每个分类直方图对应一个分类精度。
假设Hist(i)为分类精度为i时的分类直方图(i∈N)，则有：
Figure PCTCN2021092161-appb-000001
因为电压脉冲值与粒子体积可等价，所以分类直方图Hist(i)实际上是一个：体积-频率直方图，即一个体积-频率相关的函数，所以上式可写为：
Figure PCTCN2021092161-appb-000002
其中，i表示分类精度，V表示粒子的体积(脉冲值大小)，t表示检测时间。
步骤203，获取预设数量的参考分界线，通过参考分界线将每个分类直方图分割为预设数量的参考子分类直方图，参考分界线用于表征参考分界粒子体积。
本实施例根据生理学知识，将PLT细胞理论计算的理论体积最大值MaxV设置为配置参数，并确定MaxV在各个分类精度对应的分类直方图中对应的横轴的位置，然后再各个分类直方图中分别取1/8MaxV、2/8MaxV、3/8MaxV、4/8MaxV和5/8MaxV作为5个参考分界线，每个分类直方图中通过这5个参考分界线分割为5个区域，即得到5个参考子分类直方图，即横轴的1/8MaxV到MaxV的第一参考子分类直方图、横轴的2/8MaxV到MaxV的第二参考子分类直方图，横轴的3/8MaxV到MaxV为第三参考子分类直方图、横轴的4/8MaxV到MaxV为第四参考子分类直方图、横轴的5/8MaxV到MaxV为第五参考子分类直方图。
步骤204，在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，即得到预设数量的分界线。
在一实施例中，步骤204中在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，包括：
在每个参考子分类直方图中查找粒子数量最小值对应的粒子体积。
若参考子分类直方图中粒子数量最小值只有一个，则将该粒子数量最小值对应的粒子体积作为该参考分类直方图的分界线。
若所述参考分类直方图中粒子数量最小值具有多个，则将对应的多个粒子体积中最大的粒子体积作为该参考分类直方图的分界线。
本实施例在1/8MAXV到MAXV中寻找第一参考子分类直方图中纵轴的最小值，如有多个最小值，则以最右边(即粒子体积最大)为分界线1；在2/8MAXV到MAXV中寻找第二参考子分类直方图中纵轴的最小值，如有多个最小值，则以最右边(即粒子体积最大)为分界线2；同理在3/8MAXV到MAXV中可得到分界线3，在4/8MAXV到MAXV中可得到分界线4，在5/8MAXV到MAXV中可得到分界线5。综上，可得到5个分界线。
步骤205，基于分界线将每个分类直方图分割为预设数量的子分类直方图。
基于上述得到的5个分界线，将各个分类精度对应的分类直方图可切割为5个子分类直方图，即横轴的分界线1至MaxV为子分类直方图1、横轴的分界线2至MaxV为子分类直方图2、横轴的分界线3至MaxV为子分类直方图3、横轴的分界线4至MaxV为子分类直方图4、横轴的分界线5至MaxV为子分类直方图5。
步骤206，基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量。
在一实施例中，步骤206中基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量，包括：
在每个分类直方图中统计每个分界线之前的粒子，得到每个分类直方图中预设数量的第一粒子集合。
本实施例在每个分类精度对应的分类直方图中，统计分界线1之前的粒子总和，记为Num1；统计分界线2之前的粒子数之和，记为Num2；统计分界线3之前的粒子数之和，记为Num3；统计分界线4之前的粒子数之和，记为Num4；统计分界线5之前的粒子数之和，记为Num5。
将所有分类直方图中的第一粒子集合进行统计，得到预设数量的第二粒子集合。
本实施例将所有分类直方图中的Num1取出组成一个集合记为S1；将所有分类直方图中的Num2取出组成一个集合记为S2；将所有分类直方图中的Num3取出组成一个集合记为S3；将所有分类直方图中的Num4取出组成一个集合记为S4；将所有分类直方图中的Num5取出组成一个集合记为S5。
对每个第二粒子集合中的粒子按照粒子体积进行排序。
本实施例将集合S1中的元素排序后取中值记为M1；将集合S2中的元素排序后取中值记为M2；将集合S3中的元素排序后取中值记为M3；将集合S4中的元素排序后取中值记为M4；将集合S5中的元素排序后取中值记为M5。
取所有第二粒子集合中排序后粒子体积为中间位置对应的粒子数量，得到粒子数量向量m T＝[M1,M2,M3,M4,M5]，粒子数量向量的维数与预设数量相同。
获取预设权重向量，所述预设权重向量的维数与预设数量相同。
假设预设权重向量w＝[w 1,w 2,w 3,w 4,w 5]，其中w 1+w 2+w 3+w 4+w 5＝1。
基于预设权重向量和粒子数量向量，得到第一类粒子的数量。本实施例通过M＝w×m则可得到第一类粒子的数量M。
步骤207，获取预设的显示直方图函数，其中显示直方图函数用于表征第一类粒子的体积与第一类粒子的数量的对应关系。
步骤208，基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，其中第一分界线用于在分类直方图中区分第一类粒子与第二类粒子。
假设预设的显示直方图函数为F(v)，其中v表示粒子体积，v∈[0,127]；
在一实施例中，当v＝l时存在
Figure PCTCN2021092161-appb-000003
则确定v＝l为第一分界线，其中l为第一类粒子的体积。
由于在实际检测过程中，
Figure PCTCN2021092161-appb-000004
很难达到，因此另一种实施例中，当v＝l时存在
Figure PCTCN2021092161-appb-000005
则确定v＝l为第一分界线，其中l为第一类粒子的体积。
请参考图5，图5为一种实施例的PLT粒子检测装置的结构示意图，所述的PLT粒子检测装置包括脉冲信号获取单元301、分类单元302、获取参考子分类直方图单元302、分界线确定单元304、子分类直方图确定单元305、第一类粒子数量确定单元306、显示直方图函数获取单元307和第一分界线确定单元308。
脉冲信号获取单元301用于获取检测区域中的血液样本中的各种粒子经过电场时产生的脉冲信号，其中粒子至少包括第一类粒子和第二类粒子。其中，第一类粒子为血小板聚集粒子(PLT粒子)，第二类粒子为红细胞粒子(RBC)。
本实施例中的检测区域为血小板和红细胞检测通道中检测器102的小孔107，当血液样本中的各种粒子依次经过小孔107时引起电压的变化，从而产生电压脉冲，在检测时间内所产生的电压脉冲形成电压脉冲信号f(t)，t为检测时间，t≥0，电压脉冲信号f(t)为关于检测时间t的函数。
分类单元302用于对脉冲信号按照多个分类精度分别进行分类，得到多个分类直方图，其中分类直方图用于表征粒子体积与粒子数量的对应关系。
在本实施例中，分类精度i为从1到N的自然数，将电压脉冲信号f(t)转换为N个电压脉冲值-粒子数的直方图，该直方图的横轴表示脉冲的大小，纵轴表示粒子的数量，由于粒子的体积越大，电压脉冲值越大，因此电压脉冲值可等价为粒子的体积，即得到N个分类直方图，每个分类直方图对应一个分类精度。
假设Hist(i)为分类精度为i时的分类直方图(i∈N)，则有：
Figure PCTCN2021092161-appb-000006
因为电压脉冲值与粒子体积可等价，所以分类直方图Hist(i)实际上是一个：体积-频率直方图，即一个体积-频率相关的函数，所以上式可写为：
Figure PCTCN2021092161-appb-000007
其中，i表示分类精度，V表示粒子的体积(脉冲值大小)，t表示检测时间。
获取参考子分类直方图单元303用于获取预设数量的参考分界线，通过参考分界线将每个分类直方图分割为预设数量的参考子分类直方图，其中参考分界线用于表征参考分界粒子体积。
本实施例根据生理学知识，将PLT细胞理论计算的理论体积最大值MaxV设置为配置参数，并确定MaxV在各个分类精度对应的分类直方图中对应的横轴的位置，然后再各个分类直方图中分别取1/8MaxV、2/8MaxV、3/8MaxV、 4/8MaxV和5/8MaxV作为5个参考分界线，每个分类直方图中通过这5个参考分界线分割为5个区域，即得到5个参考子分类直方图，即横轴的1/8MaxV到MaxV的第一参考子分类直方图、横轴的2/8MaxV到MaxV的第二参考子分类直方图，横轴的3/8MaxV到MaxV为第三参考子分类直方图、横轴的4/8MaxV到MaxV为第四参考子分类直方图、横轴的5/8MaxV到MaxV为第五参考子分类直方图。
分界线确定单元304用于在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，即得到预设数量的分界线。
在一实施例中，在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，包括：
在每个参考子分类直方图中查找粒子数量最小值对应的粒子体积。
若参考子分类直方图中粒子数量最小值只有一个，则将该粒子数量最小值对应的粒子体积作为该参考分类直方图的分界线。
若所述参考分类直方图中粒子数量最小值具有多个，则将对应的多个粒子体积中最大的粒子体积作为该参考分类直方图的分界线。
本实施例在1/8到中寻找第一参考子分类直方图中纵轴的最小值，如有多个最小值，则以最右边(即粒子体积最大)为分界线1；在2/8到中寻找第二参考子分类直方图中纵轴的最小值，如有多个最小值，则以最右边(即粒子体积最大)为分界线2；同理在3/8到中可得到分界线3，在4/8到中可得到分界线4，在5/8到中可得到分界线5。综上，可得到5个分界线。
子分类直方图确定单元305用于基于分界线将每个分类直方图分割为预设数量的子分类直方图。
基于上述得到的5个分界线，将各个分类精度对应的分类直方图可切割为5个子分类直方图，即横轴的分界线1至MaxV为子分类直方图1、横轴的分界线2至MaxV为子分类直方图2、横轴的分界线3至MaxV为子分类直方图3、横轴的分界线4至MaxV为子分类直方图4、横轴的分界线5至MaxV为子分类直方图5。
第一类粒子数量确定单元306用于基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量。
在一实施例中，基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量，包括：
在每个分类直方图中统计每个分界线之前的粒子，得到每个分类直方图中预设数量的第一粒子集合。
本实施例在每个分类精度对应的分类直方图中，统计分界线1之前的粒子总和，记为Num1；统计分界线2之前的粒子数之和，记为Num2；统计分界线3之前的粒子数之和，记为Num3；统计分界线4之前的粒子数之和，记为Num4；统计分界线5之前的粒子数之和，记为Num5。
将所有分类直方图中的第一粒子集合进行统计，得到预设数量的第二粒子集合。
本实施例将所有分类直方图中的Num1取出组成一个集合记为S1；将所有分类直方图中的Num2取出组成一个集合记为S2；将所有分类直方图中的Num3取出组成一个集合记为S3；将所有分类直方图中的Num4取出组成一个集合记为S4；将所有分类直方图中的Num5取出组成一个集合记为S5。
对每个第二粒子集合中的粒子按照粒子体积进行排序。
本实施例将集合S1中的元素排序后取中值记为M1；将集合S2中的元素排序后取中值记为M2；将集合S3中的元素排序后取中值记为M3；将集合S4中的元素排序后取中值记为M4；将集合S5中的元素排序后取中值记为M5。
取所有第二粒子集合中排序后粒子体积为中间位置对应的粒子数量，得到粒子数量向量m T＝[M1,M2,M3,M4,M5]，粒子数量向量的维数与预设数量相同。
获取预设权重向量，所述预设权重向量的维数与预设数量相同。
假设预设权重向量w＝[w 1,w 2,w 3,w 4,w 5]，其中w 1+w 2+w 3+w 4+w 5＝1。
基于预设权重向量和粒子数量向量，得到第一类粒子的数量。本实施例通过M＝w×m则可得到第一类粒子的数量M。
显示直方图函数获取单元307用于获取预设的显示直方图函数，其中显示直方图函数用于表征第一类粒子的体积与第一类粒子的数量的对应关系。
第一分界线确定单元308用于基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，其中第一分界线用于在分类直方图中区分第一类粒子与第二类粒子。
假设预设的显示直方图函数为F(v)，其中v表示粒子体积，v∈[0,127]；
在一实施例中，当v＝l时存在
Figure PCTCN2021092161-appb-000008
则确定v＝l为第一分界线，其中l为第一类粒子的体积。
由于在实际检测过程中，
Figure PCTCN2021092161-appb-000009
很难达到，因此另一种实施例中，当v＝l时存在
Figure PCTCN2021092161-appb-000010
则确定v＝l为第一分界线，其中l为第一类粒子的体积。
请参考图6，图6为一种实施例的分类直方图中第一分界线(PLT-RBC分界线)的位置示意图，其与采用现有技术得到第一分界线的图2相比，更加准确 的对血小板聚集粒子(PLT粒子)和红细胞粒子(RBC粒子)进行了识别与检测。
本领域技术人员可以理解，上述实施方式中各种方法的全部或部分功能可以通过硬件的方式实现，也可以通过计算机程序的方式实现。当上述实施方式中全部或部分功能通过计算机程序的方式实现时，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：只读存储器、随机存储器、磁盘、光盘、硬盘等，通过计算机执行该程序以实现上述功能。例如，将程序存储在设备的存储器中，当通过处理器执行存储器中程序，即可实现上述全部或部分功能。另外，当上述实施方式中全部或部分功能通过计算机程序的方式实现时，该程序也可以存储在服务器、另一计算机、磁盘、光盘、闪存盘或移动硬盘等存储介质中，通过下载或复制保存到本地设备的存储器中，或对本地设备的系统进行版本更新，当通过处理器执行存储器中的程序时，即可实现上述实施方式中全部或部分功能。
以上应用了具体个例对本发明进行阐述，只是用于帮助理解本发明，并不用以限制本发明。对于本发明所属技术领域的技术人员，依据本发明的思想，还可以做出若干简单推演、变形或替换。

Claims (10)

  1. 一种血液细胞分析仪的PLT粒子检测方法，其特征在于，包括：
    获取检测区域中的血液样本中的各种粒子经过电场时产生的脉冲信号，所述粒子至少包括第一类粒子和第二类粒子；
    对脉冲信号按照多个分类精度分别进行分类，得到多个分类直方图，所述分类直方图用于表征粒子体积与粒子数量的对应关系；
    获取预设数量的参考分界线，通过所述参考分界线将每个分类直方图分割为预设数量的参考子分类直方图，所述参考分界线用于表征参考分界粒子体积；
    在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，即得到预设数量的分界线；
    基于所述分界线将每个分类直方图分割为预设数量的子分类直方图；
    基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量；
    获取预设的显示直方图函数，所述显示直方图函数用于表征第一类粒子的体积与第一类粒子的数量的对应关系；
    基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，所述第一分界线用于在分类直方图中区分第一类粒子与第二类粒子。
  2. 如权利要求1所述的方法，其特征在于，所述第一类粒子为血小板聚集粒子，第二类粒子为红细胞粒子。
  3. 如权利要求1所述的方法，其特征在于，所述在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，包括：
    在每个参考子分类直方图中查找粒子数量最小值对应的粒子体积；
    若所述参考子分类直方图中粒子数量最小值只有一个，则将该粒子数量最小值对应的粒子体积作为该参考分类直方图的分界线；
    若所述参考分类直方图中粒子数量最小值具有多个，则将对应的多个粒子体积中最大的粒子体积作为该参考分类直方图的分界线。
  4. 如权利要求1所述的方法，其特征在于，所述基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量，包括：
    在每个分类直方图中统计每个分界线之前的粒子，得到每个分类直方图中预设数量的第一粒子集合；
    将所有分类直方图中的第一粒子集合进行统计，得到预设数量的第二粒子集合；
    对每个第二粒子集合中的粒子按照粒子体积进行排序；
    取所有第二粒子集合中排序后粒子体积为中间位置对应的粒子数量，得到 粒子数量向量，所述粒子数量向量的维数与预设数量相同；
    获取预设权重向量，所述预设权重向量的维数与预设数量相同；
    基于预设权重向量和粒子数量向量，得到第一类粒子的数量。
  5. 如权利要求1所述的方法，其特征在于，所述基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，包括：
    假设预设的显示直方图函数为F(v)，其中v表示粒子体积，v∈[0,127]；
    当v＝l时存在
    Figure PCTCN2021092161-appb-100001
    则确定v＝l为第一分界线，其中l为第一类粒子的体积，M为第一类粒子的数量；
    或者，假设预设的显示直方图函数为F(v)，其中v表示粒子体积，v∈[0,127]；
    当v＝l时存在
    Figure PCTCN2021092161-appb-100002
    则确定v＝l为第一分界线，其中l为第一类粒子的体积，M为第一类粒子的数量。
  6. 一种血液细胞分析仪的PLT粒子检测装置，其特征在于，包括：
    脉冲信号获取单元，用于获取检测区域中的血液样本中的各种粒子经过电场时产生的脉冲信号，所述粒子至少包括第一类粒子和第二类粒子；
    分类单元，用于对脉冲信号按照多个分类精度分别进行分类，得到多个分类直方图，所述分类直方图用于表征粒子体积与粒子数量的对应关系；
    获取参考子分类直方图单元，用于获取预设数量的参考分界线，通过所述参考分界线将每个分类直方图分割为预设数量的参考子分类直方图，所述参考分界线用于表征参考分界粒子体积；
    分界线确定单元，用于在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，即得到预设数量的分界线；
    子分类直方图确定单元，用于基于所述分界线将每个分类直方图分割为预设数量的子分类直方图；
    第一类粒子数量确定单元，用于基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量；
    显示直方图函数获取单元，用于获取预设的显示直方图函数，所述显示直方图函数用于表征第一类粒子的体积与第一类粒子的数量的对应关系；
    第一分界线确定单元，用于基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，所述第一分界线用于在分类直方图中区分第一类粒子与第二类粒子。
  7. 如权利要求6所述的装置，其特征在于，所述在每个参考子分类直方图中对参考分界线进行修正，得到每个参考子分类直方图中的分界线，包括：
    在每个参考子分类直方图中查找粒子数量最小值对应的粒子体积；
    若所述参考子分类直方图中粒子数量最小值只有一个，则将该粒子数量最小值对应的粒子体积作为该参考分类直方图的分界线；
    若所述参考分类直方图中粒子数量最小值具有多个，则将对应的多个粒子体积中最大的粒子体积作为该参考分类直方图的分界线。
  8. 如权利要求6所述的装置，其特征在于，所述基于所有分类直方图中对应的子分类直方图，确定第一类粒子的数量，包括：
    在每个分类直方图中统计每个分界线之前的粒子，得到每个分类直方图中预设数量的第一粒子集合；
    将所有分类直方图中的第一粒子集合进行统计，得到预设数量的第二粒子集合；
    对每个第二粒子集合中的粒子按照粒子体积进行排序；
    取所有第二粒子集合中排序后粒子体积为中间位置对应的粒子数量，得到粒子数量向量，所述粒子数量向量的维数与预设数量相同；
    获取预设权重向量，所述预设权重向量的维数与预设数量相同；
    基于预设权重向量和粒子数量向量，得到第一类粒子的数量。
  9. 如权利要求6所述的装置，其特征在于，所述基于预设的显示直方图函数和第一类粒子的数量，确定第一分界线，包括：
    假设预设的显示直方图函数为F(v)，其中v表示粒子体积，v∈[0,127]；
    当v＝l时存在
    Figure PCTCN2021092161-appb-100003
    则确定v＝l为第一分界线，其中l为第一类粒子的体积，M为第一类粒子的数量；
    或者，假设预设的显示直方图函数为F(v)，其中v表示粒子体积，v∈[0,127]；
    当v＝l时存在
    Figure PCTCN2021092161-appb-100004
    则确定v＝l为第一分界线，其中l为第一类粒子的体积，M为第一类粒子的数量；
    所述第一类粒子为血小板聚集粒子，第二类粒子为红细胞粒子。
  10. 一种计算机可读存储介质，其特征在于，包括程序，所述程序能够被处理器执行以实现如权利要求1-5中任一项所述的方法。
PCT/CN2021/092161 2020-11-23 2021-05-07 血液细胞分析仪的plt粒子检测方法和装置 WO2022105141A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011320383.2 2020-11-23
CN202011320383.2A CN112557281B (zh) 2020-11-23 2020-11-23 血液细胞分析仪的plt粒子检测方法和装置

Publications (1)

Publication Number Publication Date
WO2022105141A1 true WO2022105141A1 (zh) 2022-05-27

Family

ID=75044787

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/092161 WO2022105141A1 (zh) 2020-11-23 2021-05-07 血液细胞分析仪的plt粒子检测方法和装置

Country Status (2)

Country Link
CN (1) CN112557281B (zh)
WO (1) WO2022105141A1 (zh)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112557281B (zh) * 2020-11-23 2022-06-24 深圳市科曼医疗设备有限公司 血液细胞分析仪的plt粒子检测方法和装置
CN113052086B (zh) * 2021-03-29 2024-02-27 深圳市科曼医疗设备有限公司 一种白细胞分类方法、装置、计算机设备和存储介质
CN113218845B (zh) * 2021-07-08 2021-11-23 深圳市帝迈生物技术有限公司 一种样本分析仪及其计数异常的检测方法
CN114729871A (zh) * 2021-08-05 2022-07-08 深圳迈瑞动物医疗科技有限公司 一种样本分析装置、动物用分析装置和样本分析方法
CN117501096A (zh) * 2021-12-31 2024-02-02 深圳迈瑞动物医疗科技股份有限公司 一种样本分析装置和样本分析方法

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4202625A (en) * 1978-08-18 1980-05-13 Ortho Diagnostics, Inc. Method and apparatus for discriminating red blood cells from platelets
US6228652B1 (en) * 1999-02-16 2001-05-08 Coulter International Corp. Method and apparatus for analyzing cells in a whole blood sample
US20070105234A1 (en) * 2005-10-20 2007-05-10 University Of Utah Research Foundation Diagnosing equine hyperelastosis cutis
CN101387599A (zh) * 2007-09-13 2009-03-18 深圳迈瑞生物医疗电子股份有限公司 一种区分粒子群落的方法及粒子分析仪
CN106290081A (zh) * 2016-08-16 2017-01-04 江苏康尚生物医疗科技有限公司 一种区分粒子群落的方法及粒子分析仪
CN110383037A (zh) * 2017-06-20 2019-10-25 深圳迈瑞生物医疗电子股份有限公司 一种血小板聚集识别的方法、装置和细胞分析仪
WO2019206310A1 (zh) * 2018-04-28 2019-10-31 深圳迈瑞生物医疗电子股份有限公司 血液分析方法、血液分析系统及存储介质
CN111849736A (zh) * 2019-04-25 2020-10-30 深圳市帝迈生物技术有限公司 动物血液细胞测量方法及动物血液分析设备
CN112557281A (zh) * 2020-11-23 2021-03-26 深圳市科曼医疗设备有限公司 血液细胞分析仪的plt粒子检测方法和装置

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA1091350A (en) * 1976-11-04 1980-12-09 John L. Haynes Particle-density measuring system
JP2714629B2 (ja) * 1988-03-23 1998-02-16 東亜医用電子株式会社 粒子計数方法および装置
JP4817270B2 (ja) * 1997-05-13 2011-11-16 シスメックス株式会社 粒子測定装置
JP3521381B2 (ja) * 1998-02-23 2004-04-19 リオン株式会社 粒子計数装置
JP5006107B2 (ja) * 2007-05-30 2012-08-22 シスメックス株式会社 表示方法および血液分析装置
CN101226133B (zh) * 2008-01-28 2010-04-14 宁波大学 一种血细胞脉冲信号的分类识别方法
CN106501160A (zh) * 2016-09-08 2017-03-15 长春迪瑞医疗科技股份有限公司 一种粒子分类方法及粒子分类装置
CN107817208B (zh) * 2016-09-12 2024-07-02 深圳市帝迈生物技术有限公司 一种血细胞计数装置及其白细胞计数修正方法
CN111912978A (zh) * 2019-05-09 2020-11-10 深圳迈瑞生物医疗电子股份有限公司 白细胞分类计数的方法、装置和血液分析仪
CN111812012B (zh) * 2020-06-29 2023-08-11 迈克医疗电子有限公司 有核红细胞区域的识别方法、装置及血液分析仪

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4202625A (en) * 1978-08-18 1980-05-13 Ortho Diagnostics, Inc. Method and apparatus for discriminating red blood cells from platelets
US6228652B1 (en) * 1999-02-16 2001-05-08 Coulter International Corp. Method and apparatus for analyzing cells in a whole blood sample
US20070105234A1 (en) * 2005-10-20 2007-05-10 University Of Utah Research Foundation Diagnosing equine hyperelastosis cutis
CN101387599A (zh) * 2007-09-13 2009-03-18 深圳迈瑞生物医疗电子股份有限公司 一种区分粒子群落的方法及粒子分析仪
CN106290081A (zh) * 2016-08-16 2017-01-04 江苏康尚生物医疗科技有限公司 一种区分粒子群落的方法及粒子分析仪
CN110383037A (zh) * 2017-06-20 2019-10-25 深圳迈瑞生物医疗电子股份有限公司 一种血小板聚集识别的方法、装置和细胞分析仪
WO2019206310A1 (zh) * 2018-04-28 2019-10-31 深圳迈瑞生物医疗电子股份有限公司 血液分析方法、血液分析系统及存储介质
CN111849736A (zh) * 2019-04-25 2020-10-30 深圳市帝迈生物技术有限公司 动物血液细胞测量方法及动物血液分析设备
CN112557281A (zh) * 2020-11-23 2021-03-26 深圳市科曼医疗设备有限公司 血液细胞分析仪的plt粒子检测方法和装置

Also Published As

Publication number Publication date
CN112557281A (zh) 2021-03-26
CN112557281B (zh) 2022-06-24

Similar Documents

Publication Publication Date Title
WO2022105141A1 (zh) 血液细胞分析仪的plt粒子检测方法和装置
CN110444248B (zh) 基于网络拓扑参数的癌症生物分子标志物筛选方法及系统
JP5636381B2 (ja) 細胞集団識別のための機器間の方法およびシステム
US10222320B2 (en) Identifying and enumerating early granulated cells (EGCs)
US8154273B2 (en) Detecting and handling coincidence in particle analysis
US7457709B2 (en) Systems and methods for particle counting
CN107389536B (zh) 基于密度-距离中心算法的流式细胞粒子分类计数方法
WO2018151680A1 (en) Methods and devices for identifying population clusters in data
CN116046643B (zh) 辅助诊断信息提供装置及血液分析系统
CN110887818B (zh) 一种血液样本的分析方法和血液细胞分析仪及存储介质
US20210318223A1 (en) Method for analyzing nucleated red blood cells, blood cell analyzer, and storage medium
EP2252872B1 (en) High resolution classification
CN114093421B (zh) 一种判别淋巴瘤分子亚型的方法、装置和存储介质
WO2020102953A1 (zh) 分析细胞的方法、细胞分析设备与计算机可读存储介质
CN112424582A (zh) 血液样本检测的方法、血液样本检测仪和存储介质
EP4010682B1 (en) Detecting and reporting subpopulations of neutrophils
CN106290081A (zh) 一种区分粒子群落的方法及粒子分析仪
CN113470770A (zh) 基于误差预测模型的血小板检测系统
CN114222907B (zh) 检测和报道中性粒细胞亚群
LU504621B1 (en) Animal Blood Cell Measurement Method, and Animal Blood Analysis Equipment
CN115372207A (zh) 样本检测方法、装置及计算机可读存储介质
CN108037060A (zh) 粒子计数方法、实现所述方法的粒子计数装置和粒子分析仪
CN118072831A (zh) 一种基于宏基因组的病原识别方法、装置、介质及产品
CN116448649A (zh) 血小板聚集的报警方法、装置、样本分析仪及存储介质
JPS59230139A (ja) 粒子計数装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21893297

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 09/10/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 21893297

Country of ref document: EP

Kind code of ref document: A1