WO2024001155A1 - 基于显微放大数字图像的血液细胞分析方法及系统 - Google Patents

基于显微放大数字图像的血液细胞分析方法及系统 Download PDF

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WO2024001155A1
WO2024001155A1 PCT/CN2023/071249 CN2023071249W WO2024001155A1 WO 2024001155 A1 WO2024001155 A1 WO 2024001155A1 CN 2023071249 W CN2023071249 W CN 2023071249W WO 2024001155 A1 WO2024001155 A1 WO 2024001155A1
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volume
microscopically
cell
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blood
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French (fr)
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王志平
刘亚慧
房祥飞
汪椿树
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深圳安侣医学科技有限公司
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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
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • 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
    • G01N15/0205—Investigating particle size or size distribution by optical means
    • G01N15/0227—Investigating particle size or size distribution by optical means using imaging; using holography
    • 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
    • G01N15/06—Investigating concentration of particle suspensions
    • 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
    • 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
    • G01N15/06—Investigating concentration of particle suspensions
    • G01N15/075—Investigating concentration of particle suspensions by optical means
    • 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
    • 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
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1486—Counting the particles

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  • This application belongs to the technical field of obtaining the characteristics and parameters of each component in a target sample based on microscopically amplified digital images of cell suspensions, and particularly relates to the technical field of calculating various cell concentrations and cell volumes in blood samples based on digital images.
  • Flow cytometry is usually used for cell type identification and calculation of cell volume and number. Flow cytometers all need to identify individual cells in a small sample in sequence; therefore, it is necessary to design precise fluid channels for the flow of individual cells, and to cooperate with the corresponding complex optical system design to adapt to the measurement of photoelectric parameters of individual cells. capture. It requires complex flow channel design and optical system design. The system hardware design is complex and costly, and is very prone to failure. Regular maintenance is usually required to ensure that the flow channel and optical system maintain normal working conditions; and such a single-cell flow channel, cell recognition Low efficiency; when the shape characteristics of cells change, the accuracy of identification decreases.
  • a blood cell analysis method based on bright-field micro-magnified digital images is designed, which can identify and count different cell types based on bright-field micro-magnified digital images, and calculate the volumes of different types of cells; this application
  • the blood cell analysis method in performs whole blood cell analysis with minimal hardware cost. And because this method is based on bright field images, it is very intuitive and has better accuracy; there is neither the design of the sheath flow mechanism in flow cells nor the design of complex spectrophotometers; the entire technical solution is extremely simple, from research and development to use. The maintenance system is very efficient and the cost is extremely low.
  • WBC is the abbreviation of "whitebloodcell” in English, which means white blood cells in Chinese; in the blood analyzer, WBC means the concentration of white blood cells, and the unit is "cells/L";
  • RBC is the abbreviation of "red bloodcell” in English, which means red blood cells in Chinese; in a blood analyzer, RBC means the concentration of red blood cells, and the unit is "pieces/L";
  • HCT is the abbreviation of "hematocrit” in English. HCT is also called hematocrit (PCV), which means hematocrit in Chinese. In a hematology analyzer, HCT means the volume ratio of red blood cells to whole blood after anticoagulation backlog; unit yes%;
  • HGB is the abbreviation of "hemoglobin” in English, and the Chinese meaning is hemoglobin; in the blood analyzer, HGB means the hemoglobin content per unit volume of blood, that is, the hemoglobin concentration, the unit is "g/L"; CH is the English “corpuscular hemoglobin” The abbreviation of CH in Chinese means hemoglobin of red blood cells; in the blood analyzer, CH means the hemoglobin content of a single red blood cell, and the unit is "pg";
  • MCH is the abbreviation of "mean corpuscular hemoglobin” in English, and the Chinese meaning is the average corpuscular hemoglobin content; in the hematology analyzer, MCH means the average corpuscular hemoglobin content of a single red blood cell, and the unit is "pg" picogram;
  • MCHC is the abbreviation of "mean corpuscular hemoglobin concentration" in English.
  • the Chinese meaning is the average corpuscular hemoglobin concentration; in the hematology analyzer, the meaning of MCHC is the average corpuscular hemoglobin content per unit volume of red blood cells, and the unit is "g/L";
  • MCHC HGB ⁇ RBC ⁇ MCV
  • MCH HGB ⁇ RBC.
  • the technical problem to be solved by this application is to avoid the above-mentioned shortcomings of the prior art, and propose a blood cell analysis method based on microscopically amplified digital images; and can perform calculation and analysis of various blood cell volumes based on microscopically amplified digital images. .
  • the technical solution of this application to solve the above technical problems is a blood cell analysis method based on microscopic amplification of digital images, which is used to calculate the volume of target cells in blood.
  • the microscopic amplification of digital images is based on a single layer of blood cells spread out in suspension.
  • a set of microscopically magnified digital images obtained; a set of microscopically magnified digital images includes N ⁇ M microscopically magnified digital images; N and M are both natural numbers greater than or equal to 1; including step 15A: from N ⁇ M pieces
  • the described blood cell analysis method based on microscopically amplified digital images also includes step 15AA of calculating the third volume correction coefficient CVW3;
  • step 15AA includes: step 15AA1: take the same blood cell sample to be analyzed to obtain the target cells. Average cell volume ZSC;
  • Step 15AA2 Take the same blood cell sample to be analyzed as in step 15AA1, perform pretreatment to prepare a cell suspension, and inject the cell suspension into the imaging target area; spread the blood cell monolayer in the suspension , and obtain a microscopically magnified digital image of a monolayer of blood cells spread in suspension;
  • the blood cell analysis method based on microscopically amplified digital images includes, step 4M: According to the volume VTC of each single target cell, add up the volume VTC of each single target cell and average it to calculate and obtain the average volume of the target cells.
  • the blood cell analysis method based on microscopically amplified digital images includes step 4M2: outputting a single target cell volume VTC histogram according to each single target cell volume VTC; the histogram is used to count the volume distribution patterns of different target cells.
  • the described blood cell analysis method based on microscopically amplified digital images includes, step 4M3: obtaining each target hemoglobin content CH, and outputting a CH-CV joint scatter plot according to each target cell volume VTC and each target hemoglobin content CH; The CH-CV joint scatter plot is used to calculate the hemoglobin distribution patterns of target cells of different volumes.
  • the blood cell analysis method based on microscopically amplified digital images includes, step 4M4: displaying at least one CH range indicator line and at least one CV range indicator line on the CH-CV joint scatter plot.
  • the blood cell analysis method based on microscopically amplified digital images includes, step 4M6: displaying at least one CHC range indicator line and at least one CV range indicator line on the CHC-CV joint scatter plot.
  • a blood cell analysis system used for blood cell analysis, including a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, it implements the above A blood cell analysis method based on microscopically magnified digital images.
  • a readable storage medium on which a computer program is stored characterized in that when the program is executed by a processor, it implements the above-mentioned blood cell analysis method based on microscopically amplified digital images.
  • the target cell concentration and volume are calculated based on the number of target cells in the microscopically enlarged digital image.
  • the method is simple and the accuracy of counting and volume calculation is high. Therefore, the calculation of cell concentration is The accuracy is also high.
  • a cell suspension containing blood cells is built into the imaging target area.
  • the cell suspension holding chamber is a specially designed chamber.
  • the cell suspension is basically a single layer of cells spread out for cell suspension imaging.
  • the second beneficial effect of this application is that it provides a very accurate basis for the statistical analysis of the volume of a single target cell, and also enables the statistical analysis of the volume of a single target cell to be carried out, which provides a deeper understanding of clinical practice. layer of valuable information.
  • statistical analysis of various target cell volumes has very important clinical value.
  • the third beneficial effect of this application is that based on the information in the microscopically enlarged digital image, the volume of platelets and white blood cells is calculated using a spherical model, which is closer to clinical practice.
  • the fourth beneficial effect of this application is that based on the information in the microscopically enlarged digital image, the volume of cells can be calculated at the same time, and the cost is low, without the need for centrifugation steps in advance, and no complicated flow cytometry is required.
  • Cell counting and spectrophotometric measurement procedures All calculations are only based on obtaining microscopically enlarged digital images with sufficient magnification and sufficient information, subverting the working idea of the original cell analyzer. It is an AI-based big data image processing technology in the field of cell analysis.
  • the application is the true digitization of cell analyzers.
  • Figure 1 is a schematic block diagram of a microscopic image acquisition device
  • Figure 2 is a schematic diagram of the optical part of the microscopic image acquisition device
  • Figure 3 is a schematic diagram of the imaging target area; the number 100 in the figure is the imaging target area, including the chip base that carries the cell suspension, that is, the kit chip; the number 200 is the cell suspension tiled area, and the number 300 is the selected first-level field of view Target imaging area;
  • Figure 4 is a schematic diagram of the first-level field of view of the imaging target area; it can be seen from the figure that the first-level field of view target imaging area 300 in Figure 3 is divided into 16 first-level fields of view 310;
  • Figure 5 is a schematic diagram of the hierarchical visual field of the imaging target area; in the figure, the first-level visual field target imaging area 300 is divided into 16 first-level visual fields 310; one of the first-level visual fields 310 is divided into 25 second-level visual fields 510;
  • Figure 6 is a partial enlarged view of a first-level visual field in Figure 5.
  • the first-level visual field is divided into 25 second-level visual fields 510;
  • Figure 7 is the second schematic diagram of the hierarchical visual field of the imaging target area in the hierarchical visual field digital image acquisition method; in the figure, the first-level visual field target imaging area 300 is divided into nine first-level visual fields 310; one of the first-level visual fields 310 is divided into 16 secondary visions 510.
  • the microscopic imaging system on which a graded field of view digital image acquisition method is based includes a main controller, a microscopic imaging component, a driving component and an illumination source component;
  • the microscopic imaging component includes a lens
  • the component and the camera component, the lens component is also called the lens component, the lens component and the camera component are combined and move together, the microscopic imaging component is used to obtain a microscopically magnified digital image within the imaging target area;
  • the microscopic imaging component and driver The components are connected, and the driving component controls the distance of the microscopic imaging component relative to the imaging target area;
  • the driving component is electrically connected to the main controller, and the driving component accepts instructions from the main controller and can drive the microscopic imaging component to move along the imaging optical axis to adjust the display.
  • the distance of the micro-imaging component relative to the imaging target area is used to obtain clear microscopic magnified digital images;
  • the imaging target area is set between the illumination light source component and the microscopic imaging component; the illumination
  • reference numeral 600 is a microscopic imaging component
  • reference numeral 620 is a camera component
  • reference numeral 610 is a lens assembly
  • reference numeral 100 is a target imaging area
  • reference numeral 700 is an illumination source component
  • lens assembly 610 is set above the imaging target area for A microscopically magnified digital image of the imaging target area is formed
  • the camera assembly 620 is used to obtain a digitized image of the microscopically magnified digital image.
  • a part 300 of the imaging target area 200 is selected, and the imaging target area of this part is divided into N1 first-level fields of view; the size of each first-level field of view is set to different sizes or the same size, and N1 is greater than or equal to A natural number of 2; divide the imaging target area corresponding to each primary field of view into M2 secondary fields of view; set the size of each secondary field of view to different sizes or the same size, M2 is a natural number greater than or equal to 2; select any first-level field of view Field of view, use any of the secondary fields of view as the focus target, adjust the distance between the imaging target area and the microscopic imaging component, so that the microscopic imaging component can obtain a clear microscopic magnified digital image of the secondary field of view; maintain at this focal length In the state, move the microscopic imaging component in the horizontal direction, so that the microscopic imaging component sequentially acquires M2 clear microscopic magnified digital images corresponding to the secondary field of view.
  • the size of the imaging target area is 12 mm ⁇ 14 mm; that is, the area labeled 200 in Figure 3.
  • Select no less than a quarter of the imaging target area that is, the area labeled 300 in Figure 3, and divide it into N1 first-level fields of view; the microscopic magnification range is 20 times to 100 times.
  • the specific imaging target area size can be adjusted according to the degree of dilution between body fluid and diluent in the cell suspension.
  • the blood cells in the cell suspension may include stained cells or unstained cells.
  • N1 is equal to 9; M2 is equal to 16.
  • the actual number of segmented areas can be adjusted based on segmented area size and magnification to obtain the best combination.
  • the blood cells in the cell suspension include stained blood cells and unstained blood cells; the volume ratio of the blood volume to the staining reagent in the cell suspension ranges from 1:200 to 1:260.
  • the cell concentration in this application refers to the number of cells per unit volume, so the essence of concentration calculation is cell counting.
  • the microscopically magnified digital image is a set of microscopically magnified digital images obtained based on a single layer of blood cells laid out in suspension; a set of microscopically magnified digital images includes N ⁇ M microscopically magnified digital images.
  • N and M are both natural numbers greater than or equal to 1; select X microscopically magnified digital images from the N Select the target cells in the X microscopically enlarged digital images, and obtain the number NTC of the target cells in the selected.
  • the micro-magnification, the size of the camera field of view used for digital image acquisition, and the dilution concentration of the suspension are selected to balance the selection, as long as the amount can meet the basic requirements of statistics.
  • a cell suspension containing blood cells is built into the imaging target area, and the height of the cavity used to hold the cell suspension can be used as the height H of the cell suspension; the cell suspension holding cavity is specially designed In the chamber, the cell suspension is in a flat state of a single layer of cells for cell suspension imaging.
  • the cell suspension imaging process please refer to the patent application number CN2020112669182, titled “Cell Suspension Sample Imaging Method and System and Kit” for details.
  • a blood cell analysis method for blood target cell volume calculation especially for platelet volume calculation, that is, in one of the platelet volume calculation methods, at least one image obtained based on the above-mentioned graded field of view digital image acquisition method is used.
  • a microscopically magnified digital image; the target cells in the blood are platelets; including step 15A: using an AI algorithm to identify the target cells in the microscopically magnified digital image, and obtaining the area STC of each target cell in the microscopically magnified digital image. ;
  • step 15AA also includes step 15AA of calculating the platelet volume correction coefficient CPLT;
  • step 15AA includes: step 15AA1: take the same blood cell sample to be analyzed, and use an external device to obtain the average cell volume ZSC of the target cells;
  • a blood cell analysis method for calculating the concentration of target cells in blood especially for calculating the concentration of white blood cells, that is, in one of the methods for calculating the concentration of white blood cells, N2 ⁇ M2 obtained based on the graded field of view digital image acquisition method
  • a microscopically magnified digital image; the target cells in the blood are white blood cells; including step 11A: using an AI algorithm to identify the target cells in each microscopically magnified digital image, and obtaining the number of target cells in each microscopically magnified digital image;
  • Step 11B Calculate the number of target cells NTC in all microscopically magnified digital images participating in the operation; calculate the field of view area S corresponding to all microscopically magnified digital images participating in the operation;
  • the blood cell analysis method for blood target cell volume calculation especially for leukocyte volume calculation, that is, in one of the leukocyte volume calculation methods, N2 ⁇ M2 obtained based on the graded field of view digital image acquisition method
  • a microscopically magnified digital image the target cells in the blood are white blood cells; the microscopically magnified digital image is used to calculate the red blood cell volume; including step 13A: using an AI algorithm to identify the target cells in the microscopically magnified digital image, and obtaining the microscopically magnified number Each target cell area STC in the image;
  • Step 13B Obtain the known first white blood cell volume correction coefficient CVW1;
  • Calculate the single target cell volume VTC in the microscopically magnified digital image target cell area STC 1.5 ⁇ first white blood cell volume correction Coefficient CVW1.
  • step 13AA also includes step 13AA of calculating the first white blood cell volume correction coefficient CVW1;
  • step 13AA includes: step 13AA1: take the same blood cell sample to be analyzed, and use an external device to obtain the average cell volume ZSC of the target cells;
  • step 13AA2 Take the same blood cell sample to be analyzed as in step 13AA1, perform pretreatment to prepare a cell suspension, and inject the cell suspension into the imaging target area; spread the blood cell monolayer in the suspension, and obtain the blood cell monolayer Tile the microscopically magnified digital image in the suspension;
  • step 13AA3 Use the AI algorithm to identify the target cells in the microscopically magnified digital image, obtain the area STC of each target cell in the microscopically magnified digital image, and calculate the target Average cell area STCA;
  • the embodiment of the blood cell analysis method based on microscopic magnified digital images includes step 4M: according to the individual target cell volumes VTC, add up the individual target cell volumes VTC and average them to calculate and obtain the average volume of the target cells.
  • Step 4M2 The step of outputting the VTC histogram of the volume of a single target cell according to the volume VTC of each single target cell; the histogram is used to calculate the volume distribution rules of different target cells.
  • An embodiment of a blood cell analysis system used for blood cell analysis, includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program
  • the above-mentioned blood cell analysis method based on microscopic magnified digital images is realized.
  • a computer program is stored thereon, and when the program is executed by a processor, the blood cell analysis method based on microscopically magnified digital images is implemented as described above.
  • serial numbers such as first and second in this application are only for convenience of expression and do not necessarily indicate the sequential relationship in size and timing.
  • the alphabetical numbers in step 1 are only for convenience of expression and do not necessarily indicate the sequential relationship in time series.

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Abstract

基于显微放大数字图像的血液细胞分析方法及系统，用于血液中目标细胞浓度和体积计算，识别出显微放大数字图像中的目标细胞，并获得每个目标细胞面积STC；目标细胞是血小板或白细胞；步骤15B：获取已知的第三体积校正系数CVW3；计算单个目标细胞体积VTC＝目标细胞面积STC 1.5×第三体积校正系数CVW3；第三体积校正系数CVW3包括血小板体积校正系数CPLT和第一白细胞体积校正系数CVW1。目标细胞浓度和体积计算，方法简单，计数和体积计算的准确率高；使单个目标细胞体积的统计学分析就有了非常精准的基础，也使得针对单个目标细胞体积及其联合其他参数的统计分析能展开，为临床获取了更深一层的有价值信息。

Description

基于显微放大数字图像的血液细胞分析方法及系统 技术领域
本申请属基于细胞悬浮液显微放大数字图像获取目标样本中各成分特性和参数的技术领域，尤其涉及基于数字图像进行血液样本中各种细胞浓度和细胞体积计算的技术领域。
背景技术
现有技术中，对血液细胞中各种不同细胞的浓度和体积的分析，有不同的方法。对细胞类型识别，细胞体积和数量的计算通常采用流式细胞仪。流式细胞仪，都需要对小部分样品进行单个细胞依次进行识别；因此需要为单个细胞的流过设计精密的流体通道，并配合相应的复杂光学系统设计，以适应对单个细胞进行光电参数的捕获。需要复杂的流道设计和光学系统设计，系统硬件设计复杂成本高，且非常容易出现故障，通常需要定期维护以保证流道和光学系统维持正常工作状态；且这样的单细胞流道，细胞识别效率低；当细胞的形状特征发生变化时，识别的准确性会降低。
本申请中设计了一种基于明场显微放大数字图像的血液细胞分析方法，能基于明场显微放大数字图像进行不同细胞类型的识别和计数，并计算出不同类型细胞的体积；本申请中的血液细胞分析方法用极简的硬件成本进行了全血细胞分析。且由于这样的方法基于明场图像，非常直观，准确性更好；既没有流式细胞中鞘流机构的设计，也没有复杂的分光光度计的设计；整个技术方案极简，从研发到使用维护的系统效率都很高，成本极低。
WBC是英文“whitebloodcell”的缩写，中文意思是白细胞；在血液分析仪中WBC的含义是白细胞浓度，单位是“个/L”；
RBC是英文“red bloodcell”的缩写，中文意思是红细胞；在血液分析仪中，RBC的含义是红细胞浓度，单位是“个/L”；
HCT是英文“hematocrit”的缩写，HCT又称红细胞压积(PCV)，中文意思是 红细胞比容；在血液分析仪中，HCT的含义是抗凝血积压后红细胞占全血的容积比；单位是％；
CV是英文“corpuscular volume”的缩写，中文意思是红细胞体积；单位是“fL”；MCV是英文“mean corpuscular volume”的缩写，中文意思是平均红细胞体积；在血液分析仪中MCV的含义是所有红细胞的平均体积，即平均红细胞体积，单位是“fL”飞升；
HGB是英文“hemoglobin”的缩写，中文意思是血红蛋白；在血液分析仪中HGB的含义是单位体积血液中的血红蛋白含量，即血红蛋白浓度，单位是“g/L”；CH是英文“corpuscular hemoglobin”的缩写，中文意思是红细胞的血红蛋白；在血液分析仪中CH的含义是单个红细胞的血红蛋白含量，单位是“pg”；
MCH是英文“mean corpuscular hemoglobin”的缩写，中文意思是平均红细胞血红蛋白含量；在血液分析仪中MCH的含义是单个红细胞的平均红细胞血红蛋白含量，单位是“pg”皮克；
MCHC是英文“mean corpuscular hemoglobin concentration”的缩写，中文意思是平均红细胞血红蛋白浓度；在血液分析仪中MCHC的含义是单位体积红细胞的平均红细胞血红蛋白含量，单位是“g/L”；
传统血液分析仪的计算过程中，MCHC＝HGB÷RBC÷MCV；MCHC＝MCH÷MCV＝HGB÷RBC÷MCV；MCH＝HGB÷RBC。
发明内容
本申请要解决的技术问题在于避免现有技术上述不足之处，提出了一种基于显微放大数字图像的血液细胞分析方法；并能基于显微放大数字图像进行多种血液细胞体积的计算分析。
本申请解决上述技术问题的技术方案是一种基于显微放大数字图像的血液细胞分析方法，用于血液中目标细胞体积计算，显微放大数字图像是基于血液细胞单层平铺在悬浮液中所获取的一组显微放大数字图像；一组显微放大数字图像中包括N×M幅显微放大数字图像；N和M均为大于等于1的自然数；包括步骤15A：从N×M幅显微放大数字图像选取X幅显微放大数字图像用于目标细胞浓度计算，X为大于等于1的自然数；识别出所选中X幅显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC；所述血 液中目标细胞是血小板或白细胞；步骤15B：获取已知的第三体积校正系数CVW3；计算显微放大数字图像中的单个目标细胞体积VTC＝目标细胞面积STC 1.5×第三体积校正系数CVW3；第三体积校正系数CVW3包括血小板体积校正系数CPLT和第一白细胞体积校正系数CVW1。
所述的基于显微放大数字图像的血液细胞分析方法，还包括计算第三体积校正系数CVW3的步骤15AA；步骤15AA中包括：步骤15AA1：取同一待分析的血液细胞样品，利获取目标细胞的平均细胞体积ZSC；步骤15AA2：取和步骤15AA1中同一待分析的血液细胞样品，进行预处理制得细胞悬浮液，细胞悬浮液注入成像目标区域内；使血液细胞单层平铺在悬浮液中，并获取血液细胞单层平铺在悬浮液中的显微放大数字图像；步骤15AA3：识别出显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC，并计算目标细胞面积平均值STCA；步骤13AA4：第三体积校正系数CVW3＝平均细胞体积ZSC÷目标细胞面积平均值STCA 1.5。
所述的基于显微放大数字图像的血液细胞分析方法包括，步骤4M：根据各单个目标细胞体积VTC，加总各单个目标细胞体积VTC求平均，计算获取目标细胞平均体积。
所述的基于显微放大数字图像的血液细胞分析方法包括，步骤4M2：根据各单个目标细胞体积VTC，输出单个目标细胞体积VTC直方图的步骤；直方图用于统计不同目标细胞体积分布规律。
所述的基于显微放大数字图像的血液细胞分析方法包括，步骤4M3：获取各目标血红蛋白含量CH，并根据各目标细胞体积VTC和各目标血红蛋白含量CH输出CH-CV联合散点图的步骤；CH-CV联合散点图用于统计不同体积目标细胞的血红蛋白分布规律。
所述的基于显微放大数字图像的血液细胞分析方法包括，步骤4M4：在CH-CV联合散点图上展示至少一条CH范围指示线和至少一条CV范围指示线的步骤。
所述的基于显微放大数字图像的血液细胞分析方法包括，步骤4M5：获取各目标细胞血红蛋白浓度CHC；或获取各目标血红蛋白含量CH，计算CHC＝目标血红蛋白含量CH÷各目标细胞体积VTC；并根据各目标细胞的体积和目标 细胞血红蛋白浓度CHC输出CHC-CV联合散点图的步骤；CHC-CV联合散点图用于统计不同体积目标细胞的血红蛋白分布规律。
所述的基于显微放大数字图像的血液细胞分析方法包括，步骤4M6：在CHC-CV联合散点图上展示至少一条CHC范围指示线和至少一条CV范围指示线的步骤。
一种血液细胞分析系统，用于血液细胞分析，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如上述的基于显微放大数字图像的血液细胞分析方法。
一种可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如上述的基于显微放大数字图像的血液细胞分析方法。
同现有技术相比较本申请的有益效果之一，基于显微放大数字图像中的目标细胞数量进行目标细胞浓度和体积计算，方法简单，计数和体积计算的准确率高，因此细胞浓度的计算准确率也高。成像目标区域内置有包含血液细胞的细胞悬浮液，其中的细胞悬浮液盛放腔体是经由特殊设计的腔体，细胞悬浮液中基本是单层细胞平铺的状态用于细胞悬浮液成像。
同现有技术相比较本申请的有益效果之二，使单个目标细胞体积的统计学分析就有了非常精准的基础，也使得针对单个目标细胞体积的统计分析能展开，为临床获取了更深一层的有价值信息。在多种贫血症的分型中，各种目标细胞体积的统计分析具有非常重要的临床价值。
同现有技术相比较本申请的有益效果之三，基于显微放大数字图像中的信息，对血小板、白细胞的体积计算采用球形模型进行计算，更接近于临床实际。
同现有技术相比较，本申请的有益效果之四，基于显微放大数字图像中的信息，能同时进行细胞的体积计算，且成本低廉，无需事先进行离心的步骤，也无需复杂的流式细胞计数和分光光度计的测量过程。所有的计算都只是基于获取了足够放大倍数，且带有足够多信息的显微放大数字图像，颠覆了原有细胞分析仪的工作思路，是一种基于AI大数据图像处理技术在细胞分析领域的应用，是细胞分析仪的真正数字化。
附图说明
图1是显微图像获取装置的示意框图；
图2是显微图像获取装置中光学部分的组成示意图；
图3是成像目标区域的示意图；图中标号100是成像目标区域，包括了承载细胞悬浮液的芯片底座即试剂盒芯片；标号200是细胞悬浮液平铺区域，标号300是选取的一级视野目标成像区域；
图4是成像目标区域一级视野的示意图；图中可见图3中的一级视野目标成像区域300被划分成了16个一级视野310；
图5是成像目标区域分级视野的示意图；图中一级视野目标成像区域300被划分成了16个一级视野310；其中一个一级视野310又被划分成了25个二级视野510；
图6是图5中一个一级视野的局部放大图，该一级视野被划分成了25个二级视野510；
图7是分级视野数字图像获取方法中成像目标区域分级视野的示意图之二；图中一级视野目标成像区域300被划分成了9个一级视野310；其中一个一级视野310又被划分成了16个二级视野510。
具体实施方式
以下结合各附图对本申请的实施方式做进一步详述。
如图1至图3所示，一种分级视野数字图像获取方法其所基于的显微成像系统中，包括主控制器，显微成像组件，驱动组件和照明光源组件；显微成像组件包括透镜组件和相机组件，透镜组件也称镜头组件，透镜组件和相机组件组合在一起共同移动，显微成像组件用于获取成像目标区域范围内的显微放大后的数字化图像；显微成像组件和驱动组件连接，驱动组件控制显微成像组件相对于成像目标区域的距离；驱动组件和主控制器电连接，驱动组件接受主控制器指令，能带动显微成像组件沿着成像光轴移动，调整显微成像组件相对于成像目标区域的距离，以获取清晰的显微放大数字图像；成像目标区域设置在照明光源组件和显微成像组件之间；照明光源组件用于成像照明。
图2中，标号600是显微成像组件，标号620是相机组件，标号610是透镜组件；标号100是目标成像区域；标号700是照明光源组件；透镜组件610设置在成像目标区域上方，用于形成成像目标区域的显微放大数字图像；相机组件620用于获取该显微放大数字图像的数字化图像。
如图3所示，选择成像目标区域200中的一部分300，将该部分的成像目 标区域划分成N1个一级视野；各一级视野范围大小设定成不同大小或相同大小，N1为大于等于2的自然数；将每个一级视野对应成像目标区域划分成M2个二级视野；各二级视野范围大小设定成不同大小或相同大小，M2为大于等于2的自然数；任选一个一级视野，以其中任意一个二级视野为聚焦目标，调整成像目标区域和显微成像组件的间距，使显微成像组件能获取该个二级视野的清晰的显微放大数字图像；在该焦距维持的状态下，在水平方向移动显微成像组件，使显微成像组件依次获取该二级视野对应的M2幅清晰的显微放大数字图像。
如图3所示，一种分级视野数字图像获取方法的实施例中，成像目标区域大小是12mm×14mm；即图3中标号为200的区域。选择成像目标区域中不小于四分之一的区域，即图3中标号为300的区域，并将其划分成N1个一级视野；显微放大倍数范围是20倍至100倍。除了选择成像目标区域中的四分之一区域，还可以选择成像目标区域中的三分之一区域或五分之一区域或其他面积的大小。具体的成像目标区域面积大小，可跟随细胞悬浮液中体液和稀释液之间的稀释程度进行调整。细胞悬浮液中的血液细胞既包括染色后的细胞，也可以是没有染色后的细胞。
如图4至图6所示的实施例中，N1等于16；M2等于25；N1的取值范围是10至20。如图7所示的实施例中，N1等于9；M2等于16。实际分割的区域数量可以根据分割区域大小和放大倍数进行调整以获得最佳的组合。细胞悬浮液中的血液细胞包括染色后的血液细胞和没有染色的血液细胞；细胞悬浮液中血液体积和染色试剂的体积比范围是1:200至1：260。
本申请中的细胞浓度是指单位体积内的细胞个数，因此浓度计算的实质是进行细胞计数。本申请中，显微放大数字图像是基于血液细胞单层平铺在悬浮液中所获取的一组显微放大数字图像；一组显微放大数字图像中包括N×M幅显微放大数字图像；N和M均为大于等于1的自然数；从N×M幅显微放大数字图像选取X幅显微放大数字图像用于目标细胞的浓度或体积计算，X为大于等于1的自然数；识别出所选中X幅显微放大数字图像中的目标细胞，并获得所选中X幅显微放大数字图像中的目标细胞数量NTC；X可以是1，当然也可以是其他数值；具体的数值，可以根据显微放大倍数、数字图像获取用的相机视野大小以及悬浮液的稀释浓度来平衡选择，数量只要能满足统计学上的基本要求 即可。
成像目标区域内置有包含血液细胞的细胞悬浮液，其中用于盛放细胞悬浮液的腔体的高度可用作是细胞悬浮液的高度H；其中的细胞悬浮液盛放腔体是经由特殊设计的腔体，细胞悬浮液中是单层细胞平铺的状态用于细胞悬浮液成像。关于细胞悬浮液成像过程，具体请参考专利申请号为CN2020112669182，名称为“细胞悬浮液样品成像方法和系统及试剂盒”的专利申请。
一种用于血液目标细胞体积计算，尤其是用于血小板体积计算的血液细胞分析方法的实施例中，即血小板体积计算方法之一中，基于上述的分级视野数字图像获取方法所获取的至少一幅显微放大数字图像；所述血液中目标细胞是血小板；包括步骤15A：利用AI算法识别出显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC；步骤15B：获取已知的血小板体积校正系数CPLT；计算显微放大数字图像中的单个目标细胞体积VTC＝目标细胞面积STC 1.5×血小板体积校正系数CPLT。
上述实施例中，还包括计算血小板体积校正系数CPLT的步骤15AA；步骤15AA中包括：步骤15AA1：取同一待分析的血液细胞样品，利用外部设备获取目标细胞的平均细胞体积ZSC；步骤15AA2：取和步骤15AA1中同一待分析的血液细胞样品，进行预处理制得细胞悬浮液，细胞悬浮液注入成像目标区域内；使血液细胞单层平铺在悬浮液中，并获取血液细胞单层平铺在悬浮液中的显微放大数字图像；步骤15AA3：利用AI算法识别出显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC，并计算目标细胞面积平均值STCA；步骤13AA4：血小板体积校正系数CPLT＝平均细胞体积ZSC÷目标细胞面积平均值STCA 1.5。
一种用于血液中目标细胞浓度计算，尤其是用于白细胞浓度计算的血液细胞分析方法的实施例中，即白细胞浓度计算方法之一中，基于分级视野数字图像获取方法所获取的N2×M2幅显微放大数字图像；血液中目标细胞是白细胞；包括步骤11A：利用AI算法识别出每一幅显微放大数字图像中的目标细胞，并获得每一幅显微放大数字图像目标细胞数量；步骤11B：计算参与运算的所有显微放大数字图像中的目标细胞数量NTC；计算参与运算的所有显微放大数字图像对应的视野面积S；步骤11C：获取已知成像目标区域中细胞悬浮液的高度H； 计算血液中目标细胞浓度＝目标细胞数量NTC÷(视野面积S×H)。
另一种用于血液目标细胞体积计算，尤其是用于白细胞体积计算的血液细胞分析方法的实施例中，即白细胞体积计算方法之一中，基于分级视野数字图像获取方法所获取的N2×M2幅显微放大数字图像；血液中目标细胞是白细胞；显微放大数字图像用于红细胞体积计算；包括步骤13A：利用AI算法识别出显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC；步骤13B：获取已知的第一白细胞体积校正系数CVW1；计算显微放大数字图像中的单个目标细胞体积VTC＝目标细胞面积STC 1.5×第一白细胞体积校正系数CVW1。
上述实施例中，还包括计算第一白细胞体积校正系数CVW1的步骤13AA；步骤13AA中包括：步骤13AA1：取同一待分析的血液细胞样品，利用外部设备获取目标细胞的平均细胞体积ZSC；步骤13AA2：取和步骤13AA1中同一待分析的血液细胞样品，进行预处理制得细胞悬浮液，细胞悬浮液注入成像目标区域内；使血液细胞单层平铺在悬浮液中，并获取血液细胞单层平铺在悬浮液中的显微放大数字图像；步骤13AA3：利用AI算法识别出显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC，并计算目标细胞面积平均值STCA；步骤13AA4：第一白细胞体积校正系数CVW1＝平均细胞体积ZSC÷目标细胞面积平均值STCA 1.5。
基于显微放大数字图像的血液细胞分析方法的实施例中包括，步骤4M：根据各单个目标细胞体积VTC，加总各单个目标细胞体积VTC求平均，计算获取目标细胞平均体积。步骤4M2：根据各单个目标细胞体积VTC，输出单个目标细胞体积VTC直方图的步骤；直方图用于统计不同目标细胞体积分布规律。
一种血液细胞分析系统的实施例中，用于血液细胞分析，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现上述的基于显微放大数字图像的血液细胞分析方法。一种可读存储介质的实施例中，其上存储有计算机程序，该程序被处理器执行时实现如上述的基于显微放大数字图像的血液细胞分析方法。
本申请中的第一、第二这样的序号只是为了表达的方便，并不必然表示大小和时序上的顺序关系。步骤1的字母序号，也只是为了表达的方便，并不必 然表示时序上的顺序关系。
如图1至图22所示以上所述仅为本申请的实施例，并非因此限制本申请的专利范围，凡是利用发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (5)

  1. 一种基于显微放大数字图像的血液细胞分析方法，用于血液中目标细胞体积计算，其特征在于，
    显微放大数字图像是基于血液细胞单层平铺在悬浮液中所获取的一组显微放大数字图像；一组显微放大数字图像中包括N×M幅显微放大数字图像；N和M均为大于等于1的自然数；
    包括步骤15A：从N×M幅显微放大数字图像选取X幅显微放大数字图像用于目标细胞浓度计算，X为大于等于1的自然数；识别出所选中X幅显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC；所述血液中目标细胞是血小板或白细胞；
    步骤15B：获取已知的第三体积校正系数CVW3；计算显微放大数字图像中的单个目标细胞体积VTC＝目标细胞面积STC 1.5×第三体积校正系数CVW3；
    第三体积校正系数CVW3包括血小板体积校正系数CPLT和第一白细胞体积校正系数CVW1；
    所述的基于显微放大数字图像的血液细胞分析方法，
    还包括计算第三体积校正系数CVW3的步骤15AA；
    步骤15AA中包括：
    步骤15AA1：取同一待分析的血液细胞样品，利获取目标细胞的平均细胞体积ZSC；
    步骤15AA2：取和步骤15AA1中同一待分析的血液细胞样品，进行预处理制得细胞悬浮液，细胞悬浮液注入成像目标区域内；使血液细胞单层平铺在悬浮液中，并获取血液细胞单层平铺在悬浮液中的显微放大数字图像；
    步骤15AA3：识别出显微放大数字图像中的目标细胞，并获得显微放大数字图像中的每个目标细胞面积STC，并计算目标细胞面积平均值STCA；
    步骤13AA4：第三体积校正系数CVW3＝平均细胞体积ZSC÷目标细胞面积平均值STCA 1.5。
  2. 根据权利要求1所述的基于显微放大数字图像的血液细胞分析方法，其特征在于，包括，
    步骤4M：根据各单个目标细胞体积VTC，加总各单个目标细胞体积VTC求平 均，计算获取目标细胞平均体积。
  3. 根据权利要求1所述的基于显微放大数字图像的血液细胞分析方法，其特征在于，包括，
    步骤4M2：根据各单个目标细胞体积VTC，输出单个目标细胞体积VTC直方图的步骤；直方图用于统计不同目标细胞体积分布规律。
  4. 一种血液细胞分析系统，用于血液细胞分析，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1至3中任一所述的基于显微放大数字图像的血液细胞分析方法。
  5. 一种可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1至3中任一所述的基于显微放大数字图像的血液细胞分析方法。
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