WO2023240854A1 - 基于显微放大数字图像的血红蛋白分析方法及系统 - Google Patents

基于显微放大数字图像的血红蛋白分析方法及系统 Download PDF

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WO2023240854A1
WO2023240854A1 PCT/CN2022/124646 CN2022124646W WO2023240854A1 WO 2023240854 A1 WO2023240854 A1 WO 2023240854A1 CN 2022124646 W CN2022124646 W CN 2022124646W WO 2023240854 A1 WO2023240854 A1 WO 2023240854A1
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target
area
hemoglobin
target cell
gray value
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PCT/CN2022/124646
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English (en)
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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G06T2207/10061—Microscopic image from scanning electron microscope

Definitions

  • This application belongs to the technical field of obtaining the characteristics and parameters of each component in a target sample based on microscopic amplification of digital images of cell suspensions, and particularly relates to the technical field of analyzing specific components in cells based on digital images, specifically involving the use of microscopic amplification of cell suspensions.
  • Beer-Lambert's law is one of the commonly used principles in determining the content of various substances. As shown in Figure 1, its physical meaning is that when a beam of parallel monochromatic light passes vertically through a uniform non-scattering light-absorbing material, its absorbance A is proportional to the concentration c of the light-absorbing material and the thickness of the absorption layer b, and is proportional to the light transmission Degree T is inversely related.
  • c is the concentration of the light-absorbing material
  • b is the thickness of the absorbing layer
  • b is often replaced by l, which has the same meaning.
  • the applicable conditions for Beer-Lambert's law include: (1) The incident light is parallel monochromatic light and illuminated vertically; (2) The light-absorbing material is a uniform non-scattering system; (3) There is no interaction between light-absorbing particles; (4) Radiation The interaction with matter is limited to light absorption.
  • Blood is composed of blood cells (target cells, white blood cells, platelets) and plasma.
  • the unanticoagulated blood will coagulate naturally after separation (or during centrifugation), and the upper layer of light yellow transparent liquid is separated as serum, the middle layer of white solids is white blood cells and platelets, the bottom layer of red solids is target cells, and normal hemoglobin is coated. Covered within the cell membrane. Since most blood cells in whole blood samples are coated with cell membranes, blood samples will naturally stratify when placed in conventional test tube containers and do not have uniform non-scattering properties. Therefore, in the prior art, when a blood cell analyzer detects the hemoglobin content HGB in each liter of blood cell sample, the most commonly used method is the HiCN assay.
  • HiCN measurement method namely hemoglobin cyanide (HiCN) spectrophotometry
  • HiCN hemoglobin cyanide
  • the measurement results of this method are the traceability standards for other hemoglobin measurement methods.
  • the measurement principle of cyanomethemoglobin spectrophotometry is that the ferrous ions (Fe 2+ ) in hemoglobin (except sulfated hemoglobin) are oxidized by potassium ferricyanide into ferric ions (Fe 3+ ), and the hemoglobin is converted into methemoglobin. Methemoglobin combines with cyanide ions (CN) to form stable cyanated methemoglobin (HiCN).
  • cyanomethemoglobin When detected with a spectrophotometer, cyanomethemoglobin has a broad absorption peak at a wavelength of 540 nm, and its absorbance at 540 nm is proportional to its concentration in the solution.
  • the HiCN assay requires hemolysis first, allowing the hemoglobin to combine with the hemolytic agent to form a hemoglobin derivative, so that the sample has uniform non-scattering characteristics before Beer-Lambert's law can be used.
  • the hemolysis process will destroy the overall structure of the cells. Therefore, during the blood analysis process, it is usually considered to count the blood cells first and then proceed. Hemolysis; in this way, the analysis process of whole blood is limited to this and must be carried out in a specific order; and adding a hemolytic agent in the middle process also makes the entire control process more complicated and reduces the overall efficiency.
  • hemoglobin content HGB needs to be hemolyzed first, so the opportunity to accurately obtain the hemoglobin in a single red blood cell is lost. Therefore, the hemoglobin content HGB output by a traditional blood cell analyzer can only be a quantitative analysis of the sample output. As a result, the quantitative analysis results cannot be accurately drilled down to the level of individual red blood cells, nor can the analysis results be drilled down to a deeper cellular level.
  • each red blood cell the size of each red blood cell, the hemoglobin content contained in each red blood cell, and its distribution patterns and characteristics all represent corresponding physiological or pathological meanings.
  • WBC is the abbreviation of "white blood cell” 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 "pieces/L";
  • RBC is the abbreviation of "red blood cell” in English, and the Chinese meaning is red blood cells; 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 hematology analyzer, HGB means the hemoglobin content per unit volume of blood, that is, the hemoglobin concentration, and the unit is "g/L";
  • CH is the abbreviation of "corpuscular hemoglobin” in English, and the Chinese meaning is the 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 solution of the present invention overcomes the shortcomings of the existing technology and proposes a hemoglobin analysis method based on microscopically magnified digital images obtained by laying a single layer of blood cells in suspension.
  • the microscopically magnified digital image acquired in the suspension combines the Beer-Lambert law with the microscopically magnified digital image.
  • it is applied to obtain the hemoglobin content of a single cell, and brings hemoglobin analysis with a minimalist system design. Entering the era of hemoglobin analysis at the cellular level.
  • On the basis of obtaining the hemoglobin content of a single cell it can also complete the traditional hemoglobin content detection target, and the system is extremely simple and low-cost. Not only is the complexity of the system's optical path design reduced, the optical path is maintenance-free, and the operation and control process are also simplified, greatly improving the overall efficiency of hemoglobin detection.
  • the technical solution of this application to solve the above technical problems is a hemoglobin analysis method based on a microscopically amplified digital image.
  • the hemoglobin analysis method based on microscopically enlarged digital images includes, in step 6B: each target cell in the target image is an independent single cell.
  • the hemoglobin analysis method based on microscopic magnified digital images includes step 6JA of obtaining the first hemoglobin absorption coefficient K HGB ;
  • step 6JA includes: step 6JA1: take the same amount of blood cell sample to be analyzed and obtain it using a hemoglobin tester Hemoglobin content HGB and red blood cell concentration RBC in each liter of blood cell sample;
  • Step 6JA2 Take the same amount of cell sample to be analyzed as in step 6JA1, perform pretreatment to prepare a cell suspension, and inject the cell suspension into the imaging target area; make the blood The cell monolayer is tiled in the suspension, and a microscopic magnified digital image of the blood cell monolayer tiled in the suspension is obtained;
  • Step 6JA4 In the target image obtained in step 6JA3, calculate based on the average gray value Gc of each target cell area and the average
  • the first absorbance ⁇ 1 of each target cell lg (the average gray value of the blank area Gb/the average gray value of the target cell area Gc);
  • Step 6JA5 Obtain the first absorbance ⁇ 1 of each target cell in step 6JA4, and obtain the first absorbance ⁇ 1 of each target cell.
  • Step 6JA6 Obtain the area STC of each target cell, and find the average area SVTC of all target cells;
  • Step 6JA7: First hemoglobin absorption coefficient K HGB Mean value of first absorbance ⁇ 1 ⁇ Hemoglobin content bc corresponding to unit area;
  • the first hemoglobin absorption coefficient K HGB is a constant value corresponding to the target sample to be tested, or a constant value corresponding to the target sample to be tested and obtained from a table lookup in a data table.
  • a hemoglobin analysis method based on microscopically magnified digital images are based on microscopically magnified digital images obtained by flattening a single layer of blood cells in suspension; including step 6A: identifying the microscopically magnified digital images.
  • the hemoglobin analysis method based on microscopic magnified digital images includes step 7JA of obtaining the first hemoglobin content correction coefficient CHGB1; step 7JA includes: step 7JA1: take the same amount of blood cell sample to be analyzed, and use a hemoglobin tester to obtain Hemoglobin content HGB and red blood cell concentration RBC in each liter of blood cell sample; Step 7JA2: Take the same amount of cell sample to be analyzed as in step 7JA1, perform pretreatment to prepare a cell suspension, and inject the cell suspension into the imaging target area; make the blood The cell monolayer is tiled in the suspension, and a microscopic magnified digital image of the blood cell monolayer tiled in the suspension is obtained; Step 7JA3: In the microscopic magnified digital image obtained in step 7JA2, select the corresponding target cells The target picture; the target picture includes the target cell area and the blank area; Step 7JA4: In the target picture obtained in step 7JA3, calculate based on the average gray value Gc of each target cell area and the average gray value Gb of
  • the first absorbance ⁇ 1 of each target cell lg (the average gray value of the blank area Gb/the average gray value of the target cell area Gc);
  • Step 7JA5 Obtain the first absorbance ⁇ 1 of each target cell in step 7JA4, and calculate the first Absorbance ⁇ 1 average;
  • Step 7JA6 Obtain the area STC of each target cell, and obtain the average area SVTC of the target cells;
  • the first absorbance ⁇ 1 mean ⁇ the average area of the target cells SVTC.
  • the first hemoglobin content correction coefficient CHGB1 is a constant value corresponding to the target sample to be tested, or a constant value corresponding to the target sample to be tested obtained from a table lookup in a data table.
  • a hemoglobin analysis method based on microscopically magnified digital images are based on microscopically magnified digital images obtained by flattening a single layer of blood cells in suspension; including step 6A: identifying the microscopically magnified digital images.
  • the hemoglobin analysis method based on microscopic magnified digital images includes step 8DA of obtaining the first hemoglobin concentration correction coefficient CHC1; step 8DA includes: step 8DA1: take the same amount of blood cell sample to be analyzed, and obtain it using a hemoglobin tester Hemoglobin content HGB, red blood cell concentration RBC and mean red blood cell volume MCV in each liter of blood cell sample; Step 8DA2: Take the same amount of cell sample to be analyzed as in step 8DA1, perform preprocessing to prepare a cell suspension, and inject the cell suspension into the imaging target area; lay a monolayer of blood cells in the suspension, and obtain a microscopically magnified digital image of a monolayer of blood cells tiled in the suspension; Step 8DA3: In the microscopically magnified digital image obtained in step 8DA2, select Obtain the target picture corresponding to each target cell; the target picture includes the target cell area and the blank area; Step 8DA4: In the target picture obtained in step 8DA3, the average gray value Gc of each target cell
  • the first hemoglobin concentration correction coefficient CHC1 is a constant value corresponding to the target sample to be tested, or a constant value corresponding to the target sample to be tested obtained from a table lookup in a data table.
  • the hemoglobin analysis method based on microscopic magnified digital images includes step 6M: According to the hemoglobin content CH of each target cell, add up the hemoglobin content CH of each target cell and average it to calculate the average hemoglobin content MCH of the target cells.
  • the hemoglobin analysis method based on microscopically enlarged digital images includes step 6M2: outputting a histogram of the hemoglobin content CH of the target cells according to the hemoglobin content CH of each target cell; the histogram is used to count the hemoglobin distribution patterns of different target cells.
  • the hemoglobin analysis method based on microscopic magnified digital images includes step 6M3: obtaining the volume of each target cell, and outputting a CH-CV joint scatter plot according to the volume of each target cell and the content of each target hemoglobin CH; CH-CV joint Scatter plots are used to calculate the distribution patterns of hemoglobin in target cells of different volumes.
  • the hemoglobin analysis method based on microscopically magnified digital images includes step 6M4: displaying at least one CH range indicator line and at least one CV range indicator line on the CH-CV joint scatter plot.
  • a hemoglobin analysis method based on a microscopically amplified digital image is a microscopically amplified digital image obtained under illumination of a broad spectrum illumination light source; the microscopically amplified digital image is an R containing at least three color component information.
  • /G/B three-channel microscopic magnified digital image; R/G/B three channels are red channel, green channel and blue channel respectively.
  • the hemoglobin analysis method based on a microscopically amplified digital image includes: the microscopically amplified digital image is a microscopically amplified digital image obtained under the illumination of a specific light source; the specific light source is a purple light source with a central wavelength including 418nm; the specific light source The central wavelength range is between 380nm and 440nm, or the central wavelength range of the specific light source is between 400nm and 420nm; the microscopic magnified digital image is an R/G/B three-channel microscope containing at least three color component information. Enlarge digital image.
  • the hemoglobin analysis method based on microscopically amplified digital images includes, in step 6C: calculating the target cells based on the average gray value of the blue channel of the target cell area in the target image and the average gray value of the blue channel of the blank area in the target image.
  • the hemoglobin analysis method based on microscopically amplified digital images includes, in step 6C: calculating the target cells based on the average gray value of any channel in the target cell area in the target image and the average gray value of any channel in the blank area of the target image.
  • the technical solution of the present application to solve the above technical problems can also be a hemoglobin analysis method based on microscopically amplified digital images.
  • the microscopically amplified digital images are based on microscopically amplified digital images obtained by laying a single layer of blood cells in suspension.
  • the technical solution of the present application to solve the above technical problems can also be an electronic device, including 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 hemoglobin analysis method based on microscopic magnified digital images is realized.
  • the technical solution of the present application to solve the above technical problems can also be a readable storage medium on which a computer program is stored.
  • the program is executed by a processor, the above-mentioned hemoglobin analysis method based on microscopically amplified digital images is implemented.
  • the technical solution of the present application to solve the above technical problems can also be a microscopic imaging system for obtaining microscopically amplified digital images for hemoglobin analysis, including a main controller, a microscopic imaging component, a driving component and an illumination source component;
  • the imaging component includes a lens component and a camera component.
  • the microscopic imaging component is used to obtain a microscopically magnified digital image within the imaging target area.
  • the microscopic imaging component is connected to a driving component, and the driving component controls the microscopic imaging component relative to the imaging target. The distance between the areas; the driving component is electrically connected to the main controller.
  • the driving component accepts instructions from the main controller and can drive the microscopic imaging component to move along the imaging optical axis, and adjust the distance of the microscopic imaging component relative to the imaging target area to obtain clear
  • a microscopically magnified digital image
  • the imaging target area is set between the illumination light source component and the microscopic imaging component;
  • the imaging target area contains a tiled suspension of a monolayer of blood cells;
  • the microscopically magnified digital image is a monolayer of blood cells
  • the camera assembly includes a black and white camera assembly or a color camera assembly;
  • the illumination light source assembly is a broad spectrum illumination light source; or the illumination light source assembly is a specific light source;
  • the specific light source is The central wavelength includes a purple light source of 418nm;
  • the central wavelength range of the specific light source is between 380nm and 440nm, or the central wavelength range of the specific light source is between 400nm and 420nm.
  • the microscopic imaging component also includes a narrow-band filter arranged in the optical path before the light enters the camera component; the narrow-band filter can transmit light in a central wavelength range between 380nm and 440nm or between 400nm and 420nm. .
  • one of the beneficial technical effects of this application is to combine the Beer-Lambert law with the microscopically magnified digital image based on the microscopically magnified digital image obtained by laying a monolayer of blood cells in suspension.
  • Application can obtain the hemoglobin content of a single target cell, allowing the clinical perspective of observing hemoglobin content to go deep into the hemoglobin content at the single cell level from the hemoglobin content of the whole sample.
  • the cell morphology in the cell suspension is intact, and the measurement of hemoglobin is directly for a single intact cell, with high accuracy and no need for hemolysis.
  • the second beneficial technical effect of this application is that it can obtain the hemoglobin content of a single target cell, so that the statistical analysis of a single hemoglobin content has a very accurate basis, and it also makes the statistics of a single hemoglobin content The analysis can be carried out to obtain a deeper level of valuable information for clinical use. In the classification of various anemias, statistical analysis of single-cell hemoglobin content has very important clinical value.
  • the third beneficial technical effect of this application is that in the cell suspension, the cell morphology remains intact, and the volume measurement of the cells will be more accurate.
  • the combination of the volume of a single cell and the hemoglobin content of a single cell will The hemoglobin content and the volume of a single hemoglobin are combined for statistical analysis to obtain a deeper, multi-dimensional and valuable information for clinical use.
  • the statistical analysis of single cell hemoglobin content combined with single hemoglobin volume has extremely important clinical value.
  • the fourth beneficial technical effect of this application is that the AI algorithm can identify a single target cell in a microscopically enlarged digital image, and each target cell in the target image is an independent single cell; therefore, the target cell area STC , the first absorbance ⁇ 1 of the target cell, and the hemoglobin content CH of the target cell can be calculated at the level of a single cell; therefore, the precision of the calculation is higher; and the precision of the calculation increases and enriches with the increase and enrichment of calculation examples in the AI algorithm. , the calculation accuracy will be improved accordingly.
  • the fifth beneficial technical effect of this application is that the first hemoglobin absorption coefficient K HGB , the first hemoglobin content correction coefficient CHGB1 and the first hemoglobin concentration correction coefficient CHC1 can obtain different types of targets through table lookup.
  • the corresponding parameters of cells are simple and fast, simplifying the calculation process and improving the overall calculation efficiency.
  • the sixth beneficial technical effect of the present application is that the first hemoglobin absorption coefficient K HGB and the first hemoglobin content correction coefficient CHGB1 can be measured by introducing a hemoglobin tester with corresponding precision or higher precision in the prior art. And the acquisition of the first hemoglobin concentration correction coefficient CHC1 ensures the consistency and accuracy of the coefficient, which is more suitable for hemoglobin content measurement scenarios. These coefficients can also be obtained according to the sample type by introducing a comparison reference to the standard instrument, which further improves the compatibility and scalability of the system and can be applied to the hemoglobin content test of a variety of samples.
  • the seventh beneficial technical effect of this application is that the central wavelength range of the specific light source is between 380nm and 440nm, or the central wavelength of the filter is set, making full use of the characteristics of the light source and the filter.
  • the eighth beneficial technical effect of this application is that the blue channel or any channel of the microscopically amplified digital image obtained under white light or other wide spectrum light sources is used. It improves the digital characteristics of the image, which is equivalent to filtering out the corresponding light signals of other channels. It enhances the relative signal amount at 418nm, which is the strongest absorption peak of blood cells, in a disguised manner. It improves the signal-to-noise ratio at the strongest absorption peak and can go further. Improve measurement and calculation accuracy.
  • the ninth beneficial technical effect of this application is that the average hemoglobin content MCH of the target cells is calculated through the hemoglobin content CH of each target cell, and the average hemoglobin concentration MCHC is calculated based on the hemoglobin concentration CHGBs of each target cell, both of which are direct operations. And the measurement process is closer to the real situation, avoiding the process errors introduced by conversion when measuring derivatives.
  • Figure 1 is a schematic diagram of Beer-Lambert’s law
  • Figure 2 is a schematic block diagram of the measurement principle of hemoglobin in blood in the prior art
  • Figure 3 is a schematic diagram of the blood absorption spectrum; it can be seen in the figure that blood exhibits obvious absorption peaks in multiple spectral intervals such as 420nm, 540nm, 580nm, etc.; it can be seen in the figure that the absorption peak near 418nm is relative to the absorption peak between 540nm and 580nm. More obvious and prominent, which means that blood has stronger absorption characteristics near 418nm;
  • Figure 4 is a schematic diagram of the components of the optically related parts of the microscopic imaging system used to obtain microscopically magnified digital images;
  • Figure 5 is a schematic diagram of the optical path of the microscopic imaging system based on Figure 4;
  • Figure 6 is a specific microscopic magnified digital image used for hemoglobin analysis; it can be seen in the image that the cells in the blood are in a single layer tile state;
  • Figure 7 is a schematic diagram of a microscopically enlarged digital image using an AI algorithm to identify multiple target cells; the identified target cells in the figure have all been selected as a frame;
  • Figure 8 is a schematic diagram of a longitudinal cross-section of cells contained in the solution under observation; b in the figure is the longitudinal length of the cells;
  • Figures 9 and 10 are schematic diagrams of a target picture containing a target cell in Figure 7; the target picture is any selected picture in Figure 7; in Figures 9 and 10, the circles represent cells; in Figure 9
  • the grid is a diagram of an area; the outer frame in Figure 10 is the boundary of the target image, which includes the cells in the middle and the blank area outside the cells;
  • Figure 11 is a schematic diagram of the results of a comparative test using the HiCN measurement method in the prior art and the method in this application, that is, the ANLV test method or the Anlu test method; multiple groups of samples are compared and tested in the table;
  • Figure 13 is a CH histogram of hemoglobin content of a single target cell in a cat blood sample
  • Figure 14 is a CH histogram of the hemoglobin content of a single target cell in a canine blood sample
  • Figure 15 is a CH histogram of the hemoglobin content of a single target cell in another dog blood sample; in Figures 13 to 15, the abscissa is the hemoglobin content of the target cell in pg, and the ordinate is the number of target cells in units;
  • Figure 16 is a CV-CH joint scatter plot of cat blood samples
  • Figure 17 is a CV-CH joint scatter plot of canine blood samples
  • Figure 18 is a CV-CH joint scatter plot of another dog blood sample
  • the ordinate is the target cell hemoglobin content CH value in pg
  • the abscissa is the target cell volume in fl.
  • Hb derivatives In the existing technology, the light absorption properties of Hb derivatives at specific wavelengths (530-550 nm) are usually used; the spectral absorption properties of blood itself are rarely used.
  • the ferrous ions (Fe2+) in hemoglobin (except SHb) are oxidized to ferric iron particles (Fe3+) by potassium ferricyanide in the hemolytic agent, and the hemoglobin is converted into methemoglobin.
  • Methemoglobin combines with cyanide ions (CN-) to form stable HiCN, which is an Hb derivative.
  • the maximum absorption peak of HiCN is 540nm. This combination determines that the absorption characteristics of hemoglobin cannot be purely utilized, especially the absorption characteristics near 418nm. Since it has gone through the hemolysis process and the original hemoglobin content is calculated through the hemoglobin derivative content, it is not a direct measurement of the hemoglobin content, and measurement errors will also be introduced in the process.
  • each cell in the sample will also have uniform non-scattering characteristics, so the digital image obtained based on the single layer of cell tiles can be for component content analysis.
  • it eliminates the hemolysis process and simplifies the operation process; on the other hand, it can also select the band with the strongest absorption characteristics of blood cells for hemoglobin concentration analysis.
  • Figure 3 it is the absorption spectrum curve of blood cells. From this curve, it can be seen that the strongest absorption peak of blood cells is near 418nm. If the absorption features can be extracted near this band, a better signal-to-noise ratio can be obtained, and the measurement Accuracy is also easier to achieve and better.
  • the basis for cell analysis based on microscopic magnified digital images is that after diluting the blood cells, the monolayer cells maintain the original 3D cell morphology in the liquid base and take pictures to obtain bright-field microscopic magnified digital images; Based on the bright-field microscopic magnified digital image, the hemoglobin content was analyzed and measured based on the identification of cell types.
  • 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 assembly
  • lens assembly 610 is set above the imaging target area for A microscopically magnified image of the imaging target area is formed
  • the camera component 620 is used to obtain digital image information of the microscopically magnified image.
  • the illumination light source assembly 700 can output at least two illumination beams for illumination of the imaging target area according to the control instructions given by the main controller, a first illumination beam and a second illumination beam; the first illumination beam is a beam with a first central wavelength of 418 nm. ;
  • the second illumination beam is white light or a beam of a second central wavelength; the white light is a mixed broad spectrum light; the second central wavelength can also be a beam of other central wavelengths, such as 540nm, 580nm, etc.
  • the camera assembly includes a black and white camera assembly or a color camera assembly; the illumination light source assembly is a wide spectrum illumination light source; or the illumination light source assembly is a specific light source; the specific light source is a purple light source with a central wavelength including 418nm; the central wavelength range of the specific light source is between 380nm and between 440nm, or the central wavelength range of the specific light source is between 400nm and 420nm.
  • the microscopic imaging component also includes a narrow-band filter disposed in the optical path before the light enters the camera component; the narrow-band filter has a central wavelength range of 380 nm that can transmit light to 440nm or between 400nm and 420nm.
  • a microscopically magnified digital image is a microscopically magnified digital image obtained under the illumination of a broad spectrum illumination light source; or a microscopically magnified digital image is a microscopically magnified digital image obtained under the illumination of a specific light source;
  • the specific light source is the central wavelength Including a 418nm purple light source;
  • the central wavelength range of the specific light source is between 380nm and 440nm or between 400nm and 420nm;
  • the microscopic magnified digital image is an R/G/B three-channel microscopic magnified digital image containing at least three color component information , the three R/G/B channels are the red channel, green channel and blue channel respectively.
  • the incident light source enhances the light intensity near the hemoglobin absorption peak band and can further highlight the light absorption near the absorption peak.
  • the change in quantity further improves the signal-to-noise ratio and makes the calculation results more accurate.
  • Figure 5 is the optical path of the microscopic imaging system based on Figure 4; the exit light intensity I0 of the light source passes through the solution being observed, the part of the absorbed light intensity is the absorbed light intensity Id, and the exit light intensity I1 enters the CMOS in the camera component
  • the imaging unit acquires microscopic magnified digital images.
  • the solution under observation is a suspension of monolayer cells, which is a dilution of blood. Blood diluents also include staining solutions with staining functions.
  • Figure 8 is a schematic longitudinal cross-section of cells contained in the solution being observed.
  • the middle part is the cell
  • the cell is surrounded by the suspension
  • the thickness of the cell is b.
  • Figure 9 is a schematic diagram of a target image containing a target cell; where I 0 is the incident light intensity, which is represented by the gray value of the blank area in the algorithm of this application; I t is the outgoing light intensity, which is represented in the algorithm of this application It is represented by the gray value of the cell area.
  • the microscopically enlarged digital image described in this application can be a grayscale image; each pixel in the image can represent a grayscale value ranging from 0 (black) to 255 (white). Between 0-255 represents different gray levels.
  • the microscopically enlarged digital image described in this application can also be a color image: the color image is composed of three grayscale images corresponding to different color channels, one is a grayscale image corresponding to the red channel, and the other is a grayscale image corresponding to the green channel. degree image, and the other is the blue channel corresponding to the grayscale image.
  • a hemoglobin analysis method based on microscopically magnified digital images, it is used to calculate the hemoglobin concentration in blood cells.
  • the microscopically amplified digital images are based on microscopically amplified numbers obtained by flattening a monolayer of blood cells in suspension. Image; that is, the blood cells in the microscopically magnified digital image are generally in a single layer.
  • the suspension can be regular saline or a specific dilution containing or without dye. In principle, the suspension can be used as long as the absorption spectrum of other substances does not overlap with the absorption spectrum of the target cells.
  • the microscopically magnified digital image can also be obtained under the illumination of a wide-spectrum illumination light source; the microscopically magnified digital image is an R/G/B three-channel microscopically magnified digital image that contains information on multiple color components. Whether it is irradiated by a specific light source with a clear central wavelength range or under broad spectrum irradiation conditions, as long as its central wavelength range includes 418nm or one of the absorption peaks of other blood cells.
  • the basic data required for hemoglobin measurement is changed from the light intensity obtained by a specific optical detector into the gray value information in the digital image, which greatly simplifies the hardware structure of the entire device; it is carried out with minimal hardware costs.
  • Analytical determination of hemoglobin content is based on bright field images, it is very intuitive and has better accuracy; there is no complicated spectrophotometer design, and no hemolytic agent is needed for the release and binding process of hemoglobin; 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.
  • the embodiment of the hemoglobin analysis method based on the microscopically magnified digital image includes step 6A: identifying multiple target cells in the microscopically magnified digital image; that is, the hemoglobin analysis is performed based on the target cells having been identified; identifying Output target cells include: red blood cells and reticulocytes.
  • the method of identifying multiple target cells in microscopically magnified digital images can be traditional image processing methods or AI algorithms. There are relatively mature identification and counting algorithms based on AI algorithms for cell type identification and counting in microscopic magnified digital images. Any algorithm in the existing technology can be used, and will not be described again here. To identify multiple target cells in microscopically magnified digital images, traditional image recognition methods or AI algorithms can be used.
  • Each target cell in the target image is an independent single cell.
  • the cells in the target pictures corresponding to each target cell are independent single cells. When the identified cell areas overlap, they will not be used in the subsequent calculation process.
  • the hemoglobin content corresponding to unit area bc, that is, the target cell hemoglobin content CH (target cell area STC/first hemoglobin absorption coefficient
  • the hemoglobin content bc corresponding to the unit area of the target cell is cleverly calculated first, and is used as an operation unit for subsequent calculation of the target cell hemoglobin content CH.
  • As an operation unit it is directly multiplied by the target cell area STC to obtain the unit.
  • the hemoglobin content corresponding to the volume avoids the measurement and calculation process of the length of a single cell on the absorption light path, and reduces the errors introduced thereby.
  • the step 6JA of obtaining the first hemoglobin absorption coefficient K HGB is also included; the step 6JA of obtaining the first hemoglobin absorption coefficient K HGB is also included; step 6JA includes: 6JA1: Take the same amount of blood cell samples to be analyzed, and use a hemoglobin tester to obtain the hemoglobin content HGB and red blood cell concentration RBC in each liter of blood cell sample; Step 6JA2: Take the same amount of cell samples to be analyzed as in step 6JA1, and pre- Process to prepare a cell suspension, which is injected into the imaging target area; a monolayer of blood cells is spread in the suspension, and a microscopic magnified digital image of a monolayer of blood cells spread in the suspension is obtained; step 6JA3: In the microscopic magnified digital image obtained in step 6JA2, select the target image corresponding to each target cell; the target image includes the target cell area and the blank area; step 6JA4: In the
  • the first hemoglobin absorption coefficient K HGB is a constant value corresponding to the target sample to be tested, or a constant value corresponding to the target sample to be tested and obtained from a table lookup in a data table.
  • step 7JA of obtaining the first hemoglobin content correction coefficient CHGB1;
  • step 7JA includes: step 7JA1: take the same amount of blood cell sample to be analyzed, and use The hemoglobin tester obtains the hemoglobin content HGB and red blood cell concentration RBC in each liter of blood cell sample;
  • Step 7JA2 Take the same amount of cell sample to be analyzed as in step 7JA1, perform preprocessing to prepare a cell suspension, and inject the cell suspension into the imaging target area within; spread the blood cell monolayer in the suspension, and obtain a microscopically magnified digital image of the blood cell monolayer tiled in the suspension;
  • Step 7JA4 In the target picture obtained in step 7JA3, the average grayscale value Gc of each target cell area and the average grayscale of the blank area
  • the first hemoglobin content correction coefficient CHGB1 is a constant value corresponding to the target sample to be tested, or a constant value corresponding to the target sample to be tested obtained from a table lookup in a data table.
  • Step 8D Obtain the known first hemoglobin concentration correction coefficient CHC1
  • the first hemoglobin concentration correction coefficient CHC1 is a known parameter, which is equivalent to Kb in Beer-Lambert's law being the product Kb of the molar absorption coefficient and the absorption layer thickness b. For a single cell, the thickness of each single cell is different; but in a statistical sense, the average thickness of the target cell is close to a constant.
  • Some embodiments of the hemoglobin analysis method based on microscopic amplification of digital images also include step 8DA of obtaining the first hemoglobin concentration correction coefficient CHC1;
  • step 8DA includes: step 8DA1: take the same amount of blood cell sample to be analyzed, and use The hemoglobin tester obtains the hemoglobin content HGB, red blood cell concentration RBC, and mean red blood cell volume MCV in each liter of blood cell sample;
  • Step 8DA2 Take the same amount of cell sample to be analyzed as in step 8DA1, and perform preprocessing to prepare a cell suspension.
  • Step 8DA3 The microscopic magnified digital image obtained in step 8DA2 In the image, select the target picture corresponding to each target cell; the target picture includes the target cell area and the blank area;
  • Step 8DA5 The value of each target cell obtained in step 8DA4 The first absorbance ⁇ 1, and find the mean value of the first absorbance ⁇ 1;
  • step 6G Use the AI algorithm to identify the target cells in the microscopically magnified digital image, and obtain the area STC of the single target cell in the microscopically magnified digital image; and obtain the known average cell height b;
  • ⁇ (CHGBs) means the sum of the hemoglobin concentrations CHGBs of all target cells.
  • Some embodiments of the hemoglobin analysis method based on microscopically amplified digital images include step 6M2: the step of outputting a histogram of the hemoglobin content CH of the target cells according to the hemoglobin content CH of each target cell; the histogram is used to count the hemoglobin content CH of different target cells. Hemoglobin distribution patterns.
  • the sample involved in Figure 13 is a normal healthy cat blood sample, and the hemoglobin content HGB is within the normal range.
  • the sample in Figure 14 is a normal healthy dog blood sample.
  • the sample in Figure 15 is another dog blood sample. In Figure 15, it can be seen that the distribution of single red blood cells with hyperchromic red blood cells shifts to the left, and the MCH value is lower than the reference value range (22pg-27pg), indicating the possibility of anemia.
  • Some embodiments of the hemoglobin analysis method based on microscopic amplification of digital images include step 6M3: obtaining the volume of each target cell, and outputting a CH-CV joint scatter plot according to the volume of each target cell and the content of each target hemoglobin CH. ; The CH-CV joint scatter plot is used to count the hemoglobin distribution patterns of target cells of different volumes; Step 6M4: Display at least one CH range indicator line and at least one CV range indicator line on the CH-CV joint scatter plot.
  • CH-CV joint scatter plot also called CV-CH joint scatter plot, provides statistical information reference for clinical anemia research. Especially combined with the CH range indicator line and the CV range indicator line, the normal range of CV-CH can be indicated with clear lines, which is very intuitive for clinicians.
  • Figure 16 shows the CV-CH scatter plot of a healthy cat blood sample; the figure also shows the CH reference range of normal cats, 38 to 54 fL, corresponding to two CH range indicator lines; the CV reference range, 11 to 18 fL, corresponding to Two CV range indicator lines; displaying the above reference range on the CH-CV joint scatter plot can very clearly show the tendency of the distribution pattern; it is very intuitive for clinical use and is easy for doctors to refer to.
  • Figure 17 shows a schematic diagram of the CV-CH scatter plot of a healthy dog blood sample; the figure also shows the CH reference range of normal dogs from 22 to 27 fL, and the CV reference range from 60 to 76 fL; the above reference ranges are displayed on the CH-CV
  • the joint scatter plot can clearly show the tendency of the distribution pattern; it is very intuitive for clinical use and is easy for doctors to refer to.
  • Figure 18 shows the CV-CH scatter plot of another dog blood sample; it is mainly concentrated in the lower left, showing that both CH and CV values are small, and the clinical manifestation is simple microcytic anemia or microcytic hypochromic anemia; Common diseases may include chronic infection, poisoning, inflammation, liver disease, uremia, malignant tumors, rheumatic diseases, etc., such as chronic inflammation, uremia; iron deficiency anemia, chronic hemolysis, globin production disorder anemia, sideroblasts Anemia etc.
  • Some embodiments of the hemoglobin analysis method based on microscopic magnified digital images include step 6M: According to the hemoglobin content CH of each target cell, add up the hemoglobin content CH of each target cell and average it to calculate and obtain the average hemoglobin content MCH of the target cell.
  • step 6N Obtain the known mean corpuscular volume MCV;
  • the blue channel and the gray value of any channel are used to calculate the corresponding parameters.
  • the blue channel can highlight the spectral characteristic information near the 418nm absorption peak.
  • the image signal-to-noise ratio of the blue channel is high, and only the blue channel is used for calculation, which improves calculation efficiency.
  • the information of any channel contains not only the spectral characteristic information near the 418nm absorption peak, but also the spectral characteristic information between 530-560nm, which can integrate the absorption of blood cells at each absorption peak for subsequent calculations.
  • the calculation amount can be reduced when using a single channel calculation; at the same time, the characteristics of white light or other broad spectrum light sources are also taken into account to ensure that the corresponding absorption characteristic information can be extracted.
  • the microscopically magnified digital images are based on microscopically magnified digital images obtained by flattening a monolayer of blood cells in suspension; including step 9A: identifying Microscopically magnify the target cell area and blank area in the digital image; the target cell area includes target cell area A corresponding to a single target cell and/or target cell area B where multiple cells overlap; or the target cell area only selects the target cell area corresponding to a single target cell.
  • the target cell area A corresponding to a single target cell refers to the situation where the target cell area A is displayed independently by a single target cell. There are as many target cell areas A as there are independent cells.
  • the multi-cell overlapping target cell area B refers to a whole target cell area B formed by two or more cells adhering together; there are as many target cell areas B as there are overlapping cells.
  • target cell area A As shown in Figure 7, most cells are scattered independently, and such independent single target cells correspond to target cell area A; in Figure 7, some cells overlap, such as multi-cell overlapping target cell area B; for For the calculation of hemoglobin content, whether target cell area A is used alone, target cell area B is used in combination, or target cell area B is used alone, the hemoglobin content CH of each target cell can be measured.
  • a hemoglobin analysis method based on microscopically magnified digital images is used; multiple target cells in the microscopically magnified digital images are identified; target pictures corresponding to each target cell are selected; the target pictures include target cell areas and blank areas; calculation
  • the first absorbance ⁇ 1 of the target cell lg (the average gray value of the blank area Gb/the average gray value of the target cell area Gc); obtain the area STC of each target cell in the microscopically enlarged digital image, and calculate the hemoglobin content of each target cell.
  • Target cell hemoglobin content CH first absorbance ⁇ 1 ⁇ target cell area STC ⁇ first hemoglobin content correction coefficient CHGB1.
  • Target cell hemoglobin concentration CHGBs first absorbance ⁇ 1 ⁇ first hemoglobin concentration correction coefficient CHC1.

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Abstract

基于显微放大数字图像的血红蛋白分析方法；基于目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；获取目标细胞血红蛋白含量CH=(STC/K HGB)×lg(Gb/Gc)。目标细胞血红蛋白含量CH＝第一吸光度α1×目标细胞面积STC×第一血红蛋白含量校正系数CHGB1。应用比尔－朗伯定律，能精准获取单个目标细胞的血红蛋白含量，使临床上观察血红蛋白含量的视角从全样本血红蛋白含量深入到单个细胞层面的血红蛋白含量，使针对单个血红蛋白含量的统计分析得以展开，为临床获取了更深一层的有价值信息。且整个测量系统极简，光路和液路免维护，操作和控制过程也极简，大大提升了血红蛋白检测综合效率。

Description

基于显微放大数字图像的血红蛋白分析方法及系统 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细胞中特定组分分析的技术领域，具体涉及利用细胞悬浮液显微放大数字图像进行血红蛋白含量分析的技术领域。
背景技术
比尔－朗伯定律是各类物质含量测定中常用的原理之一。如图1所示，其物理意义是当一束平行单色光垂直通过某一均匀非散射的吸光物质时，其吸光度A与吸光物质的浓度c及吸收层厚度b成正比，而与透光度T成反相关。比尔－朗伯定律的数学表达式：A＝lg(1/T)＝Kbc；其中A为吸光度，T为透射比，透射比等于出射光光强It除以入射光光强I0,K为摩尔吸收系数。它与吸收物质的性质及入射光的波长λ有关；c为吸光物质的浓度，b为吸收层厚度，b也常用l替换，含义一致。比尔－朗伯定律适用的条件包括：(1)入射光为平行单色光且垂直照射；(2)吸光物质为均匀非散射体系；(3)吸光质点之间无相互作用；(4)辐射与物质之间的作用仅限于光吸收。
现有技术中，血细胞分析仪进行每升血液细胞样品中的血红蛋白含量HGB检测时，其原理示意如图2所示；向血液加入溶血剂后，目标细胞释放出血红蛋白，血红蛋白与溶血剂结合形成血红蛋白衍生物，即Hb衍生物，Hb衍生物会均匀分散在样本中，使样本具有了均匀非散射特性，因此能利用Hb衍生物在特定波长(530～550nm)下的吸光特性，即使用比尔－朗伯定律进行吸光度的测量，根据被吸收光量的变化测定液体中的Hb衍生物含量，而Hb衍生物的含量和HGB含量对应，因此可以通过上述方法测量得到HGB含量。HGB含量通常的单位是g/L；即单位体积中的血红蛋白质量。
血液由血细胞(目标细胞、白细胞、血小板)和血浆组成。离体后未抗凝处理的血液会自然凝固(或离心时)，分离出上层淡黄色透明液体为血清，中间层白色固体为白细胞和血小板，最下层红色固体为目标细胞，血红蛋白正常是被包覆在细胞膜内。由于在全血样本中的血液细胞大都被细胞膜包覆，血液样本在常规的试管类容器中放置时会自然分层，并不具有均匀的非散射特性。因此现有技术中，血细胞分析仪进行每升血液细胞样品中的血红蛋白含量HGB检测时，最常用的方法是HiCN测定法。HiCN测定法即氰化高铁血红蛋白(hemoglobin cyanide，HiCN)分光光度法是世界卫生组织和国际血液学标准化委员会推荐的参考方法，该方法的测定结果是其他血红蛋白测定方法的溯源标准。氰化高铁血红蛋白分光光度法的测量原理是血红蛋白(除硫化血红蛋白外)中的亚铁离子(Fe 2+)被高铁氰化钾氧化成高铁离子(Fe 3+)，血红蛋白转化成高铁血红蛋白。高铁血红蛋白与氰根离子(CN)结合，生成稳定的氰化高铁血红蛋白(HiCN)。用分光光度计检测时，氰化高铁血红蛋白在波长540nm处有一个较宽的吸收峰，它在540nm处的吸光度同它在溶液中的浓度成正比。HiCN测定法需要先进行溶血，让血红蛋白与溶血剂结合形成血红蛋白衍生物，从而使样本具有了均匀非散射特性，才能使用比尔－朗伯定律。上述血红蛋白的测量过程中，由于需要使用溶血剂将血红蛋白从目标细胞中溶解出来， 溶血过程会破坏掉细胞的整体构造，因此在血液分析过程中，通常会考虑先进行血液细胞的计数，再进行溶血；这样对全血的分析过程就受限于此，必须按特定的顺序进行；且在中间过程中加入溶血剂，也使得整个操控过程变得更复杂，降低了整体效率。
由于现有技术中，血红蛋白含量HGB检测时，需要先进行溶血，因此也失去准确获取单个红细胞中的血红蛋白的机会，因此传统血细胞分析仪所输出的血红蛋白含量HGB只能是针对样本输出一个定量分析的结果，无法将该定量分析结果准确地深入到单个红细胞的层面，也无法将分析结果进入到更深层的细胞层面。
然而在实际临床应用和研究中，每个红细胞的大小，每个红细胞中所含有的血红蛋白含量，其分布规律和特征都代表着相应的生理或病理含义。
名词解释：
WBC是英文“white blood cell”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 blood cell”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
本发明的技术方案克服了现有技术的缺点，提出了一种基于血液细胞单层平铺在悬浮液中所获取的显微放大数字图像的血红蛋白分析方法，巧妙利用血液细胞单层平铺在悬浮液中所获取的显微放大数字图像，将比尔－朗伯定律和显微放大数字图像结合，在这样的场景中应用获取单个细胞的血红蛋白含量，以极简的系统设计地将血红蛋白分析带入了细胞层面的血红蛋白分析时代。在获取单个细胞血红蛋白含量的基础上，也能完成传统的血红蛋白含量检测目标，且系统极简，成本低廉。不仅系统的光路设计复杂度降低，光路免维护，操作和控制过程也极简，大大提升了血红蛋白检测的综合效率。
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红 蛋白分析方法，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显微放大数字图像；包括步骤6A：识别出显微放大数字图像中的多个目标细胞；步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤6I：获取已知的第一血红蛋白吸收系数K HGB；步骤6J：计算目标细胞的单位面积对应的血红蛋白含量bc＝第一吸光度α1/第一血红蛋白吸收系数K HGB；步骤6K：获取显微放大数字图像中的各目标细胞面积STC；步骤6L：计算获得各目标细胞血红蛋白含量CH＝目标细胞面积STC×目标细胞的单位面积对应的血红蛋白含量bc，即目标细胞血红蛋白含量CH＝(目标细胞面积STC/第一血红蛋白吸收系数K HGB)×lg(空白区平均灰度值Gb/目标细胞区平均灰
Figure PCTCN2022124646-appb-000001
基于显微放大数字图像的血红蛋白分析方法包括，在步骤6B中：目标图片中各个目标细胞是独立的单个细胞。
基于显微放大数字图像的血红蛋白分析方法包括，还包括获取第一血红蛋白吸收系数K HGB的步骤6JA；步骤6JA中包括：步骤6JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；步骤6JA2：取和步骤6JA1同一份量待分析的细胞样品，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6JA3：在步骤6JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤6JA4：在步骤6JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤6JA5：以步骤6JA4获取的各目标细胞的第一吸光度α1，并求获取第一吸光度α1均值；步骤6JA6：获取各目标细胞面积STC，并求所有目标细胞的平均面积SVTC；步骤6JA7：第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷单位面积对应的血红蛋白含量bc；第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷(每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷目标细胞的平均面积SVTC)＝第一吸光度α1均值×红细胞浓度RBC×目标细胞的平均面积SVTC÷每升血液细胞样品中的血红蛋白含量HGB。
第一血红蛋白吸收系数K HGB为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；包括步骤6A：识别出显微放大数字图像中的多个目标细胞；步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤7I：获取已知的第一血红蛋白含量校正系数CHGB1；步骤7K：获取显微放大数字图像中的各目标细胞面积STC；步骤7J：计算目标细胞血红蛋白含量CH＝第一吸光度α1×目 标细胞面积STC×第一血红蛋白含量校正系数CHGB1。
基于显微放大数字图像的血红蛋白分析方法包括，还包括获取第一血红蛋白含量校正系数CHGB1的步骤7JA；步骤7JA中包括：步骤7JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；步骤7JA2：取和步骤7JA1同一份量待分析的细胞样品，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7JA3：在步骤7JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤7JA5：以步骤7JA4获取的各目标细胞的第一吸光度α1，并求第一吸光度α1均值；步骤7JA6：获取的各目标细胞面积STC，并获取目标细胞的平均面积SVTC；步骤7JA7：第一血红蛋白含量校正系数CHGB1＝每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷第一吸光度α1均值÷目标细胞的平均面积SVTC。
第一血红蛋白含量校正系数CHGB1为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；包括步骤6A：识别出显微放大数字图像中的多个目标细胞；步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤8D：获取已知的第一血红蛋白浓度校正系数CHC1；步骤8E：计算单个目标细胞血红蛋白浓度CHGBs＝第一吸光度α1×第一血红蛋白浓度校正系数CHC1。
基于显微放大数字图像的血红蛋白分析方法包括，还包括获取第一血红蛋白浓度校正系数CHC1的步骤8DA；步骤8DA中包括：步骤8DA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB、红细胞浓度RBC和平均红细胞体积MCV；步骤8DA2：取和步骤8DA1同一份量待分析的细胞样品，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8DA3：在步骤8DA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤8DA4：在步骤8DA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤8DA5：以步骤8DA4获取的各目标细胞的第一吸光度α1，并求第一吸光度α1均值；步骤8DA6：第一血红蛋白浓度校正系数CHC1＝每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷平均红细胞体积MCV÷第一吸光度α1均值。
第一血红蛋白浓度校正系数CHC1为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6F：获取显微放大数字图像中的所有各目标细胞血红蛋白浓度CHGBs，计算平均血红蛋白浓度MCHC＝ Σ(CHGBs)÷所有目标细胞数量NTC。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6G：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单个目标细胞面积STC；并获取已知的细胞平均高度b；步骤6H：计算单个目标红细胞血红蛋白含量CH＝单个目标细胞面积STC×单个目标细胞血红蛋白浓度CHGBs×细胞平均高度b。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6M：根据各目标细胞血红蛋白含量CH，加总各目标细胞血红蛋白含量CH求平均，计算获取目标细胞平均血红蛋白含量MCH。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6M2：根据各目标细胞血红蛋白含量CH，输出目标细胞血红蛋白含量CH的直方图的步骤；直方图用于统计不同目标细胞的血红蛋白分布规律。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6M3：获取各目标细胞的体积，并根据各目标细胞的体积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。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6M4：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。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6N：获取已知的平均红细胞体积MCV；步骤6P：计算平均血红蛋白浓度MCHC＝目标细胞平均血红蛋白含量MCH÷平均红细胞体积MCV。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6Q：获取已知的红细胞浓度RBC；步骤6R：计算单位体积血液中的血红蛋白含量HGB＝目标细胞平均血红蛋白含量MCH×红细胞浓度RBC。
基于显微放大数字图像的血红蛋白分析方法，所述显微放大数字图像是在宽光谱的照明光源照射下获取的显微放大数字图像；显微放大数字图像是包含至少三种颜色分量信息的R/G/B三通道显微放大数字图像；R/G/B三通道分别是红色通道、绿色通道和蓝色通道。
基于显微放大数字图像的血红蛋白分析方法包括，所述显微放大数字图像是在特定光源照射下获取的显微放大数字图像；所述特定光源是中心波长包括418nm的紫色光源；所述特定光源的中心波长范围在380nm至440nm之间，或所述特定光源的中心波长范围在400nm至420nm之间；显微放大数字图像是包含至少三种颜色分量信息的R/G/B三通道显微放大数字图像。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6C中：以目标图片中目标细胞区蓝色通道的平均灰度值和目标图片中空白区蓝色通道的平均灰度值，计算该目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值/目标细胞区蓝色通道的平均灰度值)；步骤6JA4中，在步骤6JA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值和各目标图片中空白区蓝色通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值/目标细胞区蓝色通道的平均灰度值)；步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值Gc和各目标图片中空白区蓝色通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值Gb/目标细胞区蓝色通道的平均灰度值Gc)；步骤6DA4：在步骤6DA3获取的目标图片中，以各目标细胞 区蓝色通道的平均灰度值Gc和各目标图片中空白区蓝色通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值Gb/目标细胞区蓝色通道的平均灰度值Gc)。
基于显微放大数字图像的血红蛋白分析方法包括，步骤6C中：以目标图片中目标细胞区任一通道的平均灰度值和目标图片中空白区任一通道的平均灰度值，计算该目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；步骤6JA4中，在步骤6JA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值和各目标图片中空白区任一通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值Gc和各目标图片中空白区任一通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值Gb/目标细胞区任一通道的平均灰度值Gc)；步骤6DA4中，在步骤6DA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值和各目标图片中空白区任一通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；任一通道包括红色通道、绿色通道和蓝色通道。
本申请解决上述技术问题的技术方案还可以是一种基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；包括步骤9A：识别出显微放大数字图像中的目标细胞区和空白区；目标细胞区包括单目标细胞对应的目标细胞区A和/或多细胞重叠的目标细胞区B；步骤9C：以目标图片中目标细胞区平均灰度值Gc和空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤9I：获取已知的第一血红蛋白吸收系数K HGB；步骤9J：计算目标细胞区单位面积对应的血红蛋白含量bc＝第一吸光度α1/第一血红蛋白吸收系数K HGB；步骤9K：获取显微放大数字图像中的目标细胞区对应的目标细胞总面积ASTC和目标细胞个数NC；步骤9L：计算获得各目标细胞血红蛋白含量CH＝目标细胞总面积ASTC×目标细胞的单位面积对应的血红蛋白含量bc÷目标细胞个数NC，即目标细胞血红蛋白含量CH＝(目标细胞总面积ASTC÷目标细胞个数NC÷第一血红蛋白吸收系数K HGB)×lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)＝
Figure PCTCN2022124646-appb-000002
本申请解决上述技术问题的技术方案还可以是一种电子设备，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红蛋白分析方法。
本申请解决上述技术问题的技术方案还可以是一种可读存储介质，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红蛋白分析方法。
本申请解决上述技术问题的技术方案还可以是一种显微成像系统，用于获取血红蛋白分析用显微放大数字图像，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，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；成像目标区域中盛放有血液细胞单层平铺的悬浮液；显微放大数字图像是血液细胞单层平铺在悬浮液中的显微放大数字图像；所述相机组件包括黑白相机组件或彩色相机组件；所述照明光源组件是宽光谱照明光源；或所述照明光源组件是特定光源；特定光源是中心波长包括418nm的紫色光源；所述特定光源的中心波长范围在380nm至440nm之间，或所述特定光源的中心波长范围在400nm至420nm之间。所述显微成像组件还包括设置在光线进入相机组件之前的光路中的窄带滤光片；窄带滤光片，所能透过光线的中心波长范围在380nm至440nm之间或在400nm至420nm之间。
同现有技术相比较，本申请的有益技术效果之一是，基于血液细胞单层平铺在悬浮液中所获取的显微放大数字图像，将比尔－朗伯定律和显微放大数字图像结合应用，能获取单个目标细胞的血红蛋白含量，使临床上观察血红蛋白含量的视角从全样本血红蛋白含量能深入到单个细胞层面的血红蛋白含量。且在细胞悬浮液中的细胞形态完整，血红蛋白的测量是直接针对单个完整细胞的，准确性高，也无须溶血。
同现有技术相比较，本申请的有益技术效果之二是，能获取单个目标细胞的血红蛋白含量，使单个血红蛋白含量的统计学分析就有了非常精准的基础，也使得针对单个血红蛋白含量的统计分析能展开，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，单细胞血红蛋白含量的统计分析具有非常重要的临床价值。
同现有技术相比较，本申请的有益技术效果之三是，在细胞悬浮液中，细胞形态保持完整，细胞的体积测量也会更准确，单个细胞体积和单个细胞血红蛋白含量的联合，将单个血红蛋白含量和单个血红蛋白的体积联合进行统计学分析，为临床获取了更深一层的多维度的有价值信息。尤其是在多种贫血症的分型中，单细胞血红蛋白含量联合单个血红蛋白的体积的统计分析具有极其重要的临床价值。
同现有技术相比较，本申请的有益技术效果之四是，AI算法能识别出显微放大数字图像中的单个目标细胞，目标图片中各个目标细胞是独立的单个细胞；因此目标细胞面积STC，目标细胞的第一吸光度α1，目标细胞血红蛋白含量CH都可以做到针对单个细胞的层面的计算；因此计算的精细度更高；并且计算的精细程度随着AI算法中算例的增加和丰富，计算的准确性也会相应地提高。
同现有技术相比较，本申请的有益技术效果之五是，第一血红蛋白吸收系数K HGB、第一血红蛋白含量校正系数CHGB1以及第一血红蛋白浓度校正系数CHC1能通过查表的方式获取不同种类目标细胞的相应参数，简单快速，简化了计算过程，提高了整体的计算效率。
同现有技术相比较，本申请的有益技术效果之六是，能通过引入现有技术中相应精度或更高精度的血红蛋白测试仪进行第一血红蛋白吸收系数K HGB、第一血红蛋白含量校正系数CHGB1以及第一血红蛋白浓度校正系数CHC1的获取，确保了系数的一致性和准确性，更适合血红蛋白含量测定的场景。这些系数还能通过引入对标仪器的对比参照，跟随样品种类进行获取，进一步提高了系统的可兼容性和扩展性，能适用于多种样品的血红蛋白含量测试。
同现有技术相比较，本申请的有益技术效果之七是，特定光源的中心波长范围在380nm至440nm之间，或滤光片的中心波长设定，充分利用光源和滤光片的特性，自然地增强了418nm即血液细胞最强吸收峰处的原始信号量，提高了图像质量， 尤其是在最强吸收峰处的信噪比，能提高测量和计算的准确性。
同现有技术相比较，本申请的有益技术效果之八是，在白光或其他宽光谱光源下获取的显微放大数字图像，采用显微放大数字图像的蓝色通道或任一通道，是利用了图像数字的特性，相当于滤除了其他通道相应的光信号，变相地增强了418nm即血液细胞最强吸收峰处的相对信号量，提高了最强吸收峰处的信噪比，能更进一步提高测量和计算准确性。
同现有技术相比较，本申请的有益技术效果之九是，通过各目标细胞血红蛋白含量CH计算目标细胞平均血红蛋白含量MCH，通过各目标细胞血红蛋白浓度CHGBs计算平均血红蛋白浓度MCHC，都是直接的运算和测量过程，更接近于真实情况，避免了通过衍生物测量时，换算引入的过程误差。
附图说明
图1是比尔－朗伯定律的示意图；
图2是现有技术中血液中血红蛋白测量原理示意框图；
图3是血液吸收光谱的示意图；图中可见，血液在420nm，540nm，580nm等多个光谱区间内都呈现出明显的吸收峰；图中可见，418nm附近的吸收峰相对540nm-580nm的吸收峰更为明显和突出，意味着在418nm附近血液有更强烈的吸收特征；
图4是用于获取显微放大数字图像的显微成像系统中和光学相关部分的组成示意图；
图5是基于图4的显微成像系统的光路示意图；
图6是用于血红蛋白分析得一具体的显微放大数字图像；图像中可见，血液中的细胞都处于单层平铺的状态；
图7是显微放大数字图像经过AI算法识别出其中的多个目标细胞的示意图；图中被识别出来的目标细胞都做了框选示意；
图8是选取了被观察溶液中含有细胞的一个纵向剖视示意图；图中的b为细胞的纵向长度；
图9和图10是包含了图7中一个目标细胞的目标图片示意图；目标图片是图7中任意一个被选出来的图片；图9和图10中，其中圆形示意为细胞；图9中的网格是一个区域示意；图10中的外框是目标图片的边界，其中包括了中间的细胞和细胞外围的空白区域；
图11是采用现有技术中的HiCN测定法和本申请中的方法即ANLV测试法即安侣测试法进行对比测试的结果示意；表中对比测试了多组样本；
图12是图11中表格呈现数据的一个最小二乘线性回归分析示意图；图中是散点是ANLV测试法即安侣测试法的HGB数据，直线为HiCN测定法测试的HGB数据；图中可见ANLV测试法即安侣测试法的HGB和HiCN测定法有非常强相关性；从线性统计图表计算出R2＝0.9757，表明本申请中的ANLV测试法即安侣测试法和HiCN测定法的测试数据具有非常强的相关性。
图13是一猫血样本的单个目标细胞血红蛋白含量CH直方图；
图14是一犬血样本的单个目标细胞血红蛋白含量CH直方图；
图15是另一犬血样本的单个目标细胞血红蛋白含量CH直方图；图13至图15中，横坐标是目标细胞血红蛋白含量CH值单位为pg，纵坐标是目标细胞的数量单位为个；
图16是猫血样本的CV-CH联合散点图；
图17是犬血样本的CV-CH联合散点图；
图18是另一犬血样本的CV-CH联合散点图；
图16至图18中，纵坐标是目标细胞血红蛋白含量CH值单位为pg，横坐标是目标细胞的体积单位为fl。
具体实施方式
以下结合各附图对本发明内容做进一步详述。需要说明的是，本申请中方法步骤中的带序号的编号仅为了标识区分，并不必然表示时间或空间上的顺序关系。
随着人工智能即AI的进步，AI算法越来越被普遍地应用到数字图像处理中，在血液细胞分析领域，目前还没有见到基于血液样本数字图像并利用AI算法进行细胞参数分析的产品，尤其是利用显微放大数字图像进行血红蛋白浓度和含量的分析测定。血红蛋白浓度的分析测定通常需要利用血液的光学吸收特性；如图3所示，是血液吸收光谱的示意图；图中可见血液细胞在418nm附近和540nm-580nm附近均有吸收峰，418nm附近的吸收峰相对540nm-580nm的吸收峰更为明显和突出，意味着在418nm附近血液有更强烈的吸收特征。而现有技术中通常是利用Hb衍生物在特定波长(530～550nm)下的吸光特性；而很少利用血液本身的光谱吸收特性。血红蛋白(除SHb外)中的亚铁离子(Fe2+)被溶血剂中的高铁氰化钾氧化为高铁粒子(Fe3+)，血红蛋白转化为高铁血红蛋白。高铁血红蛋白与氰离子(CN-)结合，生成稳定的HiCN即Hb衍生物。HiCN最大吸收峰为540nm。这个结合决定了无法纯粹利用血红蛋白的吸收特性，尤其是418nm附近的吸收特性。由于经过了溶血的过程，且是通过血红蛋白衍生物含量来计算的血红蛋白原始含量，并不是直接进行血红蛋白含量的测定，过程中也会引入测量误差。
对血液样本来说，若能将血液样本中的细胞做到单层细胞平铺，也会让样本中各个细胞具有了均匀非散射特性，因此就能基于单层细胞平铺所获取的数字图像来进行组分含量分析。一方面免去了溶血过程，简化了操作过程；另一方面还能选取血细胞本身吸收特性最强的波段进行血红蛋白浓度分析。如图3所示，是血细胞的吸收光谱曲线，从该曲线可见，血细胞的最强吸收峰在418nm附近；若能在该波段附近进行吸收特征的提取将能获得较好的信噪比，测量准确性也更容易做到更好。
本申请中，基于显微放大数字图像进行细胞分析的基础是，对血液细胞进行稀释后，单层细胞在液基中保持原有细胞3D形貌进行拍照，获取明场显微放大数字图像；基于明场显微放大数字图像在完成了细胞类型识别的基础上，进行血红蛋白含量的分析测量。
如图4所示，是获取显微放大数字图像所采用的显微成像系统中，光学相关部分的组成示意图。图4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图像；相机组件620用于获取该显微放大图像的数字化图像信息。照明光源组件700能依主控制器给予的控制指令输出至少两种照明光束用于成像目标区域的照明，第一照明光束和第二照明光束；第一照明光束是第一中心波长在418nm的光束；第二照明光束是白光或第二中心波长的光束；白光是混合的宽光谱光；第二中心波长还可以是其他中心波长的光束，如540nm，580nm等。相机组件包括黑白相机组件或彩色相机组件；照明光源组件是宽光谱照明光源；或照明光源组件是特定光源；特定光源是中心波长包括418nm的紫色光源；所述特定光源的中心波长范围在380nm至440nm之间，或所述特定光源的中心波长范围在400nm至420nm之间。
在一些附图中未显示的实施例中，显微成像组件还包括设置在光线进入相机组件之前的光路中的窄带滤光片；窄带滤光片，所能透过光线的中心波长范围在 380nm至440nm之间或在400nm至420nm之间。
基于上述，显微放大数字图像是在宽光谱的照明光源照射下获取的显微放大数字图像；或显微放大数字图像是在特定光源照射下获取的显微放大数字图像；特定光源是中心波长包括418nm的紫色光源；特定光源的中心波长范围在380nm至440nm之间或400nm至420nm之间；显微放大数字图像是包含至少三种颜色分量信息的R/G/B三通道显微放大数字图像，R/G/B三通道分别是红色通道、绿色通道和蓝色通道。
采用中心波长范围在380nm至440nm之间的特定光源，是中心波长包括418nm的紫色光源，在入射光源上增强了血红蛋白吸收峰波段附近的光强，能更进一步地突出该吸收峰附近的光吸收量的变化，进一步提高了信噪比，使计算的结果更为准确。
图5是基于图4的显微成像系统的光路；光源的出射光强I0经过被观察溶液，被吸收掉的那部分光强是吸收光强Id，出射光强I1进入到相机组件中的CMOS成像单元，获取显微放大数字图像。被观察溶液是单层细胞平铺的悬浮液，是血液的稀释液。血液的稀释液也包括了具有染色功能的染色液。
图8是选取了被观察溶液中含有细胞的一个纵向剖视示意图，图中，中间部分为细胞，细胞周围是悬浮液，细胞的厚度为b。
图9是包含了一个目标细胞的目标图片示意图；其中I 0是入射光强，在本申请的算法中采用空白区的灰度值来表示；I t是出射光强，在本申请的算法中采用细胞区的灰度值来表示。
本申请中所述的显微放大数字图像，可以是灰度图像；图像中每个像素可以由0(黑)到255(白)的值表示灰度值。0-255之间表示不同的灰度级。本申请中所述的显微放大数字图像，也可以是彩色图像：彩色图像中由三幅不同颜色通道对应的灰度图组成，一个为红色通道对应的灰度图，一个为绿色通道对应灰度图像，另一个为蓝色通道对应灰度图像。
一种基于显微放大数字图像的血红蛋白分析方法的实施例中，用于血液细胞中血红蛋白浓度计算，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显微放大数字图像；即显微放大数字图像中的血液细胞大致都处于单层平铺的状态。悬浮液可以是常规的生理盐水，也可以是特定的含有染色剂或不含染色剂的稀释液。悬浮液原则上只要其中其他物质的吸收光谱不和目标细胞的吸收光谱重叠即可。
显微放大数字图像也可以在宽光谱的照明光源照射下获取的显微放大数字图像；显微放大数字图像是包含多种颜色分量信息的R/G/B三通道显微放大数字图像。无论是中心波长范围明确的特定光源照射下，还是在宽光谱照射条件下，只要其中心波长范围包括418nm或其他血细胞的吸收峰之一即可。
在本申请中，血红蛋白测定所需要的基础数据从特定光学探测器获得的光强，变成了数字图像中的灰度值信息，大大简化了整个设备的硬件结构；用极简的硬件成本进行血红蛋白含量的分析测定。且由于这样的方法基于明场图像，非常直观，准确性更好；既没有复杂的分光光度计的设计，也无需溶血剂进行血红蛋白的释放和结合过程；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。
由于是对血液细胞直接成像，没有溶血的过程，因此能发挥血液自身在418nm这一最高吸收峰波段的特性，能在数字图像处理中获得最大的灰度变化范围，在此基础上进行灰度值的计算，信噪比相对较好，计算的结果准确。
基于显微放大数字图像的血红蛋白分析方法的实施例中，包括步骤6A：识别出显微放大数字图像中的多个目标细胞；即血红蛋白分析是基于已经识别出目标细胞的基础上进行的；识别出的目标细胞包括：红细胞和网织红细胞。识别出显微放大数字图像中的多个目标细胞的方法可以是传统的图像处理方法，也可以是AI算法。基于AI算法对显微放大数字图像中的细胞类型识别和计数已有较为成熟的识别和计数算法，可以利用现有技术中任意一种算法，在此不再赘述。识别显微放大数字图像中的多个目标细胞，可以采用传统的图像识别方法，也可以采用AI算法。
基于显微放大数字图像的血红蛋白分析方法的实施例中，还包括步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；目标图片中目标细胞区平均灰度值Gc相当于透射光的光强；目标图片中空白区平均灰度值Gb相当于入射光的光强。在步骤6B中：目标图片中各个目标细胞是独立的单个细胞。各目标细胞相应的目标图片中的细胞是独立的单个细胞，当识别出的细胞区域有重叠时，不会被用于后续的计算过程。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，包括以下步骤，步骤6I：获取已知的第一血红蛋白吸收系数K HGB；步骤6J：计算目标细胞的单位面积对应的血红蛋白含量bc＝第一吸光度α1/第一血红蛋白吸收系数K HGB；步骤6K：获取显微放大数字图像中的各目标细胞面积STC；步骤6L：计算获得各目标细胞血红蛋白含量CH＝目标细胞面积STC×目标细胞的单位面积对应的血红蛋白含量bc，即目标细胞血红蛋白含量CH＝(目标细胞面积STC/第一血红蛋白吸收系数
Figure PCTCN2022124646-appb-000003
上述运算中，巧妙地先计算目标细胞的单位面积对应的血红蛋白含量bc，并将其作为一个运算单元进行后续的目标细胞血红蛋白含量CH计算，作为一个运算单元直接和目标细胞面积STC乘积，获得单位体积对应的血红蛋白含量；避免了单个细胞在吸收光路上的长度的测量和计算过程，减小了因此引入的误差。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，还包括获取第一血红蛋白吸收系数K HGB的步骤6JA；还包括获取第一血红蛋白吸收系数K HGB的步骤6JA；步骤6JA中包括：步骤6JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；步骤6JA2：取和步骤6JA1同一份量待分析的细胞样品，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6JA3：在步骤6JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤6JA4：在步骤6JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤6JA5：以步骤6JA4获取的各目标细胞的第一吸光度α1，并求获取第一吸光度α1均值；步骤6JA6：获取各目标细胞面积STC，并求所有目标细胞的平均面积SVTC；步骤6JA7：第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷单位面积对应的血红蛋白含量bc；第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷(每升血液细胞样品中的血红蛋 白含量HGB÷红细胞浓度RBC÷目标细胞的平均面积SVTC)＝第一吸光度α1均值×红细胞浓度RBC×目标细胞的平均面积SVTC÷每升血液细胞样品中的血红蛋白含量HGB。
第一血红蛋白吸收系数K HGB为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，包括，步骤7I：获取已知的第一血红蛋白含量校正系数CHGB1；步骤7K：获取显微放大数字图像中的各目标细胞面积STC；步骤7J：计算目标细胞血红蛋白含量CH＝第一吸光度α1×目标细胞面积STC×第一血红蛋白含量校正系数CHGB1。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，还包括获取第一血红蛋白含量校正系数CHGB1的步骤7JA；步骤7JA中包括：步骤7JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；步骤7JA2：取和步骤7JA1同一份量待分析的细胞样品，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7JA3：在步骤7JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤7JA5：以步骤7JA4获取的各目标细胞的第一吸光度α1，并求第一吸光度α1均值；步骤7JA6：获取的各目标细胞面积STC，并获取目标细胞的平均面积SVTC；步骤7JA7：第一血红蛋白含量校正系数CHGB1＝每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷第一吸光度α1均值÷目标细胞的平均面积SVTC。
第一血红蛋白含量校正系数CHGB1为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
基于显微放大数字图像的血红蛋白分析方法的另一些实施例中包括，步骤8D：获取已知的第一血红蛋白浓度校正系数CHC1；步骤8E：计算单个目标细胞血红蛋白浓度CHGBs＝第一吸光度α1×第一血红蛋白浓度校正系数CHC1。第一血红蛋白浓度校正系数CHC1是一个已知的参数，相当于比尔－朗伯定律中的K为摩尔吸收系数和吸收层厚度b的乘积Kb。就单一细胞来说，各单一细胞的厚度是不同；但就统计学意义上来说，目标细胞的平均厚度是接近于常数的。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，还包括获取第一血红蛋白浓度校正系数CHC1的步骤8DA；步骤8DA中包括：步骤8DA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB、红细胞浓度RBC和平均红细胞体积MCV；步骤8DA2：取和步骤8DA1同一份量待分析的细胞样品，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8DA3：在步骤8DA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；步骤8DA4：在步骤8DA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤8DA5：以步骤8DA4获取的各目标细胞的第一吸光度α1，并求第一吸光度α1均值；步骤8DA6：第一血红蛋白浓度校 正系数CHC1＝每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷平均红细胞体积MCV÷第一吸光度α1均值。第一血红蛋白浓度校正系数CHC1为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，包括，步骤6F：获取显微放大数字图像中的所有各目标细胞血红蛋白浓度CHGBs，计算平均血红蛋白浓度MCHC＝Σ(CHGBs)÷所有目标细胞数量NTC。步骤6G：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单个目标细胞面积STC；并获取已知的细胞平均高度b；步骤6H：计算单个目标红细胞血红蛋白含量CH＝单个目标细胞面积STC×单个目标细胞血红蛋白浓度CHGBs×细胞平均高度b。Σ(CHGBs)的意思是所有各目标细胞血红蛋白浓度CHGBs加总。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，包括，步骤6M2：根据各目标细胞血红蛋白含量CH，输出目标细胞血红蛋白含量CH的直方图的步骤；直方图用于统计不同目标细胞的血红蛋白分布规律。
图13涉及的样本为正常健康猫血样本，血红蛋白含量HGB处于正常范围。图14中样本为正常健康犬血样本。图15中样本为另一犬血样本，图15中可见高色素性红细胞单个红细胞分布靠左移，MCH数值低于参考值范围(22pg-27pg)，存在贫血的可能性。
虽然在附图中没有显示，但是在实际应用中，若可单个目标细胞血红蛋白含量CH分布中心发生左右偏移时，往往指示了一些异常；这些异常的偏移信息通常是临床病理的表现特征之一；有了准确的单个目标细胞血红蛋白含量CH直方图，就很容易看清楚目标细胞血红蛋白含量CH分布情况，目标细胞血红蛋白含量CH的中心含量位置也能反映出样品的病理特征。
由于有了单个目标细胞血红蛋白含量CH的信息，因此能基于此进行准确的统计分析，输出如图13至图15的单个目标细胞血红蛋白含量CH直方图，为临床上更进一步的血红蛋白分析和研究提供了更微观层面的数据及其统计信息参考。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，包括步骤6M3：获取各目标细胞的体积，并根据各目标细胞的体积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；步骤6M4：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。CH-CV联合散点图也称CV-CH联合散点图为临床的贫血研究提供了统计信息参考。尤其是结合CH范围指示线和CV范围指示线，可以将正常范围的CV-CH都能有清晰的线条指示，对临床医生来说非常的直观。
图16中展示了一健康猫血样本的CV-CH散点图示意；图中还展示了正常猫的CH参考范围38～54fL，对应两条CH范围指示线；CV参考范围11～18fL，对应两条CV范围指示线；将上述参考范围展示在CH-CV联合散点图上，能非常清晰地展示出分布规律的倾向性；对临床来说非常直观，便于医生参考。图17中展示了一健康犬血样本的CV-CH散点图示意；图中还展示了正常犬的CH参考范围22～27fL，CV参考范围60～76fL；将上述参考范围展示在CH-CV联合散点图上，能非常清晰地展示出分布规律的倾向性；对临床来说非常直观，便于医生参考。图18中展示了另一犬血样本的CV-CH散点图示意；主要集中左下方，体现为CH与CV值都偏小， 临床体现为单纯小细胞性贫血或小红细胞低色素性贫血；常见疾病可能有慢性感染、中毒、炎症、肝病、尿毒症、恶性肿瘤、风湿性疾病等，如慢性炎症、尿毒症；缺铁性贫血、慢性溶血、珠蛋白生产障碍性贫血、铁粒幼细胞贫血等。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，包括，步骤6M：根据各目标细胞血红蛋白含量CH，加总各目标细胞血红蛋白含量CH求平均，计算获取目标细胞平均血红蛋白含量MCH。步骤6N：获取已知的平均红细胞体积MCV；步骤6P：计算平均血红蛋白浓度MCHC＝目标细胞平均血红蛋白含量MCH÷平均红细胞体积MCV。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，步骤6C中：以目标图片中目标细胞区蓝色通道的平均灰度值和目标图片中空白区蓝色通道的平均灰度值，计算该目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值/目标细胞区蓝色通道的平均灰度值)；步骤6JA4中，在步骤6JA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值和各目标图片中空白区蓝色通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值/目标细胞区蓝色通道的平均灰度值)；步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值Gc和各目标图片中空白区蓝色通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值Gb/目标细胞区蓝色通道的平均灰度值Gc)；步骤8DA4：在步骤8DA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值Gc和各目标图片中空白区蓝色通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值Gb/目标细胞区蓝色通道的平均灰度值Gc)。由于血红蛋白的特征吸收峰在418nm，以及530-560nm之间，而对显微放大数字图像来说，蓝色通道中所呈现的特征吸收峰也会相对红色通道更为明显。因此采用蓝色通道和任一通道的灰度值进行相应参数的计算。蓝色通道中能突出418nm吸收峰附近的光谱特征信息。蓝色通道的图像信噪比较高，只采用蓝色通道进行计算，提高了计算效率。
基于显微放大数字图像的血红蛋白分析方法的一些实施例中，步骤6C中：以目标图片中目标细胞区任一通道的平均灰度值和目标图片中空白区任一通道的平均灰度值，计算该目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；步骤6JA4中，在步骤6JA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值和各目标图片中空白区任一通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值Gc和各目标图片中空白区任一通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值Gb/目标细胞区任一通道的平均灰度值Gc)；步骤8DA4中，在步骤8DA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值和各目标图片中空白区任一通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；任一通道包括红色通道、绿色通道和蓝色通道。任一通道的信息中既包含了418nm吸收峰附近的光谱特征信息，也包含了530-560nm之间的光谱特征信息，能综合血液细胞在各个吸收峰上吸收量用于后续的计算。采用单一通道计算时可降低运算量；同时也兼顾了白光或其他宽光谱光源的特性，确保能提取到相应的吸收特征信息。
一种基于显微放大数字图像的血红蛋白分析方法的一些实施例中，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显微放大数字图像；包括步 骤9A：识别出显微放大数字图像中的目标细胞区和空白区；目标细胞区包括单目标细胞对应的目标细胞区A和/或多细胞重叠的目标细胞区B；或目标细胞区只选取单目标细胞对应的目标细胞区A；步骤9C：以目标图片中目标细胞区平均灰度值Gc和空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤9I：获取已知的第一血红蛋白吸收系数K HGB；步骤9J：计算目标细胞区单位面积对应的血红蛋白含量bc＝第一吸光度α1/第一血红蛋白吸收系数K HGB；步骤9K：获取显微放大数字图像中的目标细胞区对应的目标细胞总面积ASTC和目标细胞个数NC；步骤9L：计算获得各目标细胞血红蛋白含量CH＝目标细胞总面积ASTC×目标细胞的单位面积对应的血红蛋白含量bc÷目标细胞个数NC，即目标细胞血红蛋白含量CH＝(目标细胞总面积ASTC÷目标细胞个数NC÷第一血红蛋白吸收系数K HGB)×lg(空白区平均灰度值Gb/目标细胞区平均灰度值
Figure PCTCN2022124646-appb-000004
单目标细胞对应的目标细胞区A，是指目标细胞区A都是单个目标细胞独立显示的情形，独立细胞有多少个就有多少个目标细胞区A。多细胞重叠的目标细胞区B，是指两个或多个细胞粘连在一起形成的一整块的目标细胞区B；有多少个细胞重叠区域就有多少个目标细胞区B。
如图7中大部分细胞都是独立分散的，这样的独立单目标细胞对应的目标细胞区A；图7中还有部分细胞是有重叠的，这样的多细胞重叠的目标细胞区B；对血红蛋白含量计算来说，无论是单独采用目标细胞区A还是联合采用目标细胞区B，还是单独采用目标细胞区B，都能测出各目标细胞血红蛋白含量CH。
本申请中，基于显微放大数字图像的血红蛋白分析方法；识别出显微放大数字图像中的多个目标细胞；选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；获取显微放大数字图像中的各目标细胞面积STC，计算获得各目标细胞血红蛋白含量
Figure PCTCN2022124646-appb-000005
目标细胞血红蛋白含量CH＝第一吸光度α1×目标细胞面积STC×第一血红蛋白含量校正系数CHGB1。目标细胞血红蛋白浓度CHGBs＝第一吸光度α1×第一血红蛋白浓度校正系数CHC1。将比尔－朗伯定律和显微放大数字图像结合应用，使整个测量系统极简，光路和液路免维护，操作和控制过程也极简，大大提升了血红蛋白检测的效率。
以上所述仅为本发明的实施例，并非因此限制本发明的申请范围，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，均同理包括在本发明的申请保护范围内。

Claims (25)

  1. 一种基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；
    包括步骤6A：识别出显微放大数字图像中的多个目标细胞；
    步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤6I：获取已知的第一血红蛋白吸收系数K HGB；
    步骤6J：计算目标细胞的单位面积对应的血红蛋白含量bc＝第一吸光度α1/第一血红蛋白吸收系数K HGB；
    步骤6K：获取显微放大数字图像中的各目标细胞面积STC；
    步骤6L：计算获得各目标细胞血红蛋白含量CH＝目标细胞面积STC×目标细胞的单位面积对应的血红蛋白含量bc，即
    Figure PCTCN2022124646-appb-100001
    Figure PCTCN2022124646-appb-100002
    Figure PCTCN2022124646-appb-100003
    还包括获取第一血红蛋白吸收系数K HGB的步骤6JA；
    步骤6JA中包括：
    步骤6JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；
    步骤6JA2：取和步骤6JA1同一份量待分析的细胞样品，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6JA3：在步骤6JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤6JA4：在步骤6JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤6JA5：以步骤6JA4获取的各目标细胞的第一吸光度α1，并求获取第一吸光度α1均值；
    步骤6JA6：获取各目标细胞面积STC，并求所有目标细胞的平均面积SVTC；
    步骤6JA7：第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷单位面积对应的血红蛋白含量bc；
    第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷(每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷目标细胞的平均面积SVTC)＝第一吸光度α1均值×红细胞浓度RBC×目标细胞的平均面积SVTC÷每升血液细胞样品中的血红蛋白含量HGB。
  2. 根据权利要求1所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    在步骤6B中：目标图片中各个目标细胞是独立的单个细胞。
  3. 根据权利要求1所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，第一血红蛋白吸收系数K HGB为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
  4. 一种基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；
    包括步骤6A：识别出显微放大数字图像中的多个目标细胞；
    步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤7I：获取已知的第一血红蛋白含量校正系数CHGB1；
    步骤7K：获取显微放大数字图像中的各目标细胞面积STC；
    步骤7J：计算目标细胞血红蛋白含量CH＝第一吸光度α1×目标细胞面积STC×第一血红蛋白含量校正系数CHGB1；
    还包括获取第一血红蛋白含量校正系数CHGB1的步骤7JA；
    步骤7JA中包括：
    步骤7JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；
    步骤7JA2：取和步骤7JA1同一份量待分析的细胞样品，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7JA3：在步骤7JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤7JA5：以步骤7JA4获取的各目标细胞的第一吸光度α1，并求第一吸光度α1均值；
    步骤7JA6：获取的各目标细胞面积STC，并获取目标细胞的平均面积SVTC；
    步骤7JA7：第一血红蛋白含量校正系数CHGB1＝每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷第一吸光度α1均值÷目标细胞的平均面积SVTC。
  5. 根据权利要求4所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，第一血红蛋白含量校正系数CHGB1为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
  6. 一种基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；
    包括步骤6A：识别出显微放大数字图像中的多个目标细胞；
    步骤6B：在显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤6C：以目标图片中目标细胞区平均灰度值Gc和目标图片中空白区平均灰度值 Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤8D：获取已知的第一血红蛋白浓度校正系数CHC1；
    步骤8E：计算单个目标细胞血红蛋白浓度CHGBs＝第一吸光度α1×第一血红蛋白浓度校正系数CHC1；
    还包括获取第一血红蛋白浓度校正系数CHC1的步骤8DA；
    步骤8DA中包括：
    步骤8DA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB、红细胞浓度RBC和平均红细胞体积MCV；步骤8DA2：取和步骤8DA1同一份量待分析的细胞样品，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8DA3：在步骤8DA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤8DA4：在步骤8DA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤8DA5：以步骤8DA4获取的各目标细胞的第一吸光度α1，并求第一吸光度α1均值；
    步骤8DA6：第一血红蛋白浓度校正系数CHC1＝每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷平均红细胞体积MCV÷第一吸光度α1均值。
  7. 根据权利要求6所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，第一血红蛋白浓度校正系数CHC1为与待检目标样本相应的恒定值，或从一数据表格中查表获取的与待检目标样本相应的恒定数值。
  8. 根据权利要求6所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6F：获取显微放大数字图像中的所有各目标细胞血红蛋白浓度CHGBs，计算平均血红蛋白浓度MCHC＝Σ(CHGBs)÷所有目标细胞数量NTC。
  9. 根据权利要求6所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6G：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单个目标细胞面积STC；并获取已知的细胞平均高度b；
    步骤6H：计算单个目标红细胞血红蛋白含量CH＝单个目标细胞面积STC×单个目标细胞血红蛋白浓度CHGBs×细胞平均高度b。
  10. 根据权利要求1或4或9任意一项所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6M：根据各目标细胞血红蛋白含量CH，加总各目标细胞血红蛋白含量CH求平均，计算获取目标细胞平均血红蛋白含量MCH。
  11. 根据权利要求1或4或9任意一项所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6M2：根据各目标细胞血红蛋白含量CH，输出目标细胞血红蛋白含量CH的直方图的步骤；直方图用于统计不同目标细胞的血红蛋白分布规律。
  12. 根据权利要求1或4或9任意一项所述的基于显微放大数字图像的血红蛋白分 析方法，其特征在于，包括，
    步骤6M3：获取各目标细胞的体积，并根据各目标细胞的体积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。
  13. 根据权利要求12所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6M4：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。
  14. 根据权利要求10所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6N：获取已知的平均红细胞体积MCV；
    步骤6P：计算平均血红蛋白浓度MCHC＝目标细胞平均血红蛋白含量MCH÷平均红细胞体积MCV。
  15. 根据权利要求10所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，包括，
    步骤6Q：获取已知的红细胞浓度RBC；
    步骤6R：计算单位体积血液中的血红蛋白含量HGB＝目标细胞平均血红蛋白含量MCH×红细胞浓度RBC。
  16. 根据权利要求1或4或6任意一项所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，
    所述显微放大数字图像是在宽光谱的照明光源照射下获取的显微放大数字图像；
    显微放大数字图像是包含至少三种颜色分量信息的R/G/B三通道显微放大数字图像；R/G/B三通道分别是红色通道、绿色通道和蓝色通道。
  17. 根据权利要求1或4或6任意一项所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，
    所述显微放大数字图像是在特定光源照射下获取的显微放大数字图像；
    所述特定光源是中心波长包括418nm的紫色光源；显微放大数字图像是包含至少三种颜色分量信息的R/G/B三通道显微放大数字图像。
  18. 根据权利要求17所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，
    步骤6C中：以目标图片中目标细胞区蓝色通道的平均灰度值和目标图片中空白区蓝色通道的平均灰度值，计算该目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值/目标细胞区蓝色通道的平均灰度值)；
    步骤6JA4中，在步骤6JA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值和各目标图片中空白区蓝色通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值/目标细胞区蓝色通道的平均灰度值)；
    步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值Gc和各目标图片中空白区蓝色通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值Gb/目标细胞区蓝色通道的平均灰度值Gc)；
    步骤8DA4：在步骤8DA3获取的目标图片中，以各目标细胞区蓝色通道的平均灰度值Gc和各目标图片中空白区蓝色通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区蓝色通道的平均灰度值Gb/目标细胞区蓝色通道的平均灰度值Gc)。
  19. 根据权利要求16所述的基于显微放大数字图像的血红蛋白分析方法，其特征在于，
    步骤6C中：以目标图片中目标细胞区任一通道的平均灰度值和目标图片中空白区任一通道的平均灰度值，计算该目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；
    步骤6JA4中，在步骤6JA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值和各目标图片中空白区任一通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；
    步骤7JA4：在步骤7JA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值Gc和各目标图片中空白区任一通道的平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值Gb/目标细胞区任一通道的平均灰度值Gc)；
    步骤8DA4中，在步骤8DA3获取的目标图片中，以各目标细胞区任一通道的平均灰度值和各目标图片中空白区任一通道的平均灰度值，计算各目标细胞的第一吸光度α1＝lg(空白区任一通道的平均灰度值/目标细胞区任一通道的平均灰度值)；
    任一通道包括红色通道、绿色通道和蓝色通道。
  20. 一种基于显微放大数字图像的血红蛋白分析方法，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显微放大数字图像；
    包括步骤9A：识别出显微放大数字图像中的目标细胞区和空白区；
    目标细胞区包括单目标细胞对应的目标细胞区A和/或多细胞重叠的目标细胞区B；步骤9C：以目标图片中目标细胞区平均灰度值Gc和空白区平均灰度值Gb，计算该目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；步骤9I：获取已知的第一血红蛋白吸收系数K HGB；
    步骤9J：计算目标细胞区单位面积对应的血红蛋白含量bc＝第一吸光度α1/第一血红蛋白吸收系数K HGB；
    步骤9K：获取显微放大数字图像中的目标细胞区对应的目标细胞总面积ASTC和目标细胞个数NC；
    步骤9L：计算获得各目标细胞血红蛋白含量CH＝目标细胞总面积ASTC×目标细胞的单位面积对应的血红蛋白含量bc÷目标细胞个数NC，即
    Figure PCTCN2022124646-appb-100004
    Figure PCTCN2022124646-appb-100005
    Figure PCTCN2022124646-appb-100006
    还包括获取第一血红蛋白吸收系数K HGB的步骤6JA；
    步骤6JA中包括：
    步骤6JA1：取同一份量的待分析的血液细胞样品，利用血红蛋白测试仪获取每升血液细胞样品中的血红蛋白含量HGB和红细胞浓度RBC；
    步骤6JA2：取和步骤6JA1同一份量待分析的细胞样品，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6JA3：在步骤6JA2获取的显微放大数字图像中，选出各目标细胞相应的目标图片；目标图片包括目标细胞区和空白区；
    步骤6JA4：在步骤6JA3获取的目标图片中，以各目标细胞区平均灰度值Gc和各 目标图片中空白区平均灰度值Gb，计算各目标细胞的第一吸光度α1＝lg(空白区平均灰度值Gb/目标细胞区平均灰度值Gc)；
    步骤6JA5：以步骤6JA4获取的各目标细胞的第一吸光度α1，并求获取第一吸光度α1均值；
    步骤6JA6：获取各目标细胞面积STC，并求所有目标细胞的平均面积SVTC；
    步骤6JA7：第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷单位面积对应的血红蛋白含量bc；
    第一血红蛋白吸收系数K HGB＝第一吸光度α1均值÷(每升血液细胞样品中的血红蛋白含量HGB÷红细胞浓度RBC÷目标细胞的平均面积SVTC)＝第一吸光度α1均值×红细胞浓度RBC×目标细胞的平均面积SVTC÷每升血液细胞样品中的血红蛋白含量HGB。
  21. 一种电子设备，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20中任一所述的基于显微放大数字图像的血红蛋白分析方法。
  22. 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20中任一所述的基于显微放大数字图像的血红蛋白分析方法。
  23. 一种显微成像系统，用于获取血红蛋白分析用显微放大数字图像，其特征在于，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，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；成像目标区域中盛放有血液细胞单层平铺的悬浮液；显微放大数字图像是血液细胞单层平铺在悬浮液中的显微放大数字图像；
    所述相机组件包括黑白相机组件或彩色相机组件；
    所述照明光源组件是宽光谱照明光源；
    或所述照明光源组件是特定光源；特定光源是中心波长包括418nm的紫色光源。
  24. 根据权利要求23所述的显微成像系统，其特征在于，
    所述显微成像组件还包括设置在光线进入相机组件之前的光路中的窄带滤光片；窄带滤光片，所能透过光线的中心波长范围在380nm至440nm之间或在400nm至420nm之间。
  25. 一种血红蛋白分析系统，用于血红蛋白分析，其特征在于，包括权利要求23至24中任意一项所述的显微成像系统。
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CN103471982A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN106226247A (zh) * 2016-07-15 2016-12-14 暨南大学 一种基于高光谱显微成像技术的细胞检测方法
CN112041659A (zh) * 2018-02-06 2020-12-04 瓦罗贝克两合公司 微流体装置、系统、基础设施、其用途以及使用其用于基因工程改造的方法
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CN111445448A (zh) * 2020-03-19 2020-07-24 中国医学科学院北京协和医院 一种基于图像处理的单细胞血红蛋白测定方法及装置
CN113936005A (zh) * 2020-06-29 2022-01-14 深圳辉煌耀强科技有限公司 一种dna指数计算方法、装置、计算机设备及存储介质
CN113484256A (zh) * 2021-06-09 2021-10-08 浙江万里学院 一种泥蚶血红蛋白浓度高通量测定方法
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CN114778418A (zh) * 2022-06-17 2022-07-22 深圳安侣医学科技有限公司 基于显微放大数字图像的血红蛋白分析方法及系统

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