Image-based agglomerated particle quantitative detection method and device

Abstract

An image-based agglomerated particle quantitative detection method comprises the following steps: adding detection particles into a sample to obtain a detection sample, adding the detection sample into a detection cavity, and combining and gathering the detection particles and a target object in the detection sample to form agglomerated particles; shooting the detection cavity to obtain a microscopic image of the agglomerated microparticles or the detection microparticles; carrying out image analysis on the microscopic image, selecting the area of the microscopic image as S1, and setting the volume of the detection sample corresponding to the image with the area as S1 as V; performing image analysis on the microscopic image to obtain the area or the number of the agglomerated microparticles corresponding to the area S1 of the microscopic image, and obtaining the target object content T1 in the volume V of the detection sample corresponding to the microscopic image according to the area or the number; and obtaining the content T2 of the target object in unit volume, wherein T2 = T1 / V.

Description

Technical Field

[0001] This application belongs to the technical field of object analysis based on images, and specifically relates to a method and device for quantitatively detecting agglomerated particles based on images. Background Art

[0002] In vitro diagnostic immunology is a method of diagnosing diseases by detecting specific immune markers in body fluids. These immune markers can be antibodies, antigens, immunoglobulins, etc. Detecting their presence or changes in levels can help doctors diagnose diseases, monitor treatment effects, or predict disease progression.

[0003] The affinity reaction is crucial for the normal function of the immune system because it enables the immune system to recognize and eliminate pathogens, foreign substances, and abnormal cells in the body. In addition, the affinity reaction is also widely used in laboratory techniques and clinical diagnoses. For example, techniques such as ELISA (enzyme-linked immunosorbent assay) and immunohistochemistry utilize the affinity reaction between antibodies and antigens to detect specific molecules or cells.

[0004] In the techniques for quantitatively and qualitatively analyzing trace immune markers, there are multiple different technical paths. The mature techniques include enzyme-linked immunosorbent assay, radioimmunoassay, immunoturbidimetry, immunofluorescence analysis, and chemiluminescence immunoassay.

[0005] Chinese Patent Application No. "CN201410197209.1", "A Method for Detecting Biomacromolecules or Microorganisms", proposes "A method for detecting biomacromolecules or microorganisms. The patent with the application number "CN201410197209.1" integrates immunomagnetic enrichment and visualization detection, is easy to operate, requires a short time for the entire detection process, does not require special instrument equipment, and the method of the present invention has low requirements for the purity of antibodies, which can greatly reduce the detection cost. However, this patent only reveals that "the degree of aggregation of the aggregates is positively correlated with the concentration of the biomacromolecules or microorganisms", and uses "determine the concentration of biomacromolecules or microorganisms in the sample to be detected according to the degree of aggregation of the aggregates, specifically: obtain a photo of the aggregates, and determine the concentration of biomacromolecules or microorganisms in the sample to be detected by quantifying the gray value of the photo", rather than accurately calculating the concentration. The method proposed in this patent application can at most give a qualitative conclusion and cannot give a quantitative measurement result, so it cannot be applied to medical detection.

[0006] Because, during the process of taking photos of the test samples, for the same type of sample, the gray values of the photos taken under different thickness conditions are different. Therefore, the patent application number "CN201410197209.1" cannot accurately measure the content of the target substance in the sample. This type of technology is not mature and cannot be practically applied in the industry. Especially in medical application scenarios that require accurate measurement, it cannot meet the 8% measurement accuracy required for veterinary tests, and when applied to human medical tests, it is even more unable to meet the measurement accuracy requirements.

[0007] Chinese patent application number "CN202180041737.6" has the application title "Aggregation Induction Assay for Improving Sensitivity". In this application, it is proposed that "a system, device, and method for rapidly and accurately measuring analyte particle-binding-induced reporter particle aggregation are provided. In the presence of analyte particles of interest, the reporter particles form aggregates, and the average particle size of which increases as the analyte concentration increases. Based on the analysis of the average particle size determined from the sample frames, the presence and / or concentration of the analyte can be determined", and a "calibration curve" is proposed. This "calibration curve" is "horizontal axis = SARS-CoV-2 antibody (mg / dL), vertical axis = aggregation size (pixels)", establishing the relationship between the average aggregation area and the target analyte. After being verified by a large number of experiments, when the change in the content of the target substance fluctuates by more than 20%, the measurement deviation is huge, and it has no application value for medical measurement. This patent application is essentially the same as the Chinese patent application number "CN201410197209.1" with the application title "A Method for Detecting Biomacromolecules or Microorganisms", and it does not give how to accurately calculate the content of the target substance.

[0008] Facts have proved that there is no direct correlation between the average aggregation particle size and the content of the target substance. It is affected by various factors such as the length of time for configuring the test sample, the magnitude of the vibration force, and the temperature during configuration. It is not scientific and has no application value to use the average aggregation particle size to measure the content of the target substance. Summary of the Invention

[0009] In this application, the inventor proposes an image-based quantitative detection method for aggregated microparticles. Specific antigens / antibodies corresponding to the target substance to be tested are coated with detection microparticles of appropriate size, enabling the target substance to aggregate with the detection microparticles to form aggregated microparticle bodies that can be observed by imaging. At the same time, through technical means, the volume of the test sample corresponding to the image is obtained, so as to accurately obtain the precise quantitative analysis result of the target per unit volume in the sample, enabling the adoption of the image-based quantitative detection method for aggregated microparticles in medical application scenarios.

[0010] An image-based quantitative detection method for agglomerated particles, including adding detection particles to a sample to obtain a detection sample, adding the detection sample into a detection cavity. In the detection sample, the detection particles bind and aggregate with the target object to form agglomerated particle bodies; photographing the detection cavity to obtain a microscopic image of the agglomerated particle bodies or the detection particles; performing image analysis on the microscopic image, selecting the area of the microscopic image as S1, and the volume of the detection sample corresponding to the image with area S1 is V; performing image analysis on the microscopic image to obtain the area or quantity of the agglomerated particle bodies corresponding to the microscopic image with area S1, and obtaining the content T1 of the target object in the detection sample with volume V corresponding to the microscopic image according to the area or quantity; obtaining the content T2 of the target object per unit volume, where T2 = T1 / V.

[0011] For the above-mentioned image-based quantitative detection method for agglomerated particles, when the unit of the above-mentioned target object content T1 is "pieces", the content of the target object per unit volume is the number of target detection objects per unit volume.

[0012] For the above-mentioned image-based quantitative detection method for agglomerated particles, when the unit of the above-mentioned target object content T1 is "mass", the content of the target object per unit volume is the mass of the target detection object per unit volume.

[0013] For the above-mentioned image-based quantitative detection method for agglomerated particles, in the above-mentioned detection sample, the content of the detection particles is in pieces per unit volume.

[0014] For the above-mentioned image-based quantitative detection method for agglomerated particles, the density difference between the above-mentioned detection particles and the detection sample is greater than 0.01 and less than 0.3.

[0015] For the above-mentioned image-based quantitative detection method for agglomerated particles, the pixel area of the image sensor corresponding to the above-mentioned microscopic image is S1, the number of pixels of the image sensor corresponding to the above-mentioned microscopic image is N1, and the area S2 of the image sensor corresponding to the above-mentioned microscopic image, where S2 = S1×N1.

[0016] For the above-mentioned image-based quantitative detection method for agglomerated particles, the microscopic magnification of the above-mentioned microscopic image is K, where K is the area magnification factor, and the area S3 of the above-mentioned microscopic image is S3 = S2 / K; the height of the above-mentioned detection cavity is H; the scale accuracy of the height H of the accommodation cavity is higher than the set value; the above-mentioned set value is less than 15%.

[0017] For the above-mentioned image-based quantitative detection method for agglomerated particles, the volume V of the detection sample corresponding to the above-mentioned microscopic image is V = S3×H.

[0018] For the above-mentioned image-based quantitative detection method for agglomerated particles, the upper surface or the lower surface of the detection cavity includes scale marking lines, and the above-mentioned microscopic image obtains the above-mentioned area S1 according to the above-mentioned scale marks; the height of the above-mentioned detection cavity is H; the volume V of the detection sample corresponding to the above-mentioned microscopic image is V = S1×H.

[0019] In the above-mentioned image-based quantitative detection method for agglomerated particles, the detection sample is obtained by diluting the original sample by NX times, and the content T3 of the target substance per unit volume in the original sample is T3 = T2 × NX.

[0020] In the above-mentioned image-based quantitative detection method for agglomerated particles, the volume V of the detection sample required for detecting the target substance is greater than P milliliters, and the single-shot area of the above-mentioned captured image is SP; the volume VS corresponding to a single image of the above-mentioned captured image is VS = SP * H, and the number of images required to be captured at different positions of the detection cavity is greater than NP, where NP = P milliliters / VS.

[0021] In the above-mentioned image-based quantitative detection method for agglomerated particles, the height of the above-mentioned detection cavity is H, which is obtained by measurement.

[0022] In the above-mentioned image-based quantitative detection method for agglomerated particles, the height H of the above-mentioned detection cavity is HT, where HT is greater than 0.03 mm and less than 2 mm; the machining accuracy HW of the height H of the detection cavity, and HW / HT is less than a set value. It can also be that HT is greater than 0.1 mm and less than 2 mm.

[0023] In the above-mentioned image-based quantitative detection method for agglomerated particles, the height H of the above-mentioned detection cavity is HT, where HT is greater than 0.03 mm and less than 2 mm; the machining accuracy HW of the height H of the detection cavity, and HW / HT is less than a set value; HW / HT is less than a set value, and the above-mentioned set value is 10%.

[0024] In the above-mentioned image-based quantitative detection method for agglomerated particles, there are no bubbles in the image with an area of S1.

[0025] In the above-mentioned image-based quantitative detection method for agglomerated particles, the volume VA of the detection sample corresponding to the image with an area of S1, where the volume of the bubbles is VP, and the detection sample volume V = VA - VP.

[0026] In the above-mentioned image-based quantitative detection method for agglomerated particles, the content T1 of the target substance corresponding to the microscopic image is calculated according to the fitting formula for agglomerated particle detection.

[0027] In the above-mentioned image-based quantitative detection method for agglomerated particles, the content T1 of the target substance = the number of agglomerated particles * the average coupling number of particles * the coupling degree.

[0028] In the above-mentioned image-based quantitative detection method for agglomerated particles, the coupling degree is obtained by querying the coupling curve. The Y-axis of the coupling curve corresponds to the coupling degree, and the X-axis corresponds to the proportion of agglomerated particles in the total number of detected particles.

[0029] In the above-mentioned image-based quantitative detection method for agglomerated particles, the target substance includes any one of proteins, carbohydrates, lipids, vitamins, and small molecule hormones; the surface of the above-mentioned detected particles is labeled with antibodies, and the above-mentioned binding and aggregation is an affinity reaction;

[0030] In the above image-based quantitative detection method for aggregated particles, the target substance includes antibodies; any one of proteins, carbohydrates, lipids, vitamins, and small molecule hormones is included on the surface of the detection particles, and the above binding and aggregation is an affinity reaction;

[0031] In the above image-based quantitative detection method for aggregated particles, specific antigens or antibodies are labeled on the surface of the detection particles, and the above binding and aggregation is a specific antigen-antibody binding;

[0032] In the above image-based quantitative detection method for aggregated particles, specific antibodies are labeled on the surface of the detection particles, and the above binding and aggregation is an immune reaction;

[0033] In the above image-based quantitative detection method for aggregated particles, the aggregated particles are formed by the binding of cell surface antigens in the sample to the antibodies on the surface of the particles;

[0034] In the above image-based quantitative detection method for aggregated particles, the aggregated particles are formed by the binding of cell surface antibodies in the sample to the antigens on the surface of the particles;

[0035] In the above image-based quantitative detection method for aggregated particles, antibodies / antigens modified with quenching groups are labeled on the surface of the detection particles. When the detection particles are attracted and aggregated by the corresponding antigens / antibodies, the fluorescent groups are quenched and no longer emit fluorescence. The more aggregated, the less fluorescence;

[0036] In the above image-based quantitative detection method for aggregated particles, antibodies / antigens modified with fluorescent groups are labeled on the surface of the detection particles. When the detection particles are attracted and aggregated by the corresponding antigens / antibodies, the fluorescent groups are excited to emit fluorescence. The more aggregated, the more fluorescence.

[0037] In the above image-based quantitative detection method for aggregated particles, the detection particles are natural particles or artificial polymer particles; the artificial polymer particles include polystyrene microspheres and iron oxide microspheres;

[0038] In the above image-based quantitative detection method for aggregated particles, the diameter of the detection particles is greater than 0.1 micrometer;

[0039] In the above image-based quantitative detection method for aggregated particles, the number of detection particles in the aggregated particles is greater than a set value, and the above set value is less than 5.

[0040] In the above image-based quantitative detection method for aggregated particles, the sample includes any one of body fluids or excreta;

[0041] In the above image-based quantitative detection method for aggregated particles, the sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, and tissue fluid;

[0042] The above-mentioned method for quantitatively detecting aggregated particles based on images, the sample includes any one of urine and feces; the feces is diluted feces.

[0043] The above-mentioned method for quantitatively detecting aggregated particles based on images, the sample is from humans, animals or plants.

[0044] The above-mentioned method for quantitatively detecting aggregated particles based on images, the sample includes any one of human, cat or dog blood or serum samples.

[0045] The above-mentioned method for quantitatively detecting aggregated particles based on images, the target substance includes any one of immunoproteins, inflammatory factors, viruses, and pathogenic microorganisms in body fluids or excreta.

[0046] The above-mentioned method for quantitatively detecting aggregated particles based on images, irradiate the above sample with a light source, and the light source is a white light source or a blue light source.

[0047] The above-mentioned method for quantitatively detecting aggregated particles based on images, irradiate the above sample with an excitation light source to excite the antigen / antibody modified with a fluorescent group to obtain fluorescence emission, and the microscopic image is a fluorescence image.

[0048] The above-mentioned method for quantitatively detecting aggregated particles based on images, the image analysis includes performing binarization processing on the above microscopic image to obtain a binarized image, and obtaining the area or quantity of the aggregated particle bodies from the binarized image.

[0049] An apparatus for quantitatively detecting aggregated particles based on images, obtaining the content of the target substance based on any one of the above-mentioned methods.

[0050] A computing and processing device is used for all or part of the above method, and the memory of the computing and processing device includes all or part of the data of the above method.

[0051] A data storage device stores all or part of the program code for any one of the above methods.

[0052] A data storage device stores all or part of the data for any one of the above methods.

[0053] The technical effects of the above technical solutions include: Tiny target substances that are invisible under ordinary microscopic magnification are aggregated through particle conjugates to form aggregated particle bodies. The area presented by the aggregated particle bodies in the image is visible under ordinary microscopic magnification. Through such combination, the recognition and measurement of smaller target substances can be achieved with the aid of an ordinary microscope. It expands the application depth of the ordinary microscope and can detect and analyze tinier invisible substances with minimized hardware costs.

[0054] The technical effects of the above technical solution include: natural particles or artificial polymer particles; the artificial polymer particles include polystyrene microspheres and iron oxide microspheres; as the main body for identification, their concentration and size are controllable and they are easy to obtain.

[0055] The technical effects of the above technical solution include: detecting particles adsorbed or conjugated with antibodies or antigens, enabling the measurement of immune-related parameters based on microscopic magnified images for identification.

[0056] The technical effects of the above technical solution include: a wide range of sample adaptability, and various body fluids and excreta can be used as long as they can be made into a suspension and developed and photographed in the chip.

[0057] The technical effects of the above technical solution include: the concentration of the detection particles is known, and based on the binding degree of the target analyte to the detection particles and the unit volume content of the aggregated particle bodies, the unit volume content of the target analyte is calculated, cleverly converting the measurement of the tiny target into the binding degree, thus enabling measurement.

[0058] The technical effects of the above technical solution include: the aggregated particle bodies are excited by light to emit fluorescence, expanding the application depth of fluorescence microscopy and enabling the detection and analysis of deeper tiny invisible substances with minimized hardware costs.

[0059] The technical effects of the above technical solution include: the detection chip can be used to photograph images of the aggregated particle bodies, and it is a chip dedicated to this scenario. The height H of the detection cavity in the detection chip is used to calculate the sample volume, enabling accurate measurement of volume-related parameters.

[0060] The technical effects of the above technical solution include: the density difference between the detection particles and the detection sample is within a reasonable range, the detection particles can fully contact the target in the detection sample, and at the same time, they can be effectively stratified for accurate measurement.

[0061] The technical effects of the above technical solution include: through the pixel area of the image sensor and the area magnification factor, the area S2 can be effectively obtained. Combining with the height H of the accommodation cavity, the volume of the detection sample corresponding to the microscopic image can be obtained, which is crucial for high-precision measurement. Without accurate volume, quantitative detection of antigen-antibody detection cannot be carried out.

[0062] The technical effects of the above technical solution include: through the chip manufacturing process, lines with a scale below 1 micron can be marked on the upper or lower surface of the detection cavity. Using the lines as scale marking lines, there is no need to care about the accuracy of focusing or the size of the image pixels, and the scale relationship in the image can be directly obtained.

[0063] The technical effects of the above technical solution include: for different detection objects, the basic volume of the samples to be detected is different. By calculating the volume corresponding to a single picture, the number of pictures required can be calculated based on the basic volume. By taking pictures at different positions, the volume of more detection samples can be obtained.

[0064] The technical effects of the above technical solution include: the height error can be controlled to meet the measurement accuracy requirements, and the production and processing difficulty can be reduced.

[0065] The technical effects of the above technical solution include: the steam drum is excluded and does not participate in the calculation. This treatment can greatly improve the measurement accuracy.

[0066] The technical effects of the above technical solution include: the empirical formula can be coupled to the measurement parameters from multiple angles, and the measurement results can be obtained from multiple angles.

[0067] The technical effects of the above technical solution include: the coupling degree can be measured according to different detection targets to form a coupling curve. Compared with the fitting formula, the measurement and calibration of the coupling degree are relatively simple and the calculation is more convenient.

[0068] The technical effects of the above technical solution include: in the affinity reaction, binding and aggregation phenomena can also occur. On the detection particles, modification with solid-phase antigen or avidin can also form a visual aggregation phenomenon, and the content of the target substance can be determined.

[0069] The technical effects of the above technical solution include: setting the minimum number in the aggregated microparticles can eliminate the interference of invalid aggregation.

[0070] The technical effects of the above technical solution include: when the diameter of the detection particles is greater than 0.1 micrometer, they can be effectively observed under an ordinary optical microscope, and at the same time, the aggregation effect meets the requirements.

[0071] The technical effects of the above technical solution include: the detection particles in the reagent adsorb or couple with antibodies or antigens to form particle conjugates. The particle conjugates aggregate to form aggregated microparticles, increasing the granularity of the aggregated microparticles in the microscopic image and making it easier to perform identification and quantitative calculation.

[0072] The technical effects of the above technical solution include: the diameter of the detection particles is greater than 0.1 micrometer and is still visible under an ordinary microscope, having recognizability.

[0073] The technical effects of the above technical solution include: the detection particles are non-transparent and can have corresponding features in the microscopic image for subsequent identification and quantitative calculation.

[0074] The technical effects of the above technical solution include: the detection particles are colored particles, and subsequent identification and quantitative calculation can be performed based on the color characteristics in the microscopic image.

[0075] The technical effects of the above technical solution include: the calculation method for detecting the content of the target substance based on the particulate aggregation image can obtain the content per unit volume or the number per unit volume of the target substance, and deepen the parameter calculation depth to a more microscopic level.

[0076] The technical effects of the above technical solution include: the acquisition of the test sample volume is calculated according to the pixel area or the number of pixel points of the image sensor, making the measurement of the volume a digital process based on the image, which is very simple and avoids various conventional volume measurement devices and algorithms.

[0077] The technical effects of the above technical solution include: there are various image processing methods for identifying and obtaining the number, area or volume of the aggregated particulate bodies, which are easy to obtain.

[0078] The technical effects of the above technical solution include: the number, area or volume of the non-aggregated particulate bodies can also be used for the evaluation of the content of the target substance. Both the non-aggregated and the aggregated ones can be used for the evaluation of the content of the target substance, and they can be mutually complementary and verified.

[0079] The technical effects of the above technical solution include: the target substance is not only a substance with antigen activity; it can also be a substance with a cell structure, and the applicable types of target substances are diverse. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 is a schematic diagram of the steps of a method for detecting a target substance based on particulate aggregation;

[0081] Figure 2 is a schematic diagram of the substance to be detected that is invisible in the sample;

[0082] Figure 3 is a schematic diagram of the structures of traditional monoclonal antibodies, heavy chain antibodies, and nanobodies;

[0083] Figure 4 is a schematic diagram of an antibody or antigen coated on microparticles such as latex or magnetic beads;

[0084] Figure 5 is a schematic diagram of the coated microparticles adsorbing and aggregating with each other;

[0085] Figure 6 is a micrograph showing that the detection microparticles are relatively uniform without the target substance;

[0086] Figure 7 is a micrograph showing that the detection microparticles are aggregated with the target substance;

[0087] Figure 8 is the aggregated microparticles identified by AI;

[0088] Figure 9It is a schematic diagram of a detection area corresponding to an image in a captured array image;

[0089] Figure 10 It is a top view schematic diagram of a detection chip;

[0090] Figure 11 It is a cross-sectional schematic diagram of an internal detection cavity and a sample injection channel of a detection chip;

[0091] Figure 12 It is a cross-sectional schematic diagram of an internal detection cavity, a sample injection channel, and an exhaust channel of a detection chip;

[0092] Figure 13 It is a calculation method for detecting the content of a target substance based on a particulate aggregation image;

[0093] Figure 14 It is a schematic diagram for obtaining the number, area, or volume of aggregated particulate bodies through AI image recognition;

[0094] Figure 15 It is a schematic diagram for obtaining the number, area, or volume of aggregated particulate bodies by binarizing a test image area;

[0095] Figure 16 It is a schematic diagram for obtaining an image by binarizing a test image area.

[0096] Figure 17 It is a schematic block diagram of a quantitative detection method for aggregated particulate bodies based on an image.

[0097] Figure 18 It is a schematic diagram of a captured image of a low-concentration target substance;

[0098] Figure 19 It is a schematic diagram of a captured image of a high-concentration target substance;

[0099] Figure 20 It is a schematic diagram of affinity aggregation;

[0100] Specific implementation

[0101] Method

[0102] The following further details the content of this application in conjunction with each attached drawing. It should be noted that the following is a description of the preferred embodiments of the present invention and does not constitute any limitation to the present invention. The description of the preferred embodiments of the present invention is only for the illustration of the general principles of the present invention. The numbers such as "first", "second", "A", and "B" involved in the present invention are only for the convenience of description and do not represent the chronological or spatial order relationship. The combinations of letters and numbers "TA", "TB", and "H" involved in the present invention are only for the convenience of description, and the specific meanings are determined by the specific words they represent.

[0103] Such asFigure 17 , an image-based quantitative detection method for agglomerated particles. Detection particles are added to a sample to obtain a detection sample, and the detection sample is added to a detection cavity. In the detection sample, the detection particles bind and aggregate with the target substance to form agglomerated particle bodies; the detection cavity is photographed to obtain a microscopic image of the agglomerated particle bodies or the detection particles; the microscopic image is subjected to image analysis, and the area of the microscopic image is selected as S1, and the volume of the detection sample corresponding to the image with the area S1 is V;

[0104] The microscopic image is subjected to image analysis to obtain the area or number of the agglomerated particle bodies corresponding to the area S1 of the microscopic image, and the content T1 of the target substance in the detection sample with the volume V corresponding to the microscopic image is obtained according to the area or number;

[0105] The content T2 of the target substance per unit volume is obtained as T2 = T1 / V.

[0106] It can be that the unit of the content T1 of the target substance is pieces, and the content of the target substance per unit volume is the number of the target detection substances per unit volume.

[0107] It can be that the unit of the content T1 of the target substance is mass, and the content of the target substance per unit volume is the mass of the target detection substances per unit volume.

[0108] It can be that in the detection sample, the content of the detection particles is per unit volume; the content of the detection particles is determined, which can facilitate quantitative calculation.

[0109] It can be that the density difference between the detection particles and the detection sample is greater than 0.01 and less than 0.3. The density difference between the detection particles and the detection sample cannot be too large or too small. If the density difference is too large, the detection particles will sink to the bottom in a short time and cannot fully contact the target substance in the detection sample, and the detection result cannot be accurately obtained; if the density difference is too small, the detection particles cannot effectively sink to the bottom or float, and an effective image cannot be photographed, and the detection result cannot be accurately obtained either;

[0110] The pixel area of the image sensor corresponding to the microscopic image is S1, the number of pixels of the image sensor corresponding to the microscopic image is N1, the area of the image sensor corresponding to the microscopic image is S2, and the area S2 = S1 × N1; the microscopic magnification of the microscopic image is K, K is the area magnification, and the area S3 of the microscopic image is S3 = S2 / K; the height of the detection cavity is H; the scale accuracy of the height H of the accommodation cavity is higher than the set value; the set value is less than 15%; the volume of the detection sample corresponding to the microscopic image is V, and V = S3 × H.

[0111] Through the pixel area of the image sensor and the area magnification, the area S2 can be effectively obtained. Combining with the height H of the accommodation cavity, the volume of the detection sample corresponding to the microscopic image can be obtained, which is crucial for high-precision measurement. Without an accurate volume, quantitative detection of antigen-antibody detection cannot be carried out.

[0112] It can be that scale marking lines are included on the upper surface or the lower surface of the detection cavity, and the area S1 is obtained from the microscopic image according to the scale markings; the height of the detection cavity is H; the volume of the detection sample corresponding to the microscopic image is V, and V = S1×H.

[0113] Through the chip manufacturing process, scribing with a scale below 1 micron can be marked on the upper surface or the lower surface of the detection cavity. Using the scribing as the scale marking line, one does not need to care about the accuracy of focusing, nor does one need to care about the size of the image pixels. The scale relationship in the image can be directly obtained, such as the size of the test area. However, the width and accuracy of the scribing made by the chip process are related to the manufacturing process. The more precise it is, the higher the price. This method can be used in scenarios that require high measurement accuracy.

[0114] It can be that the detection sample is obtained by diluting the original sample by NX times, and the content of the target substance per unit volume in the original sample T3 = T2×NX.

[0115] It can be that the volume V of the detection sample to be detected for the detection target is greater than P milliliters, the area of a single captured image is SP, the volume VS corresponding to a single captured image of the captured image is VS = SP*H, and the number of images that need to be captured at different positions of the detection cavity is greater than NP, and NP = P milliliters / VS.

[0116] For different detection objects, the basic volumes of the samples to be detected are different. By calculating the volume corresponding to a single image, the number of images required can be calculated according to the basic volume. By capturing images at different positions, the volume of more detection samples can be obtained.

[0117] The height of the detection cavity is H, which is obtained by measurement; to obtain the volume of the detection sample, the height of the detection cavity needs to be accurately known. With the current production level, it can be achieved within 1 micron at a reasonable processing cost. If the height of the detection cavity is 0.5 millimeters, the error in height can be controlled within 2%, meeting the measurement accuracy requirements for most scenarios. H can be marked or measured on-site.

[0118] It can be that the height H of the detection cavity is HT, where HT is greater than 0.03 millimeters and less than 2 millimeters; the processing accuracy HW of the height H of the detection cavity, and HW / HT is less than the set value; due to the limitation of capturing microscopic images, the height of the cavity cannot be too high. After determining the height of the cavity, the processing accuracy of H can be determined. If the cavity height is too low, such as 0.1 millimeter, according to the accuracy requirement of 1%, the processing accuracy needs to reach one micron. If the cavity height is 0.5 millimeters, according to the accuracy requirement of 1%, the processing accuracy requirement is 0.5 microns, which is difficult to achieve with the current common process. Therefore, it is better for the value of HT to be between 0.5 and 1 millimeter.

[0119] The height H of the detection cavity is HT, where HT is greater than 0.03 mm and less than 2 mm; the machining accuracy HW of the height H of the detection cavity satisfies HW / HT less than a set value; HW / HT is less than the set value, and the set value is 10%. In the general field of qualitative detection, the requirements for machining accuracy can be relaxed, such as a 10% requirement. When the cavity height is 0.5 mm, the machining accuracy can reach 0.05, that is, plus or minus 0.025, which can generally be achieved by precision injection molding processes.

[0120] In the image with a selected area of S1, there are no bubbles; if bubbles are found in the captured picture, the picture can be not selected, or the area of the bubble part can be excluded through an algorithm.

[0121] If the image with an area of S1 corresponds to a detection sample volume of VA, where the volume of the bubbles is VP, the detection sample volume V = VA - VP. For example, in the bubble part, a circular or square area is deducted, and the volume of the bubble part is subtracted. The substances inside the steam drum are also not involved in the calculation. This kind of processing can greatly improve the measurement accuracy.

[0122] The content T1 of the target object corresponding to the microscopic image is calculated according to the agglomerated particle detection fitting formula. Since there are multiple evaluation parameters for the degree of polymerization of agglomerated particles, such as the number of detected particles in the agglomeration, the total area, or the total volume of the agglomerated particles, different evaluation parameters can be directly coupled with the corresponding antigen-antibody quality or quantity to establish an empirical formula. According to the formula, in subsequent measurements, the antigen-antibody quality or quantity can be directly calculated according to the empirical formula.

[0123] It can also be that the content of the target object T1 = the number of agglomerated particles * the average coupling number of particles * the coupling degree. The degree of binding of the detected particles to antigens or antibodies is different. The amount of antigen or antibody bound by one detected particle is called the coupling degree. Through a large number of experiments, it is found that there are different degrees of agglomeration.

[0124] The coupling degree is obtained by querying the coupling curve. The Y-axis of the coupling curve corresponds to the coupling degree, and the X-axis corresponds to the proportion of agglomerated particles in the total number of detected particles.

[0125] The coupling degree can be measured according to different detection targets to make a coupling curve. Compared with the fitting formula, the measurement and calibration of the coupling degree are relatively simple and the calculation is more convenient.

[0126] It can be that the target object includes any one of proteins, carbohydrates, lipids, vitamins, and small molecule hormones; the surface of the detected particles is labeled with antibodies, and the binding and aggregation are affinity reactions.

[0127] Such as Figure 20 , for the affinity reaction, binding and aggregation phenomena can also occur. On the detected particles, modification with solid-phase antigens or avidin can also form visible aggregation phenomena, and the content of the target object can be determined.

[0128] It may be that the target includes an antibody; any one of proteins, saccharides, lipids, vitamins, and small molecule hormones is included on the surface of the detection particle, and the binding and aggregation are an affinity reaction.

[0129] It may be that the surface of the detection particle is labeled with a specific antigen or antibody, and the binding and aggregation are specific antigen-antibody binding.

[0130] It may be that the surface of the detection particle is labeled with a specific antibody, and the binding and aggregation are an immune reaction.

[0131] It may be that the aggregated microsomes are the binding of the cell surface antigen in the sample to the antibody on the particle surface.

[0132] It may be that the aggregated microsomes are the binding of the cell surface antibody in the sample to the antigen on the particle surface.

[0133] It may be that the surface of the detection particle is labeled with an antibody / antigen modified with a quenching group. When the detection particle is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group is quenched and no longer emits fluorescence. The more aggregated, the less fluorescence.

[0134] It may be that the surface of the detection particle is labeled with an antibody / antigen modified with a fluorescent group. When the detection particle is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group emits fluorescence. The more aggregated, the more fluorescence.

[0135] It may be that the detection particle is a natural particle or an artificial polymer particle; the artificial polymer particle includes a polystyrene microsphere and a magnetite microsphere;

[0136] It may be that the diameter of the detection particle is greater than 0.1 micrometer; the various aggregation forces are a molecular force. If the detection particle is too large, the aggregation force cannot pull the detection particle together for aggregation. If the detection particle is too small, it cannot be observed by an ordinary optical microscope. Therefore, the diameter of the detection particle needs to be greater than a certain value. Under the current process conditions, for a 0.1-micrometer particle, the microscope can effectively observe it, and at the same time, the production cost is relatively reasonable, widely used, and there is no cost pressure.

[0137] It may be that the number of detection particles in the aggregated microsomes is greater than a set value, and the set value is less than 5.

[0138] Such as Figure 18 , or Figure 19 , in the case of a high-concentration detection sample, such as Figure 19 , the aggregated particles form an aggregation much larger than a single particle, and it is very easy to distinguish the aggregated particles.

[0139] Figure 18In it, the upper part is the original picture taken. After image sharpening, the lower binary picture is obtained. In the lower picture, the white part is the detected particles, and the phenomenon of aggregation is very rare.

[0140] Figure 19 In it, the upper part is the original picture taken. After image sharpening, the lower binary picture is obtained. In the lower picture, the white part is the detected particles, and the phenomenon of aggregation is very obvious.

[0141] In low-concentration samples, such as when the content of the target substance is zero, there will still be aggregation of 2 or several detected particles. Therefore, it is necessary to accurately identify the true antigen-antibody or affinity aggregation caused by the presence of the target. Through preliminary experiments, the basic aggregation degree of the detected particles can be calculated under the condition of zero content, and the influence of this basic aggregation degree can be eliminated during the formal measurement.

[0142] It can be that the sample includes any one of body fluids or excreta;

[0143] It can be that the sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, and tissue fluid;

[0144] It can be that the sample includes any one of urine and feces; the feces is diluted feces;

[0145] It can be that the sample comes from humans, animals, or plants;

[0146] It can be that the sample includes any one of the blood or serum samples of humans, cats, or dogs;

[0147] It can be that the target substance includes any one of immune proteins, inflammatory factors, viruses, and pathogenic microorganisms in body fluids or excreta.

[0148] It can be that the sample is irradiated with a light source, and the light source is a white light source or a blue light source;

[0149] It can be that the sample is irradiated with an excitation light source to excite the antigen / antibody modified with a fluorescent group to obtain fluorescence emission, and the microscopic image is a fluorescence image;

[0150] It can be that the image analysis includes binarizing the microscopic image to obtain a binary image, and obtaining the area or number of aggregated microsomes from the binary image.

[0151] Such as Figure 1, Schematic diagram of the steps for detecting target substances based on particle aggregation, including adding detection particles to a sample; the target substances in the sample bind to the detection particles to form particle conjugates; the particle conjugates aggregate to form aggregated particle bodies; taking an image of the sample under a microscope; analyzing the image to obtain the number, area, or volume of the aggregated particle bodies, and obtaining the number or content of the target substances based on the binding degree between the target substances and the detection particles.

[0152] Such as Figure 2 , Target substances in the sample, such as proteins, small molecules, amino acids, sugars, enzymes, etc., substances that cannot be observed or are difficult to observe under an optical microscope, cannot be directly observed and detected with an ordinary optical microscope.

[0153] Such as Figure 3 , In an antigen-antibody detection system, specific antibodies react with the substance to be detected, but the antibodies and antigens cannot be directly observed and detected with an ordinary optical microscope. Traditional monoclonal antibodies, heavy-chain antibodies, and nanobodies cannot be directly observed under a traditional microscope.

[0154] Such as Figure 4 , Coating antibodies or antigens on particles such as latex or magnetic beads, with the diameter of the particles being greater than 0.1 μm, and the particles can be directly observed and detected with an ordinary optical microscope.

[0155] Such as Figure 5 , The coated particles adsorb or aggregate with each other to form large aggregated particle bodies. By observing the number, area, or volume of the aggregated particle bodies under a microscope, the number or content information of the target substances can be obtained.

[0156] Such as Figure 6 , For the actually captured detection particles, when there is no target substance in the sample, the detection particles show a relatively uniform distribution, and only a very small number of particles aggregate into about two small clusters. For large-sized detection particles (such as 1000 nm magnetic bead detection particles), when there is no target substance in the sample or the content is extremely low, the detection particles have good dispersibility.

[0157] Such as Figure 7 , For the actually captured detection particles, when the sample contains target substances, some particles aggregate into clusters. As the concentration of the corresponding target substance increases, the individually dispersed detection particles aggregate to form aggregated detection particles, and the aggregated detection particles are clearly distinguishable after aggregation.

[0158] Such as Figure 8 , When detecting, add detection particles with a fixed concentration, identify the number of aggregated detection particles, and quantify the antigen concentration. By identifying and counting the aggregated particles, and based on the binding degree between the particles and the target substances, the number or content of the target substances can be calculated. The binding degree between the particles and the target substances is calculated based on the particle size, the amount of coated antibodies, and the degree of aggregation.

[0159] The above detection method; the particles can be natural particles or artificial polymer particles; the artificial polymer particles include polystyrene microspheres and magnetite microspheres;. The polymer particles can be polystyrene microspheres, i.e., latex particles. The polystyrene microspheres, i.e., latex particles, adsorb proteins. Through adsorption, the invisible particles can expand into visible particles. By detecting the quantity and size of the visible particles, the amount of protein can be detected.

[0160] The surface of polystyrene can be subjected to various modifications, such as hydrophilic modification. The surface modification groups of the polymer particles mainly include functional groups such as polysaccharides, acrylamides, polyvinyl alcohols, and polyamines. After modification, the polystyrene can selectively adsorb different target substances. Therefore, the present invention can be applied not only to the detection of antibodies and antigens, but also to the detection of various target substances or non-target substances.

[0161] Detecting particles adsorb or conjugate antibodies or antigens. The detection of antibodies and antigens is a very important detection item. The modification methods or coating methods of magnetic bead microspheres in various existing technologies can all be applied to the modification and coating of the particles in this application. The modified or coated particles can selectively aggregate when encountering the target to be detected.

[0162] The sample includes body fluids or excreta; the body fluids include serum, plasma, whole blood, saliva, local body fluid effusions, and the excreta include urine and feces; the feces are diluted feces.

[0163] According to the corresponding sample area and sample height of the captured image, the sample volume is obtained, and the unit volume content of the aggregated particulate matter is calculated based on the quantity of the aggregated particulate matter and the sample volume.

[0164] Such as Figure 9 , during the microscopic imaging process, the field of view is relatively small, and the volume corresponding to each imaging is very small. By selecting the sample area and sample height corresponding to the area in the image, the volume calculated for each imaging can be obtained. By increasing the number of captured images, a larger sample volume can be obtained, improving the detection accuracy.

[0165] Based on the binding degree between the target analyte and the detection particles and the unit volume content of the aggregated particulate matter, the unit volume content of the target analyte is calculated.

[0166] Such as Figure 9 , by identifying the aggregated particulate matter in the sample volume, the content or quantity per unit volume corresponding to the sample volume can be calculated, and the detection result can be converted into a measurement index in the existing technology, such as virus content, the content per unit volume of a specific protein value, etc.

[0167] The aggregated microsomes are irradiated with excitation light, and the aggregated microsomes are excited by the excitation light to emit fluorescence, and the number or area or volume or fluorescence intensity of the aggregated microsomes emitting fluorescence is obtained.

[0168] Such as Figure 10 , a detection chip for detecting antibodies and antigens, includes a sampling channel 1010, a detection cavity 1020, and a sampling port 1011; the detection cavity is used to accommodate a sample; one end of the sampling channel is communicated with the detection cavity; the other end of the sampling channel is communicated with the sampling port, and the sampling port is communicated with the external atmosphere; the upper and lower parts of the detection cavity include transparent windows, and external illumination light can enter the detection cavity through the transparent windows; when placed horizontally, the sampling port is higher than the top of the detection cavity; the sample includes detection microparticles, and the detection microparticles are adsorbed or conjugated with antibodies or antigens; the target analyte in the sample binds to the detection microparticles to form microparticle conjugates; the microparticle conjugates aggregate to form aggregated microsomes; through the transparent windows, an image of the sample can be taken; the number or area or volume of the aggregated microsomes is obtained.

[0169] Such as Figure 11 , a cross-sectional schematic diagram of the detection cavity and the sampling channel of the detection chip, the sample is accommodated inside the detection cavity 1120, the sampling channel 1110 is communicated with the sampling port 1111, the sampling port 1111 is higher than the upper surface 1121 of the detection cavity, and the sample will not overflow from the sampling port 1111. At the same time, due to the internal pressure of the liquid, air will be automatically discharged.

[0170] The upper and lower surfaces of the detection cavity are transparent windows, and light can be introduced to obtain an image of the aggregated microsomes inside.

[0171] The above-mentioned detection chip; the detection microparticles are natural microparticles or artificial polymer microparticles; the artificial polymer microparticles include polystyrene microspheres and iron oxide microspheres; ; Such as Figure 12 , it may further include an exhaust channel 1210 and an exhaust port 1211; one end of the exhaust channel is communicated with the detection cavity; the other end of the exhaust channel is communicated with the exhaust port, and the exhaust port is communicated with the external atmosphere; when placed horizontally, the exhaust port is higher than the top of the detection cavity.

[0172] The above-mentioned detection chip; the polymer microparticles include polystyrene microspheres, i.e., latex microparticles.

[0173] According to the area of the sample corresponding to the captured image and the height of the detection cavity, the sample volume is obtained. The height H of the detection cavity is used to calculate the sample volume, and the sample volume = the area of the sample corresponding to the image × the height H of the detection cavity.

[0174] The unit volume content of the target analyte is calculated based on the binding degree between the target analyte and the microparticles and the unit volume content of the aggregated microparticles. The unit volume content of the aggregated microparticles is calculated based on the number of aggregated microparticles and the sample volume. The number of aggregated microparticles is obtained from the image; the unit volume content of the aggregated microparticles = the number of aggregated microparticles ÷ the sample volume; the unit volume content of the target analyte = the unit volume content of the aggregated microparticles × the binding degree between the target analyte and the microparticles.

[0175] Such as Figure 9 , during the process of taking pictures, multiple pictures are taken to increase the sample volume. Among the taken images, all or part are selected, such as Figure 9 , the area of the selected image corresponds to an area inside the chip that is 0.3 mm long, 0.2 mm wide, and 0.4 mm high. The height is determined by the height of the internal cavity of the chip.

[0176] Furthermore, the unit volume content of the target analyte is calculated based on the binding degree between the target analyte and the microparticles and the unit volume content of the aggregated microparticles.

[0177] Within a certain concentration range, the binding degree between the target analyte and the microparticles is relatively fixed. With the unit volume content of the aggregated microparticles, multiplying by the binding degree gives the unit volume content of the target analyte. The binding degree of the microparticles is obtained through a calibration experiment.

[0178] The sample can be serum, and the target analyte can be various viruses, bacteria, antibodies caused by various pathogens, and the microparticles are coated with the corresponding antigens.

[0179] A reagent for antibody-antigen detection, comprising detection microparticles, and the detection microparticles adsorb or conjugate with antibodies or antigens; during the detection process, the reagent is mixed with the sample; the target analyte in the sample binds to the detection microparticles to form microparticle conjugates; the microparticle conjugates aggregate to form aggregated microparticles; an image of the aggregated microparticles is obtained, and the amount of antibodies or antigens in the sample is obtained by analyzing the number of aggregated microparticles through the image.

[0180] The detection particles are added to various detection liquids. The concentration of the detection particles can be pre-prepared so that the detection particles are dispersed in the detection liquid. During the test, they are added according to the volume ratio, which facilitates the detection process. With the detection particles in the reagent, they can serve as the focus target of the optical microscopy system to assist in focusing. The detection particles can be natural particles or artificial polymer particles; the artificial polymer particles include polystyrene microspheres and iron oxide microspheres;. The polymer particles can be polystyrene microspheres, i.e., latex particles. The diameter of the detection particles can be greater than 0.1 micrometer. The diameter of the detection particles can be 0.3 - 3 micrometers. If the diameter is too small, such as less than 0.3 micrometers, it cannot be seen under a conventional microscope magnification of 40 times. If it is greater than 3 micrometers, the antigen-antibody binding force cannot cause them to aggregate, and it is difficult to agglomerate, so detection cannot be carried out.

[0181] Such as Figure 13 , a calculation method for detecting the content of the target substance based on the particle aggregation image, includes selecting one or more microscopic test pictures as the calculation selection pictures; selecting all or part of them as the test image area in the calculation selection pictures; obtaining the test sample volume according to the test image area and the sample height; obtaining the number or area or volume of the aggregated particulate bodies in the test image area; obtaining the number or content of the target substance according to the number or area or volume information of the aggregated particulate bodies; dividing the number or content of the target substance by the test sample volume to obtain the content per unit volume or the number per unit volume of the target substance.

[0182] The method for calculating the sample volume can be to obtain the test image area by multiplying the pixel area of the image sensor by the number of pixel points in the test image area; obtain the test sample area by dividing the test image area by the microscope magnification; multiply the test sample area by the test sample height to obtain the test sample volume.

[0183] Such as Figure 14 , obtaining the number or area or volume of the aggregated particulate bodies in the test image area can be to obtain the number or area or volume of the aggregated particulate bodies through AI image recognition.

[0184] Such as Figure 15 , obtaining the number or area or volume of the aggregated particulate bodies in the test image area can be to binarize the test image area, calculate the area of the binarized image, and obtain the area of the large-mass substances.

[0185] Such as Figure 16 , photos of the target substance at 3 concentrations taken by the detection device of "Shenzhen Anlv Medical Technology Co., Ltd." are used. Picture A corresponds to a very low content of the target analyte in the sample, Picture B contains a small amount of the target analyte, and Picture C contains a high concentration of the target analyte. The pictures are binarized to obtain pictures A1, B1, and C1, and it can be clearly seen that the area of the aggregated particles changes with the content of the target analyte.

[0186] The number, area or volume of unagglomerated microsomes in the test image region is identified through AI image recognition. The more the number, area or volume of unagglomerated microsomes identified, the less the content of the target substance.

[0187] The target substance may be a substance with antigenic activity, and the substance with antigenic activity includes any one of protein, nucleic acid and polysaccharide.

[0188] The target substance may also be a substance with cell structure, and the substance with cell structure includes any one of platelets, blood parasites and red blood cells.

[0189] Although the present invention is described and illustrated according to preferred embodiments and several alternative schemes, the invention will not be limited by the specific description in this specification. Other additional alternatives or equivalent components can also be used to practice the present invention.

Claims

1. An image-based quantitative detection method for agglomerated particles, characterized in that, including Detecting particles are added to a sample to obtain a detection sample, and the detection sample is added into a detection cavity. In the detection sample, the detecting particles bind and aggregate with the target to form aggregated particulate bodies; The detection cavity is photographed to obtain a microscopic image of the aggregated particulate bodies or the detecting particles; Image analysis is performed on the microscopic image. The area of the microscopic image is selected as S1, and the volume of the detection sample corresponding to the image with area S1 is V; Image analysis is performed on the microscopic image to obtain the area or quantity of the aggregated particulate bodies corresponding to the microscopic image with area S1. According to the area or quantity, the content T1 of the target in the detection sample with volume V corresponding to the microscopic image is obtained; The content T2 of the target per unit volume is obtained, where T2 = T1 / V.

2. The method for quantitatively detecting agglomerated particles based on images according to claim 1, wherein Any one or more of the following features are included; TA1: The unit of the target content T1 is "pieces", and the content of the target per unit volume is the number of target analytes per unit volume; TA2: The unit of the target content T1 is "mass", and the content of the target per unit volume is the mass of the target analytes per unit volume; TA3: In the detection sample, the content of the detecting particles is "pieces per unit volume"; TA4: The density difference between the detecting particles and the detection sample is greater than 0.01 and less than 0.

3.

3. The image-based quantitative detection method for aggregated particles according to claim 1, wherein The pixel area of the image sensor corresponding to the microscopic image is S1, the number of pixels of the image sensor corresponding to the microscopic image is N1, and the area S2 of the image sensor corresponding to the microscopic image, where S2 = S1×N1; The microscopic magnification of the microscopic image is K, where K is the area magnification factor. The area S3 of the microscopic image is S3 = S2 / K; the height of the detection cavity is H; the scale accuracy of the dimension accommodating the height H of the cavity is higher than a set value; the set value is less than 15%; The volume of the detection sample corresponding to the microscopic image is V, where V = S3×H.

4. The image-based quantitative detection method for aggregated particles according to claim 1, wherein The upper surface or the lower surface of the detection cavity includes scale marking lines, and the area S1 is obtained for the microscopic image according to the scale marking; the height of the detection cavity is H; the volume of the detection sample corresponding to the microscopic image is V, where V = S1×H.

5. The image-based quantitative detection method for aggregated particles according to claim 1, wherein Any one or more of the following features are included; TB10: The detection sample is obtained by diluting the original sample by NX times, and the content T3 of the target per unit volume in the original sample is T3 = T2×NX; TB20: The volume V of the detection sample required for detecting the target is greater than P milliliters. The area of a single photographed image is SP; the volume VS corresponding to a single photographed image is VS = SP*H, and the number of images to be photographed at different positions of the detection cavity is greater than NP, where NP = P milliliters / VS.

6. The image-based quantitative detection method for aggregated particles according to claim 1, wherein Any one or more of the following features are included; TC10: The height H of the detection cavity is obtained by measurement; TC20: The height H of the detection cavity is HT, where HT is greater than 0.03 mm and less than 2 mm; the machining accuracy HW of the height H of the detection cavity satisfies HW / HT less than a set value; (Step 3: Volume calculation expansion 8-1:) TC30: The height H of the detection cavity is HT, where HT is greater than 0.03 mm and less than 2 mm; the machining accuracy HW of the height H of the detection cavity satisfies HW / HT less than a set value; HW / HT is less than the set value, and the set value is 10%.

7. The image-based quantitative detection method for agglomerated particles according to claim 1, wherein it includes any one or more of the following features; TD10: In an image with an area of S1, there are no bubbles; TD20: The detection sample volume corresponding to the image with an area of S1 is VA, where the volume of the bubbles is VP, and the detection sample volume V = VA - VP.

8. The image-based quantitative detection method for agglomerated particles according to claim 1, wherein the content T1 of the target object corresponding to the microscopic image is calculated according to the agglomerated particle detection fitting formula.

9. The image-based quantitative detection method for agglomerated particles according to claim 1, wherein the content T1 of the target object = the number of agglomerated particles * the average coupling number of the particles * the coupling degree.

10. The image-based quantitative detection method for agglomerated particles according to claim 9, wherein the coupling degree is obtained by querying the coupling curve. The Y-axis of the coupling curve corresponds to the coupling degree, and the X-axis corresponds to the proportion of the agglomerated particles in the total number of detected particles.

11. The method for quantitatively detecting agglomerated particles based on images according to claim 1, wherein it includes any one or more of the following features; Feature TA11: The target object includes any one of proteins, carbohydrates, lipids, vitamins, and small molecule hormones; the surface of the detection particle is labeled with an antibody, and the binding and aggregation are affinity reactions; Feature TA12: The target object includes an antibody; the surface of the detection particle includes any one of proteins, carbohydrates, lipids, vitamins, and small molecule hormones, and the binding and aggregation are affinity reactions; Feature TA21: The surface of the detection particle is labeled with a specific antigen or antibody, and the binding and aggregation are specific antigen-antibody binding; Feature TA22: The surface of the detection particle is labeled with a specific antibody, and the binding and aggregation are immune reactions; Feature TA51: The agglomerated particle is the binding of the cell surface antigen in the sample to the antibody on the surface of the particle; Feature TA52: The agglomerated particle is the binding of the cell surface antibody in the sample to the antigen on the surface of the particle; Feature TA31: The surface of the detection particle is labeled with an antibody / antigen modified with a quenching group. When the detection particle is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group is quenched and no longer emits fluorescence. The more aggregated, the less fluorescence; Feature TA32: The surface of the detection particle is labeled with an antibody / antigen modified with a fluorescent group. When the detection particle is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group emits fluorescence. The more aggregated, the more fluorescence.

12. The method for quantitatively detecting agglomerated particles based on images according to claim 1, wherein it includes any one or more of the following features; Feature TB10: The detection particle is a natural particle or an artificial polymer particle; the artificial polymer particle includes polystyrene microspheres, iron oxide microspheres; Feature TB20: The diameter of the detected particles is greater than 0.1 micrometer; Feature TB30: The number of detected particles in the agglomerated particulate matter is greater than a set value, and the set value is less than 5.

13. The method for quantitatively detecting agglomerated particles based on an image according to claim 1, wherein including any one or more of the following features; Feature TC10: The sample includes any one of body fluids or excreta; Feature TC20: The sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, tissue fluid; Feature TC30: The sample includes any one of urine, feces; the feces is diluted feces; Feature TC40: The sample is from a human, an animal or a plant; Feature TC50: The sample includes any one of blood or serum samples of a human, a cat or a dog; Feature TC60: The target includes any one of immunoproteins, inflammatory factors, viruses, pathogenic microorganisms in body fluids or excreta.

14. The method for quantitatively detecting agglomerated particles based on an image according to claim 1, wherein including any one or more of the following features; Feature TD10: irradiating the sample with a light source, the light source being a white light source or a blue light source; Feature TD20: irradiating the sample with an excitation light source to excite the antigen / antibody modified with a fluorescent group to obtain fluorescence emission, and the microscopic image being a fluorescence image; Feature TD30: The image analysis includes performing binarization processing on the microscopic image to obtain a binarized image, and obtaining the area or number of agglomerated particulate matter from the binarized image.

15. An image-based quantitative detection device for agglomerated particles, characterized in that obtaining the content of the target based on the method according to any one of claims 1 to 12.

16. A computing and processing device, characterized in that including any one of the following technical features: TJ1: for running all or part of the method according to any one of claims 1 to 12 or claims 14 to 15; TJ2: The memory of the computing and processing device includes all or part of the data of the method according to any one of claims 1 to 12 or claims 14 to 15.

17. A data storage device, characterized in that including any one of the following technical features: TK1: storing all or part of the program code for executing the method according to any one of claims 1 to 12 or claims 14 to 15; TK2: storing all or part of the data of the method according to any one of claims 1 to 12 or claims 14 to 15.

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