Parasite egg detection method and system

Abstract

The invention discloses a parasite egg detection method and system, and relates to the technical field of egg detection. Comprising the following steps: collecting a certain volume of a test sample, putting the test sample into a centrifugal tube, performing centrifugal operation, and reserving a precipitation part at the bottom of the tube; re-suspending the centrifuged precipitate part in normal saline, injecting the mixed liquid into a microfluidic chip, arranging a protruding electrode in the microfluidic chip, and coating the surface of the electrode with a specific antibody to capture eggs; current at different moments is calculated, and meanwhile microscope images of the electrodes are collected; establishing a Yo lov5s model, and training the Yo lov5s model by taking the contour of the parasite egg as a label; inputting a microscope image of the electrode into the Yo lov5s model, identifying a parasitic ovum contour in the image, and performing feature extraction on a contour image part; and jointly judging whether the parasitic eggs exist in the sample based on the change of the current at different moments in combination with the characteristic parameters, and if so, calculating the concentration of the parasitic eggs based on the change of the current and the number of the parasitic eggs.

Description

Technical Field

[0001] The present invention relates to the technical field of worm egg detection, and in particular to a method and system for detecting parasite eggs. Background Art

[0002] Parasitic infections are a widespread public health problem worldwide. The spread of parasitic diseases is closely related to people's living conditions, hygiene habits, and environmental factors. Parasite eggs are a critical stage in their life cycle. They can survive in the environment for a long time and are transmitted in a variety of ways, including water, soil, and food, posing a serious threat to human health. Therefore, rapid and accurate detection of the presence of parasite eggs has become an important task in public health testing and disease control.

[0003] Traditional parasite egg detection methods mostly rely on manual identification and counting under a microscope, which is not only cumbersome to operate, but also largely affected by the subjective experience of the operator, resulting in limited accuracy and reliability of the test results. In addition, traditional methods usually take a long time to process samples and analyze results, which is not ideal in rapid response public health emergencies. Therefore, there is an urgent need for a novel, rapid, and automated parasite egg detection technology to improve detection efficiency and accuracy.

[0004] In recent years, the development of microfluidics and machine learning has provided new solutions for parasite egg detection. Microfluidic chips have the characteristics of high sensitivity and high throughput, which can effectively process and analyze samples. Deep learning models based on computer vision (such as Yolov5s) can efficiently extract features from microscope images for automatic identification. The combination of the two can greatly improve the efficiency and accuracy of parasite egg detection and solve the shortcomings of traditional methods.

[0005] In the prior art, publication number CN104700112B discloses a method for detecting parasite eggs in feces based on morphological features, which mainly completes the preprocessing of the image, grayscaling, binarization, filling and connected area area screening of the image; the detection part expands and corrodes the preprocessed binary image, calculates the connected area area, eccentricity, ellipse hardness and circularity characteristic parameters and detects parasite eggs. However, in complex fecal samples, parasite eggs may be mixed with other impurities and cells. Traditional image processing methods may be difficult to effectively separate and identify targets, and are highly dependent on the quality of the image and shooting conditions. Light changes, background impurities and the degree of mixing of samples will directly affect the effects of grayscaling and binarization, resulting in inaccurate feature extraction, thereby affecting the final detection results, which is easy to cause misjudgment or missed judgment. The accuracy and effectiveness of the detection are reduced.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The object of the present invention is to provide a method and system for detecting parasite eggs to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for detecting parasite eggs, comprising the following specific steps:

[0010] A certain volume of test sample is collected from the uniformly mixed sample to be tested, the collected test sample is placed in a centrifuge tube, physiological saline is added, and the tube is placed in a centrifuge for centrifugation. After the centrifugation is completed, the upper layer of liquid is removed and the sediment at the bottom of the tube is retained;

[0011] The precipitate after centrifugation is resuspended in physiological saline, mixed evenly, and the flow rate of the mixed liquid injection is set. Based on the flow rate, the mixed liquid is injected into a microfluidic chip, wherein a fluid channel is provided in the microfluidic chip, and a protruding electrode is provided in the flow channel. The electrode surface is coated with a specific antibody for adsorbing parasite eggs;

[0012] Obtain the time series of the potential changes of the electrodes in the microfluidic chip during the entire injection process, calculate the current at different times based on the time series data of the potential changes, and collect the microscope image of the electrodes in the microfluidic chip after the mixed liquid is completely injected into the microfluidic chip;

[0013] Collect microscope pictures of parasite eggs of different types, mark the outlines of the parasite eggs in the collected pictures, establish a Yolov5s model, use the pictures with the marked outlines of the parasite eggs as input to the model, and use the outlines of the parasite eggs as labels to train the Yolov5s model;

[0014] Inputting the microscope image of the electrode in the microfluidic chip into the trained Yolov5s model, identifying the contour of the parasite egg in the microscope image of the electrode in the microfluidic chip, performing feature extraction on the image portion identified as the parasite egg, and obtaining feature parameters, wherein the feature parameters include contour area, contour perimeter and circularity;

[0015] Based on the changes in current at different times combined with characteristic parameters, it is determined whether there are parasite eggs in the sample. If there are, the concentration of the parasite eggs is calculated based on the current changes and the number of parasite eggs.

[0016] Furthermore, the collected test sample was placed in a centrifuge tube, and physiological saline was added to the centrifuge tube at a ratio of 1:2 based on the volume of the collected test sample. The centrifuge tube containing the test sample was placed in a centrifuge, and the centrifuge speed was set to 3000 RMP. The centrifugation time was 10 minutes, wherein RMP represents revolutions per minute. After centrifugation, the precipitate at the bottom of the tube was obtained, and the same volume of physiological saline as the precipitate was taken, and the precipitate after centrifugation was resuspended in physiological saline.

[0017] Further, the flow rate of the mixed liquid injection is set, and the mixed liquid is injected into the microfluidic chip based on the flow rate, and the volume of the mixed liquid, the cross-sectional area of ​​the fluid channel of the microfluidic chip and the time required for injection are determined. The formula for setting the mixed liquid injection flow rate based on the determined injection parameters is:

[0018]

[0019] In the formula, v represents the injection flow rate, V is the volume of the mixed liquid, and t z represents the required time for the set injection, and A is the cross-sectional area of ​​the fluid channel of the microfluidic chip;

[0020] The mixed liquid is repeatedly injected into the microfluidic chip to allow the mixed liquid to fully react with the electrodes in the fluid channel of the microfluidic chip.

[0021] Furthermore, a protruding electrode is provided in the flow channel, and the electrode surface is coated with specific antibodies for adsorbing parasite eggs, wherein the specific antibodies are proteins produced by B cells, have high specificity, and can recognize and bind to specific antigen molecules. The specific antigen molecules include glycoproteins and lipids on the surface of parasite eggs. Coating the specific antibodies on the electrode surface increases its ability to capture parasite eggs. In the microfluidic chip, when the mixed liquid passes through the flow channel, the eggs will contact the antibodies on the electrode surface and be captured.

[0022] Furthermore, the time series E(t) of the electrode potential change in the microfluidic chip during the whole process of repeatedly injecting the mixed liquid into the microfluidic chip is collected, and the formula for calculating the current at different times based on the time series data of the potential change is:

[0023]

[0024] In the formula, I(t) represents the current on the electrode surface at time t, I0 is the current on the electrode surface at the initial time, R is the gas constant, and T is the air temperature;

[0025] Based on the calculated electrode surface current at different times, the maximum change of the electrode surface current during the entire test process is calculated, where the formula for calculating the maximum change of the electrode surface current is:

[0026] ΔI max =max|I(t)-I0|

[0027] In the formula, ΔI max Indicates the maximum change in current on the electrode surface;

[0028] After the experiment, the location of the electrodes in the microfluidic chip was placed under a microscope to collect images, and the images collected under the microscope were input into the trained Yolov5s model.

[0029] Furthermore, the step of training the Yolov5s model includes: collecting microscope images of different types of parasite eggs, marking the outlines of different parasite eggs in the microscope images of parasite eggs by manual labeling, inputting the marked microscope images into the Yolov5s model, and using the outlines of the parasite eggs as labels to train the Yolov5s model, and finally obtaining an image with the image under the microscope as input and the outlines of the parasite eggs in the image marked as output.

[0030] Furthermore, the collected images of the electrodes in the microfluidic chip under a microscope are input into the trained Yolov5s model, and the model identifies and marks the parasite eggs captured on the electrodes, extracts the marked contour images, performs feature extraction, and obtains feature parameters;

[0031] The formula for calculating the contour area using the pixel counting method is:

[0032] M i =E i *α

[0033] Where M i represents the area of ​​the i-th marker contour, E i represents the number of pixels in the i-th marker contour image, α represents the actual area of ​​each pixel, where i is the index of the marker contour, i = 1, 2, …, n, where n represents the total number of marker contours;

[0034] The formula for calculating the contour perimeter is:

[0035]

[0036] Where, L i represents the contour perimeter of the i-th marked contour, P i,j represents the jth boundary pixel in the i-th marker contour, P i,j+1 represents the j+1th boundary pixel in the i-th marker contour, that is, the neighboring pixel of the j-th boundary pixel, d(P i,j ,P i,j+1) represents the adjacent points P in the contour i,j and P i,j+1 The distance between them, j is the index of the boundary pixel, j = 1, 2, ..., m, m is the total number of boundary pixels in the contour;

[0037] The circularity of the contour is calculated based on the contour area and contour perimeter, and the specific formula is:

[0038]

[0039] In the formula, C i is the contour circularity;

[0040] The parasite egg determination coefficient is generated based on the characteristic parameters, and the specific formula is as follows:

[0041] xs i =ω1*C i +ω2*M i +ω3*L i

[0042] In the formula, xs i represents the judgment coefficient of the i-th marked contour, ω1, ω2 and ω3 are the weight coefficients of contour circularity, contour area and contour perimeter, respectively, where ω1>ω2≥ω3 and ω1, ω2 and ω3 are all greater than 0.

[0043] Furthermore, the logic for determining whether there are parasite eggs in the sample based on the change of current at different times combined with characteristic parameters is as follows:

[0044] The egg presence judgment coefficient is generated based on the maximum change in the electrode surface current and the number of eggs determined by the image. The specific formula is:

[0045] pd=ΔI max +G

[0046] In the formula, pd is the judgment coefficient of the presence of eggs, and G is the number of eggs determined by the image;

[0047] When pd>yu, the sample is judged to contain parasite eggs, where yu is the judgment threshold for the presence of parasite eggs;

[0048] The logic for determining the number of parasite eggs G is as follows: the initial value of the number of parasite eggs G is limited to 0, and the preset parasite egg judgment threshold is compared with the parasite egg judgment coefficient. If 0.4*yz≤xs is satisfied i When ≤0.6*yz, the outline of the mark is judged to be a parasite egg, and the number of eggs G determined by the image is increased by 1.

[0049] Further, when it is determined that the test sample contains parasite eggs, the concentration of the parasite eggs is calculated based on the current change and the number of parasite eggs, wherein the formula for calculating the concentration of the parasite eggs is:

[0050]

[0051] Where ND represents the concentration of parasite eggs, k is the sensitivity coefficient, ω4 and ω5 are the weight coefficients of the maximum change of the electrode surface current and the number of eggs determined by the image, respectively, and W α is the total volume of the sample to be tested, W β is the volume of the test sample, where ω5>ω4 and ω4 and ω5 are both greater than 0.

[0052] The present invention also provides a parasite egg detection system, wherein the parasite egg detection system is used to perform the above-mentioned parasite egg detection method, comprising:

[0053] The sample pretreatment module is used to collect a certain volume of test samples from the evenly mixed samples to be tested, put the collected test samples into a centrifuge tube, add physiological saline, put it into a centrifuge for centrifugation, and after the centrifugation, remove the upper layer of liquid and retain the sediment at the bottom of the tube;

[0054] The parasite egg capture module is used to resuspend the precipitate after centrifugation in physiological saline, mix it evenly, set the flow rate of the mixed liquid injection, and inject the mixed liquid into the microfluidic chip based on the flow rate. The microfluidic chip is provided with a fluid channel, and the flow channel is provided with a protruding electrode. The surface of the electrode is coated with a specific antibody for adsorbing parasite eggs;

[0055] The test information acquisition module is used to obtain the time series of the potential changes of the electrodes in the microfluidic chip during the entire injection process, calculate the current at different times based on the time series data of the potential changes, and collect the microscope image of the electrodes in the microfluidic chip after the mixed liquid is completely injected into the microfluidic chip;

[0056] The recognition model training module is used to collect microscope pictures of different types of parasite eggs, mark the outlines of the parasite eggs in the collected pictures, and establish Yolov5s. The model uses the pictures with the outlines of the parasite eggs marked as the input of the model, and uses the outlines of the parasite eggs as labels to train the Yolov5s model;

[0057] An image feature extraction module is used to input the microscope image of the electrode in the microfluidic chip into the trained Yolov5s model, identify the contour of the parasite egg in the microscope image of the electrode in the microfluidic chip, extract features of the image portion identified as the parasite egg, and obtain feature parameters, wherein the feature parameters include contour area, contour perimeter and circularity;

[0058] The worm egg concentration acquisition module is used to determine whether there are parasite eggs in the sample based on the changes in current at different times combined with characteristic parameters. If so, the concentration of the parasite eggs is calculated based on the current changes and the number of parasite eggs.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] By combining microfluidic technology with deep learning models, firstly, by utilizing the efficient centrifugation and precipitation separation capabilities of microfluidic chips, combined with electrodes coated with specific antibodies, parasite eggs can be quickly and effectively extracted from complex samples, greatly shortening the time required for detection. In addition, the design of the microfluidic chip can achieve high-throughput processing of samples, making the detection process more automated and improving work efficiency. Secondly, by training the Yolov5s model and using the outline of parasite eggs under the microscope as labels for deep learning, accurate egg identification and counting can be achieved. This automated identification not only reduces the errors of manual operation, but also maintains consistency and reliability in large-scale detection. Finally, combined with the analysis of current changes and characteristic parameters, the concentration of parasite eggs in the sample can be quantitatively evaluated, realizing the automation, rapidity and high precision of parasite egg detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0062] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0064] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0065] Example:

[0066] See also Figure 1 , the present invention provides a technical solution:

[0067] A method for detecting parasite eggs, comprising the following specific steps:

[0068] Step 1: Collect a certain volume of test sample from the evenly mixed sample to be tested, put the collected test sample into a centrifuge tube, add physiological saline, put it into a centrifuge for centrifugation, remove the upper liquid after centrifugation, and retain the sediment at the bottom of the tube;

[0069] Put the collected test sample into a centrifuge tube, add physiological saline to the centrifuge tube at a ratio of 1:2 based on the volume of the collected test sample, put the centrifuge tube with the test sample into a centrifuge, set the centrifuge speed to 3000RMP, and the centrifugation time to 10 minutes, where RMP represents revolutions per minute. After centrifugation, obtain the precipitate at the bottom of the tube, take the same volume of physiological saline as the precipitate, and resuspend the precipitate after centrifugation in physiological saline.

[0070] During the centrifugation process, solid particles in the sample (such as parasite eggs) will settle to the bottom of the tube due to the centrifugal force. This is because the centrifugal force will cause the denser solid particles to overcome the buoyancy of the liquid and form a precipitate. After centrifugation, all heavier particles (including parasite eggs) will be concentrated at the bottom of the tube, while the upper liquid is mainly composed of lighter liquid components. Therefore, after removing the upper liquid, the precipitate at the bottom of the tube will retain the eggs.

[0071] Step 2: resuspending the precipitate after centrifugation in physiological saline, mixing it evenly, setting the flow rate of the mixed liquid injection, and injecting the mixed liquid into a microfluidic chip based on the flow rate. The microfluidic chip is provided with a fluid channel, and a protruding electrode is provided in the flow channel. The electrode surface is coated with specific antibodies for adsorbing parasite eggs;

[0072] The flow rate of the mixed liquid injection is set, and the mixed liquid is injected into the microfluidic chip based on the flow rate. The volume of the mixed liquid, the cross-sectional area of ​​the fluid channel of the microfluidic chip and the time required for injection are determined. The formula for setting the mixed liquid injection flow rate based on the determined injection parameters is:

[0073]

[0074] In the formula, v represents the injection flow rate, V is the volume of the mixed liquid, and t z represents the required injection time, A is the cross-sectional area of ​​the fluid channel of the microfluidic chip, wherein the required injection time is set and modified according to the actual situation;

[0075] The mixed liquid is repeatedly injected into the microfluidic chip to allow the mixed liquid to fully react with the electrodes in the fluid channel of the microfluidic chip.

[0076] Several protruding electrodes are provided in the flow channel, and the electrode surfaces are coated with specific antibodies for adsorbing parasite eggs. The specific antibodies are proteins produced by B cells, which are highly specific and can recognize and bind to specific antigen molecules. The specific antigen molecules include glycoproteins and lipids on the surface of parasite eggs. Coating specific antibodies on the electrode surface increases its ability to capture parasite eggs. In the microfluidic chip, when the mixed liquid passes through the flow channel, the eggs will contact the antibodies on the electrode surface and be captured.

[0077] For different types of parasite eggs, commonly used specific antibodies are usually antibodies against the surface features of these eggs, such as specific antigens against liver fluke eggs (such as egg shell proteins). Antibodies extracted from infected animals or synthesized monoclonal antibodies can be used. Specific antigens on the surface of whipworm eggs are usually the shell components of the eggs, and antibodies against the surface features of roundworm eggs, such as antibodies against specific glycoproteins of the egg shell. Therefore, different types of parasite eggs can be captured by coating different specific antibodies on the protruding electrodes.

[0078] Step 3: Obtain the time series of the potential change of the electrodes in the microfluidic chip during the entire injection process, calculate the current at different times based on the time series data of the potential change, and collect a microscope image of the electrodes in the microfluidic chip after the mixed liquid is completely injected into the microfluidic chip;

[0079] During the whole process of repeatedly injecting the mixed liquid into the microfluidic chip, the time series E(t) of the electrode potential change in the microfluidic chip is collected. The formula for calculating the current at different times based on the time series data of the potential change is:

[0080]

[0081] Where I(t) represents the current on the electrode surface at time t, I0 is the current on the electrode surface at the initial time, R is the gas constant, usually 8.314 J / (mol·K), and T is the air temperature;

[0082] Based on the calculated electrode surface current at different times, the maximum change of the electrode surface current during the entire test process is calculated, where the formula for calculating the maximum change of the electrode surface current is:

[0083] ΔI max =max|I(t)-I0|

[0084] In the formula, ΔI max Indicates the maximum change in current on the electrode surface;

[0085] After the experiment, the location of the electrodes in the microfluidic chip was placed under a microscope to collect images, and the images collected under the microscope were input into the trained Yolov5s model.

[0086] The presence of eggs may adsorb on the electrode, reducing the availability of reactants and thus reducing the current. For example, the molecular structure on the surface of the eggs may compete with the electrode surface for adsorption, hindering the reactants from participating in the reaction.

[0087] Step 4: Collect microscope pictures of parasite eggs of different types, mark the outlines of the parasite eggs in the collected pictures, establish a Yolov5s model, use the pictures with the outlines of the parasite eggs marked as input to the model, and use the outlines of the parasite eggs as labels to train the Yolov5s model;

[0088] The steps of training the Yolov5s model include: collecting microscope images of different types of parasite eggs, marking the outlines of different parasite eggs in the microscope images of parasite eggs by manual labeling, inputting the marked microscope images into the Yolov5s model, and using the outlines of the parasite eggs as labels to train the Yolov5s model, and finally obtaining an image with the image under the microscope as input and the outlines of the parasite eggs in the image marked as output.

[0089] Step 5: inputting the microscope image of the electrode in the microfluidic chip into the trained Yolov5s model, identifying the contour of the parasite egg in the microscope image of the electrode in the microfluidic chip, performing feature extraction on the image portion identified as the parasite egg, and obtaining feature parameters, wherein the feature parameters include contour area, contour perimeter and circularity;

[0090] The collected images of the electrodes in the microfluidic chip under a microscope are input into the trained Yolov5s model. The model identifies and marks the parasite eggs captured on the electrodes, extracts the marked contour images, performs feature extraction, and obtains feature parameters.

[0091] The formula for calculating the contour area using the pixel counting method is:

[0092] M i =E i *α

[0093] Where M i represents the area of ​​the i-th marker contour, E i represents the number of pixels in the i-th marker contour image, α represents the actual area of ​​each pixel, where i is the index of the marker contour, i = 1, 2, …, n, where n represents the total number of marker contours;

[0094] The formula for calculating the contour perimeter is:

[0095]

[0096] Where, L i represents the contour perimeter of the i-th marked contour, P i,j represents the jth boundary pixel in the i-th marker contour, P i,j+1 represents the j+1th boundary pixel in the i-th marker contour, that is, the neighboring pixel of the j-th boundary pixel, d(P i,j ,P i,j+1 ) represents the adjacent points P in the contour i,j and P i,j+1 The distance between them, j is the index of the boundary pixel, j = 1, 2, ..., m, m is the total number of boundary pixels in the contour;

[0097] The circularity of the contour is calculated based on the contour area and contour perimeter, and the specific formula is:

[0098]

[0099] In the formula, C i is the contour circularity;

[0100] The parasite egg determination coefficient is generated based on the characteristic parameters, and the specific formula is as follows:

[0101] xs i =ω1*C i +ω2*M i +ω3*L i

[0102] In the formula, xs i represents the judgment coefficient of the i-th marked contour, ω1, ω2 and ω3 are the weight coefficients of contour circularity, contour area and contour perimeter, respectively. Since the circularity of the parasite egg contour has the greatest influence on the judgment of whether it is a parasite egg, the contour area and contour perimeter have less influence on the judgment than the circularity, it is set that ω1>ω2≥ω3 and ω1, ω2 and ω3 are all greater than 0.

[0103] Step 6: Based on the change of current at different times and the characteristic parameters, determine whether there are parasite eggs in the sample. If there are, calculate the concentration of the parasite eggs based on the change of current and the number of parasite eggs.

[0104] The logic for judging whether there are parasite eggs in the sample based on the changes in current at different times combined with characteristic parameters is as follows:

[0105] The egg presence judgment coefficient is generated based on the maximum change in the electrode surface current and the number of eggs determined by the image. The specific formula is:

[0106] pd=ΔI max +G

[0107] In the formula, pd is the judgment coefficient of the presence of eggs, and G is the number of eggs determined by the image;

[0108] When pd>yu, the sample is judged to contain parasite eggs, where yu is the judgment threshold for the presence of parasite eggs;

[0109] The logic for determining the number of eggs G is as follows: the initial value of the number of eggs G is limited to 0, and the preset parasite egg judgment threshold yz is compared with the parasite egg judgment coefficient. If 0.4*yz≤xs is satisfied i When ≤0.6*yz, the outline of the mark is judged to be a parasite egg, and the number of eggs G determined by the image is increased by 1;

[0110] The worm egg existence judgment threshold yu and the preset parasite egg judgment threshold yz can be set according to expert experience and the actual type of worm eggs to be detected;

[0111] The combination of current change and image recognition to determine whether a sample contains parasite eggs can avoid most missed judgments and misjudgments, that is, when the electrode is disturbed by impurities such as non-parasite eggs, resulting in changes in potential and current, but the image recognition does not detect the eggs, which causes errors. At the same time, the microscope image of the electrode in the microfluidic chip is collected, not the image of the mixed liquid, which eliminates the amount of calculation for image processing and recognition, reduces the interference of impurities in the image, reduces computing resources, and increases the effectiveness of recognition;

[0112] When it is determined that the test sample contains parasite eggs, the concentration of the parasite eggs is calculated based on the current change and the number of parasite eggs, wherein the formula for calculating the concentration of the parasite eggs is:

[0113]

[0114] Where ND represents the concentration of parasite eggs, k is the sensitivity coefficient, ω4 and ω5 are the weight coefficients of the maximum change of the electrode surface current and the number of eggs determined by the image, respectively, and W α is the total volume of the sample to be tested, W β is the volume of the test sample. Since the change of current may be affected by the environment, the weight coefficient of the number of eggs determined by the image is set to be greater than the maximum change of the current on the electrode surface, that is, ω5>ω4 and ω4 and ω5 are both greater than 0.

[0115] See also Figure 2 The present invention also provides a parasite egg detection system, wherein the parasite egg detection system is used to perform the above-mentioned parasite egg detection method, comprising:

[0116] The sample pretreatment module is used to collect a certain volume of test samples from the evenly mixed samples to be tested, put the collected test samples into a centrifuge tube, add physiological saline, put it into a centrifuge for centrifugation, and after the centrifugation, remove the upper layer of liquid and retain the sediment at the bottom of the tube;

[0117] The parasite egg capture module is used to resuspend the precipitate after centrifugation in physiological saline, mix it evenly, set the flow rate of the mixed liquid injection, and inject the mixed liquid into the microfluidic chip based on the flow rate. The microfluidic chip is provided with a fluid channel, and the flow channel is provided with a protruding electrode. The surface of the electrode is coated with a specific antibody for adsorbing parasite eggs;

[0118] The test information acquisition module is used to obtain the time series of the potential changes of the electrodes in the microfluidic chip during the entire injection process, calculate the current at different times based on the time series data of the potential changes, and collect the microscope image of the electrodes in the microfluidic chip after the mixed liquid is completely injected into the microfluidic chip;

[0119] The recognition model training module is used to collect microscope pictures of different types of parasite eggs, mark the outlines of the parasite eggs in the collected pictures, establish a Yolov5s model, use the pictures with the marked outlines of the parasite eggs as input to the model, and use the outlines of the parasite eggs as labels to train the Yolov5s model;

[0120] An image feature extraction module is used to input the microscope image of the electrode in the microfluidic chip into the trained Yolov5s model, identify the contour of the parasite egg in the microscope image of the electrode in the microfluidic chip, extract features of the image portion identified as the parasite egg, and obtain feature parameters, wherein the feature parameters include contour area, contour perimeter and circularity;

[0121] The worm egg concentration acquisition module is used to determine whether there are parasite eggs in the sample based on the changes in current at different times combined with characteristic parameters. If so, the concentration of the parasite eggs is calculated based on the current changes and the number of parasite eggs.

[0122] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0123] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for detecting parasite eggs, characterized in that: The specific steps include: A certain volume of test sample is collected from the uniformly mixed sample to be tested, the collected test sample is placed in a centrifuge tube, physiological saline is added, and the tube is placed in a centrifuge for centrifugation. After the centrifugation is completed, the upper layer of liquid is removed and the sediment at the bottom of the tube is retained; The precipitate after centrifugation is resuspended in physiological saline, mixed evenly, and the flow rate of the mixed liquid injection is set. Based on the flow rate, the mixed liquid is injected into a microfluidic chip, wherein a fluid channel is provided in the microfluidic chip, and a protruding electrode is provided in the flow channel. The electrode surface is coated with a specific antibody for adsorbing parasite eggs; Obtain the time series of the potential changes of the electrodes in the microfluidic chip during the entire injection process, calculate the current at different times based on the time series data of the potential changes, and collect the microscope image of the electrodes in the microfluidic chip after the mixed liquid is completely injected into the microfluidic chip; Collect microscope pictures of parasite eggs of different types, mark the outlines of the parasite eggs in the collected pictures, establish a Yolov5s model, use the pictures with the marked outlines of the parasite eggs as input to the model, and use the outlines of the parasite eggs as labels to train the Yolov5s model; Inputting the microscope image of the electrode in the microfluidic chip into the trained Yolov5s model, identifying the contour of the parasite egg in the microscope image of the electrode in the microfluidic chip, performing feature extraction on the image portion identified as the parasite egg, and obtaining feature parameters, wherein the feature parameters include contour area, contour perimeter and circularity; Based on the changes in current at different times combined with characteristic parameters, it is determined whether there are parasite eggs in the sample. If there are, the concentration of the parasite eggs is calculated based on the current changes and the number of parasite eggs.

2. A method for detecting parasite eggs according to claim 1, characterized in that: Put the collected test sample into a centrifuge tube, add physiological saline to the centrifuge tube at a ratio of 1:2 based on the volume of the collected test sample, put the centrifuge tube with the test sample into a centrifuge, set the centrifuge speed to 3000RMP, and the centrifugation time to 10 minutes, where RMP represents revolutions per minute. After centrifugation, obtain the precipitate at the bottom of the tube, take the same volume of physiological saline as the precipitate, and resuspend the precipitate after centrifugation in physiological saline.

3. A method for detecting parasite eggs according to claim 2, characterized in that: The flow rate of the mixed liquid injection is set, and the mixed liquid is injected into the microfluidic chip based on the flow rate. The volume of the mixed liquid, the cross-sectional area of ​​the fluid channel of the microfluidic chip and the time required for injection are determined. The formula for setting the mixed liquid injection flow rate based on the determined injection parameters is: In the formula, v represents the injection flow rate, V is the volume of the mixed liquid, and t z represents the required time for the set injection, and A is the cross-sectional area of ​​the fluid channel of the microfluidic chip; The mixed liquid is repeatedly injected into the microfluidic chip to allow the mixed liquid to fully react with the electrodes in the fluid channel of the microfluidic chip.

4. The method for detecting parasite eggs according to claim 1, characterized in that: Several protruding electrodes are provided in the flow channel, and the electrode surfaces are coated with specific antibodies for adsorbing parasite eggs. The specific antibodies are proteins produced by B cells, which are highly specific and can recognize and bind to specific antigen molecules. The specific antigen molecules include glycoproteins and lipids on the surface of parasite eggs. Coating specific antibodies on the electrode surface increases its ability to capture parasite eggs. In the microfluidic chip, when the mixed liquid passes through the flow channel, the eggs will contact the antibodies on the electrode surface and be captured.

5. The method for detecting parasite eggs according to claim 1, characterized in that: During the whole process of repeatedly injecting the mixed liquid into the microfluidic chip, the time series E(t) of the electrode potential change in the microfluidic chip is collected. The formula for calculating the current at different times based on the time series data of the potential change is: In the formula, I(t) represents the current on the electrode surface at time t, I0 is the current on the electrode surface at the initial time, R is the gas constant, and T is the air temperature; Based on the calculated electrode surface current at different times, the maximum change of the electrode surface current during the entire test process is calculated, where the formula for calculating the maximum change of the electrode surface current is: ΔI max =max|I(t)-I0| In the formula, ΔI max Indicates the maximum change in current on the electrode surface; After the experiment, the location of the electrodes in the microfluidic chip was placed under a microscope to collect images, and the images collected under the microscope were input into the trained Yolov5s model.

6. A method for detecting parasite eggs according to claim 5, characterized in that: The steps of training the Yolov5s model include: collecting microscope images of different types of parasite eggs, marking the outlines of different parasite eggs in the microscope images of parasite eggs by manual labeling, inputting the marked microscope images into the Yolov5s model, and using the outlines of the parasite eggs as labels to train the Yolov5s model, and finally obtaining an image with the image under the microscope as input and the outlines of the parasite eggs in the image marked as output.

7. A method for detecting parasite eggs according to claim 6, characterized in that: The collected images of the electrodes in the microfluidic chip under a microscope are input into the trained Yolov5s model. The model identifies and marks the parasite eggs captured on the electrodes, extracts the marked contour images, performs feature extraction, and obtains feature parameters. The formula for calculating the contour area using the pixel counting method is: M i =E i *a Where M i represents the area of ​​the i-th marker contour, E i represents the number of pixels in the i-th marker contour image, α represents the actual area of ​​each pixel, where i is the index of the marker contour, i = 1, 2, …, n, where n represents the total number of marker contours; The formula for calculating the contour perimeter is: Where, L i represents the contour perimeter of the i-th marked contour, P i,j represents the jth boundary pixel in the i-th marker contour, P i,j+1 represents the j+1th boundary pixel in the i-th marker contour, that is, the neighboring pixel of the j-th boundary pixel, d(P i,j ,P i,j+1 ) represents the adjacent points P in the contour i,j and P i,j+1 The distance between them, j is the index of the boundary pixel, j = 1, 2, ..., m, m is the total number of boundary pixels in the contour; The circularity of the contour is calculated based on the contour area and contour perimeter, and the specific formula is: In the formula, C i is the contour circularity; The parasite egg determination coefficient is generated based on the characteristic parameters, and the specific formula is as follows: xs i =ω1*C i +ω2*M i +ω3*L i In the formula, xs i represents the judgment coefficient of the i-th marked contour, ω1, ω2 and ω3 are the weight coefficients of contour circularity, contour area and contour perimeter, respectively, where ω1>ω2≥ω3 and ω1, ω2 and ω3 are all greater than 0.

8. A method for detecting parasite eggs according to claim 7, characterized in that: The logic for judging whether there are parasite eggs in the sample based on the changes in current at different times combined with characteristic parameters is as follows: The egg presence judgment coefficient is generated based on the maximum change in the electrode surface current and the number of eggs determined by the image. The specific formula is: pd=ΔI max +G In the formula, pd is the judgment coefficient of the presence of eggs, and G is the number of eggs determined by the image; When pd>yu, the sample is judged to contain parasite eggs, where yu is the judgment threshold for the presence of parasite eggs; The logic for determining the number of eggs G is as follows: the initial value of the number of eggs G is limited to 0, and the preset parasite egg judgment threshold yz is compared with the parasite egg judgment coefficient. If 0.4*yz≤xs is satisfied i When ≤0.6*yz, the outline of the mark is judged to be a parasite egg, and the number of eggs G determined by the image is increased by 1.

9. A method for detecting parasite eggs according to claim 8, characterized in that: When it is determined that the test sample contains parasite eggs, the concentration of the parasite eggs is calculated based on the current change and the number of parasite eggs, wherein the formula for calculating the concentration of the parasite eggs is: Where ND represents the concentration of parasite eggs, k is the sensitivity coefficient, ω4 and ω5 are the weight coefficients of the maximum change of the electrode surface current and the number of eggs determined by the image, respectively, and W α is the total volume of the sample to be tested, W β is the volume of the test sample, where ω5>ω4 and ω4 and ω5 are both greater than 0.

10. A parasite egg detection system, characterized in that: The parasite egg detection system is used to implement the parasite egg detection system method according to any one of claims 1 to 9, comprising: The sample pretreatment module is used to collect a certain volume of test samples from the evenly mixed samples to be tested, put the collected test samples into a centrifuge tube, add physiological saline, put it into a centrifuge for centrifugation, and after the centrifugation, remove the upper layer of liquid and retain the sediment at the bottom of the tube; The parasite egg capture module is used to resuspend the precipitate after centrifugation in physiological saline, mix it evenly, set the flow rate of the mixed liquid injection, and inject the mixed liquid into the microfluidic chip based on the flow rate. The microfluidic chip is provided with a fluid channel, and the flow channel is provided with a protruding electrode. The surface of the electrode is coated with a specific antibody for adsorbing parasite eggs; The test information acquisition module is used to obtain the time series of the potential changes of the electrodes in the microfluidic chip during the entire injection process, calculate the current at different times based on the time series data of the potential changes, and collect the microscope image of the electrodes in the microfluidic chip after the mixed liquid is completely injected into the microfluidic chip; The recognition model training module is used to collect microscope pictures of different types of parasite eggs, mark the outlines of the parasite eggs in the collected pictures, establish a Yolov5s model, use the pictures with the marked outlines of the parasite eggs as input to the model, and use the outlines of the parasite eggs as labels to train the Yolov5s model; An image feature extraction module is used to input the microscope image of the electrode in the microfluidic chip into the trained Yolov5s model, identify the contour of the parasite egg in the microscope image of the electrode in the microfluidic chip, extract features of the image portion identified as the parasite egg, and obtain feature parameters, wherein the feature parameters include contour area, contour perimeter and circularity; The worm egg concentration acquisition module is used to determine whether there are parasite eggs in the sample based on the changes in current at different times combined with characteristic parameters. If so, the concentration of the parasite eggs is calculated based on the current changes and the number of parasite eggs.

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