Digital PCR (Polymerase Chain Reaction) quantitative detection method and equipment

Abstract

The embodiment of the invention discloses a digital PCR quantitative detection method and device. The scheme can comprise the following steps: acquiring a digital PCR fluorescence image; determining the liquid drop position of each liquid drop in the fluorescence image; determining a first pixel value of a non-droplet area in the fluorescence image based on the droplet position; based on the first pixel value, correcting the pixel value of the pixel point in each liquid drop to obtain a corrected liquid drop pixel value; obtaining a second pixel value of each liquid drop according to the corrected liquid drop pixel value in each liquid drop; and determining a detection result of the fluorescence image according to the second pixel value of each liquid drop.

Description

Technical Field

[0001] The present application relates to the field of algorithm analysis technology, and in particular to a digital PCR quantitative detection method and device. Background Art

[0002] In the existing technology, digital PCR technology has shown great application potential in the fields of disease detection and biochemical analysis due to its high sensitivity and high specificity in the detection of low-abundance targets. However, in the current digital PCR experiment, due to the influence of some objective factors, such as interference generated in the transmission channel, reflection of foreign objects on the chip, uneven light source, etc., there is strong noise interference in the fluorescent image, which makes it difficult to identify and extract effective reaction unit information, making the accuracy of detection based on digital PCR low. Summary of the invention

[0003] The embodiments of the present specification provide a digital PCR quantitative detection method and device to solve the problem of low accuracy of detection results in existing digital PCR quantitative detection methods.

[0004] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0005] The digital PCR quantitative detection method provided in the embodiments of this specification includes:

[0006] Acquire digital PCR fluorescence images;

[0007] determining a droplet position of each droplet in the fluorescent image;

[0008] Based on the position of the liquid drop, determining a first pixel value of a non-liquid drop region in the fluorescent image;

[0009] Based on the first pixel value, correcting the pixel value of the pixel point in each droplet to obtain a corrected droplet pixel value;

[0010] Obtaining a second pixel value of each droplet according to the corrected droplet pixel value in each droplet;

[0011] The detection result of the fluorescent image is determined according to the second pixel value of each droplet.

[0012] A digital PCR quantitative detection device provided in an embodiment of the present specification may include a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above-mentioned digital PCR quantitative detection method.

[0013] At least one embodiment of the present specification can achieve the following beneficial effects: by acquiring a digital PCR fluorescence image; determining the droplet position of each droplet in the fluorescence image; based on the droplet position, determining the first pixel value of the non-droplet area in the fluorescence image; based on the first pixel value, correcting the pixel value of the pixel point in each droplet to obtain the corrected droplet pixel value; based on the corrected droplet pixel value in each droplet, obtaining the second pixel value of each droplet; based on the second pixel value of each droplet, determining the detection result of the fluorescence image. Thus, the accuracy of the detection result can be improved based on the corrected droplet pixel value. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a schematic diagram of a digital PCR quantitative detection method provided in the embodiments of this specification;

[0016] Figure 2 is a schematic diagram of a window sliding in a fluorescent image provided by an embodiment of this specification;

[0017] Figure 3 is a schematic diagram of a detection image provided by an embodiment of this specification;

[0018] Figure 4 The embodiment of this specification provides the corresponding Figure 1 A structural schematic diagram of a digital PCR quantitative detection device. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of one or more embodiments of this specification.

[0020] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0021] In the existing technology, digital PCR technology has shown great application potential in the fields of disease detection and biochemical analysis due to its high sensitivity and high specificity in the detection of low-abundance targets. However, in the current digital PCR experiment, due to the influence of some objective factors, such as interference generated in the transmission channel, reflection of foreign objects on the chip, uneven light source, etc., there is strong noise interference in the fluorescent image, which makes it difficult to identify and extract effective reaction unit information. Existing image analysis algorithms cannot effectively eliminate the influence of these factors.

[0022] In order to solve the defects in the prior art, this solution provides the following embodiments:

[0023] Figure 1 A schematic diagram of a digital PCR quantitative detection method provided in an embodiment of this specification. From a program perspective, the execution subject of the process can be a program or application client installed on an application server or detection platform.

[0024] like Figure 1 As shown, the process may include the following steps:

[0025] Step 102: Acquire a digital PCR fluorescence image.

[0026] In the embodiments of this specification, the fluorescent image may represent a picture taken of the sample to be detected under the illumination of a specific excitation light. The sample to be detected may include a droplet to be detected and a solution dissolved in the droplet. The specific excitation light may include green fluorescence, red fluorescence, and purple fluorescence, etc. The fluorescent image may be a picture of the sample to be detected taken under a microscope and magnified N times. The fluorescent image may include pictures taken under illumination of different fluorescent colors.

[0027] Step 104: Determine the droplet position of each droplet in the fluorescent image.

[0028] In the embodiments of this specification, the position of the droplet can represent the position of the droplet in the fluorescent image, and can also be understood as the position of the area representing the droplet in the fluorescent image. The fluorescent image can contain one or more droplets. After determining the position of each droplet in the fluorescent image, the pixel coordinates of the boundary of each droplet can be recorded to facilitate the subsequent screening of the droplets by determining the position of the droplet and the size of the droplet based on the pixel coordinates of the boundary of each droplet; it can also be to record information such as the shape, length and pixel coordinates of the center point of the droplet to determine the position and size of the droplet; it can also be based on other information that can record the position and size of the droplet.

[0029] Step 106: Based on the position of the liquid droplet, determine a first pixel value of a non-liquid droplet area in the fluorescent image.

[0030] The non-droplet area in the embodiments of this specification may represent other areas except the droplets, and the non-droplet area in the fluorescent image may be determined based on the droplet position, and the pixel value of the non-droplet area may be further determined. It is understandable that after removing the area corresponding to the droplet position in the fluorescent image, the remaining part is the non-droplet area. The first pixel value may be the pixel value of each pixel point in the non-droplet area, and may also represent statistical values ​​such as the average value and percentile value obtained from the pixel values ​​of each pixel point in the area. Different droplets may correspond to different non-droplet areas, and the non-droplet area corresponding to a droplet may represent the non-droplet area around the droplet.

[0031] Step 108: Based on the first pixel value, correct the pixel value of the pixel point in each droplet to obtain a corrected droplet pixel value.

[0032] In the embodiments of the present specification, the pixel values ​​of each pixel point contained in any of the droplets may be different or the same. The corrected droplet pixel value may represent the pixel value of each pixel point in each droplet on the basis of a unified background value. It can be understood that the reference value of the corrected droplet pixel value is the same.

[0033] Step 110: Obtain a second pixel value of each droplet according to the corrected droplet pixel value in each droplet.

[0034] In the embodiments of this specification, the second pixel value can be an RGB value or a grayscale value. The grayscale value is used as an example for the following description. The larger the grayscale value, the closer the color is to white, indicating that the droplet area is brighter; the smaller the grayscale value, the closer the color is to black, indicating that the droplet area is darker. The grayscale value of each droplet can represent statistical values ​​such as the average value of the pixel value of each pixel point in any droplet. So that the brightness of the droplet can be determined according to the size of the grayscale value.

[0035] Step 112: Determine the detection result of the fluorescent image according to the second pixel value of each droplet.

[0036] In the embodiments of the present specification, the droplets belonging to the target substance can be determined according to the second pixel value of each droplet, and then the concentration of the target substance can be determined according to the proportion of the droplets belonging to the target substance, thereby obtaining an accurate detection result.

[0037] It should be understood that the order of some steps in the methods described in one or more embodiments of this specification can be interchanged according to actual needs, or some steps can be omitted or deleted.

[0038] Figure 1The method in the embodiment of the present invention obtains a digital PCR fluorescence image; determines the droplet position of each droplet in the fluorescence image; determines the first pixel value of the non-droplet area in the fluorescence image based on the droplet position; corrects the pixel value of the pixel point in each droplet based on the first pixel value to obtain the corrected droplet pixel value; obtains the second pixel value of each droplet based on the corrected droplet pixel value in each droplet; and determines the detection result of the fluorescence image based on the second pixel value of each droplet. Thus, the accuracy of the detection result can be improved based on the corrected droplet pixel value.

[0039] based on Figure 1 The method, the examples of this specification also provide some specific implementation plans of the method, which are described below.

[0040] Optionally, the position of the droplet in the fluorescent image can be determined based on the position of the droplet in the bright field image to improve the accuracy of droplet detection. In the embodiments of this specification, determining the droplet position of each droplet in the fluorescent image can specifically include:

[0041] Acquire a bright field image corresponding to the fluorescent image;

[0042] Using an adaptive threshold segmentation algorithm to identify position information of each droplet in the bright field image;

[0043] The droplet position of each droplet in the fluorescent image is determined according to the position information.

[0044] The bright field image in the embodiment of this specification may represent an image of the sample to be tested taken under natural light or incandescent light. The sample to be tested and the sample to be tested taken by the fluorescence image are the same sample. The image size, shooting angle, shooting area and other conditions of the bright field image and the fluorescence image may be the same. The adaptive threshold segmentation algorithm can identify the bright field image according to the set parameters, so that it can determine whether it is a droplet according to the set parameters, output the position of the material determined as a droplet, and obtain the position information of each droplet in the bright field image. The parameters of the adaptive segmentation algorithm may include parameters such as threshold step size, spot color, spot area and spot shape. The user can set the parameters of the adaptive threshold segmentation algorithm based on actual needs. For example, the identification of droplets does not need to set parameters such as spot shape and spot color that do not affect the identification result. The use of the adaptive threshold segmentation algorithm can make the droplet identification task simpler, faster and more efficient. In practical applications, other existing segmentation algorithms can also be used to identify the droplet area in the bright field image, such as the fuzzy C-means algorithm, the inter-class threshold algorithm and the regional growing algorithm.

[0045] In practical applications, since the bright field image can more accurately identify the position of the droplet, the position of the droplet in the corresponding fluorescent image can be determined by referring to the position of the droplet in the bright field image. The server can determine the position of the droplet in the fluorescent image based on the position information of the droplet contained in the recognition result. For example, based on the pixel coordinate position of the droplet in the bright field image, the same pixel coordinate position in the corresponding fluorescent image can be determined as the position of the droplet.

[0046] The accuracy of the detection result can be improved by processing the bright field image and the fluorescent image. Optionally, after obtaining the bright field image corresponding to the fluorescent image, the following steps may be further included:

[0047] The bright field image is subjected to noise reduction and / or image enhancement processing.

[0048] The noise reduction in the embodiments of this specification can be performed by using filters. In practical applications, a suitable filter can be selected based on experience; at least two different filters can also be used to process the bright field image, and the signal-to-noise ratio of each processed image can be calculated according to the signal-to-noise ratio formula, and the signal-to-noise ratios are arranged. The filter corresponding to the processed image with the largest signal-to-noise ratio is selected as a noise reduction tool, and other existing noise reduction methods can also be used to reduce the noise of the bright field image. After the bright field image is denoised, the processed image can be enhanced by a restrictive adaptive image enhancement method, or other existing image enhancement algorithms can be used to process the denoised image, such as a point operation algorithm and a neighborhood enhancement algorithm. So that the position of the droplets in the image can be better identified. When processing the fluorescent image, the fluorescent image can also be denoised and enhanced, or only denoised or enhanced.

[0049] Due to lighting or other reasons, the brightness of the non-droplet area in the fluorescent image may be inconsistent, making the overall brightness of the non-droplet area uneven, which can easily affect the detection results, such as considering some brighter areas as droplets. In order to reduce the impact on the detection results, the non-droplet area can be processed. Optionally, the first pixel value of the non-droplet area in the fluorescent image described in the embodiments of this specification may specifically include:

[0050] Setting a sliding window to slide in the fluorescent image with a preset step length;

[0051] For any window area in the sliding process, determining the background pixel value corresponding to each pixel in the non-droplet area of ​​the any window area;

[0052] The background pixel values ​​corresponding to the respective pixels are counted to obtain the first pixel value of the arbitrary window area.

[0053] The sliding window size in the embodiments of this specification can be set based on the maximum length of the droplet, for example, the side length of the window can be consistent with the maximum length of the droplet, or a value obtained by slightly floating above or below the maximum length of the droplet, etc. The window can be a square, rectangular, etc. The length of the preset step size can be less than or equal to the side length of the window, so that the window can cover the entire image during the sliding process to avoid omissions and inaccurate detection results.

[0054] In the embodiment of this specification, the background pixel value may represent the pixel value of the non-droplet area, or may be understood as the pixel value of the solution. During the sliding process, the number of droplets contained in the window may be zero. When the number of droplets contained in the window is zero, the background pixel value in the window may not be processed. The number of droplets contained in the window being zero may indicate that none of the pixel points in the droplet is in the window. The window may contain a complete droplet or a portion of a droplet. The server may determine whether the window contains a droplet by judging whether the position of the droplet in the fluorescent area is in the window area. The server may take the statistical values ​​such as the mean weight, median, mode or upper quartile after statistically analyzing the background pixel value as the pixel value of the non-droplet area in the window, which may be understood as the first pixel value. Thus, the dark corner phenomenon caused by the light source can be solved by processing the background pixel value, or the refractive index of the light is different due to factors such as a large bubble somewhere in the chip carrying the droplet and the solution, and there will be a bright spot, or the overall brightness of the image is uneven due to some very tiny impurities and other reasons.

[0055] In order to better explain the window, Figure 2 is a schematic diagram of a window sliding in a fluorescent image provided in an embodiment of this specification. Figure 2 As shown: the blank square in the figure can represent the fluorescent image; the black square in the figure can represent the window area; the circle in the figure can represent the droplet, and the other droplets in the fluorescent image are not illustrated one by one; the arrow in the figure can represent the sliding direction of the window; except for the droplet, the remaining black part of the black square can represent the background area.

[0056] Determining the pixel value of the non-droplet area according to the background pixel value can reduce the influence of the non-droplet area on the detection result. Optionally, the background pixel value corresponding to each pixel in the embodiment of this specification is counted, which can specifically include:

[0057] Calculate at least one of the mean, median, and upper quartile of each background pixel value;

[0058] Alternatively, the counting of background pixel values ​​corresponding to the pixels may specifically include:

[0059] Sorting the background pixel values ​​corresponding to the pixels according to the pixel values ​​to obtain sorted background pixel values;

[0060] Part of the background pixel values ​​located before the first preset number of bits and / or after the second preset number of bits are removed from the sorted background pixel values, and statistics are collected on the remaining background pixel values.

[0061] In order to make the calculated first pixel value more consistent with the brightness of the non-droplet area of ​​the window area in the embodiment of this specification, the pixel values ​​of some pixels with larger pixel values ​​and smaller pixel values ​​can be removed, and then calculated to obtain a standard value of the non-droplet area of ​​the window area. For example, any value among the calculated statistical values ​​such as the mean, median and upper quartile can be used as the background pixel value of the window, so that the pixel value of the non-droplet area of ​​the window area can have a standard value, which can be used as a reference value for comparison with each pixel point in the droplet. Among them, the statistical value corresponding to some background pixel values ​​can also be used as the standard value.

[0062] In the embodiments of this specification, the first preset number of digits may be 10%, 5%, 3%, etc.; the second preset number of digits may be 90%, 95%, 97%, etc.; for example, after the background pixel values ​​of the pixels of the non-droplet area collected in the window area are sorted from small to large or from large to small, the 10% of the background pixel values ​​arranged in the front may be removed, and the 10% of the background pixel values ​​arranged in the back may be removed, and the remaining 80% of the background pixel values ​​are selected for calculation to obtain the pixel standard value of the non-droplet area in the window area. The number of the removed part of the pixel points arranged in the front may be different from the number of the removed part of the pixel points arranged in the back. The pixel standard value may be any one of the statistical values ​​such as the mean, median, upper quartile or mode of the remaining background pixel values.

[0063] The pixel value of the droplet may be corrected according to the pixel value of the pixel point in the window area so as to obtain a more accurate detection result. Optionally, in the embodiment of this specification, based on the first pixel value, the pixel value of the pixel point in each droplet is corrected to obtain the corrected droplet pixel value, which may specifically include:

[0064] For any window area in the fluorescent image, determine a third pixel value of each pixel point in the droplet in the any window area;

[0065] The first pixel value of the arbitrary window area is respectively subtracted from the third pixel value of each pixel point in the droplet in the arbitrary window area to obtain a corrected droplet pixel value of the droplet in the arbitrary window area.

[0066] In the embodiments of this specification, the corrected droplet pixel values ​​include the corrected pixel values ​​of each pixel in the droplet. The corrected droplet pixel values ​​are all relative pixel values ​​obtained based on the first pixel value of the non-droplet area, so that the brightness of each droplet can be represented by any corrected droplet pixel value under the same benchmark, and then the brightness of each droplet can be compared by the corrected droplet pixel value. For example, by subtracting the first pixel value from the pixel value of each pixel point, the relative background pixel value of each pixel point can be made to be 0, and the detection result will not be affected by the different background pixel values; it can also be understood that the detection result will not be inaccurate due to uneven background brightness.

[0067] The corrected pixel values ​​of each pixel point of each droplet can be processed so as to better distinguish the brightness of each droplet. Optionally, the corrected pixel value of a droplet in any window area in the embodiment of this specification includes the corrected pixel value corresponding to each pixel point of the droplet in any window area;

[0068] The obtaining the second pixel value of each droplet according to the corrected droplet pixel value in each droplet may specifically include:

[0069] Determining the corrected pixel value corresponding to each pixel point in each droplet in the fluorescent image according to the corrected pixel value corresponding to each pixel point in the droplet in each window area;

[0070] For any one of the droplets, the corrected droplet pixel value corresponding to each pixel point in the droplet is counted according to a preset statistical method to obtain a statistical value;

[0071] The statistical value is used as the second pixel value of any one of the droplets.

[0072] The second pixel value in the embodiments of this specification may be an unprocessed RGB value or a grayscale value that has been grayscaled. The server may determine the pixel points that belong to the same droplet based on the position of the droplet and the size of the droplet, and process the pixel values ​​of the pixel points that belong to the same droplet to obtain a pixel value for representing the droplet, which may be used as the second pixel value; or the pixel value may be grayscaled to obtain a grayscale value representing the pixel value of the droplet area as the second pixel value. In practical applications, for methods that require grayscale processing to obtain grayscale values, the pixel values ​​of each pixel point may be grayscaled first to obtain the grayscale values ​​of each pixel point, and the grayscale values ​​of each pixel point may be processed to obtain a grayscale value for the droplet.

[0073] The preset statistical method in the embodiments of this specification can average the corrected droplet pixel values ​​of each pixel point belonging to the same droplet; or arrange the corrected droplet pixel values ​​of each pixel point from small to large or from large to small, and determine the median; or determine the largest value in the corrected droplet pixel values ​​of each pixel point; or arrange the corrected droplet pixel values ​​of each pixel point from small to large, and determine the value located at 75% of the position in the entire arrangement. The statistical values ​​can be different depending on the preset statistical method. When calculating the statistical values ​​of each droplet in the same fluorescent image, the statistical values ​​of all droplets can be calculated using the same preset statistical method.

[0074] A portion of the corrected droplet pixel values ​​may be selected for calculation so that the calculation result can more accurately represent the brightness of the droplet. Optionally, the corrected droplet pixel values ​​corresponding to each pixel point in any droplet described in the embodiments of this specification are counted according to a preset statistical method, which may specifically include:

[0075] Sorting the corrected droplet pixel values ​​corresponding to each pixel point in any droplet according to the size of the pixel value to obtain a sorted pixel value queue;

[0076] Selecting some pixel values ​​between the third preset position and the fourth preset position in the sorted pixel value queue;

[0077] Calculate the average of the pixel values.

[0078] In the embodiment of this specification, the third preset position can be the same value as the value of the first preset position, or a value greater than the first preset position; the fourth preset position can be the same value as the value of the second preset position, or a value less than the second preset position. For example, the pixel value between 50% and 60% in the sorted pixel value queue can be selected; the pixel value between 30% and 70% in the sorted pixel value queue can also be selected, and so on. For the specific pixel value selection, the user can determine it according to the distribution of the pixel values ​​of each corrected droplet in the same droplet; or the selected position can be determined by calculating the standard deviation of the pixel values ​​of each corrected droplet, which is not specifically limited here. For the selected part, in addition to calculating the mean, the median, upper quartile, mode, etc. can also be calculated as a preset statistical method.

[0079] After determining the second pixel value of each droplet, a clustering algorithm may be used to classify each droplet. Optionally, the method of determining the detection result of the fluorescent image according to the second pixel value of each droplet in the embodiment of this specification may specifically include:

[0080] Using a clustering algorithm, clustering the droplets into k categories; k is greater than 2;

[0081] For any category among the multiple categories, calculate the second pixel statistics of the second pixel value of each droplet in the any category to obtain the category second pixel value of each category among the multiple categories; determine each droplet in the category with the largest category second pixel value among the categories as a positive droplet;

[0082] Droplets in other categories were identified as negative droplets;

[0083] The detection result is determined based on the positive droplets and the negative droplets.

[0084] In the embodiments of this specification, each droplet can be classified according to the gray value of the droplet and using an improved k-means clustering algorithm. Other existing clustering algorithms can also be used to classify the droplets. Most of the existing k-means clustering algorithms are binary classification models. If the classification category is 2, it is easy to classify the droplets with not too low gray values ​​and the droplets with high gray values ​​into one category, which makes it difficult to solve the problem of sample imbalance, resulting in poor detection results. The embodiments of this specification improve the k-means clustering algorithm and set the classification categories to at least 3, so that more categories can be obtained, making the classification of droplets more detailed. As for the specific number of categories to be set, it can be determined according to the actual test situation, or it can be set to a more reasonable number of categories based on empirical values, which is not specifically limited here. For example, the categories can be set to 10-15 based on empirical values.

[0085] In the embodiments of this specification, any of the above-mentioned preset statistical methods can also be used to process the second pixel values ​​of each droplet belonging to the same category. It should be noted that the same preset statistical method is used to process each category to avoid using different methods to process different categories, resulting in errors and inaccurately expressing the second pixel values ​​of each category. In practical applications, the second pixel values ​​of each category can be arranged from large to small, and each droplet contained in the first few categories can be determined as a positive droplet. The specific number of selections can be set according to the number of categories actually classified. The detection result can be calculated based on the Poisson distribution. The detection result can represent the proportion of positive droplets in all droplets, and can also be understood as the concentration of positive droplets. Negative droplets can represent droplets with smaller second pixel values.

[0086] According to the quality control requirements, the droplets that do not meet the requirements can be removed, and the remaining droplets can be processed to improve the accuracy of the detection results. Optionally, the method described in the embodiment of this specification can also include:

[0087] Remove unsatisfactory droplets in at least one of the following ways:

[0088] Removing droplets whose droplet sizes are greater than or equal to a first preset threshold value from the droplets;

[0089] Removing droplets whose droplet size is less than or equal to a second preset threshold value from the droplets;

[0090] Sorting the droplets according to the second pixel value, and taking a number of droplets sorted at a preset position as candidate droplets for removal; for any candidate droplet among the candidate droplets for removal, determining whether there are a preset number of other candidate droplets for removal within a preset range of the any candidate droplet for removal; if so, removing the any candidate droplet for removal;

[0091] Acquire the second pixel value of each droplet in the fluorescent image under different fluorescence; remove the droplets whose second pixel values ​​in the fluorescent image under different fluorescence are all greater than a preset threshold;

[0092] The determining of the detection result of the fluorescent image may specifically include:

[0093] The detection result of the fluorescent image is determined according to the second pixel value of the remaining droplet after the removal.

[0094] In the embodiment of the present specification, the first preset threshold value may be the upper quartile after arranging the diameters of the various droplets; the second preset threshold value may be the lower quartile after arranging the diameters of the various droplets; or the average value of the diameters of the various droplets may be calculated, and a random value may be added to the average value to obtain the first preset threshold value, and a random value may be subtracted from the average value to obtain the second preset threshold value. Candidate droplets for removal may also include droplets whose pixel values ​​are greater than or equal to the preset threshold value. The preset threshold value may be obtained based on expert experience; or it may be a preset threshold value determined based on the sorting position of the droplets, such as arranging the second pixel values ​​of the droplets from large to small and determining the pixel value of the droplet at 10% after the arrangement as the preset threshold value, or determining the pixel value of the droplet at 5% or 15%, etc. as the preset threshold value. The preset range may represent an area obtained by taking the candidate droplet for removal as the center of the circle and taking three times the diameter of the candidate droplet for removal or other lengths greater than the diameter of the droplet for removal as the radius. The preset number may be 3, 5, 6 or 7, etc. The server can also screen the droplets in different fluorescent images to obtain more accurate values ​​under different fluorescence, and screen out droplets that may be mistaken for droplets or have incorrect second pixel values ​​due to fluorescence, thereby improving the accuracy of the detection results. Using multiple different fluorescence to detect droplets can reduce the impact of fluorescence on the detection results.

[0095] In order to explain more clearly, the use of the digital PCR automatic detection method in this manual can make the identification of droplets more accurate. Figure 3 A schematic diagram of detecting an image is provided for the embodiment of this specification. Figure 3 As shown: 301 in the figure represents the original fluorescent image, the black part in the figure can represent the solution area, and can also be understood as the background area or the non-droplet area; the brighter or white part in the figure can represent the solution or droplets. 302 in the figure can represent the background image, an image without droplets. 303 in the figure can represent the corrected fluorescent image, the white or brighter part can represent the droplets; the black part can represent the background area.

[0096] Through the above method, the pixel values ​​can be corrected to reduce the impact of uneven background brightness of the image, the influence of fluorescence on the detection results can be reduced by processing different fluorescent images, and classification errors can be resolved through improved clustering, thereby avoiding detection errors and improving the accuracy of detection.

[0097] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above method. Figure 4 The embodiments of this specification provide corresponding to Figure 1 A schematic diagram of the structure of a digital PCR quantitative detection device. Figure 4 As shown, the device 400 may include a memory 430, a processor 410, and a computer program 420 stored in the memory, and the processor executes the computer program to implement the steps of the digital PCR quantitative detection method in any of the above embodiments.

[0098] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. Figure 4 As for the device shown, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0099] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented with "logic compiler" software, which is similar to the software compiler used when developing and writing programs. The original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that it is easy to obtain a hardware circuit that implements the logical method flow by simply performing some logic programming on the method flow using the above-mentioned hardware description languages ​​and programming it into an integrated circuit.

[0100] The controller can be implemented in any appropriate manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, AtmelAT91SAM, MicrochipPIC18F26K20, and SiliconeLabsC8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0101] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0102] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0109] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0110] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0111] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0113] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A digital PCR quantitative detection method, characterized in that: include: Acquire digital PCR fluorescence images; determining a droplet position of each droplet in the fluorescent image; Based on the position of the liquid drop, determining a first pixel value of a non-liquid drop region in the fluorescent image; Based on the first pixel value, correcting the pixel value of the pixel point in each droplet to obtain a corrected droplet pixel value; Obtaining a second pixel value of each droplet according to the corrected droplet pixel value in each droplet; The detection result of the fluorescent image is determined according to the second pixel value of each droplet.

2. The method according to claim 1, characterized in that Determining the droplet position of each droplet in the fluorescent image specifically includes: Acquire a bright field image corresponding to the fluorescent image; Using an adaptive threshold segmentation algorithm to identify position information of each droplet in the bright field image; The droplet position of each droplet in the fluorescent image is determined according to the position information.

3. The method according to claim 1, characterized in that The determining of a first pixel value of a non-droplet region in the fluorescent image specifically includes: Setting a sliding window to slide in the fluorescent image with a preset step length; For any window area in the sliding process, determining the background pixel value corresponding to each pixel in the non-droplet area of ​​the any window area; The background pixel values ​​corresponding to the respective pixels are counted to obtain the first pixel value of the arbitrary window area.

4. The method according to claim 3, characterized in that The counting of background pixel values ​​corresponding to each pixel specifically includes: Calculate at least one of the mean, median, and upper quartile of each background pixel value; Alternatively, the counting of background pixel values ​​corresponding to the pixels specifically includes: Sorting the background pixel values ​​corresponding to the pixels according to the pixel values ​​to obtain sorted background pixel values; Part of the background pixel values ​​located before the first preset number of bits and / or after the second preset number of bits are removed from the sorted background pixel values, and statistics are collected on the remaining background pixel values.

5. The method according to claim 3, characterized in that: The correcting the pixel value of the pixel point in each droplet based on the first pixel value to obtain the corrected droplet pixel value specifically includes: For any window area in the fluorescent image, determine a third pixel value of each pixel point in the droplet in the any window area; The first pixel value of the arbitrary window area is respectively subtracted from the third pixel value of each pixel point in the droplet in the arbitrary window area to obtain a corrected droplet pixel value of the droplet in the arbitrary window area.

6. The method according to claim 5, characterized in that The corrected droplet pixel value of the droplet in any one of the window regions includes the corrected pixel value corresponding to each pixel point of the droplet in any one of the window regions; The obtaining, according to the corrected droplet pixel value in each droplet, a second pixel value of each droplet specifically includes: Determining the corrected pixel value corresponding to each pixel point in each droplet in the fluorescent image according to the corrected pixel value corresponding to each pixel point in the droplet in each window area; For any one of the droplets, the corrected droplet pixel value corresponding to each pixel point in the droplet is counted according to a preset statistical method to obtain a statistical value; The statistical value is used as the second pixel value of any one of the droplets.

7. The method according to claim 6, characterized in that The step of counting the corrected droplet pixel values ​​corresponding to each pixel point in any droplet according to a preset statistical method specifically includes: Sorting the corrected droplet pixel values ​​corresponding to each pixel point in any droplet according to the size of the pixel value to obtain a sorted pixel value queue; Selecting some pixel values ​​between the third preset position and the fourth preset position in the sorted pixel value queue; Calculate the average of the pixel values.

8. The method according to claim 1, characterized in that Determining the detection result of the fluorescent image according to the second pixel value of each droplet specifically includes: Using a clustering algorithm, clustering the droplets into k categories; k is greater than 2; For any category among the multiple categories, calculate the second pixel statistics of the second pixel value of each droplet in the any category to obtain the category second pixel value of each category among the multiple categories; determine each droplet in the category with the largest category second pixel value among the categories as a positive droplet; Droplets in other categories were identified as negative droplets; The detection result is determined based on the positive droplets and the negative droplets.

9. The method according to claim 1, characterized in that: The method further comprises: Remove unsatisfactory droplets in at least one of the following ways: Removing droplets whose droplet sizes are greater than or equal to a first preset threshold value from the droplets; Removing droplets whose droplet size is less than or equal to a second preset threshold value from the droplets; Sorting the droplets according to the second pixel value, and taking a number of droplets sorted at a preset position as candidate droplets for removal; for any candidate droplet among the candidate droplets for removal, determining whether there are a preset number of other candidate droplets for removal within a preset range of the any candidate droplet for removal; if so, removing the any candidate droplet for removal; Acquire the second pixel value of each droplet in the fluorescent image under different fluorescence; remove the droplets whose second pixel values ​​in the fluorescent image under different fluorescence are all greater than a preset threshold; The determining of the detection result of the fluorescent image specifically includes: The detection result of the fluorescent image is determined according to the second pixel value of the remaining droplet after the removal.

10. A digital PCR quantitative detection device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the digital PCR quantitative detection method according to claims 1 to 9.

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