Methods of analyzing droplets based on image analysis, computer devices, and storage media

Abstract

A method for analyzing droplets based on image analysis, by obtaining a working dye channel image and a reference dye channel image corresponding to a droplet system (S1); performing a preset image processing operation on the working dye channel image and the reference dye channel image respectively (S2); taking the working dye channel image or the reference dye channel image as a target image (S3), determining a region where each droplet is located from the target image (S4); and analyzing the droplet system based on the determined region where each droplet is located (S5), which can effectively improve the accuracy of droplet analysis. A computer device and a storage medium are also provided.

Description

Technical Field

[0001] This application relates to the field of image processing technology, specifically to a method, computer device, and storage medium for analyzing droplets based on images. Background Technology

[0002] Emulsification is the process by which a liquid is uniformly dispersed into extremely small droplets in another immiscible liquid. This dispersion can efficiently and conveniently achieve emulsification and partitioning of homogeneous biochemical solutions. Droplet technology is a discontinuous flow microfluidic technique that utilizes emulsification to generate dispersed microdroplets from two immiscible liquid phases for experimental manipulation. To date, droplet technology has been widely applied in biomedical fields such as the analysis and detection of various biomolecules, including nucleic acids, proteins, polysaccharides, metal ions, and small molecules, as well as drug delivery. These droplet technology applications generally employ capillary continuous counting or planar scanning methods to obtain information such as the number, volume, and signal of droplets, thereby enabling qualitative or quantitative analysis of the analytes contained within the droplets. For example, in digital PCR, continuous counting is often used to transfer a single volume of droplets that have completed the reaction to a capillary for optical reading and analysis, obtaining information on the number of droplets and light intensity. However, this method requires a microfluidic system to maintain a stable flow rate and requires individual droplet detection and reading, resulting in low throughput and long processing time. Another alternative method is to use a planar scanning method to acquire image information of all droplets in a two-dimensional array of ordered droplets at once, and then extract and analyze the acquired image information to obtain information such as the number, volume and signal of all droplets and classify them, thereby calculating the absolute quantitative result of the analyte.

[0003] However, the planar scanning method still has significant limitations for droplet array detection, mainly in the following two aspects:

[0004] (1) Current methods are only applicable to droplet systems with a single volume. When such droplets are spread out in a container to form a two-dimensional monolayer, each droplet has an equal height in the direction perpendicular to the scanning plane. Therefore, the excitation and acquisition of the optical signal are uniform, and the optical signal intensity can well reflect information such as the volume and state of the droplets. However, once the volume of the droplets changes or the droplet system no longer has a single volume, the droplets will change in height due to the change in volume, thus affecting the uniformity of the optical signal. This makes the acquired signal unable to accurately reflect information such as the volume and state of each droplet, which will lead to a large calculation error. For example, see [reference needed]. Figure 1 The diagram illustrates the variation in optical signal intensity caused by changes in droplet volume in the planar scanning method. Figure 1In the diagram, A indicates that a single-layer two-dimensional planar single-volume droplet will not cause variations in optical signal intensity; B indicates that a single-layer two-dimensional planar multi-volume droplet will cause variations in optical signal intensity; C indicates that a squeezed quasi-two-dimensional planar single-volume droplet will not cause variations in optical signal intensity; and D indicates that a squeezed quasi-two-dimensional planar multi-volume droplet will cause variations in optical signal intensity.

[0005] (2) The spatial stability of two-dimensional or quasi-two-dimensional monolayer droplets is much lower than that of solid-state chips. They are very susceptible to external environmental disturbances, causing droplet flow, local perturbations, or Brownian motion. This can easily lead to droplet displacement or duplicate statistics when using planar scanning to capture multiple optical images. For example, see [reference needed]. Figure 2 As shown, the reference dye channel image 21 and the working dye channel image 22 were captured using a planar scanning method. By comparing the position indicated by arrow 211 in the reference dye channel image 21 and the position indicated by arrow 221 in the working dye channel image 22, it can be seen that droplet displacement occurred when the reference dye channel image 21 and the working dye channel image 22 were captured.

[0006] For the reasons mentioned above, there is an urgent need for droplet analysis methods that can effectively overcome the above drawbacks. Summary of the Invention

[0007] In view of the above, it is necessary to propose a method, computer device and storage medium for image-based droplet analysis that can accurately analyze droplets.

[0008] The image-based droplet analysis method includes: an acquisition step, acquiring a working dye channel image and a reference dye channel image corresponding to the droplet system; an image processing step, performing preset image processing operations on the working dye channel image and the reference dye channel image respectively; a target determination step, using the working dye channel image or the reference dye channel image as a target image; a region determination step, determining the region where each droplet is located from the target image; and an analysis step, analyzing the droplet system based on the determined region where each droplet is located.

[0009] Preferably, the acquisition step includes: exciting the working dye and the reference dye in the droplet system under dual / multi-channel wavelength conditions respectively, capturing fluorescence images of the channels corresponding to the working dye to obtain the working dye channel image, and capturing fluorescence images of the channels corresponding to the reference dye to obtain the reference dye channel image.

[0010] Preferably, the image processing steps include: performing background brightness unevenness correction operations on the working dye channel image and the reference dye channel image respectively; performing image enhancement processing on the reference dye channel image and the working dye channel image respectively using a preset image enhancement algorithm; and performing noise reduction processing on the working dye channel image and the reference dye channel image respectively using a preset noise reduction algorithm.

[0011] Preferably, the region determination step includes: performing local contrast enhancement processing on the target image; converting the target image to grayscale to obtain a grayscale image, and binarizing the grayscale image to obtain the binarized target image; and dividing the binarized target image into several discrete image regions, each of the several discrete image regions corresponding to a single droplet, thereby determining the region where each droplet is located.

[0012] Preferably, the method further includes a registration step before the region determination step, the registration step including: taking the one of the working dye channel image and the reference dye channel image that is not determined to be the target image as a non-target image, and using the target image as a fixed reference, performing a registration operation on the non-target image.

[0013] Preferably, the step of performing a registration operation on the non-target image using the target image as a fixed reference includes: performing a first image registration operation on the non-target image using the target image as a fixed reference to obtain a first transformation matrix, and obtaining a first registered image based on the first transformation matrix and the non-target image; and performing a second image registration operation on the first registered image using the target image as a fixed reference to obtain a second transformation matrix, and obtaining a second registered image based on the second transformation matrix and the first registered image, and using the second registered image as the registered non-target image.

[0014] Preferably, the analysis step includes: calculating the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref According to the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref Calculate the relative light intensity I of each droplet. Rela , among which, I Rela =I Abs / I Ref .

[0015] The computer device includes a memory and a processor. The memory stores at least one computer-readable instruction, and the processor executes the at least one computer-readable instruction to perform the following steps: an acquisition step, acquiring a working dye channel image and a reference dye channel image corresponding to the droplet system; an image processing step, performing preset image processing operations on the working dye channel image and the reference dye channel image, respectively; a target determination step, using either the working dye channel image or the reference dye channel image as a target image; a region determination step, determining the region where each droplet is located from the target image; and an analysis step, analyzing the droplet system based on the determined region where each droplet is located.

[0016] Preferably, the processor is configured to execute the at least one computer-readable instruction to implement the acquisition step, comprising: exciting the working dye and the reference dye in the droplet system respectively under dual / multi-channel wavelength conditions, capturing a fluorescence image of the channel corresponding to the working dye to obtain a working dye channel image, and capturing a fluorescence image of the channel corresponding to the reference dye to obtain a reference dye channel image.

[0017] Preferably, the processor is configured to execute the at least one computer-readable instruction to implement the image processing steps, including: performing background brightness unevenness correction operations on the working dye channel image and the reference dye channel image respectively; performing image enhancement processing on the reference dye channel image and the working dye channel image respectively using a preset image enhancement algorithm; and performing noise reduction processing on the working dye channel image and the reference dye channel image respectively using a preset noise reduction algorithm.

[0018] Preferably, the processor is configured to execute the at least one computer-readable instruction to implement the region determination step, including: performing local contrast enhancement processing on the target image; converting the target image to grayscale to obtain a grayscale image, and binarizing the grayscale image to obtain the binarized target image; and dividing the binarized target image into a plurality of discrete image regions, each of the plurality of discrete image regions corresponding to a single droplet, thereby determining the region where each droplet is located.

[0019] Preferably, the processor is configured to execute the at least one computer-readable instruction to perform a registration step prior to performing the region determination step, including: taking the one of the working dye channel image and the reference dye channel image that is not determined to be the target image as a non-target image, and performing a registration operation on the non-target image using the target image as a fixed reference.

[0020] Preferably, the processor is configured to execute the at least one computer-readable instruction to perform a registration operation on the non-target image using the target image as a fixed reference, comprising: performing a first image registration operation on the non-target image using the target image as a fixed reference to obtain a first transformation matrix, and obtaining a first registered image based on the first transformation matrix and the non-target image; and performing a second image registration operation on the first registered image using the target image as a fixed reference to obtain a second transformation matrix, and obtaining a second registered image based on the second transformation matrix and the first registered image, and using the second registered image as the registered non-target image.

[0021] Preferably, the processor is configured to execute the at least one computer-readable instruction to implement the analysis step, including: calculating the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref According to the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref Calculate the relative light intensity I of each droplet. Rela , among which, I Rela =I Abs / I Ref .

[0022] The non-volatile readable storage medium stores at least one computer-readable instruction, which, when executed by a processor, performs the following steps: an acquisition step, acquiring a working dye channel image and a reference dye channel image corresponding to the droplet system; an image processing step, performing preset image processing operations on the working dye channel image and the reference dye channel image respectively; a target determination step, using the working dye channel image or the reference dye channel image as a target image; a region determination step, determining the region where each droplet is located from the target image; and an analysis step, analyzing the droplet system based on the determined region where each droplet is located.

[0023] Preferably, when the at least one computer-readable instruction is executed by a processor, the acquisition step includes: exciting the working dye and the reference dye in the droplet system under dual / multi-channel wavelength conditions, capturing a fluorescence image of the channel corresponding to the working dye to obtain a working dye channel image, and capturing a fluorescence image of the channel corresponding to the reference dye to obtain a reference dye channel image.

[0024] Preferably, when the at least one computer-readable instruction is executed by the processor, the image processing steps are implemented, including: performing background brightness unevenness correction operations on the working dye channel image and the reference dye channel image respectively; performing image enhancement processing on the reference dye channel image and the working dye channel image respectively using a preset image enhancement algorithm; and performing noise reduction processing on the working dye channel image and the reference dye channel image respectively using a preset noise reduction algorithm.

[0025] Preferably, when the at least one computer-readable instruction is executed by a processor, the determining step includes: performing local contrast enhancement processing on the target image; converting the target image to grayscale to obtain a grayscale image, and binarizing the grayscale image to obtain the binarized target image; and dividing the binarized target image into several discrete image regions, each of the several discrete image regions corresponding to a single droplet, thereby determining the region where each droplet is located.

[0026] Preferably, when the at least one computer-readable instruction is executed by the processor, a registration step is performed before the region determination step, comprising: designating the one of the working dye channel image and the reference dye channel image that is not determined to be the target image as a non-target image; performing a first image registration operation on the non-target image using the target image as a fixed reference to obtain a first transformation matrix, and obtaining a first registered image based on the first transformation matrix and the non-target image; and performing a second image registration operation on the first registered image using the target image as a fixed reference to obtain a second transformation matrix, and obtaining a second registered image based on the second transformation matrix and the first registered image, and using the second registered image as the registered non-target image.

[0027] Preferably, the analysis step is implemented when the at least one computer-readable instruction is executed by a processor, including: calculating the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref According to the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref The relative light intensity I of each droplet was calculated. Rela , among which, I Rela =I Abs / I Ref .

[0028] The image analysis-based droplet method, computer device, and storage medium described in the embodiments of this application can improve the accuracy of droplet analysis. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0030] Figure 1 This diagram illustrates the variation in optical signal intensity caused by the variation in droplet volume in the planar scanning method.

[0031] Figure 2 This illustrates the droplet displacement that occurred when capturing images of the reference dye channel and the working dye channel using a planar scanning method.

[0032] Figure 3 This is a diagram of the operating environment of the droplet analysis system provided in a preferred embodiment of this application.

[0033] Figure 4 This is a flowchart of the droplet analysis method provided in a preferred embodiment of this application.

[0034] Figure 5 This diagram illustrates the secondary registration of the working dye channel image using the reference dye channel image as a fixed reference.

[0035] Figure 6 This illustrates an information table that records information about the region where each droplet is located.

[0036] Figure 7 This illustrates that after performing preset image operations on the working dye channel image and the reference dye channel image, and performing two registrations on either the working dye channel image or the reference dye channel image (which serves as the target image), droplets can be classified into two categories, negative droplets and positive droplets, based on their relative light intensity.

[0037] Figure 8 This demonstrates that after performing two registrations on the working dye channel image, the coefficient of variation of the droplet's relative light intensity is significantly reduced.

[0038] The following detailed description, in conjunction with the accompanying drawings, will further illustrate this application. Detailed Implementation

[0039] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0040] Numerous specific details are set forth in the following description to provide a thorough understanding of this application. The described embodiments are merely some, not all, of the embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0042] See Figure 3 The diagram shown is an operating environment diagram of the droplet analysis system provided in a preferred embodiment of this application.

[0043] In this embodiment, the droplet analysis system 30 operates within the computer device 3 and is used to analyze droplets based on images, such as analyzing the relative light intensity of the droplets. In this embodiment, the computer device 3 includes, but is not limited to, a memory 31, a processor 32, and at least one communication bus 33.

[0044] Those skilled in the art should understand that Figure 3 The structure of the computer device shown does not constitute a limitation of the embodiments of this application. It can be a bus-type structure or a star-type structure. The computer device 3 may also include more or fewer other hardware or software than shown, or different component arrangements.

[0045] In some embodiments, the computer device 3 includes a terminal capable of automatically performing numerical calculations and / or information processing according to pre-set or stored computer-readable instructions, the hardware of which includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices.

[0046] It should be noted that the computer device 3 described is merely an example. Other existing or future computer devices that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0047] In some embodiments, the memory 31 is used to store program code and various data, such as the droplet analysis system 30 installed in the computer device 3, and to enable high-speed, automatic access to programs or data during the operation of the computer device 3. The memory 31 includes read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other non-volatile readable storage medium capable of carrying or storing data.

[0048] In some embodiments, the at least one processor 32 may be composed of integrated circuits, such as a single-packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The at least one processor 32 is the control unit of the computer device 3, connecting various components of the computer device 3 via various interfaces and lines. It executes programs or modules stored in the memory 31 and calls data stored in the memory 31 to perform various functions of the computer device 3 and process data, such as performing image-based droplet analysis (see below for details). Figure 4 (Description).

[0049] In some embodiments, the at least one communication bus 33 is configured to enable communication between the memory 31 and the at least one processor 32, etc.

[0050] Although not shown, the computer device 3 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 32 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0051] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0052] In some embodiments, the droplet analysis system 30 may include multiple functional modules composed of program code segments. The program code of each program segment in the droplet analysis system 30 may be stored in a memory (e.g., memory 31 of computer device 1) and executed by at least one processor (e.g., processor 32) to implement the function of image-based droplet analysis (see below for details). Figure 4 (Description).

[0053] In this embodiment, the droplet analysis system 30 can be divided into multiple functional modules according to its functions. These functional modules may include: an acquisition module 301 and an execution module 302. As used in this application, a module refers to a series of computer-readable instruction segments that can be executed by at least one processor (e.g., processor 32) and perform a fixed function, stored in memory (e.g., memory 31 of computer device 1). In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0054] In this embodiment, the integrated unit, implemented as a software functional module, can be stored in a non-volatile readable storage medium. The aforementioned software functional module includes several computer-readable instructions to cause a computer device or processor to execute the methods of the various embodiments of this application, for example... Figure 4 The method shown is based on image analysis of droplets.

[0055] In a further embodiment, combined with Figure 3 The at least one processor 32 can execute the operating device of the computer device 3 and various installed applications (such as the droplet analysis system 30), program code, etc., for example, the various modules mentioned above.

[0056] The memory 31 stores program code, and the at least one processor 32 can call the program code stored in the memory 31 to execute related functions. For example, Figure 3 The various modules of the droplet analysis system 30 described herein are program codes stored in the memory 31 and executed by the at least one processor 32, thereby realizing the functions of each module to achieve the purpose of image-based droplet analysis (see below for details). Figure 4 (Description).

[0057] In one embodiment of this application, the memory 31 stores a plurality of computer-readable instructions, which are executed by the at least one processor 32 to achieve the purpose of image-based droplet analysis. Specifically, the specific implementation method of the at least one processor 32 of the aforementioned computer-readable instructions is detailed below. Figure 4 The description.

[0058] See Figure 4 The diagram shown is a flowchart of a droplet-based image analysis method provided in a preferred embodiment of this application.

[0059] In this embodiment, the image-based droplet analysis method can be applied to a computer device 3. For a computer device 3 that requires image-based droplet analysis, the image-based droplet analysis function provided by the method of this application can be directly integrated into the computer device 3, or it can be run on the computer device 3 in the form of a software development kit (SDK).

[0060] like Figure 4 As shown, the image-based droplet analysis method specifically includes the following steps. Depending on different needs, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0061] Step S1: The acquisition module 301 acquires the working dye channel image and the reference dye channel image corresponding to the droplet system.

[0062] In one embodiment, acquiring the working dye channel image and the reference dye channel image corresponding to the droplet system includes:

[0063] The working dye and reference dye in the droplet system are excited under dual / multi-channel wavelength conditions, and fluorescence images of the corresponding channels of the working dye are captured to obtain the working dye channel image, and fluorescence images of the corresponding channels of the reference dye are captured to obtain the reference dye channel image.

[0064] In one embodiment, a planar scanning method can be used to excite the working dye and reference dye in the droplet system under dual / multi-channel wavelength conditions.

[0065] In this embodiment, the working dye channel image refers to the fluorescence image of the channel corresponding to the working dye that has been captured. The reference dye channel image refers to the fluorescence image of the channel corresponding to the reference dye that has been captured.

[0066] Specifically, at least two indicator dyes of a specific concentration are added to the emulsified system and mixed thoroughly. The mixed system is emulsified into droplets, and the conditions and operations required for the reaction are applied to the droplets to ensure that all droplets in the system react completely, thereby obtaining the droplet system.

[0067] In one embodiment, the at least two indicator dyes correspond to different excitation wavelengths. The at least two indicator dyes include the working dye and the reference dye.

[0068] It should be noted that the working dye can indicate different concentrations of inclusions in the droplet system mixed with it by varying the intensity of the excitation light working signal, thereby enabling the recording of different reaction states. The reference dye is inert, neither participating in the reaction within the system nor inhibiting or interfering with the reaction itself, and generally does not exhibit different signal intensities with changes in the concentration of inclusions in the droplet system.

[0069] To illustrate this application clearly and simply, for example, a nuclease-removing aqueous solution was prepared using 5 mM SYTO-9 and 2× ROX. The solution and emulsifier were then mixed at a volume ratio of 1:10. The mixing method involved manual shaking or vortexing twice, each time for 3 seconds, to form a multi-volume droplet emulsion system. Of the two indicator dyes added, SYTO-9 was the working dye, and ROX was the reference dye. It should be noted that the working and reference dyes added are not limited to SYTO-9 and ROX. The emulsion system was loaded onto a BGISeq-500 chip and imaged under an Olympus SZX16 stereofluorescence microscope. Specifically, excitation and emission were performed using the 480nm / 535nm and 540nm / 605nm fluorescence channels, respectively, thereby obtaining fluorescence images of the SYTO-9 and ROX dye channels (i.e., working dye channel images and reference dye channel images).

[0070] It should be noted that the above-described methods for obtaining droplet systems and acquiring working dye channel images and reference dye channel images are merely illustrative examples to help those skilled in the art understand the present invention, and should not be construed as limiting the present invention.

[0071] Step S2: The execution module 302 performs preset image processing operations on the working dye channel image and the reference dye channel image, respectively.

[0072] In one embodiment, the preset image processing operations performed on the working dye channel image and the reference dye channel image respectively include, but are not limited to, correcting the background brightness unevenness of the working dye channel image and the reference dye channel image respectively; performing image enhancement processing on the reference dye channel image and the working dye channel image respectively using a preset image enhancement algorithm; and performing noise reduction processing on the working dye channel image and the reference dye channel image respectively using a preset noise reduction algorithm.

[0073] It should be noted that the order in which the execution module 302 performs the background brightness unevenness correction operation, image enhancement processing, and noise reduction processing on the working dye channel image and the reference dye channel image is not fixed.

[0074] In this embodiment, the background brightness unevenness correction operation performed on the working dye channel image and the reference dye channel image respectively may include: obtaining a mask image by using Nearest Neighbour Resampling and polynomial fitting, and then subtracting the mask image from the original image (i.e., the working dye channel image and the reference dye channel image), thereby obtaining the corrected working dye channel image and the corrected reference dye channel image.

[0075] In one embodiment, the image enhancement algorithm includes, but is not limited to, grayscale transformation algorithms, histogram equalization algorithms, and adaptive histogram equalization algorithms.

[0076] In one implementation, the execution module 302 may first perform grayscale transformation on the working dye channel image and the reference dye channel image respectively, then perform histogram equalization on each, and then perform adaptive histogram equalization on each, thereby obtaining the enhanced working dye channel image and the enhanced reference dye channel image.

[0077] In one implementation, the execution module 302 may first enhance the image using the imadjust grayscale transformation method, and then saturate a preset percentage (e.g., 1%) of the data in the image to the lowest and highest brightness levels to increase the contrast value of the output image.

[0078] In one implementation, the denoising algorithm includes, but is not limited to, nonlocal mean denoising algorithm, Gaussian scale mixture denoising algorithm, block matching three-dimensional filtering denoising algorithm, etc.

[0079] Step S3: The execution module 302 selects either the working dye channel image or the reference dye channel image as the target image.

[0080] In this embodiment, the execution module 302 designates the one of the working dye channel image and the reference dye channel image that is not used as the target image as a non-target image.

[0081] In one embodiment, the execution module 302 may also use the target image as a fixed reference to perform registration operations on the non-target image.

[0082] In one embodiment, using the working dye channel image or the reference dye channel image as the target image includes: randomly selecting one of the working dye channel image and the reference dye channel image as the target image.

[0083] In other embodiments, selecting either the working dye channel image or the reference dye channel image as the target image includes: selecting one of the working dye channel image and the reference dye channel image as the target image in response to a user operation.

[0084] In one embodiment, the registration operation on the non-target image using the target image as a fixed reference includes (a1)-(a2):

[0085] (a1) Using the target image as a fixed reference, perform a first image registration operation on the non-target image to obtain a first transformation matrix, and obtain a first registered image based on the first transformation matrix and the non-target image.

[0086] In this embodiment, the first image registration operation includes, but is not limited to, rigid transformation, similarity transformation, affine transformation, and projection transformation.

[0087] Specifically, the first registered image is equal to the product of the first transformation matrix and the non-target image.

[0088] (a2) Using the target image as a fixed reference, perform a second image registration operation on the first registered image to obtain a second transformation matrix, and obtain a second registered image based on the second transformation matrix and the first registered image, and use the second registered image as the registered non-target image.

[0089] In this embodiment, the registration method for the second image registration operation includes, but is not limited to, optical flow, B-spline, and Demons methods.

[0090] Specifically, the second registered image is equal to the product of the second transformation matrix and the first registered image.

[0091] In one embodiment, the execution module 302 may also fill the irregular edge regions of the registered non-target image with the foreground color.

[0092] For example, if the reference dye channel image is used as the target image and the working dye channel image is used as the non-target image, the execution module 302 can use the reference dye channel image as a fixed reference to perform a registration operation on the working dye channel image.

[0093] Specifically, the execution module 302 uses the reference dye channel image as a fixed reference to perform the first image registration operation on the working dye channel image to obtain a first transformation matrix, and obtains a first registered image based on the first transformation matrix and the working dye channel image. The first registered image is equal to the product of the first transformation matrix and the working dye channel image.

[0094] Then, the execution module 302 uses the reference dye channel image as a fixed reference to perform the second image registration operation on the first registered image to obtain a second transformation matrix, and obtains a second registered image based on the second transformation matrix and the first registered image. This second registered image is then used as the registered working dye channel image. The second registered image is equal to the product of the second transformation matrix and the first registered image.

[0095] In one embodiment, the execution module 302 may also fill the irregular edge regions of the registered working dye channel image with the foreground color.

[0096] It should be noted that registration of non-target images can eliminate the influence of fluid disturbances on these images. In other words, image registration solves problems such as droplet displacement, duplicate statistics, or droplet loss caused by droplet flow, local disturbances, or Brownian motion, thus improving the accuracy of image processing and data calculation.

[0097] For example, see Figure 5 As shown, Figure 5 The diagram illustrates the secondary registration of the working dye channel image using the reference dye channel image as a fixed reference. Figure 5 In image A, the left side is the reference dye channel image, and the right side is the working dye channel image; in image B, the left side is the reference dye channel image, and the right side is the working dye channel image after the first registration correction; in image C, the left side is the reference dye channel image, and the right side is the working dye channel image after the second registration correction; in image D, the left side is the reference dye channel image, and the right side is the working dye channel image with irregular black edges filled in. Figure 5As can be seen, after secondary registration of the working dye channel image, the working dye channel image and the reference dye channel image are basically aligned, thus effectively solving the problems of droplet displacement, duplicate statistics or droplet loss caused by droplet flow, local disturbance or Brownian motion, and improving the accuracy of image processing and data calculation.

[0098] It should also be noted that the registration operation on the non-target image can also be performed before the preset image processing operation is performed on the working dye channel image and the reference dye channel image.

[0099] It should also be noted that for cases where droplet flow, local disturbance, or Brownian motion will not occur, registration operations may not be performed on the non-target images. For example, when using a continuous phase material that allows for controlled phase transition, after emulsification and before capturing the working dye channel image and the reference dye channel image, the continuous phase can be solidified into a transparent solid by changing the conditions. In this way, the droplets are enclosed in a solid cavity, thus preventing flow, local disturbance, or Brownian motion that could cause droplet drift.

[0100] Step S4: Execution module 302 determines the region where each droplet is located from the target image.

[0101] In one embodiment, determining the region where each droplet is located from the target image includes (b1)-(b3):

[0102] (b1) Perform local contrast enhancement processing on the target image.

[0103] It should be noted that by performing local contrast enhancement processing on the target image, the accuracy of droplet edge extraction can be improved.

[0104] (b2) The target image is converted to grayscale to obtain a grayscale image, and the grayscale image is binarized to obtain the binarized target image.

[0105] (b3) The binarized target image is divided into several discrete image regions, each of which corresponds to a single droplet, thereby determining the region where each droplet is located.

[0106] In one embodiment, the execution module 302 can also mark the area where each droplet is located.

[0107] In this embodiment, the execution module 302 can first process the target image using a binarized image distance transform method, then obtain a grayscale terrain image of the processed target image, and then obtain the watershed dividing line of the grayscale terrain image using a dam transform method, and then divide the target image into the plurality of discrete image regions according to the dividing line.

[0108] Specifically, the execution module 302 can first process the target image using the bwdist binarized image distance transform method, then obtain a grayscale terrain image of the processed target image, and then obtain the watershed segmentation line of the grayscale terrain image using the watershed dam transform method. Furthermore, it can segment adjacent droplet regions in the target image according to the segmentation line, thereby obtaining the plurality of discrete image regions. In this embodiment, the execution module 302 can use the bwlabel method to label the region where each droplet is located, thereby assigning different label values ​​to the regions where different droplets are located.

[0109] It should be noted that when the reference dye channel image is used as the target image, since the non-target image, i.e., the working dye channel image, is registered with the reference dye channel image as a reference, the label value marked for the region where each droplet is located on the target image (i.e., the reference dye channel image) is also applicable to the non-target image, i.e., the working dye channel image (that is, the area occupied by each droplet on the working dye channel image can also be determined based on the label value). Similarly, when the working dye channel image is used as the target image, since the non-target image, i.e., the reference dye channel image, is registered with the working dye channel image as a reference, the label value marked for the region where each droplet is located on the target image, i.e., the working dye channel image, is also applicable to the non-target image, i.e., the reference dye channel image (that is, the area occupied by each droplet on the reference dye channel image can also be determined based on the label value).

[0110] Step S5: Execution module 302 analyzes the droplet system based on the determined region where each droplet is located.

[0111] In one implementation, the analysis of the droplet system based on the determined region of each droplet includes: calculating the droplet relative intensity. Rela .

[0112] In one implementation, the calculation of the relative light intensity of each droplet includes (c1)-(c2):

[0113] (c1) Calculate the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref .

[0114] In one embodiment, the average light intensity I of each droplet on the working dye channel image is... Abs The sum of the grayscale values ​​of all pixels corresponding to each droplet in the working dye channel image is divided by the total number of pixels corresponding to each droplet in the working dye channel image. The average light intensity I of each droplet in the reference dye channel image is also calculated. Ref It is equal to the sum of the gray values ​​of all pixels corresponding to each droplet in the reference dye channel image divided by the total number of pixels corresponding to each droplet in the reference dye channel image.

[0115] For example, the average light intensity I of droplet A on the working dye channel image. Abs It is equal to the sum of the grayscale values ​​of all pixels corresponding to the region where droplet A is located in the working dye channel image, divided by the total number of pixels in the region where droplet A is located in the working dye channel image. Similarly, the average light intensity I of droplet A in the reference dye channel image is... Ref It is equal to the sum of the gray values ​​of all pixels corresponding to the region where droplet A is located in the reference dye channel image divided by the total number of pixels in the region where droplet A is located in the reference dye channel image.

[0116] (c2) Based on the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref Calculate the relative light intensity I of each droplet. Rela , among which, I Rela =I Abs / I Ref .

[0117] In other embodiments, when applied to digital nucleic acid amplification detection, the light intensities of droplet digital PCR droplets differ, requiring droplet classification, such as dividing droplets into negative and positive droplets, and calculating the concentration of the target molecule based on the droplet classification results. Therefore, the execution module 302 can further calculate the relative light intensity I of each droplet. Rela Classify the droplets.

[0118] Specifically, the relative light intensity I based on each droplet Rela Classification of droplets includes:

[0119] When the relative light intensity of any droplet I Rela If the relative light intensity of any droplet is greater than a preset threshold, it is determined that the droplet contains the target analyte and that the droplet is classified as a positive droplet. Conversely, if the relative light intensity of any droplet is less than or equal to the preset threshold, it is determined that the droplet does not contain the target analyte and that the droplet is classified as a negative droplet.

[0120] For example, see Figure 6 The image shows an information table recording the region where each droplet is located. It can be seen from the image that the pixel area of ​​droplet number 1 is 41688, and the average light intensity I of droplet number 1 on the working dye channel image is... Abs The average light intensity I on the reference dye channel image is 181.3267. Ref The relative light intensity of droplet number 1 is 3.384947, which is 53.56853. Since the relative light intensity of droplet number 1 is greater than the preset threshold of 2.9, droplet number 1 is classified as a positive droplet (indicated by "+" in the information table).

[0121] For example, see Figure 7 As shown, Figure 7 This illustrates that after performing the preset image operations on the working dye channel image and the reference dye channel image, and performing two registrations on either the working dye channel image or the reference dye channel image (which serves as the target image), droplets can be classified into two categories—negative droplets and positive droplets—based on their relative light intensity. Figure 7 In the diagram, A represents the marked droplet region in the reference dye channel image; B represents the marked droplet region in the working dye channel image; C represents the droplet's relative light intensity value and pixel area calculated based on the droplet's label value (for example, the pixel area of ​​droplet number 795 (i.e., drop795) is calculated to be 28702, and the relative light intensity is 3.36); D represents the biscatter histogram of the relative light intensity and pixel area of ​​all droplets.

[0122] In one embodiment, the execution module 302 may use a clustering algorithm to determine the threshold based on the relative light intensity of each droplet in all droplets in the droplet system.

[0123] Specifically, the execution module 302 can obtain a scatter plot with the number of each droplet as the horizontal axis and the relative light intensity of each droplet as the vertical axis, and use the K-Means clustering algorithm to determine the threshold based on the obtained scatter plot.

[0124] In one embodiment, the execution module 302 can also calculate the volume v of each droplet based on the total number of pixels in the region where each droplet is located and a predetermined image pixel length conversion ratio.

[0125] Specifically, the method by which execution module 302 determines the image pixel length conversion ratio includes:

[0126] At a preset microscope magnification, take an image of a preset square using a microscope;

[0127] Calculate the total number of pixels included within the side length of the square in the captured image; and

[0128] The image pixel length conversion ratio is calculated based on the total number of pixels included in the calculated side length of the square and the actual length of the side length of the square.

[0129] For example, assuming that the total number of pixels included in the side length of the square in the captured image is 1150 pixels, and the actual side length of the square is 2mm, then the pixel length conversion ratio of the image is 1.73660264μm / pixel.

[0130] It should be noted that the preset microscope magnification refers to the magnification used by the microscope when capturing the working dye channel image and the reference dye channel image.

[0131] In one embodiment, the execution module 302 can also count the total number of all droplets in the droplet system based on the number of the tag values.

[0132] In one embodiment, the execution module 302 can also calculate the quantitative result of the contents of the droplet, i.e., the concentration of the target analyte. The concentration of the target analyte can be calculated based on the total volume of the droplet system, which is determined by the total number of positive droplets included in the droplet system.

[0133] As can be seen from the above description, the image-based droplet analysis method described in this application obtains relevant information about the droplet system, such as the contents of each droplet and classification results, based on the working dye channel image and the reference dye channel image. This can effectively solve the technical problem that the uneven light signal caused by the non-uniform droplet volume makes it impossible to accurately obtain relevant information about the droplet system based on the collected light signal.

[0134] Furthermore, during the process of capturing the working dye channel image and the reference dye channel image, even if the droplet is displaced or lost due to flow, local disturbance, or Brownian motion, resulting in the working dye channel image and the reference dye channel image not being captured based on the droplet being in the same state, this application will use the reference dye channel image as a reference to register the working dye channel image during the processing of the captured working dye channel image and the reference dye channel image, thereby making the relevant information of the droplet system analyzed based on the working dye channel image and the reference dye channel image more accurate.

[0135] For example, see Figure 8 As shown, where, Figure 8 A is an overlay of the working dye channel image without registration correction and the information of the segmented droplet region; B is a scatter plot of the relative light intensity of the droplets and the droplet pixel area (i.e., the total number of pixels) obtained from the segmented droplet region of the working dye channel image without registration correction, with a droplet relative light intensity coefficient of variation (CV) of 10.67%; C is an overlay of the working dye channel image after the first registration correction and the information of the segmented droplet region; D is a scatter plot of the relative light intensity of the droplets and the droplet pixel area obtained from the segmented droplet region of the working dye channel image after the first registration correction, with a droplet relative light intensity coefficient of variation of 7.29%; E is an overlay of the working dye channel image after the second registration correction and the information of the segmented droplet region; F is a scatter plot of the relative light intensity of the droplets and the droplet pixel area obtained from the segmented droplet region of the working dye channel image after the second registration correction, with a droplet relative light intensity coefficient of variation of 1.35%. Figure 8 The above data analysis shows that after performing two registrations on the working dye channel image, the coefficient of variation of the droplet's relative light intensity is significantly reduced. Therefore, the droplet's relevant information (such as droplet contents) calculated based on the droplet's relative light intensity obtained from the working dye channel image after two registrations will be more accurate.

[0136] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed non-volatile readable storage medium, apparatus, and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0137] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0139] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other elements or, and the singular does not exclude the plural. Multiple elements or devices recited in the apparatus claims may also be implemented by a single element or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for analyzing droplets based on images, characterized in that, The method includes: The acquisition step involves acquiring the working dye channel image and the reference dye channel image corresponding to the droplet system, wherein both the working dye channel image and the reference dye channel image are fluorescence images; The image processing steps involve performing preset image processing operations on the working dye channel image and the reference dye channel image, respectively. The target determination step involves using either the working dye channel image or the reference dye channel image as the target image. The registration step involves taking the non-target image from the working dye channel image and the reference dye channel image as a non-target image, and using the target image as a fixed reference, performing a registration operation on the non-target image. This includes: performing a first image registration operation on the non-target image using the target image as a fixed reference to obtain a first transformation matrix; obtaining a first registered image based on the first transformation matrix and the non-target image, where the first registered image is equal to the product of the first transformation matrix and the working dye channel image; and performing a second image registration operation on the first registered image using the target image as a fixed reference to obtain a second transformation matrix; obtaining a second registered image based on the second transformation matrix and the first registered image, where the second registered image is equal to the product of the second transformation matrix and the first registered image; and using this second registered image as the registered non-target image. The first image registration operation is different from the second image registration operation. The region determination step involves determining the region where each droplet is located from the target image; and The analysis steps, based on the determined region of each droplet, analyze the droplet system, including: A scatter plot is obtained with the number of each droplet in the droplet system as the horizontal axis and the relative light intensity of each droplet as the vertical axis. A threshold is determined based on the scatter plot using a clustering algorithm. Each droplet is classified according to the comparison between its relative light intensity IRela and the threshold, and is classified as either a positive droplet or a negative droplet to obtain the droplet classification result. The concentration of the target analyte is then calculated based on the droplet classification result.

2. The method for analyzing droplets based on images as described in claim 1, characterized in that, The acquisition steps include: The working dye and reference dye in the droplet system are excited under dual / multi-channel wavelength conditions, and fluorescence images of the corresponding channels of the working dye are captured to obtain the working dye channel image, and fluorescence images of the corresponding channels of the reference dye are captured to obtain the reference dye channel image.

3. The method for analyzing droplets based on images as described in claim 1, characterized in that, The image processing steps include: The background brightness unevenness correction operation is performed on the working dye channel image and the reference dye channel image respectively; The reference dye channel image and the working dye channel image are respectively enhanced using a preset image enhancement algorithm; and The working dye channel image and the reference dye channel image are denoised using a preset denoising algorithm.

4. The method for analyzing droplets based on images as described in claim 1, characterized in that, The region determination steps include: Perform local contrast enhancement processing on the target image; The target image is converted to grayscale to obtain a grayscale image, and then the grayscale image is binarized to obtain the binarized target image; and The binarized target image is divided into several discrete image regions, each of which corresponds to a single droplet, thereby determining the region where each droplet is located.

5. The method for analyzing droplets based on images as described in claim 1, characterized in that, The analysis steps also include: Calculate the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref ; Based on the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref Calculate the relative light intensity I for each droplet. Rela , among which, I Rela = I Abs / I Ref .

6. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing at least one computer-readable instruction, and the processor executing the at least one computer-readable instruction to perform the following steps: The acquisition step involves acquiring the working dye channel image and the reference dye channel image corresponding to the droplet system, wherein both the working dye channel image and the reference dye channel image are fluorescence images; The image processing steps involve performing preset image processing operations on the working dye channel image and the reference dye channel image, respectively. The target determination step involves using either the working dye channel image or the reference dye channel image as the target image. The registration step involves taking the non-target image from the working dye channel image and the reference dye channel image as a non-target image, and using the target image as a fixed reference, performing a registration operation on the non-target image. This includes: performing a first image registration operation on the non-target image using the target image as a fixed reference to obtain a first transformation matrix; obtaining a first registered image based on the first transformation matrix and the non-target image, where the first registered image is equal to the product of the first transformation matrix and the working dye channel image; and performing a second image registration operation on the first registered image using the target image as a fixed reference to obtain a second transformation matrix; obtaining a second registered image based on the second transformation matrix and the first registered image, where the second registered image is equal to the product of the second transformation matrix and the first registered image; and using this second registered image as the registered non-target image. The first image registration operation is different from the second image registration operation. The region determination step involves determining the region where each droplet is located from the target image; and The analysis steps, based on the determined region of each droplet, analyze the droplet system, including: A scatter plot is obtained with the number of each droplet in the droplet system as the horizontal axis and the relative light intensity of each droplet as the vertical axis. A threshold is determined based on the scatter plot using a clustering algorithm. Each droplet is classified according to the comparison between its relative light intensity IRela and the threshold, and is classified as either a positive droplet or a negative droplet to obtain the droplet classification result. The concentration of the target analyte is then calculated based on the droplet classification result.

7. The computer device as claimed in claim 6, characterized in that, The processor is configured to execute the at least one computer-readable instruction to implement the acquisition step, including: The working dye and reference dye in the droplet system are excited under dual / multi-channel wavelength conditions, and fluorescence images of the corresponding channels of the working dye are captured to obtain the working dye channel image, and fluorescence images of the corresponding channels of the reference dye are captured to obtain the reference dye channel image.

8. The computer device as claimed in claim 6, characterized in that, The processor is configured to execute the at least one computer-readable instruction to implement the image processing steps, including: The background brightness unevenness correction operation is performed on the working dye channel image and the reference dye channel image respectively; The reference dye channel image and the working dye channel image are respectively enhanced using a preset image enhancement algorithm; and The working dye channel image and the reference dye channel image are denoised using a preset denoising algorithm.

9. The computer device as claimed in claim 6, characterized in that, The processor is configured to execute the at least one computer-readable instruction to implement the region determination step, including: Perform local contrast enhancement processing on the target image; The target image is converted to grayscale to obtain a grayscale image, and then the grayscale image is binarized to obtain the binarized target image; and The binarized target image is divided into several discrete image regions, each of which corresponds to a single droplet, thereby determining the region where each droplet is located.

10. The computer device as claimed in claim 6, characterized in that, The processor is configured to execute the at least one computer-readable instruction to implement the analysis step, and further includes: Calculate the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref ; Based on the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref Calculate the relative light intensity I for each droplet. Rela , among which, I Rela = I Abs / I Ref .

11. A non-volatile readable storage medium, characterized in that, The non-volatile readable storage medium stores at least one computer-readable instruction, which, when executed by a processor, performs the following steps: The acquisition step involves acquiring the working dye channel image and the reference dye channel image corresponding to the droplet system, wherein both the working dye channel image and the reference dye channel image are fluorescence images; The image processing steps involve performing preset image processing operations on the working dye channel image and the reference dye channel image, respectively. The target determination step involves using either the working dye channel image or the reference dye channel image as the target image. The registration step involves taking the non-target image from the working dye channel image and the reference dye channel image as a non-target image, and using the target image as a fixed reference, performing a registration operation on the non-target image. This includes: performing a first image registration operation on the non-target image using the target image as a fixed reference to obtain a first transformation matrix; obtaining a first registered image based on the first transformation matrix and the non-target image, where the first registered image is equal to the product of the first transformation matrix and the working dye channel image; and performing a second image registration operation on the first registered image using the target image as a fixed reference to obtain a second transformation matrix; obtaining a second registered image based on the second transformation matrix and the first registered image, where the second registered image is equal to the product of the second transformation matrix and the first registered image; and using this second registered image as the registered non-target image. The first image registration operation is different from the second image registration operation. The region determination step involves identifying the region containing each droplet from the target image; and The analysis steps, based on the determined region of each droplet, analyze the droplet system, including: A scatter plot is obtained with the number of each droplet in the droplet system as the horizontal axis and the relative light intensity of each droplet as the vertical axis. A threshold is determined based on the scatter plot using a clustering algorithm. Each droplet is classified according to the comparison between its relative light intensity IRela and the threshold, and is classified as either a positive droplet or a negative droplet to obtain the droplet classification result. The concentration of the target analyte is then calculated based on the droplet classification result.

12. The non-volatile readable storage medium as described in claim 11, characterized in that, When the at least one computer-readable instruction is executed by the processor, the acquisition step is implemented, including: The working dye and reference dye in the droplet system are excited under dual / multi-channel wavelength conditions, and fluorescence images of the corresponding channels of the working dye are captured to obtain the working dye channel image, and fluorescence images of the corresponding channels of the reference dye are captured to obtain the reference dye channel image.

13. The non-volatile readable storage medium as described in claim 11, characterized in that, When the at least one computer-readable instruction is executed by a processor, it implements the image processing steps, including: The background brightness unevenness correction operation is performed on the working dye channel image and the reference dye channel image respectively; The reference dye channel image and the working dye channel image are respectively enhanced using a preset image enhancement algorithm; and The working dye channel image and the reference dye channel image are denoised using a preset denoising algorithm.

14. The non-volatile readable storage medium as described in claim 11, characterized in that, When the at least one computer-readable instruction is executed by the processor, the determining step is implemented, including: Perform local contrast enhancement processing on the target image; The target image is converted to grayscale to obtain a grayscale image, and then the grayscale image is binarized to obtain the binarized target image; and The binarized target image is divided into several discrete image regions, each of which corresponds to a single droplet, thereby determining the region where each droplet is located.

15. The non-volatile readable storage medium as claimed in claim 11, characterized in that, When the at least one computer-readable instruction is executed by the processor to implement the analysis step, it further includes: Calculate the average light intensity I of each droplet on the working dye channel image based on the region where each droplet is located. Abs And calculate the average light intensity I of each droplet on the reference dye channel image based on the region where each droplet is located. Ref ; Based on the average light intensity I of each droplet on the working dye channel Abs And the average light intensity I of each droplet on the reference dye channel image. Ref Calculate the relative light intensity I for each droplet. Rela , among which, I Rela = I Abs / I Ref .

Images