Effective droplet determination method, system, and electronic device

Abstract

The application provides an effective micro-droplet determination method, system and electronic equipment, and relates to the field of micro-droplet identification. The method first determines a to-be-determined image; wherein the to-be-determined image comprises a plurality of to-be-determined micro-droplets; then the position parameters of the current to-be-determined micro-droplet are determined in the to-be-determined image in sequence, and the coefficient of variation of the to-be-determined micro-droplet and the half-peak parameters of the gray distribution curve of the to-be-determined micro-droplet in multiple directions are determined by using the position parameters; then it is judged whether the coefficient of variation of the micro-droplet and the half-peak parameters in each direction meet the preset threshold condition; finally, the number of the half-peak parameters meeting the threshold condition is obtained, and whether the micro-droplet is effective is determined according to the number and whether the coefficient of variation of the micro-droplet meets the threshold condition. The method uses the half-peak parameters of the gray distribution curve of the to-be-determined micro-droplet in multiple directions to determine the effectiveness of the micro-droplet, reduces the influence of image noise on the determination of the effectiveness of the micro-droplet, and improves the accuracy of the determination of the micro-droplet.

Description

Technical Field

[0001] This invention relates to the field of droplet identification, and in particular to an effective method, system and electronic device for determining droplets. Background Technology

[0002] Digital PCR (Polymerase Chain Reaction, PCR) is an absolute quantification technique for nucleic acid molecules. Based on single-molecule PCR, digital PCR quantifies nucleic acids by counting molecules. It primarily employs microfluidic or droplet-based methods to disperse a large amount of diluted nucleic acid solution into microreaction chambers or droplets on a microarray. Each microreaction chamber or droplet contains no more than one nucleic acid template. After PCR cycles, microreaction chambers or droplets with one nucleic acid template molecule emit a fluorescent signal, while those without do not. Therefore, rapidly and accurately identifying positive droplets in the fluorescence image is crucial for ensuring detection accuracy.

[0003] Existing droplet identification technologies mainly use local epipolar points in an image as estimates of the droplet center to identify droplets. This identification method is easily affected by factors such as image noise, resulting in a large discrepancy between the identified droplet center and the actual location, and even erroneous results such as identifying multiple droplet centers in a single droplet. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an effective method, system and electronic device for determining microdroplets. The method uses the half-peak parameter of the gray-scale distribution curve of the microdroplet to be determined in multiple directions to determine the validity of the microdroplet, thereby reducing the influence of image noise on the determination of the validity of the microdroplet and improving the accuracy of the determination of the microdroplet.

[0005] In a first aspect, embodiments of the present invention provide an effective microdroplet determination method, the method comprising:

[0006] Identify the image to be judged; wherein the image to be judged includes multiple microdroplets to be judged;

[0007] In the image to be judged, the position parameters of the droplet to be judged are determined sequentially, and the coefficient of variation of the droplet to be judged and the half-peak parameters of its gray-level distribution curves in multiple directions are determined using the position parameters.

[0008] Determine whether the coefficient of variation of the droplet to be judged and the half-peak parameters in each direction meet the preset threshold conditions;

[0009] Obtain the number of half-peak parameters that meet the threshold condition, and determine whether the droplet is effective based on the number and the coefficient of variation.

[0010] In some implementations, the position parameters of the current droplet to be judged are determined sequentially in the image to be judged, including:

[0011] Obtain the chip region contained in the image to be judged; wherein, the chip region includes at least: the chip edge region and the chip support pillar region;

[0012] Obtain the geometric center coordinates and size parameters of all droplets contained in the judgment image, and use the geometric center coordinates and size parameters to calculate the closest distance between the droplets and the chip region;

[0013] The droplets whose closest distance meets the preset threshold are identified as the droplets to be judged, and the geometric center coordinates and size parameters of the droplets to be judged are determined as position parameters.

[0014] In some implementations, the process of determining the coefficient of variation of the droplet to be determined includes:

[0015] Within the droplet to be judged, a circular region is obtained with the center of the droplet as the center and a preset length as the radius;

[0016] Obtain the standard deviation and average value of all pixel values ​​within the circular region;

[0017] The ratio of the standard deviation of pixel values ​​to the average value of pixel values ​​is used as the coefficient of variation of the droplet to be judged.

[0018] In some implementations, the process of determining the half-peak parameter of the grayscale distribution curve of the droplet to be determined in multiple directions includes:

[0019] Obtain the geometric center coordinates and corresponding radius data of the droplet to be determined, which are contained in the position parameters;

[0020] Based on the geometric center and radius data of the droplet to be judged, the half-peak parameters of the gray-scale distribution curves of the droplet to be judged in multiple directions are determined.

[0021] In some implementations, based on the geometric center and radius data of the droplet to be determined, the half-peak parameters of the grayscale distribution curves of the droplet in multiple directions are determined, including:

[0022] A six-axis coordinate system is established with the geometric center of the droplet to be determined as the origin; the angle between each axis is 30 degrees.

[0023] Calculate the half-peak parameters of the gray-scale distribution curve corresponding to each droplet on each coordinate axis; where the half-peak parameters include: half-peak width parameter and normalized half-peak area parameter.

[0024] In some implementations, the half-peak parameter of the gray-scale distribution curve corresponding to each droplet on each coordinate axis is calculated, including:

[0025] Based on the geometric center and radius data of the droplets under each coordinate axis, obtain multiple pixels corresponding to the normal vector of the droplets along the current direction, and use the average gray level of the pixels to determine the gray level distribution curve corresponding to the droplets.

[0026] Determine the half-peak parameter using the grayscale distribution curve.

[0027] In some implementations, the step of obtaining the number of half-peak parameters that meet the threshold condition and determining whether the droplet is effective based on the number includes:

[0028] When the number of half-peak parameters that meet the threshold condition exceeds three, the droplet is determined to be a valid droplet.

[0029] If the number of half-peak parameters that meet the threshold condition does not exceed three, the droplet is identified as an invalid droplet.

[0030] Secondly, embodiments of the present invention provide an effective droplet determination system, the system comprising:

[0031] The image to be determined module is used to determine the image to be determined; wherein, the image to be determined includes multiple microdroplets to be determined;

[0032] The half-peak parameter calculation module is used to sequentially determine the position parameters of the current droplet to be judged in the image to be judged, and use the position parameters to determine the coefficient of variation of the droplet to be judged and the half-peak parameters of its gray-level distribution curves in multiple directions.

[0033] The half-peak parameter judgment module is used to determine whether the coefficient of variation of the droplet to be judged and its half-peak parameter in each direction meet the preset threshold conditions.

[0034] The effective droplet determination module is used to obtain the number and coefficient of variation of half-peak parameters that meet the threshold conditions, and to determine whether the droplet is effective based on the number and coefficient of variation.

[0035] Thirdly, embodiments of the present invention also provide an electronic device, including: a processor and a memory; the memory stores a computer program, which, when run by the processor, implements the steps of the effective droplet determination method mentioned in the first aspect above.

[0036] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the effective droplet determination method mentioned in the first aspect above.

[0037] The embodiments of the present invention bring the following beneficial effects:

[0038] This invention provides a method, system, and electronic device for determining valid droplets. When determining valid droplets in an image, the method first defines the image to be determined, which includes multiple droplets. Then, the position parameters of each droplet are sequentially determined within the image, and the coefficient of variation and half-peak parameters of their grayscale distribution curves in multiple directions are determined using these position parameters. Next, it is determined whether the coefficient of variation and half-peak parameters in each direction satisfy a preset threshold condition. Finally, the number of half-peak parameters and the coefficient of variation that satisfy the threshold condition are obtained, and the validity of the droplet is determined based on the number of parameters. This method utilizes the half-peak parameters of the grayscale distribution curves of the droplets in multiple directions to determine droplet validity, reducing the impact of image noise on droplet validity determination and improving the accuracy of droplet determination.

[0039] Other features and advantages of the invention will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 A flowchart of an effective droplet determination method provided in an embodiment of the present invention;

[0043] Figure 2 A flowchart illustrating the process of sequentially determining the position parameters of the current droplet to be determined in an image to be determined, as provided in an embodiment of the present invention.

[0044] Figure 3 A flowchart illustrating the process of determining the coefficient of variation of the droplet to be determined in an effective droplet determination method provided in an embodiment of the present invention;

[0045] Figure 4 A flowchart illustrating the process of determining the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions in an effective droplet determination method provided in an embodiment of the present invention.

[0046] Figure 5A flowchart illustrating an effective droplet determination method provided in this embodiment of the invention, which determines the half-peak parameters of the grayscale distribution curves of the droplet to be determined in multiple directions based on the geometric center and radius data of the droplet to be determined;

[0047] Figure 6 A flowchart illustrating the calculation of the half-peak parameter of the gray-scale distribution curve corresponding to each microdroplet under each coordinate axis in an effective microdroplet determination method provided by an embodiment of the present invention.

[0048] Figure 7 The flowchart of an effective droplet determination method provided in this embodiment of the invention includes obtaining the number of half-peak parameters that meet the threshold condition and determining whether the droplet is effective based on the number of parameters.

[0049] Figure 8 This is a schematic diagram of the droplets to be determined obtained in an effective droplet determination method provided in an embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram illustrating the determination of the half-peak parameter of the grayscale distribution curve of the droplet to be determined in multiple directions in an effective droplet determination method provided in an embodiment of the present invention.

[0051] Figure 10 This is a schematic diagram of an effective droplet determination system provided in an embodiment of the present invention;

[0052] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0053] icon:

[0054] 1010 - Image determination module; 1020 - Half-peak parameter calculation module; 1030 - Half-peak parameter judgment module; 1040 - Effective droplet judgment module;

[0055] 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Digital PCR (Polymerase Chain Reaction, PCR) is an absolute quantification technique for nucleic acid molecules. Based on single-molecule PCR, digital PCR quantifies nucleic acids by counting molecules. It primarily employs microfluidic or droplet-based methods to disperse a large amount of diluted nucleic acid solution into microreaction chambers or droplets on a microarray. Each microreaction chamber or droplet contains no more than one nucleic acid template. After PCR cycles, microreaction chambers or droplets with one nucleic acid template molecule emit a fluorescent signal, while those without do not. Therefore, rapidly and accurately identifying positive droplets in the fluorescence image is crucial for ensuring detection accuracy.

[0058] Existing droplet identification technologies primarily rely on local epipolar points in an image as estimates of the droplet center. This method is susceptible to image noise and other factors, leading to significant discrepancies between the identified droplet center and its actual location, and even erroneous results such as identifying multiple droplet centers within a single droplet. Therefore, this invention provides an effective droplet determination method, system, and electronic device. This method utilizes the half-peak parameters of the grayscale distribution curves of the droplet in multiple directions to determine its validity, reducing the impact of image noise on droplet validity determination and improving the accuracy of droplet identification.

[0059] To facilitate understanding of this embodiment, a detailed description of an effective microdroplet determination method disclosed in this embodiment of the invention will be provided first. Specifically, the method is as follows: Figure 1 As shown, it includes:

[0060] Step S101: Determine the image to be judged; wherein the image to be judged includes multiple microdroplets to be judged.

[0061] The image to be evaluated is a digital fluorescence image, primarily used in digital PCR (Polymerase Chain Reaction) scenarios. After PCR cycles, a microreaction chamber or droplet containing a nucleic acid template will emit a fluorescent signal, while those without will not. Generally, the image to be evaluated is a digital image with numerous droplets arranged in a pattern. The effective droplets among these droplets are those that emit fluorescent signals, corresponding to the positive spots in the fluorescence image.

[0062] Step S102: Sequentially determine the position parameters of the current droplet to be determined in the image to be determined, and use the position parameters to determine the coefficient of variation of the droplet to be determined and the half-peak parameters of its gray-scale distribution curves in multiple directions.

[0063] First, all droplets in the image to be judged need to be identified, specifically through their positional parameters. Generally, the droplets to be judged are circular or regular polygonal in shape; therefore, their respective positional parameters are determined based on their shapes. The shape of the droplets is determined using the coefficient of variation. For example, if the droplet is circular, its positional parameters mainly include the center coordinates and radius; if the droplet is hexagonal, its positional parameters mainly include the center coordinates and side length.

[0064] Once the position parameters of the droplets to be judged are determined, the grayscale distribution curves in different directions are first calculated based on the position parameters of different droplets. In practical scenarios, the calculation direction can be set to 2, 4, or 6 coordinate axes, etc., and the grayscale distribution curves are calculated according to the set calculation direction. Then, based on the obtained grayscale distribution curves, the half-peak parameters of the curves are calculated. Generally, the half-peak parameters are mainly indices such as the half-peak width value.

[0065] Step S103: Determine whether the coefficient of variation of the droplet to be judged and the half-peak parameters in each direction meet the preset threshold conditions.

[0066] The coefficient of variation and half-peak parameter are used as determination parameters for the droplets to be judged. The coefficient of variation characterizes the degree of deformation of the droplet. It can be obtained by taking the center of a known droplet as the center and the variation data of all pixels within a circle with a known length as the radius. The determination process of the half-peak parameter is carried out by comparing the half-peak parameter corresponding to each direction with a preset threshold condition, so as to accurately obtain the half-peak parameter that meets the threshold condition in each direction. Specifically, droplets that emit fluorescent signals in the droplets to be judged have higher brightness values, while droplets that do not emit fluorescent signals have lower brightness values. Since the existing technology directly uses the local extreme brightness value in the image as the estimate of the droplet center, it is logically sound. However, in real-world scenarios, it is easily affected by image noise, because the brightness value of the image noise may be greater than the local extreme brightness value in the image, ultimately leading to the use of image noise to determine the droplet center.

[0067] By determining whether the half-peak parameter in each direction meets a preset threshold condition, the half-peak parameter of the grayscale distribution curve of the droplet to be judged is evaluated from multiple directions. This achieves multi-level judgment of the droplet and avoids interference from image noise in the judgment process. It is worth mentioning that the above threshold condition is a statistically derived threshold condition. For example, the corresponding threshold is given by the relevant statistical indicators of all droplet corresponding indicators. That is, the threshold is set as the average value of all droplet corresponding indicators plus or minus three times its standard deviation.

[0068] Step S104: Obtain the number of half-peak parameters that meet the threshold condition, and determine whether the droplets are effective based on the number and the coefficient of variation.

[0069] The determination process does not require that the half-peak parameters in all directions meet the requirements. Instead, it determines whether the droplet is effective based on the number of half-peak parameters that meet the threshold conditions and the coefficient of variation.

[0070] After acquiring the image to be judged, since the image contains a large number of droplets, abnormal droplets need to be discarded, and only the droplets to be judged are further judged. Specifically, the position parameters of the droplets to be judged are determined sequentially in the image, such as... Figure 2 As shown, it includes:

[0071] Step S201: Obtain the chip region contained in the image to be determined; wherein the chip region includes at least: chip edge region and chip support pillar region.

[0072] Generally, the image to be evaluated is captured with the support of chip support pillars, and the droplets are located in the chip edge region, chip support pillar region, and other areas. Droplets in the chip edge region and chip support pillar region are generally treated as abnormal droplets.

[0073] Step S202: Obtain the geometric center coordinates and size parameters of all droplets contained in the determination image, and use the geometric center coordinates and size parameters to calculate the closest distance between the droplets and the chip region.

[0074] The process of identifying droplets in the chip edge region and chip support pillar region as abnormal droplets is mainly achieved by measuring the closest distance between the droplet and the aforementioned regions.

[0075] Step S203: The droplet whose nearest distance meets the preset threshold is identified as the droplet to be judged, and the geometric center coordinates and size parameters of the droplet to be judged are determined as position parameters.

[0076] For droplets to be determined as being in a normal state, in the process of determining whether such droplets with normal geometric positions are valid droplets, some indicators should be designed based on the droplet size and the distribution of droplet brightness, and these indicators should be used to determine whether droplets are valid or abnormal.

[0077] In real-world scenarios, the shapes of droplets to be determined do not perfectly conform to corresponding geometric shapes; some droplets may exhibit a degree of deformation. Therefore, in some implementations, it is necessary to adjust the calculation process of droplet position parameters by incorporating the corresponding coefficient of variation. During the calculation of the coefficient of variation, the droplet radius should be appropriately reduced to avoid abnormally large increases in the coefficient of variation due to estimation errors at the droplet center. Therefore, in some implementations, the process of determining the coefficient of variation of the droplet to be determined, such as... Figure 3 As shown, it includes:

[0078] Step S301: In the droplet to be judged, obtain a circular region with the center of the droplet to be judged as the center and a preset length as the radius.

[0079] The preset length in this step should be slightly smaller than the actual radius. For example, the preset length should be 1 or 2 pixels smaller than the actual radius. On the one hand, this can avoid the abnormal increase in the droplet variation coefficient caused by the estimation error of the droplet center (generally no more than two pixels). On the other hand, this can also avoid the increase in the effective droplet variation coefficient caused by the rapid decrease in the brightness of the droplet edge. Thus, the variation coefficient can be used to more easily distinguish between droplets with abnormal internal brightness (such as abnormally bright areas inside the droplet) and effective droplets.

[0080] Step S302: Obtain the standard deviation and average value of all pixel values ​​within the circular area.

[0081] After acquiring the circular region, the standard deviation and average value of the pixel values ​​within that region are calculated. This calculation involves statistically analyzing the values ​​of all pixels within the region.

[0082] Step S303: The ratio of the standard deviation of the pixel value to the average value of the pixel value is used as the coefficient of variation of the droplet to be determined.

[0083] In some implementations, the process of determining the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions, such as... Figure 4 As shown, it includes:

[0084] Step S401: Obtain the geometric center coordinates of the droplet to be determined and its corresponding radius data, which are included in the position parameters.

[0085] The geometric center coordinates of the droplet to be determined are used as the origin coordinates of the direction axis generated in the subsequent steps, while the radius data of the droplet mainly characterizes the shape of the droplet, and the direction of the direction axis in the subsequent steps is determined by the shape of the droplet.

[0086] Step S402: Based on the geometric center and radius data of the droplet to be judged, determine the half-peak parameters of the gray-scale distribution curves of the droplet to be judged in multiple directions.

[0087] After acquiring the geometric center and radius data of the droplet to be determined, corresponding coordinate axes are established from the position of its geometric center, based on the shape of the droplet. When the droplet to be determined is circular, its coordinate axes can be two perpendicular axes; while when the droplet to be determined is a regular hexagonal droplet, its coordinate axes can be six axes. Specifically, in some embodiments, step S402, which determines the half-peak parameters of the gray-scale distribution curve of the droplet to be determined in multiple directions based on the geometric center and radius data, is as follows: Figure 5 As shown, it includes:

[0088] Step S501: Establish a 6-axis coordinate system with the geometric center of the droplet to be determined as the origin; wherein the included angle between each coordinate axis is 30 degrees.

[0089] The six coordinate axes pass through the vertices and the center of the side of the hexagonal droplet, respectively, and the angle between each coordinate axis is 30 degrees.

[0090] Step S502: Calculate the half-peak parameters of the gray-scale distribution curve corresponding to each droplet on each coordinate axis; wherein, the half-peak parameters include: half-peak width parameter and normalized half-peak area parameter.

[0091] The normalized half-peak area is the half-peak area divided by the maximum value of the droplet's gray-level distribution curve in the current direction. After the coordinate axes are established, the droplet gray-level distribution curve corresponding to each of the six coordinate axes is calculated, and the corresponding half-peak parameters are determined based on the obtained gray-level distribution curves. The half-peak parameters include: half-peak width parameter and half-peak area parameter. The type of half-peak parameter is selected according to the actual scenario requirements. Specifically, in some implementations, step S502, which calculates the half-peak parameters of the gray-level distribution curve corresponding to each droplet under each coordinate axis, is as follows: Figure 6 As shown, it includes:

[0092] Step S601: Based on the geometric center and radius data of the droplets under each coordinate axis, obtain multiple pixels corresponding to the normal vector of the droplets along the current direction, and use the average gray level of the pixels to determine the gray level distribution curve corresponding to the droplets.

[0093] Step S602: Determine the half-peak parameter using the grayscale distribution curve.

[0094] When determining the grayscale distribution curve of the droplet to be judged in multiple directions, it is necessary to combine the pixel values ​​corresponding to the normal vectors of different coordinate axes for calculation. Specifically, multiple pixels corresponding to the normal vector of the droplet along the current direction can be obtained, and the grayscale distribution curve corresponding to the droplet can be determined by calculating the average grayscale of the pixels. Finally, the half-peak parameter can be determined using the grayscale distribution curve.

[0095] In determining whether a droplet is effective, it is not required that the half-peak parameters in all directions meet the requirements. Instead, the effectiveness of the droplet is determined based on the number of half-peak parameters that meet the threshold condition. Therefore, in some implementations, step S104, which involves obtaining the number of half-peak parameters that meet the threshold condition and determining whether the droplet is effective based on the number, is as follows: Figure 7 As shown, it includes:

[0096] Step S701: When the number of half-peak parameters that meet the threshold condition exceeds three, the droplet is determined as a valid droplet.

[0097] In step S702, if the number of half-peak parameters that meet the threshold condition does not exceed three, the droplet is determined to be an invalid droplet.

[0098] A droplet is considered valid only if the half-peak width and normalized half-peak area of ​​its grayscale distribution curves in more than three directions are all within normal ranges; otherwise, it is considered invalid.

[0099] In practical scenarios for determining effective droplets, the process requires designing relevant index parameters based on droplet size and brightness distribution, and ultimately identifying effective droplets based on these parameters. Specifically, the determination of effective droplets mainly utilizes indicators such as the droplet's coefficient of variation and the half-peak parameter of the droplet's grayscale distribution curves along multiple directions.

[0100] In the process of determining the droplet to be judged, specifically as follows: Figure 8 As shown, the droplet radius used in calculating the droplet variation coefficient is 1-2 pixels smaller than the true value. This can avoid the abnormal increase in the droplet variation coefficient caused by the estimation error of the droplet center; it can also avoid the increase in the effective droplet variation coefficient caused by the rapid decrease in the brightness of the droplet edge. Thus, it is easier to distinguish droplets with abnormal internal brightness through the variation coefficient.

[0101] In the process of calculating the half-peak parameter, specifically as follows: Figure 9 As shown, when calculating the half-peak width and normalized half-peak area, etc., of the gray-scale distribution curves of droplets along multiple directions, the following parameters are selected: Figure 9 The six directions are shown. A droplet is considered valid only if its half-peak width and normalized half-peak area (WHM) of the grayscale distribution curves along more than three directions are normal; otherwise, it is considered invalid. When calculating the grayscale distribution curve of a droplet along a certain direction, the average of three pixels along its normal vector direction is used. This improves the robustness of the judgment process and reduces the impact of image noise on the WHM and WHM of the droplet's grayscale distribution curves in that direction.

[0102] As can be seen from the effective droplet determination method in the above embodiments, this method uses the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions to determine the effectiveness of the droplet, which reduces the influence of image noise on the determination of droplet effectiveness and improves the accuracy of droplet determination.

[0103] Corresponding to the above embodiments of the effective droplet determination method, this invention provides an effective droplet determination system, such as... Figure 10 As shown, the system includes:

[0104] The image to be determined module 1010 is used to determine the image to be determined; wherein, the image to be determined includes multiple microdroplets to be determined;

[0105] The half-peak parameter calculation module 1020 is used to sequentially determine the position parameters of the current droplet to be judged in the image to be judged, and use the position parameters to determine the coefficient of variation of the droplet to be judged and the half-peak parameters of its gray-scale distribution curve in multiple directions.

[0106] The half-peak parameter judgment module 1030 is used to determine whether the coefficient of variation of the droplet to be judged and its half-peak parameter in each direction meet the preset threshold conditions.

[0107] The effective droplet judgment module 1040 is used to obtain the number and coefficient of variation of half-peak parameters that meet the threshold conditions, and to determine whether the droplet is effective based on the number and coefficient of variation.

[0108] In some embodiments, when the half-peak parameter calculation module 1020 sequentially determines the position parameters of the current droplet to be judged in the image to be judged, it is also used to: obtain the chip region contained in the image to be judged; wherein, the chip region includes at least: chip edge region and chip support pillar region; obtain the geometric center coordinates and size parameters of all droplets contained in the judgment image, and calculate the closest distance between the droplet and the chip region using the geometric center coordinates and size parameters; determine the droplet whose closest distance meets the preset threshold as the droplet to be judged, and determine the geometric center coordinates and size parameters of the droplet to be judged as the position parameters.

[0109] In some implementations, the half-peak parameter calculation module 1020, in the process of determining the coefficient of variation of the droplets to be determined, is also used to: compare the similarity of all droplets contained in the image to be determined using the droplet samples contained in the droplet template to obtain the average radius pixel value of all droplets contained in the image to be determined; reduce the average radius pixel value according to a preset pixel difference to determine the droplet radius threshold of the droplet template; compare the size of all droplets contained in the image to be determined using the droplet radius threshold, and determine the coefficient of variation of the droplets based on the comparison result.

[0110] In some implementations, the half-peak parameter calculation module 1020, during the process of calculating the half-peak parameters of the grayscale distribution curves of the droplet to be determined in multiple directions, is also used to: obtain the geometric center coordinates of the droplet to be determined and its corresponding radius data contained in the position parameters; and determine the half-peak parameters of the grayscale distribution curves of the droplet to be determined in multiple directions based on the geometric center and radius data of the droplet to be determined.

[0111] In some implementations, the half-peak parameter calculation module 1020, in determining the half-peak parameters of the grayscale distribution curves of the droplet to be determined in multiple directions based on the geometric center and radius data of the droplet to be determined, is also used to: establish a 6-directional coordinate axis with the geometric center of the droplet to be determined as the origin; wherein the included angle between each coordinate axis is 30 degrees; calculate the half-peak parameters of the grayscale distribution curves corresponding to the droplet under each coordinate axis; wherein the half-peak parameters include: half-peak width parameter and normalized half-peak area parameter.

[0112] In some implementations, the half-peak parameter calculation module 1020, in the process of calculating the half-peak parameter of the gray-scale distribution curve corresponding to the droplet under each coordinate axis, is also used to: obtain multiple pixels corresponding to the normal vector of the droplet along the current direction based on the geometric center and radius data of the droplet under each coordinate axis, and determine the gray-scale distribution curve corresponding to the droplet using the average gray-scale of the pixels; and determine the half-peak parameter using the gray-scale distribution curve.

[0113] In some implementations, the effective droplet determination module 1040 is further configured to: determine the droplet as an effective droplet when the number of half-peak parameters that meet the threshold condition exceeds three; and determine the droplet as an invalid droplet when the number of half-peak parameters that meet the threshold condition does not exceed three.

[0114] As can be seen from the effective droplet determination system in the above embodiments, the system uses the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions to determine the effectiveness of the droplet, which reduces the impact of image noise on the determination of droplet effectiveness and improves the accuracy of droplet determination.

[0115] The effective droplet determination system provided in this embodiment of the invention has the same technical features as the effective droplet determination method provided in the above embodiments, and therefore can solve the same technical problems and achieve the same technical effects. For the sake of brevity, any parts not mentioned in the embodiments can be referred to the corresponding content in the foregoing embodiments.

[0116] This embodiment also provides an electronic device, as shown in the structural schematic diagram below. Figure 11 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the above-described effective droplet determination method.

[0117] Figure 11 The electronic device shown also includes a bus 103 and a communication interface 104, with the processor 101, communication interface 104 and memory 102 connected via the bus 103.

[0118] The memory 102 may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0119] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0120] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0121] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the methods described in the foregoing embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, the functional units in the various embodiments of the present invention 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.

[0125] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An effective microdroplet determination method, characterized in that, The method includes: Identify an image to be judged; wherein the image to be judged includes multiple microdroplets to be judged; In the image to be judged, the position parameters of the droplet to be judged are determined sequentially, and the coefficient of variation of the droplet to be judged and the half-peak parameters of its gray-scale distribution curves in multiple directions are determined using the position parameters. Determine whether the coefficient of variation of the droplet to be determined and its half-peak parameter in each direction meet the preset threshold conditions; Obtain the number of half-peak parameters that satisfy the threshold condition, and determine whether the droplet is effective based on the number and the coefficient of variation; The process of determining the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions includes: Obtain the geometric center coordinates of the droplet to be determined and its corresponding radius data, which are included in the position parameters; Based on the geometric center of the droplet to be determined and the radius data, the half-peak parameters of the gray-scale distribution curves of the droplet to be determined in multiple directions are determined. Based on the geometric center of the droplet to be determined and the radius data, the half-peak parameters of the grayscale distribution curves of the droplet to be determined in multiple directions are determined, including: A six-axis coordinate system is established with the geometric center of the droplet to be determined as the origin; wherein the angle between each coordinate axis is 30 degrees. Calculate the half-peak parameter of the gray-scale distribution curve corresponding to the droplet under each of the coordinate axes; wherein, the half-peak parameter includes: half-peak width parameter and normalized half-peak area parameter; Calculate the half-peak parameters of the gray-scale distribution curve corresponding to each droplet on each of the coordinate axes, including: Based on the geometric center and radius data of the droplet under each coordinate axis, obtain multiple pixels corresponding to the normal vector of the droplet along the current direction, and use the average gray level of the pixels to determine the gray level distribution curve corresponding to the droplet; The half-peak parameter is determined using the grayscale distribution curve.

2. The method for determining effective microdroplets according to claim 1, characterized in that, The position parameters of the current droplet to be determined are determined sequentially in the image to be determined, including: Obtain the chip region contained in the image to be determined; wherein, the chip region includes at least: a chip edge region and a chip support pillar region; Obtain the geometric center coordinates and size parameters of all microdroplets contained in the judgment image, and use the geometric center coordinates and size parameters to calculate the nearest distance between the microdroplet and the chip region; The droplet whose nearest distance meets the preset threshold is identified as the droplet to be judged, and the geometric center coordinates and size parameters of the droplet to be judged are identified as the position parameters.

3. The method for determining effective microdroplets according to claim 1, characterized in that, The process of determining the coefficient of variation of the droplet to be determined includes: Within the droplet to be judged, a circular region is obtained with the center of the droplet as the center and a preset length as the radius; Obtain the standard deviation and average value of all pixel values ​​within the circular region; The ratio of the standard deviation of the pixel value to the average value of the pixel value is used as the coefficient of variation of the droplet to be determined.

4. The method for determining effective microdroplets according to claim 1, characterized in that, The step of obtaining the number of half-peak parameters that satisfy the threshold condition and determining whether the droplet is effective based on the number includes: When the number of half-peak parameters that meet the threshold condition exceeds three, the droplet is determined to be a valid droplet. If the number of half-peak parameters that meet the threshold condition does not exceed three, then the droplet is determined to be an invalid droplet.

5. An effective microdroplet determination system, characterized in that, The system includes: The image to be determined module is used to determine the image to be determined; wherein, the image to be determined includes multiple microdroplets to be determined; The half-peak parameter calculation module is used to sequentially determine the position parameters of the current droplet to be determined in the image to be determined, and use the position parameters to determine the coefficient of variation of the droplet to be determined and the half-peak parameters of its gray-scale distribution curve in multiple directions. The half-peak parameter determination module is used to determine whether the coefficient of variation of the droplet to be determined and its half-peak parameter in each direction meet the preset threshold conditions. An effective droplet determination module is used to obtain the number of half-peak parameters and the coefficient of variation that meet the threshold condition, and to determine whether the droplet is effective based on the number and the coefficient of variation. The half-peak parameter calculation module is also used in the process of determining the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions, and is further used to: obtain the geometric center coordinates of the droplet to be determined and its corresponding radius data contained in the position parameters; and determine the half-peak parameter of the gray-scale distribution curve of the droplet to be determined in multiple directions based on the geometric center of the droplet to be determined and the radius data. In the process of determining the half-peak parameters of the grayscale distribution curve of the microdroplet to be determined in multiple directions based on the geometric center and radius data, the half-peak parameter calculation module is also used to: establish a 6-directional coordinate axis with the geometric center of the microdroplet to be determined as the origin; wherein the included angle between each coordinate axis is 30 degrees; calculate the half-peak parameters of the grayscale distribution curve corresponding to the microdroplet under each coordinate axis; wherein the half-peak parameters include: half-peak width parameter and normalized half-peak area parameter; In the process of calculating the half-peak parameter of the gray-scale distribution curve corresponding to the microdroplet under each coordinate axis, the half-peak parameter calculation module is also used to: obtain multiple pixels corresponding to the normal vector of the microdroplet along the current direction based on the geometric center and radius data of the microdroplet under each coordinate axis, and determine the gray-scale distribution curve corresponding to the microdroplet using the average gray-scale of the pixels; and determine the half-peak parameter using the gray-scale distribution curve.

6. An electronic device, characterized in that, include: A processor and a storage device; the storage device stores a computer program that, when executed by the processor, implements the steps of the effective droplet determination method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program, when executed by a processor, implements the steps of the effective droplet determination method according to any one of claims 1 to 4.

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