CN116805280A - Digital PCR image splicing method and system based on micro-flow control - Google Patents
Digital PCR image splicing method and system based on micro-flow control Download PDFInfo
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- 238000007847 digital PCR Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 34
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- 238000004364 calculation method Methods 0.000 claims description 14
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- 238000010222 PCR analysis Methods 0.000 abstract description 5
- 239000007788 liquid Substances 0.000 description 16
- 238000001514 detection method Methods 0.000 description 9
- 239000013076 target substance Substances 0.000 description 4
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- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
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- 238000003752 polymerase chain reaction Methods 0.000 description 1
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Abstract
The application relates to a digital PCR image splicing method and a digital PCR image splicing system based on micro-flow control, wherein the digital PCR image splicing method comprises the following steps: step A: capturing images A and B of any two different positions of the microfluidic chip through a camera; and (B) step (B): acquiring a priori attributes between the image A and the image B as a set (X, Y) through a PCR instrument; step C: carrying out Gaussian filtering treatment on the image A and the image B; step D: and calculating the actual translation relation (x, y) of the image A and the image B, and splicing the image A and the image B according to the actual translation relation (x, y). The application has perfect image splicing with same resolution and obvious features but high repeatability, which is obviously not repeated, thereby effectively improving the accuracy of PCR analysis.
Description
Technical Field
The application relates to the technical field of digital PCR detection, in particular to a digital PCR image splicing method and system based on microfluidics.
Background
Watson and Crick in 1953 proposed a DNA duplex and semi-retained replication model, and later in the field of molecular biology, polymerase chain reaction PCR became a common method for qualitative and quantitative detection of target nucleic acid molecules. The PCR analysis technology in 1884 is iterated for many times, so that the results of performing qualitative analysis on the PCR product by gel electrophoresis, performing quantitative analysis on the PCR product by real-time fluorescence, performing quantitative analysis on the PCR product by taking micro-flow control as a basis and the like are produced. The digital PCR is used as the latest third-generation PCR technology in recent years, and has the characteristics of rapidness, accuracy, quantitative detection and the like.
Based on a microfluidic technology, the digital PCR is used for randomly distributing substances to be detected into small liquid drops, heating the small liquid drops to enable the small liquid drops to perform PCR reaction, and obtaining the total liquid drop number n and the liquid drop number h containing target substances through fluorescence excitation, visual detection and other methods. In digital PCR, the copy number of the target substance contained in each liquid drop is an independent event, the Poisson distribution is met, the copy number of the target substance in each liquid drop is averaged when the number of the liquid drops is enough, the obtained n and h can obtain P (0), the Poisson distribution expectation can be calculated, and then the data of the total copy number, concentration and the like of the object to be detected in the original reagent can be obtained. The digital PCR needs to detect the total number of droplets n and the number of droplets h containing the target substance, and the detection result is strongly related to n and h, so how to obtain accurate n and h is a difficulty in the digital PCR.
In digital PCR, in order to obtain n and h, a camera is generally used to collect video signals, and the field of view of the collection is often smaller than that of a microfluidic chip, so that a plurality of images are required for recording in one amplification, and the joint between the images has liquid drop texture fracture and cannot be detected. Therefore, the accuracy of n and h is ensured by splicing the amplified images at one time, and the accuracy of PCR analysis can be effectively improved by a good image splicing algorithm.
In PCR, only two steps of feature point matching and splicing are needed for the splicing of the liquid drop images. The feature points are detected by using a SHIFT algorithm, a projection matrix is obtained, and then the projection matrix is spliced, but features in the liquid drop graph are single and repeated, namely the feature vectors of non-corresponding point pairs have extremely high similarity, so that the feature points cannot be matched correctly, and the correct projection matrix cannot be obtained. The failure to splice images can lead to repeated calculation or missing calculation of the liquid drop containing the detection target at the edge of the image, and can lead to the deviation of the detection concentration result to reach more than 100% under the low concentration background, thereby seriously affecting the accuracy and the linearity of PCR detection.
Disclosure of Invention
Based on the above, it is necessary to provide a digital PCR image splicing method and system based on micro-flow control, aiming at the problems that the digital PCR images in the prior art cannot be spliced or have obvious splitting and seriously affect the accuracy of PCR detection.
In order to achieve the above object, an embodiment of the present application provides a digital PCR image stitching method based on micro-fluidic, including the following steps:
step A: capturing images A and B of any two different positions of the microfluidic chip through a camera;
and (B) step (B): acquiring a priori attributes between the image A and the image B as a set (X, Y) through a PCR instrument;
step C: and carrying out Gaussian filtering treatment on the image A and the image B, wherein the filtering formulas are respectively as follows:
wherein I is x For filtered image A, I y For filtered pictures B, I originalx For image A information, I originaly Is image B information;
step D: and calculating the actual translation relation (x, y) of the image A and the image B, and splicing the image A and the image B according to the actual translation relation (x, y).
Preferably, the calculation method of the actual translation relation (x, y) of the image a and the image B is as follows:
step D1: convolving the filtered image A and the filtered image B with a two-dimensional Sobel operator to respectively obtain edge image information of the image A and the image B, wherein a calculation formula is as follows:
I xedge ＝G*I x ，
I yedge ＝G*I y ，
wherein G is a two-dimensional Sobel operator, I xedge For the edge image information of image A, I yedge Edge image information for image B;
step D2: all K values in the prior attribute set (X, Y) are calculated, and the K values form an evaluation set K, and the calculation formula of the evaluation set K is as follows:
wherein, (i, j) e (X, Y), the (i, j) corresponding to the calculated minimum k value is (X, Y).
Preferably, the calculation method of the two-dimensional Sobel operator is as follows:
wherein G is x Is a horizontal Sobel operator, G y Is a vertical Sobel operator.
Preferably, if there are a plurality of k values, the gaussian convolution kernel size in the step C is increased by 2, and then the steps C and D are repeated to eliminate noise interference, so that the difference between the edge image information of the image a and the edge image information of the image B in the overlapping region is minimized.
Preferably, the resolution, the image file format and the bit depth of the image a and the image B in the step a are the same.
Preferably, the method of calculating the set (X, Y) in step B is as follows:
acquiring X and Y in a translation relation by priori, wherein X represents a horizontal overlapping distance between an image A and an image B, and the method comprises the following steps ofY represents the vertical distance between image A and image B, there is +.>
The set of X, Y (X, Y) was obtained a priori by PCR instrument setup.
Preferably, a 3x3 gaussian convolution kernel is used in step C.
Preferably, the interference caused by dust is reduced by a gaussian filter process.
Preferably, only a translation relationship exists between the image a and the image B in the step a, and no affine relationship exists.
The application also provides a digital PCR image splicing system based on micro-flow control, which comprises: the digital PCR image splicing method based on the micro-flow control comprises a memory, a processor and a computer program which is stored in the memory and can be executed on the processor, wherein the processor realizes the digital PCR image splicing method based on the micro-flow control when the processor executes the computer program.
The digital PCR image splicing method based on the microfluidics has the following advantages and beneficial effects:
the same resolution with obvious and unrepeated characteristics and the images with obvious and high repeatability are spliced well, so that the accuracy of PCR analysis can be effectively improved.
Drawings
Fig. 1 is a schematic flow chart of a digital PCR image stitching method based on micro-fluidic according to a preferred embodiment of the present application.
Fig. 2 is a view of an image one stitched using the digital PCR image stitching method based on micro-fluidics of the present application.
Fig. 3 is a second image stitched using the microfluidic-based digital PCR image stitching method of the present application.
Detailed Description
In order that the application may be readily understood, a more complete description of the application will be rendered by reference to the appended drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to and integrated with the other element or intervening elements may also be present. The term "mounted" and similar expressions are used herein for illustrative purposes only.
Unless defined otherwise, 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 in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In one embodiment, as shown in fig. 1, a digital PCR image stitching method based on micro-fluidic is provided, including the following steps:
s100, capturing images A and B of any two different positions of the microfluidic chip through a camera;
s200, acquiring a priori attribute between an image A and an image B as a set (X, Y) through a PCR instrument;
s300, performing Gaussian filter processing on the image A and the image B, wherein the filter formulas are respectively as follows:
wherein I is x For filtered image A, I y For filtered pictures B, I originalx For image A information, I originaly Is image B information;
s400, calculating the actual translation relation (x, y) of the image A and the image B, and splicing the image A and the image B according to the actual translation relation (x, y).
In the implementation, the resolution, the image file format and the bit depth of the image A and the image B in the step A are the same.
As can be seen from step S100, there is no affine relationship between the images a and B, and only a translational relationship exists, so X and Y in the translational relationship are acquired a priori. X represents the horizontal overlapping distance between image A and image B, there isUnits: a pixel. Y represents the vertical distance between image A and image B, i.e. image B requires pixels shifted downward with respect to image A, there is +.>Units: a pixel. The set of X, Y (X, Y) was obtained a priori by PCR instrument setup. In specific implementation, a 3×3 gaussian convolution kernel is adopted in the step S300. The high spatial resolution field of view may cause interference by dust, etc., which is reduced by the gaussian filtering process.
In specific implementation, the calculation method of the actual translation relationship (x, y) of the image A and the image B is as follows:
step 1: convolving the filtered image A and the filtered image B with a two-dimensional Sobel operator to respectively obtain edge image information of the image A and the image B, wherein a calculation formula is as follows:
I xedge ＝G*I x ，
I yedge ＝G*I y ，
wherein G is a two-dimensional Sobel operator, I xedge For the edge image information of image A, I yedge Edge image information for image B;
step 2: all K values in the prior attribute set (X, Y) are calculated, and the K values form an evaluation set K, and the calculation formula of the evaluation set K is as follows:
wherein, (i, j) e (X, Y), the (i, j) corresponding to the calculated minimum k value is (X, Y).
Since background interference, dust, liquid drop reflection and the like exist in the liquid drop image, and factors such as the state of liquid drop outlines, whether the liquid drop outlines are split and overlapped are mainly concerned for the spliced image, edge segmentation is needed, and edge image information of the image A and the image B can be obtained through the calculation formula.
In specific implementation, the calculation method of the two-dimensional Sobel operator is as follows:
wherein G is x Is a horizontal Sobel operator, G y Is a vertical Sobel operator. G x And G y For determining the edges of the image.
In the implementation, if there are a plurality of k values, the gaussian convolution kernel in the step C is added by 2, and then the steps C and D are repeated to eliminate noise interference, so that the difference between the edge image information of the image a and the edge image information of the image B in the overlapping area is minimized.
From the stitching results, as shown in fig. 2 and 3, the stitching of the images with the same resolution, which are not obviously repeated in the characteristics, is good, and the stitching of the drop images with the obvious characteristics but high repeatability is good.
The application also provides a digital PCR image splicing system based on the micro-flow control, which is characterized by comprising a memory, a processor and a computer program which is stored in the memory and can be executed on the processor, wherein the digital PCR image splicing method based on the micro-flow control is realized when the processor executes the computer program.
In summary, the digital PCR image splicing method and system based on the micro-flow control provided by the application comprise the following steps that images A and B of any two different positions of a micro-flow control chip are captured by a camera; acquiring a priori attributes between the image A and the image B as a set (X, Y) through a PCR instrument; carrying out Gaussian filtering treatment on the image A and the image B; the method comprises the steps of calculating the actual translation relation (x, y) of the image A and the image B, splicing the image A and the image B according to the actual translation relation (x, y), and splicing the images with the same resolution and obvious characteristics but high repeatability, wherein the characteristics of the images are obviously not repeated, so that the accuracy of PCR analysis can be effectively improved.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the claims. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The digital PCR image splicing method based on the microfluidics is characterized by comprising the following steps of:
step A: capturing images A and B of any two different positions of the microfluidic chip through a camera;
and (B) step (B): acquiring a priori attributes between the image A and the image B as a set (X, Y) through a PCR instrument;
step C: and carrying out Gaussian filtering treatment on the image A and the image B, wherein the filtering formulas are respectively as follows:
wherein I is x For filtered image A, I y For filtered pictures B, I originalx For image A information, I originaly Is image B information;
step D: and calculating the actual translation relation (x, y) of the image A and the image B, and splicing the image A and the image B according to the actual translation relation (x, y).
2. The digital PCR image stitching method based on micro-fluidic according to claim 1, wherein the actual translation relation (x, y) of the images a, B is calculated as follows:
step D1: convolving the filtered image A and the filtered image B with a two-dimensional Sobel operator to respectively obtain edge image information of the image A and the image B, wherein a calculation formula is as follows:
I xedge ＝G*I x ，
I yedge ＝G*I y ，
wherein G is a two-dimensional Sobel operator, I xedge For the edge image information of image A, I yedge Is a figureEdge image information like B;
step D2: all K values in the prior attribute set (X, Y) are calculated, and the K values form an evaluation set K, and the calculation formula of the evaluation set K is as follows:
wherein, (i, j) e (X, Y), the (i, j) corresponding to the calculated minimum k value is (X, Y).
3. The digital PCR image stitching method based on micro-fluidic according to claim 2, wherein the two-dimensional Sobel operator is calculated as follows:
wherein G is x Is a horizontal Sobel operator, G y Is a vertical Sobel operator.
4. The digital PCR image stitching method based on micro-fluidic according to claim 1, wherein if there are a plurality of k values, the gaussian convolution kernel size in step C is increased by 2, and steps C and D are repeated to eliminate noise interference, so that the difference between the edge image information of image a and the edge image information of image B in the overlapping area is minimized.
5. The digital PCR image stitching method based on micro-fluidic according to claim 1, wherein the resolution, image file format and bit depth of the image a and the image B in the step a are the same.
6. The microfluidic-based digital PCR image stitching method according to claim 5, wherein the set (X, Y) calculation method in step B is as follows:
acquiring X and Y in a translation relation by priori, wherein X represents a horizontal overlapping distance between an image A and an image B, and the method comprises the following steps ofY represents the vertical distance between image A and image B, there is +.>
The set of X, Y (X, Y) was obtained a priori by PCR instrument setup.
7. The microfluidic-based digital PCR image stitching method according to claim 1, wherein a 3x3 gaussian convolution kernel is employed in step C.
8. The microfluidic-based digital PCR image stitching method according to claim 1, wherein dust-induced interference is reduced by gaussian filtering.
9. The digital PCR image stitching method based on micro-fluidic according to claim 1, wherein only a translation relationship and no affine relationship exist between the image a and the image B in the step a.
10. A digital PCR image stitching system based on micro-fluidics, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the digital PCR image stitching method based on micro-fluidics according to any one of claims 1-9 when executing the computer program.
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