CN111507234A - Cell flow assay - Google Patents
Cell flow assay Download PDFInfo
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- CN111507234A CN111507234A CN202010283743.XA CN202010283743A CN111507234A CN 111507234 A CN111507234 A CN 111507234A CN 202010283743 A CN202010283743 A CN 202010283743A CN 111507234 A CN111507234 A CN 111507234A
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- 238000003556 assay Methods 0.000 title claims description 12
- 239000000523 sample Substances 0.000 claims abstract description 60
- 238000001514 detection method Methods 0.000 claims abstract description 50
- 238000002347 injection Methods 0.000 claims abstract description 15
- 239000007924 injection Substances 0.000 claims abstract description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 12
- 239000012496 blank sample Substances 0.000 claims abstract description 9
- 238000005286 illumination Methods 0.000 claims abstract description 8
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000007635 classification algorithm Methods 0.000 claims abstract description 4
- 239000000428 dust Substances 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 4
- 238000000034 method Methods 0.000 description 15
- 230000008569 process Effects 0.000 description 13
- 239000006285 cell suspension Substances 0.000 description 4
- 239000000725 suspension Substances 0.000 description 4
- 101100243951 Caenorhabditis elegans pie-1 gene Proteins 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 238000004163 cytometry Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000035899 viability Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004113 cell culture Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001976 enzyme digestion Methods 0.000 description 1
- 238000011081 inoculation Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/695—Preprocessing, e.g. image segmentation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/30242—Counting objects in image
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Abstract
The invention provides a cell flow detection method, which comprises the following steps: (A1) the light source provides illumination for the flow cell; (A2) the detection unit obtains a sample injection image I of the flow cell1(x, y); (A3) preprocessing a sample image to obtain an image I (x, y) ═ I1(x,y)‑I0(x,y)，I0(x, y) is background; (A4) segmenting the image I (x, y) to obtain a plurality of detection areas, at least part of which are far away from the central area of the image I (x, y); (A5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds a gray threshold value; (A6) and processing the peak value by utilizing a classification algorithm and a classification threshold value to obtain the sample introduction state of the flow cell, wherein the sample introduction state comprises a blank sample, a water sample and a cell sample. The invention has the advantages of accurate detection and the like.
Description
Technical Field
The invention relates to the field of biology, in particular to a cell flow detection method and a cell flow detection device in cell counting.
Background
In the functional work of cell culture, it is often necessary to know the living state of cells and identify the death and viability of cells, determine the inoculation concentration and quantity of cells, and know the survival rate and proliferation degree of cells, such as the identification of the viability of cells in a cell suspension prepared by enzyme digestion, cell counting and the like. In the traditional method, a counting plate is used for manual counting, cells in a counting cell need a long time in the counting process, and the parallel detection deviation of different personnel is large.
The cell counter is an instrument for analyzing and counting cells, reduces the tedious steps of manual operation, can automatically store all detection results, ensures the objective authenticity of data, and can analyze and process the data at a later stage. Various automatic cell counting devices are on the market more and more. Common are mainly divided into two categories: image-based cytometry and coulter impedance principle-based cytometry. The Coulter electrical impedance principle calculates the number of cells according to the potential change of the cells caused by the small holes, so the cells with similar sizes cannot be distinguished, and the image principle carries out image identification by scanning images in the visual field of an instrument and depending on the sizes of the set upper and lower limit cells, thereby effectively solving the problem that the cells with similar sizes cannot be identified. However, such analytical techniques still have drawbacks, such as:
1. if accurate analysis of the cells is required, it is first determined that the consumable card has aspirated the cell suspension. Counting results can be affected if the cell template is not successfully aspirated into the cell suspension, the suspension is cell-free, or the suspension does not fill the entire imaging area.
2. Because there is no detection control method in the process of sucking the cell suspension by the sample plate, the counting system lacks repeatability.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a cell flow detection method for improving counting precision and repeatability.
The purpose of the invention is realized by the following technical scheme:
a cell flow assay method, comprising the steps of:
(A1) the light source provides illumination for the flow cell;
(A2) the detection unit obtains a sample injection image I of the flow cell1(x,y)；
(A3) Preprocessing a sample image to obtain an image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background;
(A4) segmenting the image I (x, y) to obtain a plurality of detection areas, at least part of which are far away from the central area of the image I (x, y);
(A5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds a gray threshold value;
(A6) and processing the peak value by utilizing a classification algorithm and a classification threshold value to obtain the sample introduction state of the flow cell, wherein the sample introduction state comprises a blank sample, a water sample and a cell sample.
Compared with the prior art, the invention has the beneficial effects that:
the core of the application of the invention is that: the technical obstacle that the counting result is influenced by the fact that no cell exists in the suspension of the existing cell counting instrument or the whole imaging area is not filled with the suspension is effectively solved, the technical advantages of the cell counting instrument and the cell flow detection are brought into play, and the obvious advantages are obtained:
1. the counting precision is high;
background is deducted, interference of original factors of dust and fingerprints is eliminated, and cell technology precision is improved;
whether a blank sample plate, whether a cell sample enters or not, whether a non-particle or cell water sample enters or not are identified through the judgment of the image of the set detection area;
2. the repeatability is high;
determining a classification threshold value, and establishing a standard of a sample introduction state: whether the cell sample enters the cell sample state is determined by utilizing image recognition and the standard, and if the cell sample enters the cell sample state, cell counting is carried out, so that the repeatability is improved.
Drawings
The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are only for illustrating the technical solutions of the present invention and are not intended to limit the scope of the present invention. In the figure:
FIG. 1 is a flow chart of a cell flow assay method according to an embodiment of the invention;
FIG. 2 is a detection zone partition diagram according to an embodiment of the present invention;
FIG. 3 is one-dimensional image information of image information according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a blank sample plate according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a water sample plate according to an embodiment of the invention;
FIG. 6 is a schematic view of a cell sample plate according to an embodiment of the present invention.
Detailed Description
Fig. 1-6 and the following description depict alternative embodiments of the invention to teach those skilled in the art how to make and use the invention. Some conventional aspects have been simplified or omitted for the purpose of teaching the present invention. Those skilled in the art will appreciate that variations or substitutions from these embodiments will be within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. Thus, the present invention is not limited to the following alternative embodiments, but is only limited by the claims and their equivalents.
Example 1:
fig. 1 schematically shows a flowchart of a cell flow assay method according to example 1 of the present invention, which comprises the following steps, as shown in fig. 1:
(A1) the light source provides illumination for the flow cell;
(A2) the detection unit obtains a sample injection image I of the flow cell1(x, y); the light source, the flow cell, the filter, the lens group, and the detection unit are sequentially arranged according to the light traveling directionThe upper end and the lower end of the flow cell are respectively provided with an opening which is suitable for the flow of cells;
(A3) preprocessing a sample image to obtain an image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background, that is, before and after sample introduction, dust and the like are attached to a flow cell or a lens group, fingerprints may exist on the flow cell or the lens group, and the fingerprints need to be deducted as background, and the specific deduction mode is the prior art in the field;
(A4) segmenting the image I (x, y) to obtain a plurality of detection regions, at least a portion of which is far away from the central region of the image I (x, y), such as the border region and the corner of the plurality of images, as shown in FIG. 2;
(A5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds a gray threshold value, such as a multiple of a gray value;
(A6) and processing the peak value by utilizing a classification algorithm and a classification threshold value to obtain the sample introduction state of the flow cell, wherein the sample introduction state comprises a blank sample, a water sample and a cell sample.
In order to accurately determine the sample injection state, a uniform classification standard needs to be established, and further, the obtaining mode of the classification threshold is as follows:
(B1) the light source provides illumination for the flow cell;
(B2) the detection unit obtains an experimental sample injection image I of the flow cell1(x,y)；
(B3) Preprocessing an experimental sample image to obtain an experimental image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background, that is, before and after sample introduction, dust and the like are attached to a flow cell or a lens group, fingerprints may exist on the flow cell or the lens group, and the fingerprints need to be deducted as background, and the specific deduction mode is the prior art in the field;
(B4) segmenting the experimental image I (x, y) to obtain a plurality of detection areas, wherein at least part of the detection areas are far away from the central area of the experimental image I (x, y);
(B5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds a gray threshold value; a map of each row or column group, as shown in FIG. 3;
(B6) determining a super line segment by utilizing a peak value and SVM algorithm, and enabling the super line segment to be as follows:
Deriving b by Lagrangian multiplication or convex optimization1、b2To obtain a classification threshold value | b1|、|b2L, lagrange multiplication and convex optimization are existing algorithms.
To further improve detection accuracy, the determination of the over-line segment has a learning mode and a non-learning mode.
Example 2:
an example of application of the cell flow assay method according to invention example 1 to cell counting.
In this application example, the cell flow detection method includes the steps of:
obtaining a classification threshold value in a specific mode:
(B1) the light source provides illumination for the flow cell;
(B2) the detection unit obtains an experimental sample injection image I of the flow cell1(x,y)；
(B3) Preprocessing an experimental sample image to obtain an experimental image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background, which includes fingerprint, dust, etc.;
(B4) dividing the experimental image I (x, y) to obtain 4 detection areas 10, wherein the 4 detection areas are far away from the central area of the experimental image I (x, y) and are positioned at four corners of the image, as shown in FIG. 2;
(B5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds 5 gray values; a map of each row or column group, as shown in FIG. 3; if the peak height is higher than 5 gray values, a primary peak detection event is determined to be established, all peak events of each detection area are obtained through calculation, table 1 shows blank sample peak event experimental data, table 2 shows water sample peak event experimental data, and table 3 shows cell sample peak event experimental data:
table 1
| Blank sample | Upper left corner | Upper right corner | Lower left corner | Lower right corner |
| Pic1 | 25735.25 | 26353.94 | 25898.22 | 24679.65 |
| Pic2 | 25767.58 | 26384.96 | 25924.25 | 24702.84 |
| Pic3 | 25763.8 | 26378.45 | 25947.33 | 24723.81 |
| Pic4 | 25778.96 | 26395.84 | 25959.29 | 24733.63 |
| Pic5 | 25782.43 | 26402.64 | 25968.68 | 24744.22 |
| Pic6 | 25788.57 | 26407.83 | 25960.78 | 24738.32 |
| Pic7 | 25797.16 | 26416.6 | 25966.64 | 24744 |
| Pic8 | 25753.63 | 26371.81 | 25906.7 | 24684.93 |
| Pic9 | 25701.62 | 26320.92 | 25863.23 | 24645.27 |
| Pic10 | 25745.35 | 26364.92 | 25922.04 | 24700.09 |
| Pic11 | 25768.95 | 26389.39 | 25934.97 | 24713.94 |
| Pic12 | 25844.51 | 26466.85 | 26026.28 | 24797.37 |
| Pic13 | 25915.33 | 26538.26 | 26081.08 | 24853.8 |
| Pic14 | 25886.2 | 26510.11 | 26059.79 | 24834.61 |
| Pic15 | 25901.82 | 26523.66 | 26044.48 | 24819.26 |
| Pic16 | 25885.37 | 26507.43 | 26035.57 | 24811.1 |
| Pic17 | 25907.59 | 26532.32 | 26056.84 | 24832.22 |
| Pic18 | 25887.04 | 26510.95 | 26048.55 | 24823.98 |
| Pic19 | 25827.02 | 26450.36 | 25994.77 | 24772.41 |
| Pic20 | 25948.93 | 26575.49 | 26119.38 | 24891.73 |
| Pic21 | 25696.8 | 26316.94 | 25915.15 | 24697.59 |
| Pic22 | 25852.09 | 26479.3 | 26010.81 | 24788.61 |
| Pic23 | 25833.16 | 26457.4 | 25971.33 | 24752.14 |
| Pic24 | 25865.58 | 26493.58 | 26004.09 | 24785.64 |
| Pic25 | 25865.58 | 26491.11 | 26031.11 | 24811.37 |
| Pic26 | 25867.51 | 26492.56 | 26029.3 | 24809.17 |
| Pic27 | 25866.9 | 26493.37 | 26034.07 | 24814.68 |
| Pic28 | 25882.94 | 26510.27 | 26037.59 | 24815.35 |
| Pic29 | 25890.56 | 26516.51 | 26056.06 | 24833.55 |
| Pic30 | 25895.09 | 26520.95 | 26033.04 | 24811.56 |
| Pic31 | 25867.99 | 26494.22 | 26000.94 | 24782.66 |
| Pic32 | 26962.89 | 27615.63 | 28270.68 | 26947.1 |
| Max： | 28270.68 |
Table 2
| Water sample | Upper left corner | Upper right corner | Lower left corner | Lower right corner |
| Pic1 | 27503.52 | 28943.09 | 27105.53 | 26809.86 |
| Pic2 | 27486.71 | 28922.55 | 27084.8 | 26787.25 |
| Pic3 | 27465.47 | 28901.24 | 27086.69 | 26787.78 |
| Pic4 | 27454.23 | 28888.73 | 27076.18 | 26777.53 |
| Pic5 | 27423.66 | 28859.33 | 27053.57 | 26754 |
| Pic6 | 27451.69 | 28889.07 | 27061.06 | 26763.23 |
| Pic7 | 27458.34 | 28898.02 | 27079.17 | 26780.55 |
| Pic8 | 27451.4 | 28890.25 | 27041.42 | 26745.85 |
| Pic9 | 27462.7 | 28904.98 | 27064.11 | 26769.53 |
| Pic10 | 27517.5 | 28962.51 | 27120.46 | 26825.93 |
| Pic11 | 27523.57 | 28971.33 | 27144.27 | 26850.66 |
| Pic12 | 27598.79 | 29052.38 | 27223.94 | 26927.33 |
| Pic13 | 27615.87 | 29066.65 | 27232.17 | 26937.39 |
| Pic14 | 27628.18 | 29081.89 | 27246.53 | 26949.4 |
| Pic15 | 27632.81 | 29087.56 | 27233.48 | 26938.34 |
| Pic16 | 27625.81 | 29079.99 | 27223.21 | 26928.01 |
| Pic17 | 27612.3 | 29067.27 | 27198.26 | 26904.87 |
| Pic18 | 27597.38 | 29050.27 | 27213.15 | 26920.56 |
| Pic19 | 27599.19 | 29053.95 | 27211.73 | 26917.87 |
| Pic20 | 27599.56 | 29052.87 | 27215.6 | 26920.52 |
| Pic21 | 28181.65 | 30068.06 | 33922.08 | 27400.24 |
| Pic22 | 26587.92 | 36955.5 | 26774.45 | 25415.08 |
| Pic23 | 25654.53 | 27424.4 | 25404.67 | 24753.54 |
| Pic24 | 25539.2 | 27370.29 | 25344.54 | 24696.5 |
| Pic25 | 25477.04 | 27309.58 | 25344.91 | 24668.6 |
| Pic26 | 25248.41 | 27065.03 | 25154.26 | 24482.31 |
| Pic27 | 25224.44 | 27037.76 | 25121.96 | 24455.16 |
| Pic28 | 25234 | 27048.02 | 25116.18 | 24450.21 |
| Pic29 | 25256.6 | 27073.37 | 25139.41 | 24472.64 |
| Pic30 | 25243.7 | 27058.91 | 25092.13 | 24419.18 |
| Pic31 | 25200.41 | 27010.82 | 25041.38 | 24371.62 |
| Pic32 | 26379.23 | 28268.46 | 27275.21 | 26553.19 |
| Max： | 36955.5 |
Table 3
| Novel cell | Upper left corner | Upper right corner | Lower left corner | Lower right corner |
| Pic1 | 24790.41 | 25738.47 | 25576.25 | 25236.11 |
| Pic2 | 24805.08 | 25753.68 | 25589 | 25247.09 |
| Pic3 | 24781.69 | 25731.95 | 25597.57 | 25255.14 |
| Pic4 | 24757.79 | 25707.6 | 25563.79 | 25223.87 |
| Pic5 | 24686.86 | 25634.05 | 25507.56 | 25166.76 |
| Pic6 | 24820.17 | 25772.4 | 25602.88 | 25263.87 |
| Pic7 | 24728.11 | 25677.58 | 25534 | 25193.72 |
| Pic8 | 24762.8 | 25714.46 | 25524.09 | 25184.44 |
| Pic9 | 24786.06 | 25740.75 | 25577.14 | 25236.83 |
| Pic10 | 24887.77 | 25845.64 | 25686.49 | 25346.01 |
| Pic11 | 24872.3 | 25829.78 | 25653.71 | 25315.08 |
| Pic12 | 24834.55 | 25789.54 | 25668.68 | 25327.08 |
| Pic13 | 24839.36 | 25796.49 | 25644.31 | 25304.35 |
| Pic14 | 24935.95 | 25896.87 | 25747.74 | 25407.17 |
| Pic15 | 24924.67 | 25882.58 | 25703.25 | 25362.37 |
| Pic16 | 24963.67 | 25923.58 | 25759.13 | 25420.67 |
| Pic17 | 24964.38 | 25928.19 | 25744.24 | 25406.16 |
| Pic18 | 24838.87 | 25798.65 | 25658.02 | 25318.04 |
| Pic19 | 24860.86 | 25822.41 | 25664.86 | 25327.59 |
| Pic20 | 24917.46 | 25880.34 | 25709.92 | 25372.81 |
| Pic21 | 25042.06 | 26011.84 | 25849.11 | 25506.77 |
| Pic22 | 26235.71 | 27516.87 | 26570.52 | 26398.34 |
| Pic23 | 32455.97 | 33589.54 | 36627.01 | 38479.49 |
| Pic24 | 30933.3 | 31686.87 | 31954.51 | 32001.25 |
| Pic25 | 28807.67 | 30873.22 | 30216.96 | 31173.46 |
| Pic26 | 28458.72 | 29377.03 | 29948.5 | 29485.72 |
| Pic27 | 28549.43 | 29514.15 | 30102.78 | 29645.34 |
| Pic28 | 28653.08 | 29663.61 | 30211.47 | 29790.82 |
| Pic29 | 28661.44 | 29686.74 | 30239.93 | 29833.1 |
| Pic30 | 28663.69 | 29704.65 | 30218.68 | 29820.75 |
| Pic31 | 28708.96 | 29756.91 | 30284.09 | 29891.61 |
| Pic32 | 29897.29 | 31000.16 | 32944.69 | 32536.03 |
| Max： | 38479.49 |
(B6) Determining a super line segment by utilizing a peak value and SVM algorithm, and enabling the super line segment to be as follows:
Deriving b by Lagrangian multiplication or convex optimization1、b2To obtain a classification threshold value | b1|＝285、|b2|＝370：
That is, N285 and N370 are thresholds for the excess line segment of the blank, water sample, and cell sample, and therefore:
when N <285, consider the process from blank to blank, a K1 event;
when 370> N >285, the process from blank to non-particulate or non-cellular water sample injection is considered to be the K2 event;
when N is more than or equal to 370, the process from the blank plate to the cell sample injection is considered as the K3 event.
Then, any one of the individually set detection areas can only go through the process from the K1 event to the K3 event in the whole sample injection process; any other combination of events, such as K1 event to K2 event, K1 event to K3 event to K2 event, K2 event to K3 event, etc., is considered ineffective for the injection process. Thereby ensuring the accuracy and effectiveness of detection;
the formal detection process, as shown in fig. 1, includes the following steps:
(A1) the light source provides illumination for the flow cell;
(A2) the detection unit obtains a sample injection image I of the flow cell1(x, y); according to the light advancing direction, a light source, a flow cell, a filter, a lens group and a detection unit are sequentially arranged, wherein the upper end and the lower end of the flow cell are respectively provided with an opening which is suitable for the flow of cells;
(A3) preprocessing a sample image to obtain an image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background, that is, before and after sample introduction, dust and the like are attached to the flow cell or the lens group, and fingerprints may exist on the flow cell or the lens group, which are required to be deducted as background;
(A4) segmenting the image I (x, y) to obtain 4 detection regions 10, each of the 4 detection regions being far from the central region of the image I (x, y) at 4 corners of the image, as shown in fig. 2;
(A5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds 5 gray values;
(A6) and processing the peak value by utilizing an SVM algorithm and a classification threshold value to obtain the sample introduction state of the flow cell, wherein the sample introduction state comprises a blank sample, a water sample and a cell sample.
The results show that the cell flow detection effect completely meets the requirements, and as shown in fig. 4, the process from blank to blank is detected; as shown in fig. 5, the process from blank plate to non-particulate or non-cellular water sample injection; as shown in fig. 6, the process from white plate to cell sample injection is shown.
Claims (6)
1. A cell flow assay method, comprising the steps of:
(A1) the light source provides illumination for the flow cell;
(A2) the detection unit obtains a sample injection image I of the flow cell1(x,y)；
(A3) Preprocessing a sample image to obtain an image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background;
(A4) segmenting the image I (x, y) to obtain a plurality of detection areas, at least part of which are far away from the central area of the image I (x, y);
(A5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds a gray threshold value;
(A6) and processing the peak value by utilizing a classification algorithm and a classification threshold value to obtain the sample introduction state of the flow cell, wherein the sample introduction state comprises a blank sample, a water sample and a cell sample.
2. The cell flow assay method according to claim 1, wherein: the obtaining mode of the classification threshold value is as follows:
(B1) the light source provides illumination for the flow cell;
(B2) the detection unit obtains an experimental sample injection image I of the flow cell1(x,y)；
(B3) Preprocessing an experimental sample image to obtain an experimental image I (x, y) ═ I1(x,y)-I0(x,y)，I0(x, y) is background;
(B4) segmenting the experimental image I (x, y) to obtain a plurality of detection areas, wherein at least part of the detection areas are far away from the central area of the experimental image I (x, y);
(B5) obtaining a peak value in each row or each column group of the detection area, wherein the peak value exceeds a gray threshold value;
(B6) determining a super line segment by utilizing a peak value and SVM algorithm, and enabling the super line segment to be as follows:
Deriving b by Lagrangian multiplication or convex optimization1、b2To obtain a classification threshold value | b1|、|b2|。
3. The cell flow assay method according to claim 2, wherein: | b1|＝285，|b2|＝370。
4. The cell flow assay method according to claim 1, wherein: the detection regions are at the edges or corners of the image.
5. The cell flow assay method according to claim 1 or 2, wherein: the grayscale threshold is 5 grayscale values.
6. The cell flow assay method according to claim 1, wherein: the background is dust or a fingerprint.
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