Biological sample image processing method and system based on microfluidic optical tweezers

Abstract

The invention is suitable for the technical field of micro-fluidic optical tweezers, and provides a biological sample image processing method and system based on micro-fluidic optical tweezers. The method comprises the following steps: carrying out initial monitoring shooting on a biological sample in a micro-fluidic chip, and calculating micro-fluidic flow drag force; carrying out intermittent monitoring shooting on the biological sample, and carrying out control identification on a target object; performing dynamic analysis of particle capture, and recording dynamic motion data; selecting a representative sample image, performing trajectory processing, and creating an actual motion trajectory; performing theoretical motion analysis on the target object, and creating a hierarchical theoretical mark in the representative sample image; the method can perform dynamic analysis of particle capture, select representative sample images, create the actual motion track of a target object and create a hierarchical theoretical mark, can perform accurate and rigorous automatic marking on the motion of the target object, and can reflect the difference between the actual motion and the theoretical motion. Therefore, a visual and effective observation image is provided for research and application of the microfluidic optical tweezers.

Description

Technical Field

[0001] The present invention belongs to the technical field of microfluidic optical tweezers, and in particular relates to a biological sample image processing method and system based on microfluidic optical tweezers. Background Art

[0002] Microfluidic optical tweezers is a technology that uses laser beams to capture and manipulate tiny particles or cells.

[0003] In microfluidic chips, a high-intensity light field can be generated by focusing lasers to capture and move tiny objects such as particles, cells or molecules.

[0004] In the existing technology, biological sample image processing for microfluidic optical tweezers often requires staff to observe and then select images for manual target motion labeling. Not only is the target motion labeling not precise and rigorous, but it also fails to reflect the difference between actual and theoretical motion, making it impossible to provide intuitive and effective observation images for the research and application of microfluidic optical tweezers. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a biological sample image processing method and system based on microfluidic optical tweezers, aiming to solve the technical problems existing in the prior art mentioned in the background technology.

[0006] The embodiment of the present invention is implemented as follows:

[0007] A biological sample image processing method based on microfluidic optical tweezers, the method specifically comprising the following steps:

[0008] Perform initial monitoring and filming of biological samples in the microfluidic chip, obtain initial filming video, perform particle identification and flow analysis, and calculate microfluidic flow drag;

[0009] Perform microfluidic optical tweezers control, intermittently monitor and shoot biological samples, obtain multiple intermittent shooting images, control and identify the target object, and record multiple optical tweezers control parameters;

[0010] Performing dynamic analysis of particle capture and recording dynamic motion data based on the microfluidic flow drag and a plurality of optical tweezers control parameters;

[0011] Selecting a representative sample image from the plurality of intermittently captured images, and performing trajectory processing on the representative sample image to create an actual motion trajectory of the target object;

[0012] According to the dynamic motion data, theoretical motion analysis of the target object is performed on a plurality of intermittently captured images, and hierarchical theoretical labels are created in the representative sample images.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the initial monitoring and shooting of the biological sample in the microfluidic chip, obtaining the initial shooting video, performing particle identification and flow analysis, and calculating the microfluidic flow drag specifically include the following steps:

[0014] Performing initial monitoring and shooting of biological samples in the microfluidic chip to obtain an initial shooting video;

[0015] Obtain fluid parameters of the microfluidic chip;

[0016] Obtain particle parameters of biological samples;

[0017] Performing particle motion recognition analysis on the initially captured video to obtain particle flow velocity;

[0018] Calculate microfluidic flow drag based on fluid parameters, particle parameters, and particle flow rate.

[0019] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the microfluidic flow drag is:

[0020]

[0021] Among them, F L is the microfluidic flow drag, η is the fluid viscosity coefficient, m is the particle mass, u is the fluid flow velocity, f is the particle flow velocity, R is the particle radius, and ρ is the particle density.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the microfluidic optical tweezers control, intermittent monitoring and photographing of the biological sample, obtaining multiple intermittent photographed images, and controlling and identifying the target object, and recording multiple optical tweezers control parameters specifically include the following steps:

[0023] Conduct microfluidic optical tweezers control and determine the intermittent monitoring cycle;

[0024] According to the intermittent monitoring cycle, intermittent monitoring and photographing of the biological sample are performed to obtain a plurality of intermittent photographing images;

[0025] Performing target recognition in the plurality of intermittently captured images to obtain positions of the plurality of targets;

[0026] Based on the multiple target object positions, control identification is performed and multiple optical tweezers control parameters are recorded.

[0027] As a further limitation of the technical solution of the embodiment of the present invention, the dynamic analysis of particle capture based on the microfluidic flow drag and the multiple optical tweezers control parameters and the recording of dynamic motion data specifically include the following steps:

[0028] Construct a coordinate system;

[0029] In the coordinate system, calculating a plurality of scattering force components and a plurality of gradient force components according to the microfluidic flow drag and a plurality of the optical tweezers control parameters;

[0030] Determine multiple force component angles;

[0031] Based on the multiple scattering force components, the multiple gradient force components and the multiple force component angles, dynamic analysis of particle capture is performed and dynamic motion data is recorded.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the multiple scattering force components is:

[0033]

[0034] D=2k1 cos 2β i +k1 2 +1;

[0035] Among them, F Si is the scattering force of the target object in the i-th intermittent shooting image, D is the calculation factor, n is the refractive index, P i is the laser power corresponding to the i-th intermittent shooting image, k1 is the reflection coefficient, k2 is the refraction coefficient, α i is the incident angle of the target in the i-th intermittent image, β i is the refraction angle of the target object in the i-th intermittent image, and c is the speed of light;

[0036] The calculation formula for the multiple gradient force components is:

[0037]

[0038] Among them, F Ti is the gradient component of the target object in the i-th intermittent image.

[0039] As a further limitation of the technical solution of the embodiment of the present invention, selecting a representative sample image from the plurality of intermittently captured images, and performing trajectory processing on the representative sample image to create the actual motion trajectory of the target object specifically includes the following steps:

[0040] Arranging the plurality of intermittently captured images in time sequence and recording time sequence arrangement data;

[0041] selecting a representative sample image based on the time-series arrangement data;

[0042] Marking points in the representative sample image according to the plurality of target object positions to obtain a plurality of target object points;

[0043] A plurality of target object points are smoothly connected to create an actual motion trajectory of the target object in the representative sample image.

[0044] As a further limitation of the technical solution of the embodiment of the present invention, performing theoretical motion analysis of the target object on the plurality of intermittently captured images according to the dynamic motion data, and creating hierarchical theoretical labels in the representative sample images specifically include the following steps:

[0045] intercepting a target object image of the target object from the representative sample image;

[0046] According to the positions of the plurality of target objects, positioning and supplementing the plurality of target object images in the representative sample image to create representative supplementary marks;

[0047] According to the time sequence data, the representative supplementary mark is faded in reverse time sequence to generate a hierarchical supplementary mark;

[0048] According to the dynamic motion data, the hierarchical supplementary mark is subjected to theoretical motion identification to generate a hierarchical theoretical mark.

[0049] A biological sample image processing system based on microfluidic optical tweezers, the system comprising an initial monitoring and analysis module, an intermittent monitoring and analysis module, a particle dynamic analysis module, a motion trajectory creation module, and a theoretical marker creation module, wherein:

[0050] The initial monitoring and analysis module is used to perform initial monitoring and shooting of biological samples in the microfluidic chip, obtain the initial shooting video, perform particle identification and flow analysis, and calculate the microfluidic flow drag;

[0051] Intermittent monitoring and analysis module, used for controlling microfluidic optical tweezers, performing intermittent monitoring and shooting of biological samples, acquiring multiple intermittent shooting images, performing control and identification of target objects, and recording multiple optical tweezers control parameters;

[0052] a particle dynamic analysis module, configured to perform dynamic analysis of particle capture and record dynamic motion data based on the microfluidic flow drag and a plurality of optical tweezers control parameters;

[0053] a motion trajectory creation module, configured to select a representative sample image from the plurality of intermittently captured images, and perform trajectory processing on the representative sample image to create an actual motion trajectory of the target object;

[0054] The theoretical mark creation module is used to perform theoretical motion analysis of the target object on multiple intermittently shot images according to the dynamic motion data, and create hierarchical theoretical marks in the representative sample images.

[0055] As a further limitation of the technical solution of the embodiment of the present invention, the initial monitoring and analysis module specifically includes:

[0056] An initial monitoring and shooting unit is used to perform initial monitoring and shooting of the biological sample in the microfluidic chip to obtain an initial shooting video;

[0057] A fluid parameter acquisition unit, used to obtain fluid parameters of the microfluidic chip;

[0058] A particle parameter acquisition unit, used for acquiring particle parameters of a biological sample;

[0059] a particle motion recognition unit, configured to perform particle motion recognition analysis on the initially captured video to obtain a particle flow velocity;

[0060] The flow drag calculation unit is used to calculate the microfluidic flow drag according to fluid parameters, particle parameters and particle flow velocity.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The embodiments of the present invention perform initial monitoring and photographing of biological samples in a microfluidic chip to calculate microfluidic flow drag; perform intermittent monitoring and photographing of biological samples and control identification of target objects; perform dynamic analysis of particle capture and record dynamic motion data; select representative sample images and perform trajectory processing to create actual motion trajectories; perform theoretical motion analysis of the target object and create hierarchical theoretical labels in the representative sample images. The invention is capable of performing dynamic analysis of particle capture, selecting representative sample images, creating actual motion trajectories of the target object, and creating hierarchical theoretical labels. It is capable of accurately and rigorously automatically labeling the motion of the target object and reflecting the difference between actual motion and theoretical motion, thereby providing intuitive and effective observation images for the research and application of microfluidic optical tweezers. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of a biological sample image processing method based on microfluidic optical tweezers provided in an embodiment of the present invention is shown;

[0064] Figure 2 A flow chart showing the calculation of microfluidic flow drag in the method provided in an embodiment of the present invention is shown;

[0065] Figure 3 A flow chart showing the method for recording optical tweezers control parameters in an embodiment of the present invention is shown;

[0066] Figure 4 A flowchart of a method for recording dynamic motion data provided by an embodiment of the present invention is shown;

[0067] Figure 5A flowchart showing the actual motion trajectory of a target object in the method provided by an embodiment of the present invention is shown;

[0068] Figure 6 A flowchart of creating a hierarchical theory mark in a method provided by an embodiment of the present invention is shown;

[0069] Figure 7 The application architecture diagram of the biological sample image processing system based on microfluidic optical tweezers provided in an embodiment of the present invention is shown;

[0070] Figure 8 It shows a structural block diagram of the initial monitoring and analysis module in the system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] It is understandable that in the existing technology, for the processing of biological sample images for microfluidic optical tweezers, the staff often selects the image for manual target motion marking after observation. Not only is the target motion marking not accurate and rigorous enough, but it also cannot reflect the difference between actual motion and theoretical motion, and cannot provide intuitive and effective observation images for the research and application of microfluidic optical tweezers.

[0073] To solve the above problems, an embodiment of the present invention discloses a biological sample image processing method and system based on microfluidic optical tweezers, which performs initial monitoring and shooting of biological samples in a microfluidic chip, obtains initial shooting videos, performs particle identification and flow analysis, and calculates microfluidic flow drag; performs microfluidic optical tweezers control, intermittently monitors and shoots biological samples, obtains multiple intermittent shooting images, and performs control and identification of target objects, and records multiple optical tweezers control parameters; performs dynamic analysis of particle capture based on the microfluidic flow drag and multiple optical tweezers control parameters, and records dynamic motion data; selects representative sample images from multiple intermittent shooting images, and performs trajectory processing in the representative sample images to create the actual motion trajectory of the target object; performs theoretical motion analysis of the target object on the multiple intermittent shooting images according to the dynamic motion data, and creates hierarchical theoretical labels in the representative sample images. It can perform dynamic analysis of particle capture, select representative sample images, create the actual motion trajectory of the target object, and create hierarchical theoretical labels. It can accurately and rigorously automatically label the movement of the target object and reflect the difference between actual movement and theoretical movement, thereby providing intuitive and effective observation images for the research and application of microfluidic optical tweezers.

[0074] Specifically, Figure 1The flowchart of the biological sample image processing method based on microfluidic optical tweezers provided by an embodiment of the present invention is shown.

[0075] In a preferred embodiment of the present invention, a biological sample image processing method based on microfluidic optical tweezers comprises the following steps:

[0076] Step S101 : Initial monitoring and shooting of the biological sample in the microfluidic chip are performed to obtain an initial shooting video, perform particle identification and flow analysis, and calculate the microfluidic flow drag.

[0077] In an embodiment of the present invention, an initial monitoring video is obtained by capturing an initial video of a biological sample in a microfluidic chip, and fluid parameters such as the fluid viscosity coefficient and fluid flow rate of the microfluidic chip are obtained. In addition, particle parameters such as the particle mass, particle radius, and particle density of the biological sample are obtained. The initial video is processed frame by frame, and the images obtained by the frame-by-frame processing are subjected to particle recognition. The particle flow rate is calculated according to the time and the distance the particles move. The microfluidic flow drag is then calculated based on the fluid parameters, particle parameters, and particle flow rate. Specifically, the calculation formula for the microfluidic flow drag is:

[0078]

[0079] Among them, F L is the microfluidic flow drag, η is the fluid viscosity coefficient, m is the particle mass, u is the fluid flow velocity, f is the particle flow velocity, R is the particle radius, and ρ is the particle density.

[0080] It is understandable that during the initial monitoring and shooting process, microfluidic optical tweezers control is not performed.

[0081] Specifically, Figure 2 A flow chart for calculating microfluidic flow drag in the method provided in an embodiment of the present invention is shown.

[0082] In another preferred embodiment of the present invention, the initial monitoring and shooting of the biological sample in the microfluidic chip, obtaining the initial shooting video, performing particle identification and flow analysis, and calculating the microfluidic flow drag specifically include the following steps:

[0083] Step S1011: Initial monitoring and shooting of the biological sample in the microfluidic chip to obtain an initial shooting video.

[0084] Step S1012: Obtain fluid parameters of the microfluidic chip.

[0085] Step S1013: Obtain particle parameters of the biological sample.

[0086] Step S1014: performing particle motion recognition analysis on the initial captured video to obtain particle flow velocity.

[0087] Step S1015: Calculate the microfluidic flow drag according to the fluid parameters, particle parameters, and particle flow velocity.

[0088] Furthermore, the biological sample image processing method based on microfluidic optical tweezers further includes the following steps:

[0089] Step S102 : Control the microfluidic optical tweezers, perform intermittent monitoring and shooting of the biological sample, obtain a plurality of intermittent shooting images, perform control and recognition of the target object, and record a plurality of optical tweezers control parameters.

[0090] In an embodiment of the present invention, microfluidic optical tweezers are controlled, a light trap is constructed, the target object is captured and manipulated, and an intermittent monitoring period is determined. According to the intermittent monitoring period, the biological sample in the microfluidic chip is intermittently monitored and photographed, and a plurality of intermittently photographed images are obtained. By identifying the target object captured and manipulated in the plurality of intermittently photographed images, a plurality of target positions corresponding to the target object in the plurality of intermittently photographed images are obtained, and based on the plurality of target positions, the optical tweezers are controlled and identified for the laser power, incident angle, and refraction angle of the target object, and a plurality of optical tweezers control parameters are recorded.

[0091] It is understandable that the control of microfluidic optical tweezers changes dynamically at different target positions, including active changes in laser power and passive changes in incident angles and refraction angles at different target positions.

[0092] Specifically, Figure 3 A flow chart of recording optical tweezers control parameters in the method provided by an embodiment of the present invention is shown.

[0093] In another preferred embodiment of the present invention, the microfluidic optical tweezers control, intermittent monitoring and photographing of the biological sample, obtaining multiple intermittent photographed images, and controlling and identifying the target object, and recording multiple optical tweezers control parameters specifically include the following steps:

[0094] Step S1021 : Perform microfluidic optical tweezers control and determine an intermittent monitoring cycle.

[0095] Step S1022: perform intermittent monitoring and photographing of the biological sample according to the intermittent monitoring cycle to obtain a plurality of intermittent photographing images.

[0096] Step S1023: performing target recognition in the plurality of intermittently captured images to obtain positions of a plurality of targets.

[0097] Step S1024: performing control identification based on the multiple target object positions and recording multiple optical tweezers control parameters.

[0098] Furthermore, the biological sample image processing method based on microfluidic optical tweezers further includes the following steps:

[0099] Step S103 : performing dynamic analysis of particle capture according to the microfluidic flow drag and the plurality of optical tweezers control parameters, and recording dynamic motion data.

[0100] In an embodiment of the present invention, a coordinate system is constructed. In the coordinate system, multiple scattering force components and multiple gradient force components affecting the target are calculated based on the microfluidic flow drag and multiple optical tweezers control parameters, and the force angles corresponding to the microfluidic flow drag, multiple scattering force components, and multiple gradient force components are determined. Then, based on the multiple scattering force components, multiple gradient force components, and multiple force angles, a dynamic analysis is performed on the motion trend of the target at multiple target positions during particle capture, and dynamic motion data is recorded. Specifically, the calculation formula for the multiple scattering force components is:

[0101]

[0102] D=2k1 cos 2β i +k1 2 +1;

[0103] Among them, F Si is the scattering force of the target object in the i-th intermittent shooting image, D is the calculation factor, n is the refractive index, P i is the laser power corresponding to the i-th intermittent shooting image, k1 is the reflection coefficient, k2 is the refraction coefficient, α i is the incident angle of the target in the i-th intermittent image, β i is the refraction angle of the target object in the i-th intermittent image, and c is the speed of light;

[0104] The calculation formula for multiple gradient force components is:

[0105]

[0106] Among them, F Ti is the gradient component of the target object in the i-th intermittent image.

[0107] Specifically, Figure 4 A flow chart of recording dynamic motion data in the method provided by an embodiment of the present invention is shown.

[0108] In another preferred embodiment of the present invention, the dynamic analysis of particle capture and recording of dynamic motion data based on the microfluidic flow drag and the plurality of optical tweezers control parameters specifically include the following steps:

[0109] Step S1031: Construct a coordinate system.

[0110] Step S1032: Calculate multiple scattering force components and multiple gradient force components in the coordinate system according to the microfluidic flow drag and multiple optical tweezers control parameters.

[0111] Step S1033: Determine multiple component force angles.

[0112] Step S1034: Based on the multiple scattering force components, the multiple gradient force components, and the multiple force component angles, dynamic analysis of particle capture is performed, and dynamic motion data is recorded.

[0113] Furthermore, the biological sample image processing method based on microfluidic optical tweezers further includes the following steps:

[0114] Step S104 : Select a representative sample image from the plurality of intermittently captured images, and perform trajectory processing on the representative sample image to create an actual motion trajectory of the target object.

[0115] In an embodiment of the present invention, multiple intermittently shot images are arranged in time sequence, the time sequence arrangement data is recorded, and then, based on the time sequence arrangement data, the intermittently shot image arranged in the last time sequence is selected as a representative sample image. In the representative sample image, multiple target object positions are marked to obtain multiple target object points, and then in the representative sample image, the multiple target object points are smoothly connected to create the actual motion trajectory of the target object.

[0116] It is understandable that in the actual motion trajectory, retaining the image of the target object at the last time sequence and representing the previous actual motion situation in the form of a trajectory can accurately and intuitively reflect the actual motion situation of the target object.

[0117] Specifically, Figure 5 A flow chart of creating an actual motion trajectory of a target object in the method provided by an embodiment of the present invention is shown.

[0118] In another preferred embodiment of the present invention, selecting a representative sample image from the plurality of intermittently captured images, performing trajectory processing on the representative sample image, and creating the actual motion trajectory of the target object specifically include the following steps:

[0119] Step S1041: Arrange the plurality of intermittently captured images in time sequence and record time sequence data.

[0120] Step S1042: Select a representative sample image based on the time-series arrangement data.

[0121] Step S1043 : Marking points in the representative sample image according to the multiple target object positions to obtain multiple target object points.

[0122] Step S1044: Smoothly connect the plurality of target points to create an actual motion trajectory of the target in the representative sample image.

[0123] Furthermore, the biological sample image processing method based on microfluidic optical tweezers further includes the following steps:

[0124] Step S105 : performing theoretical motion analysis of the target object on the plurality of intermittently shot images according to the dynamic motion data, and creating hierarchical theoretical labels in the representative sample images.

[0125] In an embodiment of the present invention, a target image of a target object is captured from a representative sample image, and then the target image is positioned and supplemented in the representative sample image according to a plurality of target positions, thereby creating a representative supplement mark, and according to the time series arrangement data, the representative supplement mark is hierarchically faded in reverse time series to generate a hierarchical supplement mark, and then according to the dynamic motion data, theoretical motion identification is performed on the target images at different target positions in the hierarchical supplement mark to generate a hierarchical theoretical mark. Specifically, the theoretical motion identification is performed by identifying the theoretical motion direction of the target object through the direction of the arrow; and identifying the magnitude of the theoretical motion resultant force through the length of the arrow.

[0126] It can be understood that the reverse time-series hierarchical fading is to fade the target image at the target position earlier in the time sequence in the supplementary mark, so that the generated hierarchical supplementary mark can intuitively reflect the movement of the target object in different time sequences according to the fading level.

[0127] It can be understood that the theoretical motion resultant force is the theoretical resultant force of the microfluidic flow drag force, the scattering force component and the gradient force component on the target object at the corresponding target object position.

[0128] Specifically, Figure 6 A flowchart of creating hierarchical theoretical labels in the method provided by an embodiment of the present invention is shown.

[0129] In another preferred embodiment of the present invention, performing theoretical motion analysis of the target object on a plurality of intermittently captured images according to the dynamic motion data, and creating hierarchical theoretical labels in the representative sample images specifically include the following steps:

[0130] Step S1051 : capturing a target object image from the representative sample image.

[0131] Step S1052 : Position and supplement the plurality of target object images in the representative sample image according to the plurality of target object positions, and create representative supplementary marks.

[0132] Step S1053 : According to the time sequence arrangement data, the representative supplementary mark is faded in reverse time sequence to generate a hierarchical supplementary mark.

[0133] Step S1054: perform theoretical motion identification on the hierarchical supplementary mark according to the dynamic motion data to generate a hierarchical theoretical mark.

[0134] Further, Figure 7 The application architecture diagram of the biological sample image processing system based on microfluidic optical tweezers provided by an embodiment of the present invention is shown.

[0135] Specifically, in another preferred embodiment provided by the present invention, a biological sample image processing system based on microfluidic optical tweezers includes:

[0136] The initial monitoring and analysis module 101 is used to perform initial monitoring and shooting of the biological sample in the microfluidic chip, obtain the initial shooting video, perform particle identification and flow analysis, and calculate the microfluidic flow drag.

[0137] In an embodiment of the present invention, the initial monitoring and analysis module 101 performs initial monitoring and shooting of the biological sample in the microfluidic chip to obtain an initial shooting video, obtains fluid parameters such as the fluid viscosity coefficient and fluid flow rate of the microfluidic chip, and obtains particle parameters such as particle mass, particle radius, and particle density of the biological sample. The initial shooting video is processed frame by frame, and the images obtained by frame-by-frame processing are used for particle recognition. The particle flow rate is calculated according to time and distance traveled by the particles. Then, the microfluidic flow drag is calculated based on the fluid parameters, particle parameters, and particle flow rate. Specifically, the calculation formula of the microfluidic flow drag is:

[0138]

[0139] Among them, F L is the microfluidic flow drag, η is the fluid viscosity coefficient, m is the particle mass, u is the fluid flow velocity, f is the particle flow velocity, R is the particle radius, and ρ is the particle density.

[0140] Further, Figure 8 It shows a structural block diagram of the initial monitoring and analysis module 101 in the system provided by an embodiment of the present invention.

[0141] Specifically, in another preferred embodiment provided by the present invention, the initial monitoring and analysis module 101 specifically includes:

[0142] The initial monitoring and shooting unit 1011 is used to perform initial monitoring and shooting of the biological sample in the microfluidic chip to obtain an initial shooting video.

[0143] The fluid parameter acquisition unit 1012 is used to acquire the fluid parameters of the microfluidic chip.

[0144] The particle parameter acquisition unit 1013 is used to acquire the particle parameters of the biological sample.

[0145] The particle motion identification unit 1014 is configured to perform particle motion identification analysis on the initially captured video to obtain a particle flow velocity.

[0146] The flow drag calculation unit 1015 is used to calculate the microfluidic flow drag according to the fluid parameters, particle parameters and particle flow rate.

[0147] Furthermore, the biological sample image processing system based on microfluidic optical tweezers also includes:

[0148] The intermittent monitoring and analysis module 102 is used to control the microfluidic optical tweezers, perform intermittent monitoring and shooting of biological samples, obtain multiple intermittent shooting images, control and identify the target object, and record multiple optical tweezers control parameters.

[0149] In an embodiment of the present invention, the intermittent monitoring and analysis module 102 controls the microfluidic optical tweezers, constructs a light trap, captures and manipulates the target object, and determines an intermittent monitoring period. According to the intermittent monitoring period, the biological sample in the microfluidic chip is intermittently monitored and photographed to obtain a plurality of intermittently photographed images. By identifying the target object captured and manipulated in the plurality of intermittently photographed images, a plurality of target positions corresponding to the target object in the plurality of intermittently photographed images are obtained, and based on the plurality of target positions, the optical tweezers are controlled and identified for the laser power, incident angle, and refraction angle of the target object, and a plurality of optical tweezers control parameters are recorded.

[0150] The particle dynamic analysis module 103 is used to perform dynamic analysis of particle capture based on the microfluidic flow drag and a plurality of optical tweezers control parameters, and record dynamic motion data.

[0151] In an embodiment of the present invention, the particle dynamic analysis module 103 constructs a coordinate system. In the coordinate system, based on the microfluidic flow drag and multiple optical tweezers control parameters, it calculates multiple scattering force components and multiple gradient force components that affect the target object, and determines the force angles corresponding to the microfluidic flow drag, multiple scattering force components, and multiple gradient force components. Then, based on the multiple scattering force components, multiple gradient force components, and multiple force angles, it dynamically analyzes the motion trend of the target object at multiple target positions during particle capture and records dynamic motion data. Specifically, the calculation formula for the multiple scattering force components is:

[0152]

[0153] D=2k1 cos 2β i +k1 2 +1;

[0154] Among them, F Si is the scattering force of the target object in the i-th intermittent shooting image, D is the calculation factor, n is the refractive index, P i is the laser power corresponding to the i-th intermittent shooting image, k1 is the reflection coefficient, k2 is the refraction coefficient, α i is the incident angle of the target in the i-th intermittent image, β i is the refraction angle of the target object in the i-th intermittent image, and c is the speed of light;

[0155] The calculation formula for multiple gradient force components is:

[0156]

[0157] Among them, F Ti is the gradient component of the target object in the i-th intermittent image.

[0158] The motion trajectory creation module 104 is configured to select representative sample images from the plurality of intermittently captured images, and perform trajectory processing on the representative sample images to create an actual motion trajectory of the target object.

[0159] In an embodiment of the present invention, the motion trajectory creation module 104 arranges multiple intermittently shot images in time sequence, records the time sequence arrangement data, and then selects the intermittently shot image arranged in the last time sequence as a representative sample image based on the time sequence arrangement data. In the representative sample image, multiple target object positions are marked to obtain multiple target object points, and then in the representative sample image, the multiple target object points are smoothly connected to create the actual motion trajectory of the target object.

[0160] The theoretical mark creation module 105 is used to perform theoretical motion analysis of the target object on the plurality of intermittently shot images according to the dynamic motion data, and to create hierarchical theoretical marks in the representative sample images.

[0161] In an embodiment of the present invention, the theoretical mark creation module 105 creates a representative supplementary mark by intercepting the target image of the target object from the representative sample image, and then positioning and supplementing the target image in the representative sample image according to multiple target positions, and then performs reverse time-series hierarchical fading on the representative supplementary mark according to the time-series arrangement data to generate a hierarchical supplementary mark, and then performs theoretical motion identification on the target images of different target positions in the hierarchical supplementary mark according to the dynamic motion data to generate a hierarchical theoretical mark. Specifically, the theoretical motion identification is performed by identifying the theoretical motion direction of the target object through the direction of the arrow; and identifying the magnitude of the theoretical motion resultant force through the length of the arrow.

[0162] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0163] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0164] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A biological sample image processing method based on microfluidic optical tweezers, characterized in that: The method specifically comprises the following steps: Perform initial monitoring and shooting of biological samples in the microfluidic chip, obtain initial shooting videos, perform particle identification and flow analysis, and calculate microfluidic flow drag; Control microfluidic optical tweezers, monitor and shoot biological samples intermittently, obtain multiple intermittent shooting images, control and identify the target, and record multiple optical tweezers control parameters; According to the microfluidic flow drag and a plurality of the optical tweezers control parameters, dynamic analysis of particle capture is performed and dynamic motion data is recorded; Selecting a representative sample image from the plurality of intermittently shot images, and performing trajectory processing on the representative sample image to create an actual motion trajectory of the target object; According to the dynamic motion data, theoretical motion analysis of the target object is performed on a plurality of intermittently captured images, and hierarchical theoretical labels are created in the representative sample images.

2. The biological sample image processing method based on microfluidic optical tweezers according to claim 1, characterized in that: The initial monitoring and shooting of the biological sample in the microfluidic chip, obtaining the initial shooting video, performing particle identification and flow analysis, and calculating the microfluidic flow drag specifically include the following steps: Performing initial monitoring and shooting of biological samples in the microfluidic chip to obtain an initial shooting video; Obtain fluid parameters of the microfluidic chip; Obtain particle parameters of biological samples; Performing particle motion recognition analysis on the initially shot video to obtain particle flow velocity; Calculate microfluidic flow drag based on fluid parameters, particle parameters, and particle flow rate.

3. The biological sample image processing method based on microfluidic optical tweezers according to claim 2, characterized in that: The calculation formula of the microfluidic flow drag is: Among them, F L is the microfluidic flow drag, η is the fluid viscosity coefficient, m is the particle mass, u is the fluid flow velocity, f is the particle flow velocity, R is the particle radius, and ρ is the particle density.

4. The biological sample image processing method based on microfluidic optical tweezers according to claim 1, characterized in that: The microfluidic optical tweezers control, intermittent monitoring and shooting of biological samples, obtaining multiple intermittent shooting images, and controlling and identifying the target object, and recording multiple optical tweezers control parameters specifically include the following steps: Conduct microfluidic optical tweezers control and determine the intermittent monitoring cycle; According to the intermittent monitoring cycle, intermittent monitoring and photographing of the biological sample are performed to obtain a plurality of intermittent photographing images; In the plurality of intermittently shot images, target object recognition is performed to obtain positions of a plurality of targets; Based on the positions of the multiple target objects, control identification is performed and multiple optical tweezers control parameters are recorded.

5. The biological sample image processing method based on microfluidic optical tweezers according to claim 1, characterized in that: The method of performing dynamic analysis of particle capture according to the microfluidic flow drag and the plurality of optical tweezers control parameters and recording dynamic motion data specifically comprises the following steps: Construct a coordinate system; In the coordinate system, a plurality of scattering force components and a plurality of gradient force components are calculated according to the microfluidic flow drag force and a plurality of the optical tweezers control parameters; Determine multiple force component angles; Based on the multiple scattering force components, the multiple gradient force components and the multiple force component angles, dynamic analysis of particle capture is performed and dynamic motion data is recorded.

6. The intelligent industrial detection method based on image processing according to claim 5 is characterized in that: The calculation formulas for the multiple scattering force components are: D=2k1 cos 2β i +k1 2 +1; Among them, F Si is the scattering component of the target in the i-th intermittent image, D is the calculation factor, n is the refractive index, P i is the laser power corresponding to the i-th intermittent image, k1 is the reflection coefficient, k2 is the refraction coefficient, α i is the incident angle of the target in the i-th intermittent image, β i is the refraction angle of the target object in the i-th intermittent image, and c is the speed of light; The calculation formulas for the multiple gradient force components are: Among them, F Ti is the gradient component of the target object in the i-th intermittent image.

7. The biological sample image processing method based on microfluidic optical tweezers according to claim 4, characterized in that: The step of selecting a representative sample image from the plurality of intermittently shot images, and performing trajectory processing on the representative sample image to create an actual motion trajectory of the target object specifically comprises the following steps: Arrange the plurality of intermittently shot images in time sequence and record time sequence arrangement data; selecting a representative sample image based on the time-series arrangement data; According to the plurality of target object positions, point marking is performed in the representative sample image to obtain a plurality of target object points; A plurality of the target object points are smoothly connected to create an actual motion trajectory of the target object in the representative sample image.

8. The biological sample image processing method based on microfluidic optical tweezers according to claim 7, characterized in that: According to the dynamic motion data, the theoretical motion analysis of the target object is performed on the plurality of intermittently shot images, and in the representative sample images, the creation of hierarchical theoretical marks specifically includes the following steps: intercepting a target object image of the target object from the representative sample image; According to the positions of the plurality of target objects, in the representative sample image, positioning and supplementing the plurality of target object images, and creating representative supplementary marks; According to the time sequence arrangement data, the representative supplementary mark is faded in reverse time sequence to generate a hierarchical supplementary mark; According to the dynamic motion data, the hierarchical supplementary mark is subjected to theoretical motion identification to generate a hierarchical theoretical mark.

9. A biological sample image processing system based on microfluidic optical tweezers, characterized in that: The system includes an initial monitoring and analysis module, an intermittent monitoring and analysis module, a particle dynamic analysis module, a motion trajectory creation module and a theoretical mark creation module, wherein: The initial monitoring and analysis module is used to perform initial monitoring and shooting of biological samples in the microfluidic chip, obtain the initial shooting video, perform particle identification and flow analysis, and calculate the microfluidic flow drag; An intermittent monitoring and analysis module is used to control microfluidic optical tweezers, perform intermittent monitoring and shooting of biological samples, obtain multiple intermittent shooting images, control and identify the target object, and record multiple optical tweezers control parameters; A particle dynamic analysis module, used to perform dynamic analysis of particle capture and record dynamic motion data according to the microfluidic flow drag and a plurality of optical tweezers control parameters; A motion trajectory creation module, used for selecting a representative sample image from the plurality of intermittently shot images, and performing trajectory processing in the representative sample image to create an actual motion trajectory of the target object; The theoretical mark creation module is used to perform theoretical motion analysis of the target object on multiple intermittent shooting images according to the dynamic motion data, and create hierarchical theoretical marks in the representative sample images.

10. The biological sample image processing system based on microfluidic optical tweezers according to claim 9, characterized in that: The initial monitoring and analysis module specifically includes: An initial monitoring and shooting unit is used to perform initial monitoring and shooting of biological samples in the microfluidic chip to obtain an initial shooting video; A fluid parameter acquisition unit, used to acquire fluid parameters of the microfluidic chip; A particle parameter acquisition unit, used for acquiring particle parameters of biological samples; A particle motion identification unit, used to perform particle motion identification analysis on the initial shot video to obtain a particle flow velocity; The flow drag calculation unit is used to calculate the microfluidic flow drag according to the fluid parameters, particle parameters and particle flow velocity.

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