WO2017028341A1

WO2017028341A1 - Method for generating single-cell three-dimensional image based on optical flow analysis

Info

Publication number: WO2017028341A1
Application number: PCT/CN2015/088945
Authority: WO
Inventors: 李志�; 张光烈; 李文荣
Original assignee: 深圳大学
Priority date: 2015-08-14
Filing date: 2015-09-06
Publication date: 2017-02-23
Also published as: CN105184853B; CN105184853A

Abstract

A method for generating a single-cell three-dimensional image based on an optical flow analysis, comprising the steps: A. fabricating a light-induced dielectrophoresis chip (20), wherein the light-induced dielectrophoresis chip (20) is composed of a three-layer structure: the lower layer being ITO glass coated with a hydrogenated amorphous silicon coating layer, the upper layer being ITO glass without a coating layer, and a micro-fluidic channel being encapsulated between the upper and lower ITO glass layers for injecting a solution required for operations; B. injecting a cell and the solution into the micro-fluidic channel, and inputting a variable frequency alternating current signal to electrodes of the upper and lower ITO glass layers, and at the same time using incident light to irradiate the light-induced dielectrophoresis chip, so as to generate a non-uniform electric field in an irradiated area; C. changing the frequency and magnitude of the alternating current signal so as to control a cell motion direction, and at the same time collecting an image of the cell; and D. pre-processing the collected image, then performing feature extraction and velocity calculation, and finally reconstructing a 3D cell image. In the method, a cell two-dimensional image sequence is acquired by controlling the rotation of a single cell, and a maximum likelihood estimation method is used to realize a cell three-dimensional image generation technique based on a two-dimensional image sequence of a controllable single cell. The device used in the method is simple and low in cost, does not bring about a phototoxic function and has a relatively low requirement for a sample, thereby improving the operability.

Description

Single cell three-dimensional image generation method based on optical flow analysis

Technical field

The invention relates to the field of cell imaging, and in particular to a single cell three-dimensional image generation method based on optical flow analysis.

Background technique

Compared to earlier imaging systems, 3D imaging of living cells presents a more detailed and more accurate spatial view of the cells and their components. Technological advances have made 3D imaging an important tool for many applications, such as cell biology, developmental biology, neuroscience, and cancer research. Current technology is more accurate than ever, providing data in real time with virtually no cell preparation. The prior art single-cell three-dimensional methods mainly include the following:

1. Laser scanning confocal microscope: The laser beam is used to form a point light source through the illumination pinhole to scan every point in the intracellular focal plane. The illuminated spot on the cell is imaged at the probe pinhole, and the photomultiplier is detected after the pinhole is detected. A tube (PMT) or a cold-coupled device (cCCD) is received point by point or line by line, rapidly forming a fluorescent image on the computer monitor screen. The illumination pinhole and the detection pinhole are conjugate with respect to the focal plane of the objective lens, and the point on the focal plane is simultaneously focused on the illumination pinhole and the emission pinhole, and the point outside the focal plane is not imaged at the probe pinhole, thus obtained A confocal image is the optical cross section of a cell. Laser scanning confocal microscopy can form a three-dimensional structure image of cells by real-time scanning imaging of different layers of the same cell.

2, white light diffraction tomography imaging technology: This technology can image transparent samples such as living cells and unlabeled cells, based on traditional microscopes and white light, providing high-resolution 3D rendered images in the natural state of the cells. The objective lens scans the entire axial focal plane of the cell, produces a stack of phase-resolved images, and then reconstructs the three-dimensional structure of the object by the sparse deconvolution algorithm. A lateral resolution of 350 nm and an axial resolution of 900 nm can be obtained.

3. Lattice light microscope: The lattice light microscope has two orthogonal lenses; one lens focuses the light to produce a very thin pen-like light source that illuminates a biological sample with fluorescent molecules to produce fluorescence; Fluorescence is collected using wide field imaging, and surface scanning is used to quickly acquire 3D high definition biological images. The spatial light modulator is used to simultaneously form more than 100 pen-shaped beams to increase the scanning speed and reduce the damage to biological samples; and to control the distance and shape of each beam.

However, the above methods are insufficient:

The first method requires special equipment, which is too costly; and it produces phototoxic effects: under laser irradiation, many fluorescent dye molecules produce cytotoxins such as singlet oxygen or free radicals, limiting the scanning time and excitation light intensity. Maintaining the activity of the sample; photobleaching of the marking dye: in order to obtain a sufficient signal-to-noise ratio, the intensity of the laser must be increased; and high-intensity lasers can quickly fade the dye during continuous scanning.

The second method is not applicable to non-translucent samples. The requirements for samples are relatively high; special equipment is required, the cost is too high; and the imaging flexibility is low.

In the third way, the equipment cost is too high; the sample needs to be fluorescently treated to reduce the operability; the phototoxic effect still exists.

Therefore, the prior art has yet to be improved and developed.

Summary of the invention

In view of the above deficiencies of the prior art, an object of the present invention is to provide a single-cell three-dimensional image generation method based on optical flow analysis, which aims to solve the problem of high cost, phototoxicity, harsh conditions, etc. of the existing three-dimensional image generation method of cells. problem.

The technical solution of the present invention is as follows:

A single-cell three-dimensional image generation method based on optical flow analysis, comprising the steps of:

A. A photo-induced dielectrophoresis chip is prepared. The photo-induced dielectrophoresis chip has a three-layer structure: a three-layer structure: the lower layer is ITO glass coated with a hydrogenated amorphous silicon coating, and the upper layer is ITO glass without coating. A microfluidic channel is encapsulated between the upper and lower layers of ITO glass for injecting a solution for the desired operation;

B. Injecting the cells and the solution into the microfluidic channel, and inputting a variable frequency alternating current signal to the electrodes of the upper and lower ITO glass, and irradiating the photoinduced dielectrophoresis chip with the incident light, thereby generating a non-irradiated region in the irradiated region. Uniform electric field;

C. Change the frequency and size of the AC signal to control the direction of cell movement while collecting images of the cells;

D. Pre-process the acquired image, then perform feature extraction and velocity calculation, and finally reconstruct the 3D cell image.

The single-cell three-dimensional image generation method based on the optical flow analysis, wherein the step of fabricating the light-induced dielectrophoresis chip in the step A specifically includes:

A1, cleaning the ITO glass substrate;

A2 depositing a hydrogenated amorphous silicon coating on the ITO glass substrate;

A3, coating a photoresist on the hydrogenated amorphous silicon coating;

A4, performing plate printing on the photoresist;

A5, contact corrosion to the ITO glass substrate;

A6, removing the photoresist;

A7. A conductive adhesive is applied to the area of the ITO glass substrate that is not covered with the hydrogenated amorphous silicon coating.

The single-cell three-dimensional image generation method based on optical flow analysis, wherein the average dielectrophoretic force of the cells in a non-uniform electric field is described by the following formula:

Where F _DEP is the average dielectrophoretic force acting on the cell, R is the radius of the cell, ε _m is the dielectric constant of the solution in which the cell is located, E _rms is the root mean square value of the applied AC signal, and f _CM is Clausius-Mossotti Factor, the real part of the factor Re[f _CM ] is taken when calculating the average dielectrophoretic force.

The single-cell three-dimensional image generation method based on optical flow analysis, wherein the f _CM factor is defined as follows:

ε _p * and ε _m * are the complex dielectric constants of the cells and solutions, respectively.

The single-cell three-dimensional image generating method based on optical flow analysis, wherein the complex permittivity can be expressed as:

Where ε is the dielectric constant of the solution, σ is the conductivity, and ω is the applied AC signal Frequency of.

The single-cell three-dimensional image generating method based on optical flow analysis, wherein the cell rotation speed is:

Where E is the electric field strength, η is the viscosity of the solution, IM[f _CM ] is the imaginary part of the Clausius-Mossotti factor, and K is the coefficient.

The single-cell three-dimensional image generating method based on optical flow analysis, wherein the pre-processing comprises: Gaussian filtering processing, brightness adjustment, and template matching.

The single-cell three-dimensional image generating method based on the optical flow analysis, wherein in the step D, the maximum likelihood estimation of the model parameters is performed using a machine learning algorithm, and the parameter values minimized by the following formula are:

Where I _i+1 is a frame in the cell rotation image sequence I={I _i , i=1, . . . , n}, where M represents a set of all points on the cell, and R _i and T _i are a rotation matrix and a translation vector, respectively. , R _i m _i +T _i is a three-dimensional rotational motion model of a point m from i to i+1 on the cell, ie _mi+1 , mapping

It is to project the three-dimensional information of the cell at time i to a certain frame I _{i of the} cell rotation image.

Advantageous Effects: The method of the present invention has the following advantages: 1) a feedback control function of a light-inducing dielectrophoresis steerable platform realized by a microscopic vision algorithm; 2) a three-dimensional motion of a cell using a motion detection method based on an optical flow field Tracking to complete parameter estimation of the cell dynamics model; 3) obtaining cell two-dimensional by controlling the rotation of individual cells Image sequence, using the maximum likelihood estimation method to realize three-dimensional image generation technology of cells based on two-dimensional image sequences of controllable single cells. The method of the invention has simple equipment, low cost, no phototoxic effect, low requirements on samples, and improved operability.

DRAWINGS

1 is a flow chart of a preferred embodiment of a single cell three-dimensional image generation method based on optical flow analysis according to the present invention.

2 is a schematic view showing the structure of a light-induced dielectrophoresis platform in the present invention.

detailed description

The present invention provides a single-cell three-dimensional image generation method based on optical flow analysis. The present invention will be further described in detail below in order to clarify and clarify the objects, technical solutions and effects of the present invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Please refer to FIG. 1. FIG. 1 is a flowchart of a preferred embodiment of a single-cell three-dimensional image generation method based on optical flow analysis according to the present invention. As shown in the figure, the method includes the following steps:

S100, preparing a light-induced dielectrophoresis chip (ODEP chip), wherein the light-induced dielectrophoresis chip has a three-layer structure: the lower layer is ITO glass coated with a hydrogenated amorphous silicon coating, and the upper layer is uncoated (ie, does not contain hydrogenation) ITO glass coated with amorphous silicon, encapsulating a microfluidic channel between the upper and lower ITO glass for injecting the solution required for operation;

S200, inputting an alternating frequency signal of a variable frequency to the electrodes of the upper and lower ITO glass, and irradiating the light-induced dielectrophoresis chip with the incident light, thereby producing the irradiated region A non-uniform electric field is generated; cells and media can be injected into the microfluidic channel first (the medium is the solution to be operated, that is, the solution in which the cells are located). Then enter the AC signal.

S300, changing the frequency and size of the alternating signal to control the direction of cell movement while collecting images of the cells;

S400: Preprocessing the acquired image, then performing feature extraction and speed calculation, and finally reconstructing the 3D cell image.

Further, in the step S100, the step of fabricating the light-induced dielectrophoresis chip specifically includes:

S101, cleaning the ITO glass substrate;

Clean the surface of the ITO glass substrate to ensure the cleanliness of the contact surface.

S102, depositing a hydrogenated amorphous silicon coating (a-Si:H) on the ITO glass substrate;

A layer of hydrogenated amorphous silicon was deposited on the surface of the ITO glass substrate to a thickness of 1 micron.

S103, coating a photoresist on the hydrogenated amorphous silicon coating;

S104, performing a plate printing on the photoresist;

The stencil is to make a cover according to the specified pattern, the cover is placed on the surface of the photoresist, and the cover is irradiated with ultraviolet rays, and the uncovered photoresist is dissolved under the action of ultraviolet rays, and finally the photoresist having the same shape as the cover is obtained. Floor.

S105, contact etching to the ITO glass substrate; specifically, etching the prepared chip surface layer with oxalic acid to remove the hydrogenated amorphous silicon coating without covering the photoresist.

S106, removing the photoresist; removing the photoresist from the surface of the hydrogenated amorphous silicon coating.

S107. Applying a conductive adhesive to a region of the ITO glass substrate that is not covered with the hydrogenated amorphous silicon coating. That is, a position is added at a position where the surface of the ITO glass is not covered with the hydrogenated amorphous silicon coating. Conductive contacts.

After the upper ITO glass is cleaned, apply a conductive adhesive.

A microfluidic channel (100 micron high) is packaged between the upper and lower ITO glass, specifically a microfluidic channel is encapsulated by PDMS or double-sided tape.

In step S200, as shown in FIG. 2, a light-induced dielectrophoresis platform is first constructed. In addition to the ODEP chip 20 produced in step S100, the platform also requires an optical microscope 10, an optical projector (high resolution), a programmable signal generation circuit, and a host system. The host system includes: an image acquisition module, a microscopic vision algorithm processing module, a biochip drive controller, a virtual electrode generation module, and a display output module. The image acquisition module is configured to collect an image of the optical microscope 20, and is processed by a microscopic vision algorithm processing module and displayed by a display output module, wherein the microscopic vision algorithm processing module further drives the biochip driver controller and The virtual electrode generation module signals to control the operation of both. The biochip drive controller is coupled to the programmable signal generation circuit to vary the signal frequency and magnitude. The programmable signal generating circuit connects the ODEP chip 20 through electrodes. The optical projector is disposed below the ODEP chip 20 for illuminating the incident light. The virtual electrode generating module is coupled to the optical projector.

The optical microscope parameters are as follows:

Nikon CFI60 infinity optical system;

Electric focus, can move up and down (upper 13mm / 2mm);

Trinocular tube, light distribution: eyepiece / camera 100% / 0, 20% / 100%, 0/100%;

Eyepiece magnification: 10x;

Concentrator: waterproof, working distance: 7.2mm;

Objective lens: 20x, highly achromatic lens, nanocrystalline coating;

Stage: electric X-axis and Y-axis, resolution: 0.1 micron;

Ultraviolet cut filter block;

Fluorescence filter set: FITC/GFP.

After the platform is built, the biochip driver controller can send a signal to the programmable signal generation circuit, and then the programmable signal generation circuit inputs the variable frequency AC signal to the electrodes of the upper and lower layers of the ITO glass, and the optical projector utilizes the incident. Light illuminates the light-inducing dielectrophoresis chip to produce a non-uniform electric field in the illuminated area.

In the step S300, by changing the frequency and size of the alternating current signal, the direction and size of the dielectrophoretic force received by the cell are changed to control the direction of cell movement, and the image of the cell (2D cell image) is acquired to realize high-speed manipulation. Micro-nano entities.

Finally, in step S400, the acquired image is pre-processed, then feature extraction and velocity calculation are performed, and finally the 3D cell image is reconstructed.

Let's focus on how to control the direction of cell movement by changing the frequency and size of the AC signal.

The average dielectrophoretic force experienced by a cell in a non-uniform electric field can be described by the following formula:

Where F _DEP is the average dielectrophoretic force acting on the cell, R is the radius of the cell, ε _m is the dielectric constant of the solution in which the cell is located, E _rms is the root mean square value of the applied electric field (AC signal), f _CM is Clausius-Mossotti factor, the real part of the factor Re[f _CM ] is taken when calculating the average dielectrophoretic force, which is defined as follows:

ε _p * and ε _m * are the complex permittivity of the cell and the solution, respectively, and the complex permittivity (including ε _p * and ε _m *) in Equation 2 can be expressed as:

Where ε is the dielectric constant of the solution, σ is the conductivity, and ω is the frequency of the applied electric field (alternating current signal).

It can be seen that f _CM is a frequency dependent variable factor. Considering the alternating electric field with different frequencies, when the dielectrophoretic force and the electric field intensity change direction are the same, it is called positive dielectrophoresis; when the dielectrophoretic force and the electric field intensity change direction are opposite, it is called negative dielectrophoresis. Therefore, by changing the frequency of the applied electric field, the direction of the dielectrophoretic force to which the cells are subjected can be changed to achieve the purpose of controlling the direction of cell movement.

Since the biological cells are subjected to the polarization of the non-uniform electric field to generate the dipole moment, the torque generated by the dielectrophoretic force is balanced with the friction torque received in the medium, and the cell rotation speed is:

Where E is the electric field strength, η is the viscosity of the solution, IM[f _CM ] is the imaginary part of the Clausius-Mossotti factor, and K is the coefficient. The dielectric properties of the cells can be estimated based on the relationship between the rotational speed of the cells and the dielectric constant of the cells.

The strength and direction of the dielectrophoretic force that a cell receives depends primarily on the dielectric properties of the medium and the cell, such as shape, size, and electric field frequency. The present invention utilizes light-induced dielectrophoretic force (ODEP) (when a certain frequency band is applied, a dominant force in electro-hydraulics) to identify and manipulate organisms cell. The ODEP chip is driven by a variable frequency AC signal, and the AC signal is input through the conductive contacts of the upper and lower layers of ITO glass. At this time, only a small portion of the solution layer is divided and a uniform electric field is generated in the solution layer. When incident light illuminates the ODEP chip, the optical conductivity of a-Si:H increases by several orders of magnitude due to the increase in the number of electron-hole pairs. Since the resistance of the incident light region is reduced, the partial pressure in the solution layer is greatly increased, so that a:Si:H in the incident light region will become an effective virtual electrode to generate a non-uniform electric field. This light-induced, non-uniform electric field produces a dielectrophoretic force, ie, light-induced dielectrophoretic force (ODEP), of the particles in the polarized region. Programmatic dynamic motion is achieved through optical microscopy and host systems, and automated capture, manipulation, separation and assembly of micro-nano entities are achieved without any manual interface.

For the step S400, the principle of generating a three-dimensional image of a cell is as follows: when a polarized object is placed in a non-uniform electric field, the object moves toward the strongest or weakest part of the electric field under the action of the dipole moment, and the direction depends on the relative of the object. The polarity of the medium. According to the kinetic model of the cell under the dielectrophoretic force field, the cell is controlled by light-induced dielectric by changing the size and frequency of the AC signal that drives the light-induced dielectrophoresis biochip and matching the incident light projected onto the ODEP chip. Rotational motion under the influence of swimming force. The image sequence rotated by the cells to different positions I={I _i , i=1, . . . , n}, where n is the time point, and the three-dimensional image of the cells is a three-dimensional structure in which the cells are reconstructed from the two-dimensional image sequence. The refactoring process includes:

First, after obtaining a two-dimensional image, first pre-process:

1. Perform Gaussian filtering on the image to filter out noise in the image. The core formula of Gaussian filtering is as follows:

Where σ is the width parameter of the function, which controls the radial extent of the function.

2. Then perform brightness adjustment processing. After filtering out the noise, the overall brightness of the image is linearly transformed based on the background color:

g(x,y)=c+k(f(x,y)-a) (6) where f(x,y) and g(x,y) are respectively a point (x,y) in the image Original brightness and transformed brightness, a is the background brightness, k is the transform coefficient, and c is the brightness compensation.

3. Perform template matching processing again. The template matching is performed on the cells in the image, and the correlation coefficient ρ _{XY is} calculated by comparing the difference between the upper and lower frames of the block in the same window.

Where X and Y are the blocks in the upper and lower frames, Cov(X, Y) is the covariance of X and Y, and D(X) and D(Y) are the variances of X and Y, respectively.

Second, then feature extraction.

1. Find the local maximum of the correlation coefficient to track the peak point. From one peak point to the next peak point, it represents one rotation, and the number of rotations is estimated according to the index of the peak point.

2. The optical flow method analyzes the motion vector of the pixel. For a pixel point X(x, y) on the image, the luminance value at time t is I(x, y, t), u(x, y) and v(x, y) represent light at (x, y) The motion component in the x and y directions. Calculate based on image sequence

with

make

W=diag(W(X ₁ ),...,W(X _n )),

Where n is the number of points, diag() is the constructed diagonal matrix, and W is the Gaussian function:

Let V = (u, v) ^T , then the formula is as follows:

V=(A ^T W ² A) ^-1 A ^T W ² b (9)

Third, then calculate the speed.

1. Calculate the rotation speed ω of the cell according to the obtained number of rotation laps and the shooting interval of two adjacent image sequences, and combine the rotation model of the cell to obtain the rotation matrix K of the cell rotation. Thus the set of all points on the rotated cells M' = MK, where M is the original coordinates of all points on the cell. Speed of movement at each point

2. Using the optical flow method to obtain the two-dimensional motion velocity of the cell, and correct the three-dimensional motion velocity obtained in the previous step. For a point a, the three-dimensional motion velocity is v _3D = (x _3D , y _3D , z _3D ), the two-dimensional motion velocity is v _2D = (x _2D , y _2D ), and the correction coefficient is calculated.

Where a and b are the x-axis and y-axis correction coefficients, respectively. The resulting three-dimensional motion velocity V = Kv _3D .

Fourth, the final cell reconstitution.

Specifically, according to the rigid body model cells, the cells that m from the time t position _k _{_{(x k, y k, z}} k) after a rotational and translational motion to time t _{k + 1} position (x _{k + 1,} y _k+1 , z _k+1 ). Let the rotation matrix and the translation vector be R _k and T _k , respectively, then the three-dimensional rotational motion model of the cell is:

m _k+1 =R _k m _k +T _k (10)

Definition mapping Where M represents a collection of all points on the cell, projecting the three-dimensional information of the cell at time i to a certain frame I _{i of the} rotated image of the cell. Therefore, the three-dimensional image generation of the cells is reduced to the maximum likelihood estimation of the model parameters, and the maximum likelihood estimation of the model generation is performed using a machine learning algorithm, and the parameter values minimized by the following formula are taken.

Where I _i+1 is a frame in the cell rotation image sequence I={I _i , i=1, . . . , n}, where M represents a set of all points on the cell, and R _i and T _i are a rotation matrix and a translation vector, respectively. R _i m _i +T _i is a three-dimensional rotational motion model of a point m on the cell from i to i+1, ie, _mi+1 . Mapping

Using the trained parameters, the 3D cell model mapped to the current frame can be reconstructed from the two-dimensional image of the cell rotation. The cell rotational motion information obtained by the optical flow method of the present invention will more accurately estimate the rotation matrix Rk, thereby optimizing the three-dimensional model of cell rotation.

The method of the invention has the following advantages: 1) the feedback control function of the enhanced light-induced dielectrophoresis controllable platform realized by the microscopic vision algorithm; 2) the motion detection method based on the optical flow field is used to track the three-dimensional motion of the cell, thereby Complete the parameter estimation of the cell dynamics model; 3) Obtain the two-dimensional image sequence of the cell by controlling the rotation of the single cell, and realize the three-dimensional image generation technology of the cell based on the controllable single cell two-dimensional image sequence by the maximum likelihood estimation method. The method of the invention has simple equipment, low cost, no phototoxic effect, low requirements on samples, and improved operability.

It is to be understood that the application of the present invention is not limited to the above-described examples, and those skilled in the art can make modifications and changes in accordance with the above description, all of which are within the scope of the appended claims.

Claims

A single-cell three-dimensional image generation method based on optical flow analysis, comprising the steps of:

A. A photo-induced dielectrophoresis chip is prepared. The photo-induced dielectrophoresis chip has a three-layer structure: a three-layer structure: the lower layer is ITO glass coated with a hydrogenated amorphous silicon coating, and the upper layer is ITO glass without coating. A microfluidic channel is encapsulated between the upper and lower layers of ITO glass for injecting a solution for the desired operation;

B. Injecting the cells and the solution into the microfluidic channel, and inputting a variable frequency alternating current signal to the electrodes of the upper and lower ITO glass, and irradiating the photoinduced dielectrophoresis chip with the incident light, thereby generating a non-irradiated region in the irradiated region. Uniform electric field;

C. Change the frequency and size of the AC signal to control the direction of cell movement while collecting images of the cells;

D. Pre-process the acquired image, then perform feature extraction and velocity calculation, and finally reconstruct the 3D cell image.
The method for generating a single-cell three-dimensional image based on the optical flow analysis according to claim 1, wherein the step of fabricating the light-induced dielectrophoresis chip in the step A comprises:

A1, cleaning the ITO glass substrate;

A2 depositing a hydrogenated amorphous silicon coating on the ITO glass substrate;

A3, coating a photoresist on the hydrogenated amorphous silicon coating;

A4, performing plate printing on the photoresist;

A5, contact corrosion to the ITO glass substrate;

A6, removing the photoresist;

A7. A conductive adhesive is applied to the area of the ITO glass substrate that is not covered with the hydrogenated amorphous silicon coating.
The single-cell three-dimensional image generating method based on optical flow analysis according to claim 1, wherein the average dielectrophoretic force of the cells in the non-uniform electric field is described by the following formula:

Where F DEP is the average dielectrophoretic force acting on the cell, R is the radius of the cell, ε m is the dielectric constant of the solution in which the cell is located, E rms is the root mean square value of the applied AC signal, and f CM is Clausius-Mossotti Factor, the real part of the factor Re[f CM ] is taken when calculating the average dielectrophoretic force.
The method according to claim 1, wherein the f CM factor is defined as follows:

ε p * and ε m * are the complex dielectric constants of the cells and solutions, respectively.
The method according to claim 1, wherein the complex permittivity is expressed as:

Where ε is the dielectric constant of the solution, σ is the conductivity, and ω is the frequency of the applied AC signal.
Single-cell three-dimensional image generation based on optical flow analysis according to claim The method is characterized in that the cell rotation speed is:

Where E is the electric field strength, η is the viscosity of the solution, IM[f CM ] is the imaginary part of the Clausius-Mossotti factor, and K is the coefficient.
The single-cell three-dimensional image generating method based on optical flow analysis according to claim 1, wherein the pre-processing comprises: Gaussian filtering processing, brightness adjustment, and template matching.
The single-cell three-dimensional image generating method based on optical flow analysis according to claim 1, wherein in the step D, a maximum likelihood estimation of the model parameters is performed using a machine learning algorithm, and a parameter of the following formula is minimized. value:

Where I i+1 is a frame in the cell rotation image sequence I={I i , i=1, . . . , n}, where M represents a set of all points on the cell, and R i and T i are a rotation matrix and a translation vector, respectively. , R i m i +T i is a three-dimensional rotational motion model of a point m from i to i+1 on the cell, ie mi+1 , mapping
It is to project the three-dimensional information of the cell at time i to a certain frame I i of the cell rotation image.