WO2017028341A1 - 一种基于光流分析的单细胞三维图像生成方法 - Google Patents

一种基于光流分析的单细胞三维图像生成方法 Download PDF

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WO2017028341A1
WO2017028341A1 PCT/CN2015/088945 CN2015088945W WO2017028341A1 WO 2017028341 A1 WO2017028341 A1 WO 2017028341A1 CN 2015088945 W CN2015088945 W CN 2015088945W WO 2017028341 A1 WO2017028341 A1 WO 2017028341A1
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cell
ito glass
dimensional image
optical flow
flow analysis
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李志�
张光烈
李文荣
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Shenzhen University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
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    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/416—Systems
    • G01N27/447—Systems using electrophoresis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three-dimensional [3D] modelling for computer graphics

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  • 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.
  • 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:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a single-cell three-dimensional image generation method based on optical flow analysis comprising the steps of:
  • 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;
  • A2 depositing a hydrogenated amorphous silicon coating on the ITO glass substrate
  • a conductive adhesive is applied to the area of the ITO glass substrate that is not covered with the hydrogenated amorphous silicon coating.
  • F DEP is the average dielectrophoretic force acting on the cell
  • R is the radius of the cell
  • ⁇ 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
  • f CM is Clausius-Mossotti Factor, the real part of the factor Re[f CM ] is taken when calculating the average dielectrophoretic force.
  • ⁇ p * and ⁇ 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:
  • ⁇ is the dielectric constant of the solution
  • ⁇ is the conductivity
  • ⁇ is the applied AC signal Frequency of.
  • E is the electric field strength
  • ⁇ is the viscosity of the solution
  • IM[f CM ] is the imaginary part of the Clausius-Mossotti factor
  • 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:
  • 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.
  • 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.
  • FIG. 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.
  • FIG. 2 is a schematic view showing the structure of a light-induced dielectrophoresis platform in the present invention.
  • 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.
  • 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:
  • 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;
  • S400 Preprocessing the acquired image, then performing feature extraction and speed calculation, and finally reconstructing the 3D cell image.
  • the step of fabricating the light-induced dielectrophoresis chip specifically includes:
  • a layer of hydrogenated amorphous silicon was deposited on the surface of the ITO glass substrate to a thickness of 1 micron.
  • 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 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.
  • 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.
  • a light-induced dielectrophoresis platform is first constructed.
  • 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.
  • optical microscope parameters are as follows:
  • Electric focus can move up and down (upper 13mm / 2mm);
  • Concentrator waterproof, working distance: 7.2mm;
  • Objective lens 20x, highly achromatic lens, nanocrystalline coating
  • Fluorescence filter set FITC/GFP.
  • 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.
  • 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.
  • 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.
  • step S400 the acquired image is pre-processed, then feature extraction and velocity calculation are performed, and finally the 3D cell image is reconstructed.
  • F DEP is the average dielectrophoretic force acting on the cell
  • R is the radius of the cell
  • ⁇ 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
  • Re[f CM ] is taken when calculating the average dielectrophoretic force, which is defined as follows:
  • Equation 2 ⁇ p * and ⁇ m * are the complex permittivity of the cell and the solution, respectively, and the complex permittivity (including ⁇ p * and ⁇ m *) in Equation 2 can be expressed as:
  • ⁇ is the dielectric constant of the solution
  • ⁇ is the conductivity
  • ⁇ is the frequency of the applied electric field (alternating current signal).
  • 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.
  • E is the electric field strength
  • ⁇ is the viscosity of the solution
  • IM[f CM ] is the imaginary part of the Clausius-Mossotti factor
  • K is the coefficient.
  • 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.
  • ODEP light-induced dielectrophoretic force
  • 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.
  • a-Si:H 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.
  • 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 refactoring process includes:
  • ⁇ is the width parameter of the function, which controls the radial extent of the function.
  • g(x,y) c+k(f(x,y)-a) (6)
  • 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
  • c is the brightness compensation.
  • Cov(X, Y) is the covariance of X and Y
  • D(X) and D(Y) are the variances of X and Y, respectively.
  • the optical flow method analyzes the motion vector of the pixel.
  • the luminance value at time t is I(x, y, t)
  • u(x, y) and v(x, y) represent light at (x, y)
  • V (A T W 2 A) -1 A T W 2 b (9)
  • the correction coefficient is calculated.
  • a and b are the x-axis and y-axis correction coefficients, respectively.
  • the resulting three-dimensional motion velocity V Kv 3D .
  • 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 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.
  • 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 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.
  • 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.

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Abstract

一种基于光流分析的单细胞三维图像生成方法，包括步骤：A、制作光诱导介电泳芯片（20），所述光诱导介电泳芯片（20）由三层结构组成，下层为涂有氢化非晶硅涂层的ITO玻璃，上层是不含涂层的ITO玻璃，在上下两层ITO玻璃之间封装有一个微流体通道，用于注射所需操作的溶液；B、将细胞和溶液注射到微流体通道，并向上下两层ITO玻璃的电极输入可变频率的交流信号，同时利用入射光照射所述光诱导介电泳芯片，从而在被照射的区域产生非均匀电场；C、改变交流信号的频率及大小，以控制细胞运动方向，同时采集细胞的图像；D、对采集的图像进行预处理，然后进行特征提取以及速度计算，最后重构3D细胞图像。该方法通过控制单个细胞的旋转，获取细胞二维图像序列，利用最大似然估计方法实现基于可控单细胞的二维图像序列的细胞的三维图像生成技术。该方法所用设备简单，成本低，不会产生光毒作用，对样本的要求较低，提高了可操作性。

Description

一种基于光流分析的单细胞三维图像生成方法 技术领域
本发明涉及细胞成像领域，尤其涉及一种基于光流分析的单细胞三维图像生成方法。
背景技术
与早期的成像系统相比，活细胞的3D成像呈现了细胞及其组分的更详细、也更准确的空间视图。技术进步让3D成像成为许多应用的重要工具，如细胞生物学、发育生物学、神经科学以及癌症研究。当前的技术比以往更加准确，能实时给出数据，几乎不需要细胞制备。现有技术的单细胞三维方法主要有以下几种：
1、激光扫描共聚焦显微镜：利用激光束经照明针孔形成点光源对细胞内焦平面的每一点扫描，细胞上的被照射点，在探测针孔处成像，由探测针孔后的光电倍增管(PMT)或冷电耦器件(cCCD)逐点或逐线接收，迅速在计算机监视器屏幕上形成荧光图像。照明针孔与探测针孔相对于物镜焦平面是共轭的，焦平面上的点同时聚焦于照明针孔和发射针孔，焦平面以外的点不会在探测针孔处成像，这样得到的共聚焦图像是细胞的光学横断面。激光扫描共聚焦显微镜通过对同一细胞不同层面的实时扫描成像，进行图像叠加可构成细胞的三维结构图像。
2、白色光衍射断层扫描成像技术：该技术能够为透明样本如活细胞和未标记的细胞成像，基于传统显微镜和白光，在细胞的自然状态下提供高分辨率的3D渲染图像。物镜镜头扫描细胞的整个轴向焦面，产生一叠相位分辨图像，然后通过sparse反卷积算法重建物体的三维结构。能得到350nm的侧向分辨率以及900nm的轴向分辨率。
3、晶格光片显微镜：晶格光片显微镜有两个正交的镜头；一个镜头将光聚焦产生一条非常细的笔状光源，照射在有萤光分子的生物样品，产生荧光；另一个采用宽场成像收集荧光，由面扫描以快速取得3D高清晰度生物影像。依靠空间光调制器同时形成100多条笔状的光束，来增加扫描速度，降低对生物样品的伤害；而且能控制每条光束的距离及形状。
但上述方法均存在不足：
第一种方式需要专用的设备，成本太高；且会产生光毒作用：在激光照射下，许多荧光染料分子会产生单态氧或自由基等细胞毒素，限制扫描时间、激发光强度，以保持样品的活性；标记染料的光漂白：为了获得足够的信噪比必须提高激光的强度；而高强度的激光会使染料在连续扫描过程中迅速褪色。
第二种方式不适用于非半透明样本，对于样本的要求比较高；需要专用设备，成本过高；成像灵活度低。
第三种方式设备成本过高；需要对样本进行荧光处理，降低了可操作性；光毒作用依然存在。
因此，现有技术还有待于改进和发展。
发明内容
鉴于上述现有技术的不足，本发明的目的在于提供一种基于光流分析的单细胞三维图像生成方法，旨在解决现有的细胞三维图像生成方法成本高、有光毒作用、条件苛刻等问题。
本发明的技术方案如下：
一种基于光流分析的单细胞三维图像生成方法，其中，包括步骤：
A、制作光诱导介电泳芯片，所述光诱导介电泳芯片有三层结构组成：有三层结构组成：下层为涂有氢化非晶硅涂层的ITO玻璃，上层是不含涂层的ITO玻璃，在上下两层ITO玻璃之间封装有一个微流体通道，用于注射所需操作的溶液；
B、将细胞和溶液注射到微流体通道，并向上下两层ITO玻璃的电极输入可变频率的交流信号，同时利用入射光照射所述光诱导介电泳芯片，从而在被照射的区域产生非均匀电场；
C、改变交流信号的频率及大小，以控制细胞运动方向，同时采集细胞的图像；
D、对采集的图像进行预处理，然后进行特征提取以及速度计算，最后重构3D细胞图像。
所述的基于光流分析的单细胞三维图像生成方法，其中，所述步骤A中，制作光诱导介电泳芯片的步骤具体包括：
A1、清理ITO玻璃基质；
A2、在ITO玻璃基质上沉积氢化非晶硅涂层；
A3、在氢化非晶硅涂层上涂光刻胶；
A4、在光刻胶上进行板印；
A5、接触腐蚀至ITO玻璃基质；
A6、去除光刻胶；
A7、在ITO玻璃基质上未覆盖氢化非晶硅涂层的区域涂导电粘合剂。
所述的基于光流分析的单细胞三维图像生成方法，其中，所述细胞在非均匀电场中的所受到的平均介电泳力用如下公式描述：
Figure PCTCN2015088945-appb-000001
其中FDEP是作用到细胞上的平均介电泳力，R是细胞的半径，εm是细胞所在溶液的介电常数，Erms为所施加交流信号的均方根值，fCM为Clausius-Mossotti因子，在计算平均介电泳力时取该因子的实部Re[fCM]。
所述的基于光流分析的单细胞三维图像生成方法，其中，fCM因子定义如下:
Figure PCTCN2015088945-appb-000002
εp*和εm*分别是细胞和溶液的复介电常数。
所述的基于光流分析的单细胞三维图像生成方法，其中，所述复介电常数可表示为:
Figure PCTCN2015088945-appb-000003
其中，ε是溶液的介电常数，σ是导电率，ω是所施加交流信号 的频率。
所述的基于光流分析的单细胞三维图像生成方法，其中，细胞旋转速度为：
Figure PCTCN2015088945-appb-000004
其中E是电场强度，η是溶液的黏稠度，IM[fCM]是Clausius-Mossotti因子的虚部，K为系数。
所述的基于光流分析的单细胞三维图像生成方法，其中，所述预处理包括：高斯滤波处理、亮度调整及模板匹配。
所述的基于光流分析的单细胞三维图像生成方法，其中，所述步骤D中，使用机器学习算法对模型参数进行最大似然估计，按下式最小化的参数取值：
Figure PCTCN2015088945-appb-000005
其中Ii+1是细胞旋转图像序列I＝{Ii,i＝1,…,n}中的一帧，M代表细胞上所有点的集合，Ri和Ti分别是旋转矩阵和平移向量,Rimi+Ti是细胞上一点m从i时刻到i+1时刻的三维旋转运动模型，即mi+1，映射
Figure PCTCN2015088945-appb-000006
是将细胞在i时刻的三维信息投影到细胞旋转图像的某一帧Ii。
有益效果：本发明的方法具有如下优点：1)通过显微视觉算法实现的增强光诱导介电泳可操控平台的反馈控制功能；2)采用基于光流场的运动检测方法实现对细胞三维运动的跟踪，从而完成细胞动力学模型的参数估计；3)通过控制单个细胞的旋转，获取细胞二维 图像序列，利用最大似然估计方法实现基于可控单细胞的二维图像序列的细胞的三维图像生成技术。本发明的方法，其所用设备简单，成本低，不会产生光毒作用，对样本的要求也较低，提高了可操作性。
附图说明
图1为本发明一种基于光流分析的单细胞三维图像生成方法较佳实施例的流程图。
图2为本发明中光诱导介电泳平台的结构示意图。
具体实施方式
本发明提供一种基于光流分析的单细胞三维图像生成方法，为使本发明的目的、技术方案及效果更加清楚、明确，以下对本发明进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
请参阅图1，图1为本发明一种基于光流分析的单细胞三维图像生成方法较佳实施例的流程图，如图所示，其包括步骤：
S100、制作光诱导介电泳芯片(ODEP芯片)，所述光诱导介电泳芯片有三层结构组成：下层为涂有氢化非晶硅涂层的ITO玻璃，上层是不含涂层(即不含氢化非晶硅涂层)的ITO玻璃，在上下两层ITO玻璃之间封装有一个微流体通道，用于注射所需操作的溶液；
S200、向上下两层ITO玻璃的电极输入可变频率的交流信号，同时利用入射光照射所述光诱导介电泳芯片，从而在被照射的区域产 生非均匀电场；可先向微流体通道注入细胞和介质(介质即所需操作的溶液，也即细胞所在溶液)。然后输入交流信号。
S300、改变交流信号的频率及大小，以控制细胞运动方向，同时采集细胞的图像；
S400、对采集的图像进行预处理，然后进行特征提取以及速度计算，最后重构3D细胞图像。
进一步，所述的步骤S100中，制作光诱导介电泳芯片的步骤具体包括：
S101、清理ITO玻璃基质；
清理ITO玻璃基质的表面，保证接触面的洁净度。
S102、在ITO玻璃基质上沉积氢化非晶硅涂层(a-Si:H)；
在ITO玻璃基质表面沉积一层氢化非晶硅，厚度为1微米。
S103、在氢化非晶硅涂层上涂光刻胶；
S104、在光刻胶上进行板印；
板印是按照指定图形制作遮盖物，将遮盖物放在光刻胶表面，用紫外线照射遮盖物，没有被遮盖的光刻胶在紫外线作用下溶解，最终得到与遮盖物形状相同的光刻胶层。
S105、接触腐蚀至ITO玻璃基质；具体是用草酸腐蚀制作的芯片表层，以去除没有覆盖光刻胶的氢化非晶硅涂层。
S106、去除光刻胶；即将光刻胶从氢化非晶硅涂层表面去除。
S107、在ITO玻璃基质上未覆盖氢化非晶硅涂层的区域涂导电粘合剂。即在ITO玻璃的表面没有覆盖氢化非晶硅涂层的位置添加一 个导电触点。
而上层的ITO玻璃清理干净之后，涂导电粘合剂即可。
在上下两层ITO玻璃之间封装有一个微流体通道(100微米高)，具体是通过PDMS或是双面胶封装出一个微流体通道。
在步骤S200中，如图2所示，首先搭建光诱导介电泳平台。除了步骤S100制作的ODEP芯片20，平台还需要一台光学显微镜10、一台光学投影仪(高分辨率)、一个可编程信号发生电路和主机系统。所述主机系统包括：图像采集模块、显微视觉算法处理模块、生物芯片驱动控制器、虚拟电极生成模块以及显示输出模块。所述图像采集模块用来采集光学显微镜20的图像，并交由显微视觉算法处理模块来进行处理并通过显示输出模块来显示，所述显微视觉算法处理模块还向生物芯片驱动控制器及虚拟电极生成模块发出信号用来控制二者工作。所述生物芯片驱动控制器连接所述可编程信号发生电路来改变信号频率和大小。所述可编程信号发生电路通过电极连接所述ODEP芯片20。所述光学投影仪设置在ODEP芯片20下方，用来对其进行入射光照射。所述虚拟电极生成模块连接所述光学投影仪。
其中光学显微镜参数如下：
尼康CFI60无限远光学系统；
电动对焦，可上下移动(上13mm/下2mm)；
三目镜筒，光分布：目镜/相机100％/0，20％/100％，0/100％；
目镜放大倍率：10x；
聚光器：防水，工作距离：7.2mm；
物镜：20x，高度消色透镜，纳米结晶涂层；
载物台：电动X轴和Y轴，分辨率：0.1微米；
紫外线截止滤光块；
荧光滤波套装：FITC/GFP。
在平台搭建好后，可通过生物芯片驱动控制器向可编程信号发生电路发出信号，然后可编程信号发生电路向上下两层ITO玻璃的电极输入可变频率的交流信号，同时光学投影仪利用入射光照射所述光诱导介电泳芯片，从而在被照射的区域产生非均匀电场。
在所述步骤S300中，通过改变交流信号的频率及大小，来改变细胞所受到的介电泳力的方向与大小，以控制细胞运动方向，同时采集细胞的图像(2D细胞图像)，实现高速操纵微纳米实体。
最后在步骤S400中，对采集的图像进行预处理，然后进行特征提取以及速度计算，最后重构3D细胞图像。
下面先着重介绍下，如何实现由改变交流信号的频率及大小来控制细胞运动方向。
细胞在非均匀电场中的所受到的平均介电泳力可以用如下公式描述：
Figure PCTCN2015088945-appb-000007
其中FDEP是作用到细胞上的平均介电泳力，R是细胞的半径，εm是细胞所在溶液的介电常数，Erms为所施加电场(交流信号)的均方根值，fCM为Clausius-Mossotti因子，在计算平均介电泳力时取该因子的实部Re[fCM]，该因子定义如下:
Figure PCTCN2015088945-appb-000008
εp*和εm*分别是细胞和溶液的复介电常数，公式2中的复介电常数(包括εp*和εm*)可表示为:
Figure PCTCN2015088945-appb-000009
其中，ε是溶液的介电常数，σ是导电率，ω是所加电场(交流信号)的频率。
可以看出fCM是一个和频率相关的可变因子。考虑在施加不同频率的交变电场下，当介电泳力与电场强度变化方向相同时，称为正介电泳现象；当所受到的介电泳力与电场强度变化方向相反，称为负介电泳现象。因而可以通过改变所施加的电场的频率，来改变细胞所受到的介电泳力的方向，达到控制细胞运动方向的目的。
由于生物细胞受到非均匀电场的极化作用而产生偶极矩，根据其介电泳力所产生的转矩与所在介质中受到的摩擦力矩达到平衡，细胞旋转速度为：
Figure PCTCN2015088945-appb-000010
其中E是电场强度，η是溶液的黏稠度，IM[fCM]是Clausius-Mossotti因子的虚部，K为系数。根据细胞的旋转速度与细胞的介电常数的关系可以对细胞的介电特性进行估算。
细胞受到的介电泳力强度与方向主要取决于介质与细胞的介电特性，如形状、尺寸与电场频率。本发明利用光诱导介电泳力(ODEP)(当施加某频段，电液动力学的一种主导力)以识别与操纵生物 细胞。ODEP芯片由可变频率的交流信号驱动，交流信号通过上下两层ITO玻璃的导电触点输入，此时在溶液层只有一小部分分压，并在溶液层中产生均匀电场。当入射光照射ODEP芯片，a-Si:H的光导率由于电子空穴对数的增多而增加几个数量级。由于入射光区域电阻减小，在溶液层中的分压会大大增大，于是入射光区域的a:Si:H将成为一个有效的虚拟电极产生非均匀电场。这种光诱导的非均匀电场会极化区域内的颗粒产生介电泳力，也就是光诱导介电泳力(ODEP)。通过光学显微镜与主机系统可实现程序化的动态运动，且不需要任何手工界面而实现微纳米实体的自动化捕获、操纵、分离与组装。
对于步骤S400中，细胞三维图像生成的原理如下：当一个极化的物体置于非均匀电场时，在偶极矩的作用下物体会向电场最强或者最弱处运动，方向取决于物体相对于介质的极性。根据细胞在介电泳力场下的动力学模型，通过改变驱动光诱导介电泳生物芯片的交流信号的大小与频率，并配合相应的投射到ODEP芯片上的入射光，控制细胞在光诱导介电泳力的作用下的旋转运动。细胞旋转至不同位置的图像序列I＝{Ii,i＝1,…,n}，n为时刻点，细胞的三维图像是从二维图像序列重构细胞的三维结构。重构过程包括：
一、在获得二维图像后，先进行预处理：
1、先对图像进行高斯滤波处理，滤除掉图像中噪声。高斯滤波的核心公式如下：
Figure PCTCN2015088945-appb-000011
其中σ为函数的宽度参数,控制了函数的径向作用范围。
2、然后进行亮度调整处理。滤除噪声后，以背景色为基准，对图像整体亮度作线性变换：
g(x,y)＝c+k(f(x,y)-a)   (6)其中，f(x,y)和g(x,y)分别为图像中某点(x,y)的原始亮度和变换后的亮度，a为背景亮度，k为变换系数，c为亮度补偿。
3、再进行模板匹配处理。对图像中的细胞进行模板匹配，比对同一窗口中的块在上下两帧的差异，计算相关系数ρXY。
Figure PCTCN2015088945-appb-000012
其中X和Y分别为上下两帧中的块，Cov(X,Y)为X和Y的协方差，D(X)和D(Y)分别为X和Y的方差。
二、然后进行特征提取。
1、找到相关系数的局部最大值以跟踪峰点，从一个峰点到下一个峰点即代表旋转一圈，根据峰点的索引估算出旋转的圈数。
2、光流法分析像素的运动向量。对于图像上的一个像素点X(x,y)在t时刻的亮度值为I(x,y,t)，u(x,y)和v(x,y)表示(x,y)处光流在x和y方向的运动分量。根据图像序列，计算
Figure PCTCN2015088945-appb-000013
和
Figure PCTCN2015088945-appb-000014
令
Figure PCTCN2015088945-appb-000015
W＝diag(W(X1),…,W(Xn))，
Figure PCTCN2015088945-appb-000016
其中n为点的个数，diag()为构造对角矩阵，W为高斯函数：
Figure PCTCN2015088945-appb-000017
令V＝(u,v)T，那么其计算公式如下：
V＝(ATW2A)-1ATW2b   (9)
三、再进行速度计算。
1、根据得到的自转圈数以及相邻两个图像序列的拍摄间隔计算细胞的自转速度ω，结合细胞的旋转模型，即可得到细胞自转的旋转矩阵K。因此旋转后的细胞上所有点的集合M'＝MK，其中M为细胞上所有点的原始坐标。每个点的运动速度
Figure PCTCN2015088945-appb-000018
2、利用光流法得到的细胞二维运动速度，修正上一步得到的三维运动速度。对于某一点a，其三维运动速度为v3D＝(x3D,y3D,z3D)，二维运动速度为v2D＝(x2D,y2D)，计算修正系数
Figure PCTCN2015088945-appb-000019
其中a和b分别为x轴和y轴修正系数。最终得到的三维运动速度V＝Kv3D。
四、最后进行细胞重构。
具体的，根据细胞的刚体模型，细胞上一点m从时刻tk的位置(xk,yk,zk)经过旋转和平移，运动到时刻tk+1的位置(xk+1,yk+1,zk+1)。设旋转矩阵和平移向量分别是Rk和Tk,则细胞三维旋转运动模型为：
mk+1＝Rkmk+Tk   (10)
定义映射其中M代表细胞上所有点的集合，将细胞在i时刻的三维信息投影到细胞旋转图像的某一帧Ii。因此，细胞的三维图像生成归结为模型参数的最大似然估计，使用机器学习算法对模型产生进行最大似然估计，按下式最小化的参数取值。
Figure PCTCN2015088945-appb-000021
其中Ii+1是细胞旋转图像序列I＝{Ii,i＝1,…,n}中的一帧，M代表细胞上所有点的集合，Ri和Ti分别是旋转矩阵和平移向量,Rimi+Ti是细胞上一点m从i时刻到i+1时刻的三维旋转运动模型，即mi+1。映射
Figure PCTCN2015088945-appb-000022
是将细胞在i时刻的三维信息投影到细胞旋转图像的某一帧Ii。
利用训练好的参数，即可根据细胞旋转的二维图像重构映射为当前帧的3D细胞模型。本发明通过光流的方法获得的细胞旋转运动信息将更准确的估计旋转矩阵Rk，进而优化细胞旋转的三维模型。
本发明的方法具有如下优点：1)通过显微视觉算法实现的增强光诱导介电泳可操控平台的反馈控制功能；2)采用基于光流场的运动检测方法实现对细胞三维运动的跟踪，从而完成细胞动力学模型的参数估计；3)通过控制单个细胞的旋转，获取细胞二维图像序列，利用最大似然估计方法实现基于可控单细胞的二维图像序列的细胞的三维图像生成技术。本发明的方法，其所用设备简单，成本低，不会产生光毒作用，对样本的要求也较低，提高了可操作性。
应当理解的是，本发明的应用不限于上述的举例，对本领域普通技术人员来说，可以根据上述说明加以改进或变换，所有这些改进和变换都应属于本发明所附权利要求的保护范围。

Claims (8)

  1. 一种基于光流分析的单细胞三维图像生成方法，其特征在于，包括步骤：
    A、制作光诱导介电泳芯片，所述光诱导介电泳芯片有三层结构组成：有三层结构组成：下层为涂有氢化非晶硅涂层的ITO玻璃，上层是不含涂层的ITO玻璃，在上下两层ITO玻璃之间封装有一个微流体通道，用于注射所需操作的溶液；
    B、将细胞和溶液注射到微流体通道，并向上下两层ITO玻璃的电极输入可变频率的交流信号，同时利用入射光照射所述光诱导介电泳芯片，从而在被照射的区域产生非均匀电场；
    C、改变交流信号的频率及大小，以控制细胞运动方向，同时采集细胞的图像；
    D、对采集的图像进行预处理，然后进行特征提取以及速度计算，最后重构3D细胞图像。
  2. 根据权利要求1所述的基于光流分析的单细胞三维图像生成方法，其特征在于，所述步骤A中，制作光诱导介电泳芯片的步骤具体包括：
    A1、清理ITO玻璃基质；
    A2、在ITO玻璃基质上沉积氢化非晶硅涂层；
    A3、在氢化非晶硅涂层上涂光刻胶；
    A4、在光刻胶上进行板印；
    A5、接触腐蚀至ITO玻璃基质；
    A6、去除光刻胶；
    A7、在ITO玻璃基质上未覆盖氢化非晶硅涂层的区域涂导电粘合剂。
  3. 根据权利要求1所述的基于光流分析的单细胞三维图像生成方法，其特征在于，所述细胞在非均匀电场中的所受到的平均介电泳力用如下公式描述：
    Figure PCTCN2015088945-appb-100001
    其中FDEP是作用到细胞上的平均介电泳力，R是细胞的半径，εm是细胞所在溶液的介电常数，Erms为所施加交流信号的均方根值，fCM为Clausius-Mossotti因子，在计算平均介电泳力时取该因子的实部Re[fCM]。
  4. 根据权利要求1所述的基于光流分析的单细胞三维图像生成方法，其特征在于，fCM因子定义如下:
    Figure PCTCN2015088945-appb-100002
    εp*和εm*分别是细胞和溶液的复介电常数。
  5. 根据权利要求1所述的基于光流分析的单细胞三维图像生成方法，其特征在于，所述复介电常数可表示为:
    Figure PCTCN2015088945-appb-100003
    其中，ε是溶液的介电常数，σ是导电率，ω是所施加交流信号的频率。
  6. 根据权利要求1所述的基于光流分析的单细胞三维图像生成 方法，其特征在于，细胞旋转速度为：
    Figure PCTCN2015088945-appb-100004
    其中E是电场强度，η是溶液的黏稠度，IM[fCM]是Clausius-Mossotti因子的虚部，K为系数。
  7. 根据权利要求1所述的基于光流分析的单细胞三维图像生成方法，其特征在于，所述预处理包括：高斯滤波处理、亮度调整及模板匹配。
  8. 根据权利要求1所述的基于光流分析的单细胞三维图像生成方法，其特征在于，所述步骤D中，使用机器学习算法对模型参数进行最大似然估计，按下式最小化的参数取值：
    Figure PCTCN2015088945-appb-100005
    其中Ii+1是细胞旋转图像序列I＝{Ii,i＝1,…,n}中的一帧，M代表细胞上所有点的集合，Ri和Ti分别是旋转矩阵和平移向量,Rimi+Ti是细胞上一点m从i时刻到i+1时刻的三维旋转运动模型，即mi+1，映射
    Figure PCTCN2015088945-appb-100006
    是将细胞在i时刻的三维信息投影到细胞旋转图像的某一帧Ii。
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