Cell activity detection method and device, terminal equipment and storage medium

Abstract

The invention is suitable for the technical field of cell detection, and provides a cell viability detection method and device, terminal equipment and a storage medium, the method comprises the following steps: obtaining a plurality of initial characteristic signals of sample cells collected at different times, then carrying out autocorrelation calculation on the plurality of initial characteristic signals to obtain a plurality of autocorrelation coefficients (experiment g1, exp (tau)); and then obtaining a fitting curve similar to experiment g1, exp (tau) according to a theory, thereby analyzing to obtain a diffusion coefficient D of the sample cells, and further evaluating the activity of the sample cells according to the diffusion coefficient of the sample cells. According to the method, autocorrelation calculation is carried out on the characteristic signals of the sample cells, the autocorrelation of the signals at different time points can be measured, the dynamic change rule of the movement characteristics of the cells is revealed by analyzing the change of the autocorrelation coefficient and the diffusion coefficient along with time, and a key quantitative index is provided for accurately detecting the activity of the cells.

Description

Technical Field

[0001] The present application belongs to the field of cell detection technology, and in particular relates to a method, apparatus, terminal device and storage medium for detecting cell activity. Background Art

[0002] Cell activity refers to the comprehensive state of a cell's ability to maintain normal physiological functions, metabolic capacity, and proliferation potential under specific environmental or experimental conditions. It is a key indicator for assessing cell health. Cell activity testing is widely used in biomedical research, drug screening, toxicity testing, and other fields. For example, after a drug is applied to a cell, its effectiveness is determined by examining its activity. However, current inaccurate cell activity testing limits the scope of projects based on cell activity. Therefore, accurately measuring cell activity is a challenge that needs to be addressed. Summary of the Invention

[0003] The embodiments of the present application provide a method, apparatus, terminal device, and storage medium for detecting cell activity, which can solve the problem of inaccurate cell activity detection.

[0004] In a first aspect, the present invention provides a method for detecting cell activity, comprising:

[0005] Acquiring a plurality of initial characteristic signals of sample cells collected at different times, wherein the initial characteristic signals are used to characterize initial motion characteristics of the sample cells;

[0006] Performing autocorrelation calculation on the initial characteristic signal based on different time delays to obtain autocorrelation coefficients under different time delays;

[0007] determining the diffusion coefficient of the sample cells based on changes in the plurality of autocorrelation coefficients;

[0008] Based on the diffusion coefficient, the activity of the sample cells is determined.

[0009] In a possible implementation of the first aspect, acquiring multiple initial characteristic signals of sample cells collected at different times includes:

[0010] Acquiring a plurality of original characteristic signals of sample cells collected at a preset frequency, wherein the original characteristic signals are optical coherence tomography signals captured by an optical coherence tomography device;

[0011] The original characteristic signal is subjected to signal processing to obtain a plurality of the initial characteristic signals, wherein the signal processing includes at least one of dimensional reconstruction processing and filtering processing.

[0012] In a possible implementation of the first aspect, determining the diffusion coefficient of the sample cell based on changes in the multiple autocorrelation coefficients includes:

[0013] Obtaining a preset fitting function, wherein the fitting function is a function related to the diffusion coefficient;

[0014] The fitting function and the plurality of autocorrelation coefficients are subjected to data fitting to obtain the diffusion coefficient of the sample cells.

[0015] In a possible implementation manner of the first aspect, the fitting function is: g 1,theory (τ) is the fitting function, D is the diffusion coefficient, and τ is the preset time delay; λ0 is the wavelength of the light source when collecting the original characteristic signal, and n is the preset refractive index.

[0016] In a possible implementation manner of the first aspect, a larger diffusion coefficient indicates a higher activity of the sample cells.

[0017] In a possible implementation manner of the first aspect, the performing signal processing on the original characteristic signal to obtain the multiple initial characteristic signals includes:

[0018] Performing a first-dimensional reconstruction process on the original characteristic signal to obtain a first candidate signal, wherein the first-dimensional reconstruction includes a dimensionality increase process;

[0019] performing filtering processing on the first candidate signal to obtain a second candidate signal;

[0020] Performing a second dimensional reconstruction process on the second candidate signal to obtain a plurality of the initial characteristic signals, wherein the second dimensional reconstruction process includes converting the second candidate signal from a real number form to a complex number form.

[0021] In a possible implementation manner of the first aspect, performing data fitting on the fitting function and the plurality of autocorrelation coefficients to obtain the diffusion coefficient of the sample cells includes:

[0022] generating a true curve according to the plurality of autocorrelation coefficients, wherein the true curve represents a relative relationship between the autocorrelation coefficients and each time delay;

[0023] Obtaining a j-th candidate diffusion coefficient, and generating a j-th theoretical curve based on the j-th candidate diffusion coefficient and the fitting function, wherein the theoretical curve represents a relative relationship between a theoretical correlation coefficient and each time delay, j ≥ 1, and the candidate diffusion coefficient is selected from a preset coefficient range;

[0024] Determining a target curve from each theoretical curve according to the deviation between each theoretical curve and the true curve, wherein the target curve is the theoretical curve having the smallest deviation from the true curve among all the theoretical curves;

[0025] The candidate diffusion coefficient used when generating the target curve is determined to be the diffusion coefficient of the sample cell.

[0026] In a second aspect, an embodiment of the present application provides a device for detecting cell activity, comprising:

[0027] a signal acquisition module, configured to acquire a plurality of initial characteristic signals of sample cells collected at different times, wherein the initial characteristic signals are used to characterize the initial motion characteristics of the sample cells;

[0028] An autocorrelation calculation module is used to perform autocorrelation calculation on the initial characteristic signal based on different time delays to obtain autocorrelation coefficients under different time delays;

[0029] a coefficient calculation module, configured to determine the diffusion coefficient of the sample cells based on changes in the plurality of autocorrelation coefficients;

[0030] The activity evaluation module is used to determine the activity of the sample cells based on the diffusion coefficient.

[0031] In a third aspect, an embodiment of the present application provides a terminal device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for detecting cell activity described in any one of the first aspects above is implemented.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting cell activity described in any one of the first aspects above is implemented.

[0033] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the cell activity detection method described in any one of the above-mentioned first aspects.

[0034] The beneficial effects of the embodiments of the first aspect of the present application compared to the prior art are as follows: the present application first obtains multiple initial characteristic signals of sample cells collected at different times, and then performs autocorrelation calculations on the multiple initial characteristic signals based on different time delays to obtain multiple autocorrelation coefficients; then the diffusion coefficient of the sample cell is evaluated based on the changes of each autocorrelation coefficient over time, and then the activity of the sample cell is evaluated based on the diffusion coefficient of the sample cell. By performing autocorrelation calculations on the characteristic signals of the sample cell, the present application can measure the correlation between the signals at different time points, and by analyzing the changes of the obtained autocorrelation coefficient over time, the dynamic change law of the cell movement characteristics can be revealed. The diffusion coefficient of the sample cell is obtained based on the autocorrelation coefficient, so that the diffusion coefficient can essentially reflect the activity state of the cell, providing a key quantitative indicator for accurately detecting cell activity. Since the diffusion coefficient can more accurately reflect the activity state of the cell, the activity of the sample cell obtained based on the diffusion coefficient is more accurate.

[0035] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0037] Figure 1 1 is a flow chart of a method for detecting cell activity provided in one embodiment of the present application;

[0038] Figure 2 This is a flow chart of a method for obtaining initial characteristic signals of sample cells provided in one embodiment of the present application;

[0039] Figure 3 1 is a flow chart of a method for determining the diffusion coefficient of a sample cell provided in one embodiment of the present application;

[0040] Figure 4 1 is a schematic diagram of a curve after data fitting provided in an embodiment of the present application;

[0041] Figure 5 1 is a schematic structural diagram of a cell activity detection device provided in one embodiment of the present application;

[0042] Figure 6 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0043] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0044] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0045] In the description of this application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0046] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized.

[0047] Currently, illness in humans or animals is caused by the presence of pathogenic cells, such as cancer cells. Killing these cells with drugs is a common treatment method. When conducting drug research, it's crucial to evaluate the effectiveness of these drugs. Currently, the most common method for evaluating drugs is to apply the drug to cells and then assess the cell activity to determine whether the drug has a therapeutic effect on the cells. Therefore, accurately assessing cell activity is crucial for evaluating drug efficacy.

[0048] Based on the above problems, the present application proposes a method for detecting cell activity, which performs correlation calculation on the initial characteristic signals of sample cells to obtain multiple correlation coefficients; determines the diffusion coefficient of the sample cells based on the correlation coefficients; and evaluates the activity of the sample cells through the diffusion coefficient of the sample cells. The method of the present application can more accurately obtain the activity of the sample cells.

[0049] The following combination Figure 1 The method for detecting cell activity in the examples of the present application is described in detail.

[0050] Figure 1 A schematic flow chart of the method for detecting cell activity provided by the present application is shown, with reference to Figure 1 , the method is described in detail as follows:

[0051] S101 , acquiring a plurality of initial characteristic signals of sample cells collected at different times, wherein the initial characteristic signals are used to characterize initial motion characteristics of the sample cells.

[0052] In this embodiment, since the movement of the sample cells changes over time, it is accurate to evaluate the activity of the sample cells based on the movement of the sample cells at a certain moment. Therefore, it is necessary to collect the operating characteristics of the sample cells at different times. Evaluating the cell activity based on the movement characteristics of the sample cells at different times can make the obtained cell activity more accurate.

[0053] In one implementation, optical imaging technology is used to collect initial characteristic signals of sample cells.

[0054] Specifically, such as Figure 2 As shown, the method for obtaining the initial characteristic signal includes:

[0055] S1011 , acquiring a plurality of original characteristic signals of sample cells collected at a preset frequency, wherein the original characteristic signals are optical coherence tomography signals captured by an optical coherence tomography device.

[0056] In this embodiment, the preset frequency can be set as needed and is not limited here. The number of preset characteristic signals can also be set as needed, for example, controlling the optical coherence tomography device to collect 100 frames of signals to obtain 100 original characteristic signals.

[0057] Since the current detection of cell activity mostly uses fluorescence imaging technology and multiphoton excitation imaging technology to collect characteristic signals of sample cells, both fluorescence imaging technology and multiphoton excitation imaging technology are subject to photobleaching or phototoxicity; photobleaching refers to the irreversible chemical degradation of fluorescent molecules under continuous irradiation of laser or strong light, resulting in the phenomenon that the fluorescence signal gradually weakens or even disappears; phototoxicity refers to the phenomenon that light (especially short-wavelength, high-energy light) induces cells to produce a stress response, leading to cell damage or even death. Therefore, due to the existence of photobleaching or phototoxicity, the cell activity detected using fluorescence imaging technology and multiphoton excitation imaging technology is inaccurate. Therefore, the inventors discovered that optical coherence tomography (OCT) has the advantages of being label-free and having a deep imaging depth. Using the OCT signal of the cell can more accurately reflect the activity information of the cell, and the extracted cell activity information can be used to detect the activity of the cell. Therefore, the present application uses an optical coherence tomography device to photograph the sample cells to obtain the original characteristic signal (OCT signal) of the sample cells.

[0058] The optical coherence tomography device may include a light source and a camera, which controls the light source to illuminate the sample cells. After the light source irradiates the sample cells, a reflection signal is formed. After the camera receives the reflection signal, it first converts the reflection signal (light signal) into an electrical signal, and then passes the electrical signal through an analog-to-digital conversion circuit to convert the electrical signal into a digital signal to obtain the original characteristic signal. Among them, the light source can be an LED light source, and the camera can be a CMOS camera. In addition, the present application can also use a broadband light source and a high numerical aperture microscope objective to obtain high-resolution structural information of the sample cells.

[0059] In this embodiment, an extremely high acquisition frame rate is required when collecting the original characteristic signal, so that the acquisition time of the original characteristic signal is shortened and the signal acquisition efficiency is improved; on the other hand, it can also reduce the interference of light on the growth state of cells and improve the detection accuracy of the activity of sample cells.

[0060] S1012: Perform signal processing on the original characteristic signal to obtain a plurality of the initial characteristic signals, wherein the signal processing includes at least one of dimensional reconstruction processing and filtering processing.

[0061] In one approach, the original feature signal is input into a trained neural network model for signal processing to obtain an initial feature signal.

[0062] In another embodiment, the method for performing signal processing on the characteristic signal includes:

[0063] S10121. Perform first-dimensional reconstruction processing on the original characteristic signal to obtain a first candidate signal, wherein the first-dimensional reconstruction includes dimensionality increase processing.

[0064] In this embodiment, the original feature signal is a one-dimensional array. The spatial dimensions of the original feature signal are reconstructed and directional-calibrated to obtain a first candidate signal that is more accurately aligned with the physical space. The first candidate signal is then converted into a three-dimensional array to facilitate subsequent processing. By increasing the dimensionality of the original feature signal, more complex features can be mined by adding more dimensions. As a result, the features in the upgraded first candidate signal are more distinct, making feature extraction easier.

[0065] S10122: Filter the first candidate signal to obtain a second candidate signal.

[0066] In one approach, a singular value decomposition algorithm is used to filter the first candidate signal to obtain a second candidate signal. Specifically, the first candidate signal is linearly transformed to map it to a new coordinate system, thereby more clearly obtaining the structure and characteristics of the first candidate signal and filtering out noise signals and interference signals. Noise signals include motion artifacts caused by the overall movement of sample cells. Motion artifacts refer to false interference signals that are unrelated to the actual information and appear in the signal due to unexpected overall or local movement of sample cells during the acquisition of the original characteristic signal.

[0067] In another embodiment, a Kalman filter method is used to filter the first candidate signal to obtain a second candidate signal.

[0068] S10123, performing a second-dimensional reconstruction process on the second candidate signal to obtain a plurality of the initial characteristic signals, wherein the second-dimensional reconstruction process includes converting the second candidate signal from a real number form into a complex number form.

[0069] In this embodiment, a Hilbert transform is performed on the second candidate signal to obtain an initial feature signal. Specifically, the analytical signal is calculated frame by frame along the time / depth dimension of the second candidate signal to obtain the initial feature signal. The initial feature signal is data in complex form. Converting the second candidate signal from real form to complex form introduces a new information dimension, extending the signal from the real domain to the complex domain. On the one hand, it can simplify subsequent operations, and on the other hand, it can more comprehensively describe the characteristics of the signal.

[0070] S102: performing autocorrelation calculation on the initial characteristic signal based on different time delays to obtain autocorrelation coefficients under different time delays.

[0071] In one embodiment, an autocorrelation calculation model is used to perform autocorrelation calculation on the initial characteristic signal to obtain a plurality of autocorrelation coefficients.

[0072] Autocorrelation calculation models include: Among them, g1,exp (τ) is the autocorrelation coefficient; τ is the preset time delay, for example, τ can be 35 or 50; Represents the complex conjugate of the initial characteristic signal R1(t) corresponding to the t-th moment; R1(t+τ) represents the initial characteristic signal corresponding to the t+τ-th moment; <…< t Characterizes the overall time average, that is, the average value of the number of effective initial characteristic signals used.

[0073] For example, if τ = 35 and the initial characteristic signal is N = 100, there are 100 initial characteristic signals. In the process of calculating the autocorrelation coefficient for τ = 35, in order to better distinguish the time delay used in each calculation, iτ can be introduced, iτ = 1, 2, 3, ..., 35, and the autocorrelation coefficients for iτ = 1, 2, 3, ..., 35 are calculated, ultimately obtaining 35 autocorrelation coefficients.

[0074] As an example, when iτ=1, the numerator is calculated as: Sum the 99 products and divide by 99 to get the average. The denominator is calculated as: Sum the 100 products and divide by 100 to get the average.

[0075] When iτ=2, the numerator is calculated as: Sum the 98 products and divide by 98 to get the average. The denominator is calculated as: Sum the 100 products and divide by 100 to get the average.

[0076] Calculate sequentially until iτ=35, the numerator is calculated as: Sum the 65 products and divide by 65 to get the average. The denominator is calculated as: Sum the 100 products and divide by 100 to get the average.

[0077] Finally, 35 autocorrelation coefficients were obtained.

[0078] S103 , determining the diffusion coefficient of the sample cell based on the changes in the plurality of autocorrelation coefficients.

[0079] In one way, as Figure 3 As shown, the calculation method of the diffusion coefficient may include:

[0080] S1031: Obtain a preset fitting function, wherein the fitting function is a function related to the diffusion coefficient.

[0081] In this embodiment, the fitting function can be determined by the three-dimensional resolution and the three-dimensional flow velocity component of the cell; the three dimensions include the horizontal x-direction, the y-direction, and the axial z-direction. The fitting function is expressed as:

[0082]

[0083] Where e is the natural exponent, σ x , σ y represents the resolution in the horizontal plane, and σ x =σ y , σ z represents the axial resolution, V x , V y The horizontal velocity component representing the movement of sample cells, V z represents the axial velocity component of the sample cell movement, i is the imaginary unit, q = 2nk0, k0 is the wave number, λ0 is the wavelength of the light source when collecting the original characteristic signal, n is the preset refractive index, D is the diffusion coefficient, and τ is the preset time delay.

[0084] Considering that the random motion of the sample cells is much greater than the directional velocity motion, the velocity-related information is ignored, that is, V x =0, V y =0, V z =0;g 1,theory The function expression of (τ) can be simplified as:

[0085] S1032 , performing data fitting on the fitting function and the plurality of autocorrelation coefficients to obtain the diffusion coefficient of the sample cells.

[0086] In this embodiment, different candidate diffusion coefficients are selected to generate different fitting curves (theoretical curves). The deviations between each fitting curve and the true curve composed of autocorrelation coefficients are compared, and the fitting curve with the smallest deviation from the true curve is selected as the target curve. The candidate diffusion coefficient used in generating the target curve is the diffusion coefficient of the sample cell.

[0087] Specifically, S11 , a true curve is generated according to the autocorrelation coefficient, where the true curve represents the relative relationship between the autocorrelation coefficient and each time delay.

[0088] In this embodiment, the curve of the autocorrelation coefficient changing with time delay is plotted to obtain the true curve, such as Figure 4The solid line shown in is a true curve generated based on the autocorrelation coefficient. S12: Obtain the jth candidate diffusion coefficient. Based on the jth candidate diffusion coefficient and the fitting function, generate the jth theoretical curve. The theoretical curve represents the relative relationship between the theoretical correlation coefficient and each time delay, j ≥ 1. The candidate diffusion coefficient is selected from a preset coefficient range.

[0089] In this embodiment, a coefficient interval is preset, and a coefficient is randomly selected from the coefficient interval as a candidate diffusion coefficient.

[0090] The jth candidate diffusion coefficient is input into the fitting function to obtain the jth theoretical curve, as shown in Figure 4 The dotted line shown in is the jth theoretical curve generated according to the jth candidate diffusion coefficient.

[0091] S13 , determining a target curve from each theoretical curve according to the deviations between each theoretical curve and the true curve, wherein the target curve is the theoretical curve having the smallest deviation from the true curve among all the theoretical curves.

[0092] In one embodiment, the target curve is determined by calculating the fitting factor between each theoretical curve and the true curve, and the theoretical curve corresponding to the maximum value of the fitting factor is determined as the target curve.

[0093] The fitting factors are: Where R is the fitting factor; g 1,exp (τ) is the autocorrelation coefficient corresponding to the time τ in the true curve; is the theoretical correlation coefficient corresponding to the time τ in the theoretical curve; τ is the preset time delay; Characterizes the difference between the autocorrelation coefficient at time τ and the theoretical correlation coefficient; The squares of the absolute values ​​of the M differences are added together and the average is taken, and M is the number of autocorrelation coefficients; <g 1,exp (τ)> represents the average value of M autocorrelation coefficients; g 1,exp (τ)- <g 1,exp (τ)> represents the difference between the autocorrelation coefficient corresponding to time τ and the average value of M autocorrelation coefficients<|g 1,exp (τ)- <g 1,exp (τ)>|> 2 The representation calculates the average of the sum of the absolute values ​​of all differences and then takes the square of the mean.

[0094] S14, determining the candidate diffusion coefficient used when generating the target curve as the diffusion coefficient of the sample cell.

[0095] In this embodiment, since the target curve is the curve closest to the true curve, the candidate diffusion coefficient used in generating the target curve is determined to be the diffusion coefficient of the sample cell, which can make the obtained expansion coefficient more accurate.

[0096] In another embodiment, the calculation method of the diffusion coefficient may include:

[0097] A curve graph showing the change in autocorrelation coefficient over time is drawn, the slope of the curve graph is calculated, and the obtained slope is determined as the diffusion coefficient of the sample cells.

[0098] In another embodiment, the calculation method of the diffusion coefficient may include:

[0099] The changes in the autocorrelation coefficient over time are input into the trained convolutional neural network to obtain the diffusion coefficient of the sample cells.

[0100] S104: Determine the activity of the sample cells based on the diffusion coefficient.

[0101] In this embodiment, a larger diffusion coefficient indicates a higher activity of the sample cells.

[0102] In this embodiment, a correspondence between the diffusion coefficient and the activity value is preset. After the diffusion coefficient is obtained, the activity value of the sample cell is determined according to the preset correspondence between the diffusion coefficient and the activity value. The activity value represents the activity of the sample cell.

[0103] In one way, through Calculate the activity value of the sample cells, where A is the activity value, q is the preset coefficient, D is the diffusion coefficient, and D0 is the reference diffusion coefficient. D0 can be the average activity of multiple preset control cells.

[0104] In one way, through Calculate the activity value of the sample cells, where A is the activity value; A max is the preset maximum activity value of the sample cell under the ideal state; δ is the preset decay constant; D is the diffusion coefficient; and D0 is the reference diffusion coefficient.

[0105] The present application first obtains multiple initial characteristic signals of sample cells collected at different times, and then performs autocorrelation calculations on the multiple initial characteristic signals to obtain multiple autocorrelation coefficients; then, the diffusion coefficient of the sample cells is evaluated based on the changes of each autocorrelation coefficient over time, and then the activity of the sample cells is evaluated based on the diffusion coefficient of the sample cells. The present application can measure the correlation between signals at different time points by performing autocorrelation calculations on the characteristic signals of the sample cells, and can reveal the dynamic change law of cell movement characteristics by analyzing the changes of the obtained autocorrelation coefficients over time. The diffusion coefficient of the sample cells is obtained based on the autocorrelation coefficient, so that the diffusion coefficient can essentially reflect the activity state of the cells, providing a key quantitative indicator for accurately detecting cell activity. Since the diffusion coefficient can more accurately reflect the activity state of the cells, the activity of the sample cells obtained based on the diffusion coefficient is more accurate.

[0106] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0107] Corresponding to the detection method of cell activity described in the above embodiment, Figure 5 A structural block diagram of a cell activity detection device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0108] Reference Figure 5 The device 200 may include: a signal acquisition module 210, an autocorrelation calculation module 220, a coefficient calculation module 230 and an activity evaluation module 240.

[0109] The signal acquisition module 210 is configured to acquire a plurality of initial characteristic signals of sample cells collected at different times, wherein the initial characteristic signals are used to characterize the initial motion characteristics of the sample cells;

[0110] An autocorrelation calculation module 220 is configured to perform autocorrelation calculation on the initial characteristic signal based on different time delays to obtain autocorrelation coefficients under different time delays;

[0111] A coefficient calculation module 230 is used to determine the diffusion coefficient of the sample cells based on the changes of the multiple autocorrelation coefficients over time;

[0112] The activity evaluation module 240 is configured to determine the activity of the sample cells based on the diffusion coefficient.

[0113] In a possible implementation, the signal acquisition module 210 may be specifically configured to:

[0114] Acquiring a plurality of original characteristic signals of sample cells collected at a preset frequency, wherein the original characteristic signals are optical coherence tomography signals captured by an optical coherence tomography device;

[0115] The original characteristic signal is subjected to signal processing to obtain a plurality of the initial characteristic signals, wherein the signal processing includes at least one of dimensional reconstruction processing and filtering processing.

[0116] In a possible implementation, the coefficient calculation module 230 may be specifically configured to:

[0117] Obtaining a preset fitting function, wherein the fitting function is a function related to the diffusion coefficient;

[0118] The fitting function and the plurality of autocorrelation coefficients are subjected to data fitting to obtain the diffusion coefficient of the sample cells.

[0119] In a possible implementation, the fitting function is: g 1,theory (τ) is the fitting function, D is the diffusion coefficient, and τ is the preset time delay; λ0 is the wavelength of the light source when collecting the original characteristic signal, and n is the preset refractive index.

[0120] In a possible implementation, a larger diffusion coefficient indicates a higher activity of the sample cells.

[0121] In a possible implementation, the signal acquisition module 210 may be specifically configured to:

[0122] Performing a first-dimensional reconstruction process on the original characteristic signal to obtain a first candidate signal, wherein the first-dimensional reconstruction includes a dimensionality increase process;

[0123] performing filtering processing on the first candidate signal to obtain a second candidate signal;

[0124] Performing a second dimensional reconstruction process on the second candidate signal to obtain a plurality of the initial characteristic signals, wherein the second dimensional reconstruction process includes converting the second candidate signal from a real number form to a complex number form.

[0125] In a possible implementation, the coefficient calculation module 230 may be specifically configured to:

[0126] generating a true curve according to the plurality of autocorrelation coefficients, wherein the true curve represents a relative relationship between the autocorrelation coefficients and each time delay;

[0127] Obtaining a j-th candidate diffusion coefficient, and generating a j-th theoretical curve based on the j-th candidate diffusion coefficient and the fitting function, wherein the theoretical curve represents a relative relationship between a theoretical correlation coefficient and each time delay, j ≥ 1, and the candidate diffusion coefficient is selected from a preset coefficient range;

[0128] Determining a target curve from each theoretical curve according to the deviation between each theoretical curve and the true curve, wherein the target curve is the theoretical curve having the smallest deviation from the true curve among all the theoretical curves;

[0129] The candidate diffusion coefficient used when generating the target curve is determined to be the diffusion coefficient of the sample cell.

[0130] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0132] The present application also provides a terminal device. Figure 6 The terminal device 400 may include: at least one processor 410, a memory 420, and a computer program stored in the memory 420 and executable on the at least one processor 410. When the processor 410 executes the computer program, the steps in any of the above-mentioned method embodiments are implemented, for example Figure 1 Alternatively, when the processor 410 executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, Figure 5 The functions of the signal acquisition module 210 to the activity evaluation module 240 are shown.

[0133] For example, the computer program may be divided into one or more modules / units, one or more modules / units being stored in the memory 420 and executed by the processor 410 to complete the present application. The one or more modules / units may be a series of computer program segments capable of completing specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 400.

[0134] Those skilled in the art will understand that Figure 6 These are merely examples of terminal devices and do not constitute a limitation on the terminal devices. The terminal devices may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0135] The processor 410 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0136] The memory 420 may be an internal storage unit of the terminal device or an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. The memory 420 is used to store the computer program and other programs and data required by the terminal device. The memory 420 may also be used to temporarily store data that has been output or is about to be output.

[0137] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0138] The cell activity detection method provided in the embodiments of the present application can be applied to terminal devices such as computers, tablet computers, laptop computers, netbooks, and personal digital assistants (PDAs). The embodiments of the present application do not impose any restrictions on the specific type of terminal devices.

[0139] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0141] In the embodiments provided in this application, it should be understood that the disclosed terminal devices, apparatuses, and methods can be implemented in other ways. For example, the terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0144] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by one or more processors, it can implement the steps of the above-mentioned various method embodiments.

[0145] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by one or more processors, it can implement the steps of the above-mentioned various method embodiments.

[0146] Similarly, as a computer program product, when the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0147] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0148] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for detecting cell activity, characterized in that: include: Acquiring a plurality of initial characteristic signals of sample cells collected at different times, wherein the initial characteristic signals are used to characterize initial motion characteristics of the sample cells; Performing autocorrelation calculation on the initial characteristic signal based on different time delays to obtain autocorrelation coefficients under different time delays; determining the diffusion coefficient of the sample cells based on changes in the plurality of autocorrelation coefficients; Based on the diffusion coefficient, the activity of the sample cells is determined.

2. The method for detecting cell activity according to claim 1, wherein The step of obtaining a plurality of initial characteristic signals of sample cells collected at different times includes: Acquiring a plurality of original characteristic signals of sample cells collected at a preset frequency, wherein the original characteristic signals are optical coherence tomography signals captured by an optical coherence tomography device; The original characteristic signal is subjected to signal processing to obtain a plurality of the initial characteristic signals, wherein the signal processing includes at least one of dimensional reconstruction processing and filtering processing.

3. The method for detecting cell activity according to claim 2, wherein Determining the diffusion coefficient of the sample cell based on changes in the plurality of autocorrelation coefficients includes: Obtaining a preset fitting function, wherein the fitting function is a function related to the diffusion coefficient; The fitting function and the plurality of autocorrelation coefficients are subjected to data fitting to obtain the diffusion coefficient of the sample cells.

4. The method for detecting cell activity according to claim 3, wherein The fitting function is: g 1,theory (τ) is the fitting function, D is the diffusion coefficient, and τ is the preset time delay; λ0 is the wavelength of the light source when collecting the original characteristic signal, and n is the preset refractive index.

5. The method for detecting cell activity according to claim 1, wherein A larger diffusion coefficient indicates a higher activity of the sample cells.

6. The method for detecting cell activity according to claim 3, wherein The performing signal processing on the original characteristic signal to obtain a plurality of the initial characteristic signals includes: Performing a first-dimensional reconstruction process on the original characteristic signal to obtain a first candidate signal, wherein the first-dimensional reconstruction includes a dimensionality increase process; performing filtering processing on the first candidate signal to obtain a second candidate signal; Performing a second dimensional reconstruction process on the second candidate signal to obtain a plurality of the initial characteristic signals, wherein the second dimensional reconstruction process includes converting the second candidate signal from a real number form to a complex number form.

7. The method for detecting cell activity according to claim 3, wherein The step of performing data fitting on the fitting function and the plurality of autocorrelation coefficients to obtain the diffusion coefficient of the sample cells includes: generating a true curve according to the plurality of autocorrelation coefficients, wherein the true curve represents a relative relationship between the autocorrelation coefficients and each time delay; Obtaining a j-th candidate diffusion coefficient, and generating a j-th theoretical curve based on the j-th candidate diffusion coefficient and the fitting function, wherein the theoretical curve represents a relative relationship between a theoretical correlation coefficient and each time delay, j ≥ 1, and the candidate diffusion coefficient is selected from a preset coefficient range; Determining a target curve from each theoretical curve according to the deviation between each theoretical curve and the true curve, wherein the target curve is the theoretical curve having the smallest deviation from the true curve among all the theoretical curves; The candidate diffusion coefficient used when generating the target curve is determined to be the diffusion coefficient of the sample cell.

8. A cell activity detection device, characterized in that: include: a signal acquisition module, configured to acquire a plurality of initial characteristic signals of sample cells collected at different times, wherein the initial characteristic signals are used to characterize the initial motion characteristics of the sample cells; An autocorrelation calculation module is used to perform autocorrelation calculation on the initial characteristic signal based on different time delays to obtain autocorrelation coefficients under different time delays; a coefficient calculation module, configured to determine the diffusion coefficient of the sample cells based on changes in the plurality of autocorrelation coefficients; The activity evaluation module is used to determine the activity of the sample cells based on the diffusion coefficient.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for detecting cell activity according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for detecting cell activity according to any one of claims 1 to 7 is implemented.

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