Cell analysis method, device, equipment and storage medium

Abstract

The present application is applicable to the field of microscopic image processing, and provides a cell analysis method, device, equipment and storage medium, wherein the method includes: obtaining a target data type selected by a user; reading target video data based on a target file directory associated with the target data type; performing foreground and background image separation processing on each image frame in the target video data, and obtaining image features of the cell image when determining to separate a cell image; determining cell change information in the target video data based on the image features, the cell change information including motion change information or grayscale change information; and performing data visualization based on the cell change information. This solution can improve the efficiency of cell analysis and ensure analysis accuracy.

Description

Technical Field

[0001] The present application belongs to the field of microscopic image processing, and in particular, relates to a cell analysis method, device, equipment and storage medium. Background Art

[0002] In the microscopic world of biomedical research, microscopic imaging of cells and data analysis have become an important means for scientists to conduct cell research.

[0003] Cell-based microscopy imaging can perform a variety of processing operations on video images, such as histogram equalization, image filtering, image segmentation, etc., to extract key information required for analysis from the image.

[0004] However, this process is rather cumbersome to operate, and experimenters usually do not have programming skills. When processing video images, they often use some software to manually analyze each image. This method is time-consuming and labor-intensive. When the amount of data is large and the number of video frames and groups is large, the operation is cumbersome and inefficient. In addition, manual operation has limitations, and the accuracy of the analysis cannot be guaranteed. Summary of the invention

[0005] The embodiments of the present application provide a cell analysis method, device, equipment and storage medium to solve the problems that the cell analysis method in the prior art is time-consuming and labor-intensive, the operation is cumbersome and inefficient, and the analysis accuracy cannot be guaranteed.

[0006] A first aspect of the embodiments of the present application provides a cell analysis method, comprising:

[0007] Get the target data type selected by the user;

[0008] Reading target video data based on the target file directory associated with the target data type;

[0009] Performing foreground and background image separation processing on each image frame in the target video data, and obtaining image features of the cell image when determining that a cell image is separated;

[0010] Based on the image features, determining cell change information in the target video data, the cell change information including motion change information or grayscale change information;

[0011] Data visualization is performed based on the cell change information.

[0012] A second aspect of the embodiments of the present application provides a cell analysis device, comprising:

[0013] The acquisition module is used to obtain the target data type selected by the user;

[0014] A reading module, used for reading target video data based on a target file directory associated with the target data type;

[0015] A processing module, used for performing foreground and background image separation processing on each image frame in the target video data, and obtaining image features of the cell image when determining to separate the cell image;

[0016] A determination module, configured to determine cell change information in the target video data based on the image features, wherein the cell change information includes motion change information or grayscale change information;

[0017] A display module is used to perform data visualization based on the cell change information.

[0018] A third aspect of an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0019] A fourth aspect of 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 steps of the method described in the first aspect are implemented.

[0020] The fifth aspect of the present application provides a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying a computer-readable code. When the computer-readable code is executed in an electronic device, the processor in the electronic device executes the steps in the method described in the first aspect above.

[0021] The above scheme in the embodiment of the present application, for the video data of cells, performs feature extraction on the video image, and based on the extracted image features, captures the cell change information generated by the cells in the experiment, and obtains the motion change information and grayscale change information therein, thereby ensuring the acquisition of each data in the video that can characterize the cell reaction changes, and visually displaying these data, thereby ensuring the automation and intelligence of the cell analysis process, improving the efficiency of scientific research data analysis, and ensuring the accuracy of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0023] Figure 1is a flow chart of a cell analysis method according to some embodiments of the present application;

[0024] Figure 2 is a schematic diagram of an operation interface of some embodiments of the present application;

[0025] Figure 3 is another schematic diagram of an operation interface of some embodiments of the present application;

[0026] Figure 4 is a structural diagram of a cell analysis device according to some embodiments of the present application;

[0027] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0030] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0033] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0034] Combination Figure 1 As shown, in some embodiments, a cell analysis method is provided, comprising:

[0035] Step 101, obtaining the target data type selected by the user.

[0036] After running the program, you first need to select the data type. Optionally, the data of different types are captured videos containing multiple frames of microscopic images, and the illumination modes of the microscopic videos captured under different data types are different, such as bright field captured videos and fluorescence captured videos with different illumination modes.

[0037] In an optional implementation, after running the program, you first need to select the data type, combined with Figure 2 As shown, in a provided operation interface, a selection button for bright field video shooting is provided ( Figure 2 Bright field button in the left pane) and fluorescence video selection button ( Figure 2 (fluorescent keys in the

[0038] Users can click the buttons shown in the figure to select the target data type. Clicking the button of the desired analysis data type will jump to Figure 3 operation interface.

[0039] Step 102, reading target video data based on a target file directory associated with a target data type.

[0040] After selecting the data type, select the target video data in the corresponding file directory in the user's computer and start the subsequent analysis.

[0041] If there is background in the target video data to be analyzed, you can also read the corresponding background data and upload it to the program based on the target file directory associated with the target data type. If not, you can skip this step directly.

[0042] If the target data type selected by the user is fluorescent video, and there is a global non-constant background light intensity in the lighting mode, and the background image cannot be captured separately, then an area can be framed from the read target video data, and the average grayscale value of each image frame in the target video data under the framed area can be calculated as the background of the image frame, and applied to subsequent processing.

[0043] In an optional embodiment, Figure 3 In the operation interface, click the "Import Video" button to browse the folders on the computer and select the video data you want to analyze in the folder where the video data is saved.

[0044] Click the "Import Background" button to browse the folders on your computer and select the image background data you want to analyze from the folder where the image background data is saved. If the video you want to analyze does not require any background operations, you can skip this step.

[0045] After completing the data import, click the "Run" button to start running the program. If you find that the target data type is incorrect, you can click the "Return" button to return to Figure 2 In the operation interface shown, reselect the data type. Wait for the program to finish running to get the analysis results.

[0046] Step 103 , performing foreground and background image separation processing on each image frame in the target video data, and obtaining image features of the cell image when it is determined that the cell image is separated.

[0047] The video data includes multiple image frames. In an optional implementation process, the image frames in the target video data can be read sequentially, and each time a frame is read, it is determined whether there are cells. If not, the next video frame is read to determine whether there are cells. If it is determined that there are cells, subsequent feature extraction processing is performed based on the identified cells.

[0048] During implementation, it is specifically necessary to perform binarization processing on each image frame in the target video data with a set threshold value to separate the cell foreground image from the background image in the image.

[0049] In some optional implementations, separation of foreground and background images may be performed by extracting features from the image, and determining the foreground image and the background image based on the distribution of image features in the image frame.

[0050] Alternatively, in some optional implementations, separation of the foreground and background images may be performed by binarizing the image and then distinguishing pixels based on a gray value threshold to determine the foreground image and the background image.

[0051] Correspondingly, step 103 performs foreground and background image separation processing on each image frame in the target video data, and when determining that a cell image is separated, obtains the image features of the cell image, including:

[0052] Performing binarization processing on each image frame in the target video data;

[0053] If there is a first image region with a grayscale value greater than a threshold value in the image frame after the binarization process, determining to separate the cell image;

[0054] Cell contour detection is performed on a second image region including the first image region, and image features of the cell image are extracted from the first image region based on the detected cell contour.

[0055] The first image region with a gray value greater than the threshold can be considered as the region where the cell image is located, and the cell size and position can be preliminarily determined on this basis.

[0056] Optionally, the cell image region and the image region in the vicinity thereof can be cropped together as the second image region for subsequent analysis, so as to implement accurate contour detection and further implement effective image feature extraction.

[0057] When performing cell contour detection and realizing the frame extraction of cell contour, the specific steps are as follows:

[0058] For the cropped video image (corresponding to the second image area), the cell contours in the image are accurately detected based on image enhancement and edge detection algorithms. The edge detection algorithms include an edge detection algorithm based on a canny operator, an image segmentation algorithm based on k-means clustering, and an image segmentation algorithm based on a gray value threshold. Different algorithms correspond to different target data types, and the corresponding algorithm can be selected based on the target data type.

[0059] The extracted image features of the cell image include, for example, cell position, contour morphology, size, grayscale value and other features in the image.

[0060] In step 103, when determining to separate the cell image, the implementation process of obtaining the image features of the cell image may specifically include:

[0061] Cell outline extraction can be achieved through spatial domain image processing, image filtering, edge detection, etc. Cell parameter extraction, such as extraction of cell morphology parameters, gray value parameters, etc. When there are multiple cells, multiple cell trajectories are allocated, such as tracking multiple cells in the video image based on Kalman filtering and Hungarian algorithm at the same time to obtain the complete movement process and trajectory of the cells in the video image.

[0062] Before step 103, the imported target video data may be preprocessed to remove image noise, improve image smoothness, optimize image quality, etc.

[0063] After the data is imported, the image is preprocessed. The main operation can be image filtering, which can be achieved by setting the median filter and Gaussian filter. The median filter is used to filter out the salt and pepper noise in the image, and the Gaussian filter is used to smooth the image. At the same time, histogram equalization can also be adjusted to optimize the image quality for easy observation and analysis.

[0064] Step 104: determining cell change information in the target video data based on the image features.

[0065] Among them, the cell change information includes motion change information, grayscale change information, etc.

[0066] Optionally, the motion change information may include motion state change information, morphology change information, etc.

[0067] The information on the change in motion state may include information such as the motion trajectory, motion speed, and changes in motion distance (eg, the displacement generated by cell motion and the total distance of cell motion).

[0068] The morphological change information may include change information such as the size of the cell, the deformability of the cell (eg, oblateness), etc.

[0069] Grayscale change information refers to the change information of the grayscale value of the cell, which is used to indicate the change of fluorescence intensity, and different fluorescence intensity changes are used to characterize the intensity changes of physiological signals such as the calcium response of the cell.

[0070] In an optional implementation, different feature analyses can be performed on the cell image based on different target data types selected:

[0071] In bright field video, attention is paid to changes in physical properties such as cell morphology and displacement. Accordingly, based on image features, cell change information in the target video data is determined, including motion change information, such as motion state change information, morphology change information, etc.

[0072] Under the fluorescence video, pay attention to the gray value of the cell to form a gray change curve. By recording the gray value changes in the cell area to characterize the fluorescence intensity change curve, the changes in physiological signals such as the calcium response of the cell can be characterized.

[0073] Among them, the change in the gray value of the cell area is positively correlated with the change in the fluorescence intensity. The greater the fluorescence intensity, the greater the gray value of the cell area.

[0074] For the calcium response generated by the cell, the range of the cell in the video image is determined according to the cell outline taken by the frame, and then the average gray value of the cell in each frame of the image is calculated, so that each cell can obtain a gray value change curve, through which the gray value change curve relative to the baseline (df / f0) can be obtained, that is, the calcium response and other physiological signal change curves of the cell during the experiment. According to the calcium response and other physiological signal change curves, the peak value of the cell calcium response and other physiological signals, the number of peak values ​​of the calcium response and other physiological signals in each time period, and the area under the calcium response and other physiological signal curves can be obtained. Accordingly, based on the image features, the cell change information in the target video data is determined, which corresponds to the gray change information.

[0075] In some optional implementations, step 104 determines the cell change information in the target video data based on the image features, including:

[0076] If multiple cell contours are detected, target image features corresponding to each cell are extracted from the image features; based on the predicted movement direction of the cell image in two consecutive image frames in the target video data, the target image features of each cell in the target video data in different image frames are associated to determine the cell change information of each cell in the target video data.

[0077] When there are multiple cell contours, the cell positions detected in the current video frame and the predicted data of the cell movement direction can be used to associate the image features of each cell in different image frames to achieve cell feature tracking.

[0078] In some optional implementations, step 104 determines the cell change information in the target video data based on the image features, including:

[0079] If the image features include the grayscale value of the cell image, the average value of the grayscale value of the cell image in each image frame in the target video data is calculated; the average value is used to indicate the fluorescence intensity generated by the cell when excited by the light source; based on the average value, the grayscale change information of the cell in the target video data is generated.

[0080] The light source may be a laser light source, a mercury lamp light source or an LED (Light-emitting diode) light source.

[0081] The grayscale change information may be a grayscale value change curve.

[0082] Optionally, the average grayscale value in the cell contour area is used as the fluorescence intensity characterization parameter. In the framed cell contour area, the grayscale value of each pixel is superimposed and then divided by the total number of pixels, that is, the average grayscale value in the cell contour area.

[0083] This process forms grayscale change information based on the grayscale average of cell images in different image frames to determine the change information of physiological signals such as cell calcium response. By automatically extracting and counting the parameters representing physiological signals such as calcium response of motile cells, the changes of physiological signals such as cell calcium response can be tracked.

[0084] In some optional implementations, step 104 determines the cell change information in the target video data based on the image features, including:

[0085] If the image features include the position information of the cell image, based on the position information of the cell image in each image frame in the target video data, the movement trajectory, movement speed and / or movement distance of the cell in the target video data is determined as the movement state change information in the movement change information.

[0086] Based on the position information of the cell images in different image frames, this process calculates the characterization parameters of the cell's motion state, such as the trajectory, speed, and range of motion, to determine the changes in the motion state of the moving cells. By automatically extracting and counting the characterization parameters of the motion state of the moving cells, the changes in the motion state of the cells can be tracked.

[0087] In some optional implementations, step 104 determines the cell change information in the target video data based on the image features, including:

[0088] If the image features include contour information of the cell image, the cell shape and / or cell size of the cells in the target video data is determined as the morphological change information in the motion change information based on the contour information of the cell image in each image frame in the target video data.

[0089] Among them, the cell morphology change can be expressed by the deformability parameter:

[0090] For example, by calculating the inertia matrix of the polygon, and then using the inertia matrix to calculate the major and minor axes a and b of the polygon equivalent ellipse, the relative major and minor axis ratio of the cell equivalent ellipse is obtained.

[0091] Alternatively, after obtaining the relative major and minor axes a and b of the cell equivalent ellipse, the Taylor deformation number is calculated as (ab) / (a+b).

[0092] Alternatively, the deformability parameter can be calculated based on the cell's area A and perimeter P: .

[0093] Among them, pi is π.

[0094] This process is based on the contour information of the cell image in different image frames, and calculates the cell shape, cell size and other cell morphology parameters to determine the cell morphology changes. By automatically extracting and counting the cell morphology parameters, the morphology changes of moving cells can be tracked.

[0095] Step 105: Visualize the data based on the cell change information.

[0096] In implementation, the parameters can be sorted, filtered, counted and visualized.

[0097] Alternatively, based on the results of cell tracking and contour extraction, cells can be analyzed at different scales.

[0098] In some optional embodiments, step 105 performs data visualization based on the cell change information, including:

[0099] According to the set characteristic dimension, the motion change information is integrated and analyzed; and the data results obtained by the analysis are graphically displayed according to the set characteristic dimension.

[0100] The set feature dimension is, for example, a cell morphology dimension, a cell gray value change dimension, a cell movement state dimension, etc.

[0101] In an optional implementation process, all data in the video image can be classified based on the K-means clustering algorithm. The number of categories of the feature dimensions to be distinguished can be selected as needed, and the overall data in the video image can be integrated and qualitatively analyzed to achieve experimental data visualization.

[0102] In the embodiments of the present application, the video data of cells is used to capture the motion change information and grayscale change information generated by the cells in the experiment, ensure the acquisition of each data in the video that can characterize the changes in cell reactions, and visualize these data to ensure the automation and intelligence of the cell analysis process, improve the efficiency of scientific research data analysis, and ensure the accuracy of data analysis.

[0103] In an optional example, the above cell analysis method can be designed as an automated cell video image analysis software, which can be used in the automated image analysis of bright field video and fluorescence video. Its functions during implementation are as follows:

[0104] The imported video data is analyzed frame by frame to determine whether there are cells in the current image; if there are cells, their initial positions are determined and the cell contours are accurately framed; if there are multiple cells, the trajectory of each cell is automatically identified, and the cell trajectory is predicted based on the initial position and motion state of the cell, and the areas belonging to the same cell between every two frames are matched; after framing the cell contours, the parameters related to the cell morphology are calculated, including morphological parameters such as area, perimeter, deformability, Taylor deformation number, and the ratio of the relative major and minor axes of the cell equivalent ellipse, and the absolute position of the cell in the image is extracted to obtain the cell motion trajectory and speed.

[0105] For fluorescence video, the average grayscale value of the precisely framed cell area can be calculated to characterize the change in fluorescence intensity of the cell when it is stimulated by the light source. Finally, the data is integrated, and data irrelevant to the experiment is automatically screened out according to the conditions set by the user. At the same time, a visual interface for data analysis is provided, and data analysis visualization is completed, making it convenient for researchers to intuitively view and analyze experimental results.

[0106] In this process, the software input is image and video data. After the analysis, several types of data will be generated and visualized. The operation method is simple. The software automatically processes and analyzes the features extracted from the video images, effectively reducing the reliance on manual work and the workload of scientific researchers, while ensuring the depth and accuracy of data analysis and improving efficiency.

[0107] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0108] Based on the same inventive concept, the embodiment of the present application also provides a cell analysis device. The cell analysis device provided in the embodiment of the present application can implement each process of the embodiment of the above-mentioned cell analysis method and can achieve the same technical effect. Therefore, the specific limitations in one or more cell analysis device embodiments provided below can refer to the limitations on the cell analysis method above. To avoid repetition, it will not be repeated here.

[0109] In this embodiment, the computing side can be divided into functional modules according to the above method. For example, each function can be divided into functional modules, or two or more functions can be integrated into one processing module.

[0110] See also Figure 4 , Figure 4 This is a structural diagram of a cell analysis device provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0111] The cell analysis device 400 includes:

[0112] Acquisition module 401, used to acquire the target data type selected by the user;

[0113] A reading module 402, configured to read target video data based on a target file directory associated with the target data type;

[0114] The processing module 403 is used to perform foreground and background image separation processing on each image frame in the target video data, and when determining to separate a cell image, obtain the image features of the cell image;

[0115] A determination module 404 is used to determine cell change information in the target video data based on the image features, where the cell change information includes motion change information or grayscale change information;

[0116] The display module 405 is used to perform data visualization based on the cell change information.

[0117] Optionally, the processing module 403 is specifically configured to:

[0118] Performing binarization processing on each image frame in the target video data;

[0119] If there is a first image region with a grayscale value greater than a threshold value in the image frame after the binarization process, determining to separate the cell image;

[0120] Cell contour detection is performed on a second image region including the first image region, and image features of the cell image are extracted from the first image region based on the detected cell contour.

[0121] Optionally, the determination module 404 is specifically configured to:

[0122] If the detected cell contours are multiple, extracting target image features corresponding to each cell from the image features;

[0123] Based on the predicted movement direction of the cell image in two consecutive image frames in the target video data, the target image features of each cell in the target video data in different image frames are associated to determine the cell change information of each cell in the target video data.

[0124] Optionally, the determination module 404 is specifically configured to:

[0125] If the image features include the grayscale value of the cell image, then the average value of the grayscale value of the cell image in each image frame in the target video data is calculated; the average value is used to indicate the fluorescence intensity generated by the cell when excited by the light source;

[0126] Based on the average value, the grayscale change information of the cells in the target video data is generated.

[0127] Optionally, the determination module 404 is specifically configured to:

[0128] If the image features include the position information of the cell image, based on the position information of the cell image in each image frame in the target video data, the movement trajectory, movement speed and / or movement distance of the cell in the target video data is determined as the movement state change information in the movement change information.

[0129] Optionally, the determination module 404 is specifically configured to:

[0130] If the image features include contour information of the cell image, the cell shape and / or cell size of the cells in the target video data is determined as the morphological change information in the motion change information based on the contour information of the cell image in each image frame in the target video data.

[0131] Optionally, the display module 405 is specifically used to:

[0132] According to the set characteristic dimensions, data integration and analysis are performed on the cell change information;

[0133] The data results obtained by the analysis are graphically displayed according to the set feature dimensions.

[0134] The above integrated modules can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0135] It should be noted that the relevant contents of each step involved in the above method embodiment can all be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0136] In one embodiment, Figure 5 As shown, a computer device is provided. The computer device 5 of this embodiment includes: at least one processor 500 ( Figure 5 Only one is shown in the figure), a memory 501 and a computer program 502 stored in the memory 501 and executable on the at least one processor 500, wherein the processor 500 implements the steps of any of the above-mentioned method embodiments when executing the computer program 502.

[0137] The computer device 5 may be a computing device such as a desktop computer, a notebook, a palm computer, etc. The computer device 5 may include, but is not limited to, a processor 500 and a memory 501. Those skilled in the art will understand that Figure 5 It is only an example of the computer device 5 and does not constitute a limitation of the computer device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0138] The processor 500 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. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0139] The memory 501 may be an internal storage unit of the computer device 5, such as a hard disk or memory of the computer device 5. The memory 501 may also be an external storage device of the computer device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 5. Further, the memory 501 may also include both an internal storage unit and an external storage device of the computer device 5. The memory 501 is used to store the computer program and other programs and data required by the computer device. The memory 501 may also be used to temporarily store data that has been output or is to be output.

[0140] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be 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 in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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, which will not be repeated here.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] Those of ordinary skill 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 to be beyond the scope of this application.

[0143] In the embodiments provided in the present application, it should be understood that the disclosed apparatus / computer equipment and methods can be implemented in other ways. For example, the apparatus / computer equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, 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 through some interfaces, indirect coupling or communication connection of the apparatus or unit, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0146] If the integrated module / 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 the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can 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, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can 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.

[0147] The present application implements all or part of the processes in the above-mentioned embodiment methods, and may also be implemented through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0148] The embodiments described above 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, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A cell analysis method, characterized in that: include: Get the target data type selected by the user; There are differences in the illumination modes when shooting microscopic videos for different data types; The target data type includes fluorescence shooting video; Reading target video data based on the target file directory associated with the target data type; When the target data type is a fluorescent video and there is a global non-constant background light intensity in the illumination mode, a region is framed from the read target video data, and an average grayscale value of each image frame in the target video data under the framed region is calculated as the background of the image frame; Performing foreground and background image separation processing on each image frame in the target video data, and when determining to separate a cell image, obtaining image features of the cell image; the image features include the grayscale value of the cell image; Based on the image features, the cell change information in the target video data is determined, and the cell change information includes grayscale change information; the grayscale change information is the change information of the grayscale value of the cell, which is used to indicate the change of fluorescence intensity; different fluorescence intensity changes are used to characterize the intensity change of the physiological signal of the cell's calcium response; the fluorescence intensity change is used to count the physiological signal characterization parameters of the calcium response of the moving cells, and track the change of the physiological signal of the cell's calcium response; wherein, based on the grayscale value change curve of the cell, a grayscale value change value curve relative to the baseline is obtained as the physiological signal change curve of the cell's calcium response during the experiment, and according to the physiological signal change curve of the calcium response, the peak value of the physiological signal of the cell's calcium response in the target video data and the number of peaks of the physiological signal of the calcium response in each time period are obtained to obtain the cell change information; Data visualization is performed based on the cell change information.

2. The method according to claim 1, characterized in that The step of performing foreground and background image separation processing on each image frame in the target video data and obtaining image features of the cell image when determining that a cell image is separated includes: Performing binarization processing on each image frame in the target video data; If there is a first image region with a grayscale value greater than a threshold value in the image frame after the binarization process, determining to separate the cell image; Cell contour detection is performed on a second image region including the first image region, and image features of the cell image are extracted from the first image region based on the detected cell contour.

3. The method according to claim 2, characterized in that The determining, based on the image features, the cell change information in the target video data comprises: If the detected cell contours are multiple, extracting target image features corresponding to each cell from the image features; Based on the predicted movement direction of the cell image in two consecutive image frames in the target video data, the target image features of each cell in the target video data in different image frames are associated to determine the cell change information of each cell in the target video data.

4. The method according to claim 1, characterized in that: The determining, based on the image features, the cell change information in the target video data comprises: If the image features include the grayscale value of the cell image, then the average value of the grayscale value of the cell image in each image frame in the target video data is calculated; the average value is used to indicate the fluorescence intensity generated by the cell when excited by the light source; Based on the average value, the grayscale change information of the cells in the target video data is generated.

5. The method according to claim 1, characterized in that: The cell change information includes motion change information; and determining the cell change information in the target video data based on the image features includes: If the image features include the position information of the cell image, based on the position information of the cell image in each image frame in the target video data, the movement trajectory, movement speed and / or movement distance of the cell in the target video data is determined as the movement state change information in the movement change information.

6. The method according to claim 1, characterized in that The cell change information includes motion change information; and determining the cell change information in the target video data based on the image features includes: If the image features include contour information of the cell image, the cell shape and / or cell size of the cells in the target video data is determined as the morphological change information in the motion change information based on the contour information of the cell image in each image frame in the target video data.

7. The method according to claim 1, characterized in that The data visualization display based on the cell change information includes: According to the set characteristic dimensions, data integration and analysis are performed on the cell change information; The data results obtained by the analysis are graphically displayed according to the set feature dimensions.

8. A cell analysis device, characterized in that: include: The acquisition module is used to obtain the target data type selected by the user; There are differences in the illumination modes when shooting microscopic videos for different data types; The target data type includes fluorescence shooting video; A reading module, used for reading target video data based on a target file directory associated with the target data type; When the target data type is a fluorescent video and there is a global non-constant background light intensity in the illumination mode, a region is framed from the read target video data, and an average grayscale value of each image frame in the target video data under the framed region is calculated as the background of the image frame; A processing module, used for performing foreground and background image separation processing on each image frame in the target video data, and obtaining image features of the cell image when determining to separate the cell image; the image features include the grayscale value of the cell image; A determination module is used to determine the cell change information in the target video data based on the image features, wherein the cell change information includes grayscale change information; the grayscale change information is the change information of the grayscale value of the cell, which is used to indicate the change of fluorescence intensity; different fluorescence intensity changes are used to characterize the intensity change of the physiological signal of the cell's calcium response; the fluorescence intensity change is used to count the physiological signal characterization parameters of the calcium response of the moving cell, and track the change of the physiological signal of the cell's calcium response; wherein, based on the grayscale value change curve of the cell, a grayscale value change value curve relative to the baseline is obtained as the physiological signal change curve of the cell's calcium response during the experiment, and according to the physiological signal change curve of the calcium response, the peak value of the physiological signal of the cell's calcium response in the target video data and the number of peaks of the physiological signal of the calcium response in each time period are obtained to obtain the cell change information; A display module is used to perform data visualization based on the cell change information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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