Gene image data correction method and system, electronic equipment and storage medium
Patent Information
- Application Number
- CN202280102091.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-05
- Publication Date
- 2025-07-08
AI Technical Summary
In existing RNA cell labeling technology, the correction algorithm that relies on cell contour images is weak in robustness, resulting in inaccurate classification of gene molecules and affecting the accuracy of RNA cell labeling.
Through the gene image data correction method, the coordinate information of the initial gene molecules and background gene molecules of the target cell is determined, a Voronoi diagram is constructed, and the cells to which the background gene molecules belong are determined, reducing dependence on cell contour maps and improving correction accuracy and robustness. .
It improves the accuracy and efficiency of genetic image data correction, reduces the requirement for high-precision cell segmentation, and enhances the robustness of the correction scheme.
Smart Images
Figure CN120283256A_ABST
Abstract
Description
Gene image data correction method, system, electronic device and storage medium Technical Field
[0001] The present application relates to the technical field of gene image processing, and in particular to a gene image data correction method, system, electronic device and storage medium. Background Art
[0002] RNA cell labeling (a technology for labeling genetic information in living cells) assigns genes to cells based on the aligned cell outline image and the gene expression data corresponding to the cell. However, due to problems such as the small number of genes in cells, nuclear segmentation, and the diffusion of RNA (ribonucleic acid, a genetic information carrier present in biological cells and some viruses and viroids), the number of genes assigned to cells is far smaller than the number of genes contained in the actual cells. Therefore, the results of RNA cell labeling need to be corrected to improve the accuracy of RNA cell labeling. RNA cell labeling is a very important step in the downstream analysis of Stereo-seq (Spatio-Temporal Enhanced REsolution Omics-sequencing) spatial transcriptome sequencing technology and is also the basis for other downstream analyses. However, the current correction algorithm for RNA cell labeling relies on the cell morphology of the cell outline image. The algorithm is less robust, the final result is not accurate, and a large number of RNA gene molecules are not classified. Therefore, there is still much room for exploration and optimization to address this problem.
[0003] Summary of the Invention
[0004] The main purpose of this application is to provide a gene image data correction method, system, electronic device and storage medium to improve the defects of the existing technology such as reliance on cell morphology of cell contour images and a large number of gene molecules not being classified.
[0005] This application solves the above technical problems through the following technical solutions:
[0006] In a first aspect, the present application provides a method for correcting gene image data, the method comprising:
[0007] According to the gene image, the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells are determined;
[0008] Obtaining coordinate information of the initial gene molecule;
[0009] Determining the center point coordinates of the target cell according to the coordinate information;
[0010] Constructing a Voronoi diagram according to the center point coordinates and the coordinate information;
[0011] The cells to which the background gene molecules within the Voronoi diagram belong are determined.
[0012] Preferably, the step of determining the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells according to the gene image comprises:
[0013] Obtain microscopic and genetic images of biological samples;
[0014] Perform image registration on microscope images and gene images of biological samples;
[0015] performing cell segmentation on the microscope image to obtain a cell segmentation result;
[0016] Determining a gene image of a target cell based on the cell segmentation result and the image registration result of the gene image;
[0017] According to the gene image of the target cell, the initial gene molecules belonging to the target cell and the background gene molecules of the unknown belonging cells are determined.
[0018] Preferably, the step of obtaining the coordinate information of the initial gene molecule includes:
[0019] The coordinate information of the initial gene molecule is determined based on the gene image of the target cell.
[0020] Preferably, the step of determining the coordinates of the center point of the target cell according to the coordinate information includes:
[0021] Counting the number of molecules of the initial gene molecules;
[0022] Calculating the sum of the abscissa values and the sum of the ordinate values of the initial gene molecules;
[0023] Determine a first ratio as the abscissa value of the center point coordinate; the first ratio is the ratio of the sum of the abscissa values of the initial gene molecules to the number of the molecules;
[0024] The second ratio is determined as the ordinate value of the center point coordinate; the second ratio is the ratio of the sum of the ordinate values of the initial gene molecules to the number of molecules.
[0025] Preferably, the step of determining the coordinates of the center point of the target cell according to the coordinate information includes:
[0026] Obtaining the maximum abscissa value, the minimum abscissa value, the maximum ordinate value, and the minimum ordinate value in the initial gene molecule;
[0027] Determine a third ratio as the abscissa value of the center point coordinate; the third ratio is half of the difference between the maximum abscissa value and the minimum abscissa value;
[0028] The fourth ratio is determined as the ordinate value of the center point coordinate; the fourth ratio is half of the difference between the maximum ordinate value and the minimum ordinate value.
[0029] Preferably, the gene image data correction method further comprises:
[0030] If the background gene molecules are within different Voronoi diagram ranges, the background gene molecules are input into a Gaussian mixture model to determine the probability scores of the background gene molecules belonging to target cells within a preset distance range;
[0031] The background gene molecules are corrected to the gene molecules belonging to the target cells with high probability scores.
[0032] Preferably, the Voronoi diagram is a two-dimensional Voronoi diagram.
[0033] Preferably, the step of determining the cells to which the background gene molecules within the Voronoi diagram belong comprises:
[0034] Generate target cell gene expression information based on the corrected results of the gene molecules belonging to the target cell.
[0035] Preferably, the step of constructing a Voronoi diagram according to the center point coordinates and the coordinate information includes:
[0036] Constructing a polygonal network for all the center point coordinates;
[0037] Determining edges of a Voronoi diagram based on the polygonal network;
[0038] A Voronoi diagram is constructed according to the edges of the Voronoi diagram.
[0039] In a second aspect, the present application provides a gene image data correction system, the gene image data correction system comprising:
[0040] A determination module is used to determine the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells based on the gene image;
[0041] An acquisition module, used to obtain the coordinate information of the initial gene molecule;
[0042] The determination module is further configured to determine the center coordinates of the target cell according to the coordinate information;
[0043] A construction module, configured to construct a Voronoi diagram according to the center point coordinates and the coordinate information;
[0044] The correction module is used to determine the cells to which the background gene molecules within the Voronoi diagram belong.
[0045] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned gene image data correction method when executing the computer program.
[0046] In a fourth aspect, the present application provides a computer-readable medium having computer instructions stored thereon, which, when executed by a processor, implement the gene image data correction method as described above.
[0047] The positive progress of this application is:
[0048] The present application determines the coordinates of the center point of the target cell through the coordinate information of the initial gene molecules belonging to the target cell, and then constructs a Voronoi diagram based on the coordinates of the center point of the target cell to determine the cells to which the background gene molecules within the Voronoi diagram belong, thereby improving the correction efficiency and also improving the accuracy of the correction. In addition, the present application only needs to determine the initial gene molecules in the target cell, without having to understand the contour map of the target cell. Therefore, the requirements for high-precision cell segmentation are reduced, the dependence on the morphological image of the target cell is reduced, and the robustness of the correction scheme is improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] Figure 1a is a distribution diagram of RNA gene molecules before correction using the spatiotemporal data correction algorithm;
[0050] Figure 1b is a distribution diagram of RNA gene molecules after correction using the spatiotemporal data correction algorithm;
[0051] FIG2 is a first flow chart of the gene image data correction method according to Example 1 of the present application;
[0052] FIG3 is a second flow chart of the gene image data correction method of Example 1 of the present application;
[0053] FIG4 is a schematic diagram of the center point of a target cell in the gene image data correction method of Example 1 of the present application;
[0054] FIG5 is a third flow chart of the gene image data correction method of Example 1 of the present application;
[0055] FIG6 is a fourth flow chart of the gene image data correction method of Example 1 of the present application;
[0056] FIG7 is a Voronoi diagram illustrating the structure of the gene image data correction method according to Example 1 of the present application;
[0057] FIG8 is a fifth flow chart of the gene image data correction method of Example 1 of the present application;
[0058] FIG9 is a sixth flow chart of the gene image data correction method of Example 1 of the present application;
[0059] FIG10 is a cell gene image corrected according to the Voronoi diagram by the gene image data correction method of Example 1 of the present application;
[0060] FIG11 is a diagram showing morphological changes of several target cells after correction using the gene image data correction method of Example 1 of the present application;
[0061] FIG12a is a diagram showing the first spatial clustering effect of the mouse brain before correction of the gene image data correction method of Example 1 of the present application;
[0062] FIG12 b is a diagram showing the second spatial clustering effect of the mouse brain before correction of the gene image data correction method of Example 1 of the present application;
[0063] FIG12c is a diagram showing the first spatial clustering effect of the mouse brain after correction using the gene image data correction method of Example 1 of the present application;
[0064] FIG12d is a diagram showing the second spatial clustering effect of the mouse brain after correction using the gene image data correction method of Example 1 of the present application;
[0065] FIG13a is a diagram showing the effect of applying the UMAP algorithm (a dimensionality reduction algorithm) and the Leiden algorithm (a clustering algorithm) to the gene image data correction method of Example 1 of the present application on target cells before correction;
[0066] FIG13b is a diagram showing the effect of target cells after correction using the UMAP algorithm and the Leiden algorithm according to the gene image data correction method of Example 1 of the present application;
[0067] FIG14 is a first structural diagram of a gene image data correction system according to Example 2 of the present application;
[0068] FIG15 is a second structural diagram of the gene image data correction system according to Example 2 of the present application;
[0069] FIG16 is a schematic structural diagram of an electronic device according to Embodiment 3 of the present invention. DETAILED DESCRIPTION
[0070] The present application is further described below by way of examples, but the present application is not limited to the scope of the examples.
[0071] Example 1
[0072] During high-throughput sequencing analysis, Stereo-seq spatial transcriptome data are aligned with ssDNA (single-stranded DNA) images, and cell segmentation is performed on the ssDNA images to establish a correspondence between pixel coordinates and cells. This correspondence is then used to convert the Stereo-seq spatial transcriptome data into cellular gene expression data for downstream analysis.
[0073] However, since the cell size obtained based on ssDNA image segmentation is only the size of the cell nucleus, there is an error compared to the actual cell size. In addition, due to the small number of genes in the cells of the sequencing experiment, cell nucleus segmentation, RNA diffusion and other problems, the genes assigned to the cells are far smaller than the genes contained in the actual cells, resulting in low data accuracy in the entire sequencing process, errors in the obtained gene expression data, and low sequencing efficiency.
[0074] A Gaussian mixture model-based spatiotemporal data correction algorithm corrects RNA cell labeling results. Specifically, the spatiotemporal data correction algorithm uses a Gaussian mixture model (GMM) to calculate the probability fraction of an RNA molecule belonging to a neighboring cell. The GMM parameters are fitted based on the RNA distribution within the current cell (primarily dependent on the spatial coordinates and UMI values of ssDNA (single-stranded DNA)). The fitted GMM parameters are used to calculate the probability of RNA molecules outside the current cell boundary. As shown in Figure 1a, the probability of RNA molecules in the background is calculated (the triangles in Figure 1a represent RNA molecules in the background, and the dots in Figure 1a represent the original RNA cell labeling results). A probability threshold filtering condition is set. RNA molecules that meet the condition are classified as within the cell boundary (the quadrilaterals in Figure 1b represent RNA molecules that are reclassified as belonging to the current cell after correction). Those that do not meet the condition remain as extracellular background (the triangles in Figure 1b represent RNA molecules that remain in the background after correction). However, the spatiotemporal data correction algorithm relies on the cell morphology of the cell contour image. The algorithm is less robust and the final result is not very accurate. A large number of RNA gene molecules are not classified.
[0075] In order to overcome the above-mentioned existing defects, this embodiment provides a gene image data correction method. Referring to FIG2 , the gene image data correction method includes:
[0076] S1. Determine the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells based on the gene image.
[0077] In an optional embodiment, referring to FIG3 , step S1 includes:
[0078] S11. Obtain microscope images and gene images of biological samples.
[0079] Among them, the microscope image and gene image contain several target cells.
[0080] A microscope image can be an image obtained by photographing a biological sample using a microscope and can include the spatial information of the biological sample. A gene image can include both the spatial information and the genetic data of the biological sample. Pixels in a gene image correspond to elements of the gene matrix, and the pixel values of the pixels correspond to the values of the elements. Therefore, a gene image can include the spatial information of the same biological sample.
[0081] In this embodiment, the microscope image can be represented as an ssDNA image, but the type of microscope image is not limited and can be adjusted and selected according to actual needs. The gene image can be represented as stereo-seq spatial transcriptome data, spatial transcriptome data, or gene expression data, etc.
[0082] S12. Perform image registration on the microscope image and gene image of the biological sample.
[0083] For example, ssDNA images (i.e., microscope images) are image-aligned with Stereo-seq spatial transcriptome data (i.e., gene images) to generate cellular gene expression information.
[0084] During image registration, the microscope image is rotated and / or scaled based on its markers. A preliminary offset is calculated using the center of gravity of the microscope image and the genetic image to perform a preliminary offset. The offset of the microscope image is corrected using the markers of the two images, and image registration is performed using the offset-corrected microscope image.
[0085] S13. Perform cell segmentation on the microscope image to obtain a cell segmentation result.
[0086] That is, cell segmentation is performed on the microscope image to obtain the correspondence between the spatial position and the cells, and the cell segmentation result is generated according to the correspondence between the spatial position and the cells.
[0087] S14. Determine the gene image of the target cell based on the cell segmentation result and the image registration result of the gene image.
[0088] Based on the gene image of the target cell, the gene molecules can be divided into the gene molecules belonging to the target cell and the background gene molecules of the unknown cell.
[0089] S15. Determine the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells based on the gene image of the target cells.
[0090] S2. Obtain the coordinate information of the initial gene molecule.
[0091] The microscope image and the gene image are aligned according to the coordinates, and the coordinate information of the initial gene molecules in the cell can be obtained through the cell segmentation boundary of the microscope image.
[0092] In an optional embodiment, referring to FIG3 , step S2 includes:
[0093] S21. Determine the coordinate information of the initial gene molecule based on the gene image of the target cell.
[0094] S3. Determine the center coordinates of the target cell based on the coordinate information.
[0095] FIG4 is a rendering of the final target cell center point.
[0096] In an optional embodiment, referring to FIG5 , step S3 includes:
[0097] S31. Count the number of molecules of the initial gene molecules.
[0098] S32. Calculate the sum of the horizontal coordinate values and the sum of the vertical coordinate values of the initial gene molecules.
[0099] S33. Determine the first ratio as the horizontal coordinate value of the center point coordinate.
[0100] The first ratio is the ratio of the sum of the horizontal coordinate values of the initial gene molecules to the number of molecules;
[0101] S34. Determine the second ratio as the vertical coordinate value of the center point coordinate.
[0102] The second ratio is the ratio of the sum of the vertical coordinate values of the initial gene molecules to the number of molecules.
[0103] In an optional embodiment, referring to FIG6 , step S3 includes:
[0104] S35. Obtain the maximum abscissa value, the minimum abscissa value, the maximum ordinate value, and the minimum ordinate value in the initial gene molecule.
[0105] S36. Determine the third ratio as the horizontal coordinate value of the center point coordinate.
[0106] The third ratio is half of the difference between the maximum abscissa value and the minimum abscissa value.
[0107] S37. Determine the fourth ratio as the vertical coordinate value of the center point coordinate.
[0108] The fourth ratio is half of the difference between the maximum vertical coordinate value and the minimum vertical coordinate value.
[0109] S4. Construct a Voronoi diagram based on the center point coordinates and coordinate information.
[0110] A Voronoi diagram (also known as a Thiessen polygon or Dirichlet diagram) consists of a set of continuous polygons formed by the perpendicular bisectors of the lines connecting two adjacent points. N distinct points on a plane are partitioned according to the nearest neighbor principle; each point is associated with its nearest neighbor region.
[0111] In this embodiment, the Voronoi diagram is a two-dimensional Voronoi diagram.
[0112] In an optional embodiment, step S4 includes:
[0113] S41. Construct a polygonal network for all center point coordinates.
[0114] For example, a Delaunay (triangulation) algorithm is used to construct a triangulated network for all center points. The specific step S41 includes:
[0115] S411. Construct a super triangle that includes all center points and put them into the triangle linked list.
[0116] S412. Insert the center point of the point set in sequence, find the triangle whose circumcircle contains the insertion point in the triangle list (called the influencing triangle of the point), delete the common edges of the influencing triangles, connect the insertion point with all the vertices of the influencing triangles, and complete the insertion of a point in the Delaunay triangle list.
[0117] S413: Optimize the newly formed local triangle according to the optimization criterion and put the formed triangle into the Delaunay triangle list.
[0118] S414: Execute step S412 repeatedly until all center points are inserted.
[0119] S42. Determine the edges of the Voronoi diagram based on the polygonal network.
[0120] S43. Construct a Voronoi diagram based on the edges of the Voronoi diagram.
[0121] For example, the specific steps of step S42 and step S43 include:
[0122] S421. Analyze and organize the structure constructed by Delaunay, and establish management for each edge of the triangle and its adjacent triangles, and each vertex and its associated edge.
[0123] S422. Traverse the triangle vertices, and then traverse all edges managed by the vertex.
[0124] S423. If the edge is associated with two triangles, connect the circumcenters of the two triangles to form an edge of the primitive polygon.
[0125] S424. If the edge is associated with only one triangle (boundary triangle), take the circumcenter of the triangle as the starting point and the outward perpendicular direction of the edge as the ray direction, and draw a ray to form a closed edge with the outermost border.
[0126] S431. The traversal is completed, all Voronoi edges are found, and the Voronoi diagram is drawn based on the edges.
[0127] The two-dimensional Voronoi diagram constructed by applying the above steps in this embodiment is shown in FIG7 .
[0128] S5. Determine the cells to which the background gene molecules within the Voronoi diagram belong.
[0129] For example, based on the labels set for the background gene molecules within the Voronoi diagram, the background gene molecules within the Voronoi diagram can be determined to completely belong to the target cells, partially belong to the target cells, or not belong to the target cells at all.
[0130] In this embodiment, the coordinates of the target cell's initial gene molecules are used to determine the target cell's center coordinates. A Voronoi diagram is then constructed based on the target cell's center coordinates to identify the cells to which the background gene molecules within the Voronoi diagram belong. This improves both calibration efficiency and accuracy. Furthermore, this embodiment only requires determining the initial gene molecules within the target cell, eliminating the need for a map of the target cell's outline. This reduces the need for high-precision cell segmentation, reduces reliance on target cell morphological images, and enhances the robustness of the calibration scheme.
[0131] In an optional embodiment, referring to FIG8 , the gene image data correction method further includes:
[0132] S6. If the background gene molecules are within different Voronoi diagram ranges, the background gene molecules are input into the Gaussian mixture model to determine the probability scores of the background gene molecules belonging to the target cells within the preset distance range.
[0133] That is, a Gaussian mixture model is used to calculate the probability scores of background gene molecules belonging to several neighboring cells.
[0134] S7. Correct the background gene molecules to the gene molecules belonging to the target cells with a high probability score.
[0135] In practice, using the gene image data correction method of this embodiment, background gene molecules from unknown cells may be simultaneously attributed to multiple neighboring cells. In this embodiment, a Gaussian mixture model is used to further calculate the probability scores of background gene molecules belonging to several neighboring cells. These background gene molecules are then corrected to the gene molecules belonging to the target cell with the highest probability score, further improving the accuracy of the correction results.
[0136] In an optional embodiment, referring to FIG9 , step S5 includes:
[0137] S8. Generate target cell gene expression information based on the corrected results of the gene molecules belonging to the target cells.
[0138] The following are several sets of experimental effect diagrams to illustrate the beneficial effects of the gene image data correction method of this embodiment.
[0139] Figure 10 is a cell gene image corrected according to the Voronoi diagram. Figure 11 is a diagram showing the morphological changes of cells corrected by the gene image data correction method of this embodiment, where the black color represents the newly added gene molecules that determine the cell to which they belong.
[0140] Figures 12a and 12b show the spatial clustering of the mouse brain before correction; Figures 12c and 12d show the clustering effect after correction. The horizontal axis of Figures 12a, 12b, 12c, and 12d represents the image's position on the x-axis (abscissa axis), and the vertical axis represents the image's position on the y-axis (ordinate axis). Both axes are expressed in pixels. Comparison shows that the average number of genes per cell (Average_Cell_UMI Count) increases after correction, indicating an increase in the value of the parameter (Values).
[0141] Figure 13a shows the image before correction using the UMAP algorithm (a dimensionality reduction algorithm) and the Leiden algorithm (a clustering algorithm); Figure 13b shows the image after correction using the UMAP algorithm (a dimensionality reduction algorithm) and the Leiden algorithm (a clustering algorithm). Comparison shows that the boundaries between different classes in Figure 13b are clearer than in Figure 13a, indicating better classification results.
[0142] Example 2
[0143] This embodiment provides a gene image data correction system. Referring to FIG14 , the gene image data correction system includes:
[0144] The determination module 1 is used to determine the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells according to the gene image.
[0145] Acquisition module 2 is used to obtain the coordinate information of the initial gene molecule.
[0146] The determination module 1 is further configured to determine the center coordinates of the target cell based on the coordinate information.
[0147] Construction module 3 is used to construct a Voronoi diagram based on the center point coordinates and coordinate information.
[0148] The correction module 4 is used to determine the cells to which the background gene molecules within the Voronoi diagram belong.
[0149] In an optional embodiment, the acquisition module 2 is further configured to acquire microscope images and gene images of biological samples.
[0150] Referring to FIG15 , the gene image data correction system further includes:
[0151] The registration module 5 is used to perform image registration on the microscope image and the gene image of the biological sample.
[0152] The acquisition module 2 is further used to perform cell segmentation on the microscope image to obtain a cell segmentation result.
[0153] The determination module 1 is further used to determine the gene image of the target cell based on the cell segmentation result and the image registration result of the gene image; and is also used to determine the initial gene molecules belonging to the target cell and the background gene molecules of the unknown belonging cells based on the gene image of the target cell.
[0154] In an optional embodiment, the determination module 1 is further configured to determine the coordinate information of the initial gene molecule based on the gene image of the target cell.
[0155] In an optional embodiment, referring to FIG15 , the gene image data correction system further includes:
[0156] The statistical module 6 is used to count the number of molecules of the initial gene molecules.
[0157] The calculation module 7 is used to calculate the sum of the horizontal coordinate values and the sum of the vertical coordinate values of the initial gene molecules.
[0158] Determination module 1 is also used to determine the first ratio as the horizontal coordinate value of the center point coordinate; the first ratio is the ratio of the sum of the horizontal coordinate values of the initial gene molecules to the number of molecules; and is also used to determine the second ratio as the vertical coordinate value of the center point coordinate; the second ratio is the ratio of the sum of the vertical coordinate values of the initial gene molecules to the number of molecules.
[0159] In an optional embodiment, the acquisition module 2 is further configured to acquire the maximum abscissa value, the minimum abscissa value, the maximum ordinate value, and the minimum ordinate value in the initial gene molecule.
[0160] Determination module 1 is also used to determine the third ratio as the horizontal coordinate value of the center point coordinate; the third ratio is half of the difference between the maximum horizontal coordinate value and the minimum horizontal coordinate value; and is also used to determine the fourth ratio as the vertical coordinate value of the center point coordinate; the fourth ratio is half of the difference between the maximum vertical coordinate value and the minimum vertical coordinate value.
[0161] In an optional embodiment, the determination module 1 is further used to input the background gene molecules into the Gaussian mixture model if the background gene molecules are within different Voronoi diagram ranges, and respectively determine the probability scores of the background gene molecules belonging to the target cells within a preset distance range.
[0162] The correction module 4 is further used to correct the background gene molecules to the gene molecules belonging to the target cells with a high probability score.
[0163] In an optional embodiment, referring to FIG15 , the gene image data correction system further includes:
[0164] The generating module 8 is used to generate target cell gene expression information according to the corrected results of the gene molecules belonging to the target cells.
[0165] In an optional embodiment, the construction module 3 is further configured to construct a polygonal mesh for all center point coordinates.
[0166] The determination module 1 is further used to determine the edges of the Voronoi diagram based on the polygonal network.
[0167] Construction module 3 is further used to construct a Voronoi diagram based on the edges of the Voronoi diagram. It should be noted that the implementation principles and technical effects of each module of this embodiment can refer to the corresponding parts of the gene image data correction method of Example 1, and will not be repeated here.
[0168] Example 3
[0169] This embodiment provides an electronic device. Figure 16 is a schematic diagram of the modules of the electronic device. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, it implements the genetic image data correction method of Example 1. The electronic device 30 shown in Figure 16 is merely an example and should not limit the functionality or scope of use of the embodiments of the present invention.
[0170] As shown in FIG16 , the electronic device 30 may be a general-purpose computing device, such as a server device. Components of the electronic device 30 may include, but are not limited to, the at least one processor 31, the at least one memory 32, and a bus 33 connecting various system components (including the memory 32 and the processor 31).
[0171] The bus 33 includes a data bus, an address bus, and a control bus.
[0172] The memory 32 may include a volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322 , and may further include a read-only memory (ROM) 323 .
[0173] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.
[0174] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32 , such as the gene image data correction method of embodiment 1 of the present invention.
[0175] The electronic device 30 can also communicate with one or more external devices 34 (e.g., a keyboard, pointing device, etc.). This communication can occur via an input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 36. As shown in FIG16 , the network adapter 36 communicates with other modules of the model-generating device 30 via a bus 33. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the model-generating device 30, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.
[0176] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above may be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above may be further divided and embodied by multiple units / modules.
[0177] Example 4
[0178] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the gene image data correction method of embodiment 1 is implemented.
[0179] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.
[0180] In a possible implementation manner, the present invention may also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the gene image data correction method of embodiment 1.
[0181] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.
[0182] Although the above describes specific embodiments of the present invention, it should be understood by those skilled in the art that these are merely illustrative and that various changes or modifications may be made to these embodiments without departing from the principles and essence of the present invention. Therefore, the scope of protection of the present invention is defined by the appended claims.
Claims
1. A gene image data correction method, characterized in that: The gene image data correction method comprises: According to the gene image, the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells are determined; Obtaining coordinate information of the initial gene molecule; Determining the center point coordinates of the target cell according to the coordinate information; Constructing a Voronoi diagram according to the center point coordinates and the coordinate information; The cells to which the background gene molecules within the Voronoi diagram belong are determined.
2. The gene image data correction method according to claim 1, wherein: The step of determining the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells according to the gene image comprises: Obtain microscopic and genetic images of biological samples; Perform image registration on microscope images and gene images of biological samples; performing cell segmentation on the microscope image to obtain a cell segmentation result; Determining a gene image of a target cell based on the cell segmentation result and the image registration result of the gene image; According to the gene image of the target cell, the initial gene molecules belonging to the target cell and the background gene molecules of the unknown belonging cells are determined.
3. The gene image data correction method according to claim 1, wherein: The step of determining the center point coordinates of the target cell according to the coordinate information includes: Counting the number of molecules of the initial gene molecules; Calculating the sum of the abscissa values and the sum of the ordinate values of the initial gene molecules; Determine a first ratio as the abscissa value of the center point coordinate; the first ratio is the ratio of the sum of the abscissa values of the initial gene molecules to the number of the molecules; The second ratio is determined as the ordinate value of the center point coordinate; the second ratio is the ratio of the sum of the ordinate values of the initial gene molecules to the number of molecules.
4. The gene image data correction method according to claim 1, wherein: The step of determining the center point coordinates of the target cell according to the coordinate information includes: Obtaining the maximum abscissa value, the minimum abscissa value, the maximum ordinate value, and the minimum ordinate value in the initial gene molecule; Determine a third ratio as the abscissa value of the center point coordinate; the third ratio is half of the difference between the maximum abscissa value and the minimum abscissa value; The fourth ratio is determined as the ordinate value of the center point coordinate; the fourth ratio is half of the difference between the maximum ordinate value and the minimum ordinate value.
5. The gene image data correction method according to claim 1, wherein: The gene image data correction method further comprises: If the background gene molecules are within different Voronoi diagram ranges, the background gene molecules are input into a Gaussian mixture model to determine the probability scores of the background gene molecules belonging to target cells within a preset distance range; The background gene molecules are corrected to the gene molecules belonging to the target cells with high probability scores.
6. The gene image data correction method according to claim 1, wherein: The step of determining the cells to which the background gene molecules within the Voronoi diagram belong comprises: Generate target cell gene expression information based on the corrected results of the gene molecules belonging to the target cell.
7. The gene image data correction method according to claim 1, wherein: The step of constructing a Voronoi diagram according to the center point coordinates and the coordinate information includes: Constructing a polygonal network for all the center point coordinates; Determining edges of a Voronoi diagram based on the polygonal network; A Voronoi diagram is constructed according to the edges of the Voronoi diagram.
8. A gene image data correction system, characterized in that: The gene image data correction system comprises: A determination module is used to determine the initial gene molecules belonging to the target cells and the background gene molecules of the unknown cells based on the gene image; An acquisition module, used to obtain the coordinate information of the initial gene molecule; The determination module is further configured to determine the center coordinates of the target cell according to the coordinate information; A construction module, configured to construct a Voronoi diagram according to the center point coordinates and the coordinate information; The correction module is used to determine the cells to which the background gene molecules within the Voronoi diagram belong.
9. An electronic 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 gene image data correction method according to any one of claims 1 to 7 is implemented.
10. A computer-readable medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method for correcting genetic image data according to any one of claims 1 to 7 is implemented.