Gene image data correction method and system, electronic equipment and storage medium
Patent Information
- Application Number
- CN202280102395.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-05
- Publication Date
- 2025-08-12
AI Technical Summary
The correction algorithm of RNA cell labeling in the existing technology relies on cell contour images, and its robustness is weak, resulting in inaccurate classification of gene molecules. Many RNA gene molecules are not correctly classified, affecting the accuracy of RNA cell labeling.
By determining the difference between the gene molecule density of the target cell and the background gene molecule density in the gene image, the cells to which the background gene molecules belong are adjusted, and the convex hull algorithm and image registration technology are used to improve the accuracy of gene molecule classification.
It significantly improves the efficiency and accuracy of gene molecule classification, ensures the correct attribution of gene molecules in target cells, and enhances the accuracy of RNA cell labeling results.
Smart Images
Figure CN120476421A_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 within living cells) assigns genes to cells based on aligned cell outline images and gene expression data corresponding to the cells. However, due to issues such as the small number of genes within 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 actually contained in the 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 spatial transcriptome sequencing technology such as Stereo-seq (Spatio-Temporal Enhanced REsolution Omics-sequencing) and is the basis for other downstream analyses. 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 very 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 issue.
[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] Determining the initial number of gene molecules belonging to the target cell in the gene image and the initial contour area of the target region occupied by the gene molecules belonging to the target cell;
[0008] Determining the gene molecule density of the target cell according to the number of molecules and the initial contour area;
[0009] Determine the number of background gene molecules within a preset region of the target region and the area of the preset region;
[0010] Determining the background gene molecule density according to the number of background gene molecules and the area of the region;
[0011] The cell to which the background gene molecule belongs is determined according to the gene molecule density and the background gene molecule density.
[0012] Preferably, the step of determining the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density comprises:
[0013] If the difference between the gene molecule density and the background gene molecule density is within a preset range, the background gene molecules are corrected to the gene molecules belonging to the target cell.
[0014] Preferably, the step of determining the background gene molecule density based on the background gene molecules and the preset area includes:
[0015] Determine multiple preset area ranges of different sizes;
[0016] respectively determining the background gene molecule density corresponding to each preset region;
[0017] The step of correcting the background gene molecules to the gene molecules belonging to the target cells comprises:
[0018] The background gene molecules within a preset region with the smallest difference in density from the gene molecules of the target cell are corrected to the gene molecules belonging to the target cell.
[0019] Preferably, the step of determining the initial number of gene molecules belonging to the target cell in the gene image comprises:
[0020] Obtain microscopic and genetic images of biological samples;
[0021] Perform image registration on microscope images and gene images of biological samples;
[0022] performing cell segmentation on the microscope image to obtain a cell segmentation result;
[0023] Determining a gene image of a target cell based on the cell segmentation result and the image registration result of the gene image;
[0024] According to the gene image of the target cell, the initial molecular quantity of the gene molecules belonging to the target cell is determined.
[0025] Preferably, the step of determining the initial contour area of the target region occupied by the gene molecule belonging to the target cell comprises:
[0026] The convex hull algorithm is used to calculate the cell segmentation result to determine the initial contour area of the target region occupied by the gene molecules belonging to the target cells.
[0027] Preferably, the step of determining the gene molecule density of the target cell based on the number of molecules and the initial contour area comprises:
[0028] The ratio of the number of molecules to the initial contour area is determined as the gene molecule density of the target cell.
[0029] Preferably, the step of determining the density of background gene molecules according to the number of background gene molecules and the area of the region comprises:
[0030] The ratio of the number of background gene molecules to the area of the region is determined as the background gene molecule density.
[0031] Preferably, the step of determining the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density comprises:
[0032] Generate target cell gene expression information based on the corrected results of the gene molecules belonging to the target cell.
[0033] In a second aspect, the present application provides a gene image data correction system, the gene image data correction system comprising:
[0034] a determination module for determining an initial number of gene molecules belonging to a target cell in a gene image and an initial contour area of a target region occupied by the gene molecules belonging to the target cell; further for determining a gene molecule density of the target cell based on the number of molecules and the initial contour area; further for determining a number of background gene molecules within a preset region of the target region and an area of the preset region; further for determining a background gene molecule density based on the number of background gene molecules and the area;
[0035] A correction module is used to determine the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density.
[0036] 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.
[0037] 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.
[0038] The positive progress of this application is:
[0039] Because in practice gene molecules are evenly distributed within the actual outline of the target cell, the gene molecule density within the initial outline of the target cell should be equivalent to the density of the background gene molecules outside the initial outline of the target cell and within the actual outline of the target cell. The present application utilizes the above-mentioned cell characteristics to determine the gene molecule density of the target cell based on the initial number of gene molecules belonging to the target cell in the gene image and the area of the initial outline of the target cell. By comparing the gene molecule density of the target cell with the background gene molecule density, the background gene molecules whose difference between the two is within a preset range are classified into the gene molecules of the target cell, which greatly improves the efficiency of correcting the gene molecules in the target cell, and also increases the accuracy of correcting the gene molecules in the target cell. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Figure 1a is a distribution diagram of RNA gene molecules before correction using the spatiotemporal data correction algorithm;
[0041] Figure 1b is a distribution diagram of RNA gene molecules after correction using the spatiotemporal data correction algorithm;
[0042] FIG2 is a first flow chart of the gene image data correction method according to Example 1 of the present application;
[0043] FIG3 is a second flow chart of the gene image data correction method of Example 1 of the present application;
[0044] 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;
[0045] FIG5 is a third flow chart of the gene image data correction method of Example 1 of the present application;
[0046] FIG6 is a fourth flow chart of the gene image data correction method of Example 1 of the present application;
[0047] FIG7 a 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;
[0048] FIG7 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;
[0049] FIG7 c is a diagram showing the clustering effect of the third space of the mouse brain before correction of the gene image data correction method of Example 1 of the present application;
[0050] FIG7 d 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;
[0051] FIG7e 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;
[0052] FIG7 f is a diagram showing the third space clustering effect of the mouse brain after correction using the gene image data correction method of Example 1 of the present application;
[0053] FIG8 a 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;
[0054] FIG8 b is a diagram showing the target cell effect 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;
[0055] FIG9 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;
[0056] FIG10 is a first structural diagram of a gene image data correction system according to Example 2 of the present application;
[0057] FIG11 is a second structural diagram of the gene image data correction system according to Example 2 of the present application;
[0058] FIG12 is a schematic structural diagram of an electronic device according to Embodiment 3 of the present invention. DETAILED DESCRIPTION
[0059] The present application is further described below by way of examples, but the present application is not limited to the scope of the examples.
[0060] Example 1
[0061] 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. A correspondence is established 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.
[0062] 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.
[0063] 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.
[0064] 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:
[0065] S1. Determine the initial number of gene molecules belonging to target cells in the gene image and the initial contour area of the target region occupied by the gene molecules belonging to the target cells.
[0066] In an optional embodiment, referring to FIG3 , determining the initial number of gene molecules belonging to target cells in the gene image mainly includes:
[0067] S11. Obtain microscope images and gene images of biological samples.
[0068] Among them, the microscope image and gene image contain several target cells.
[0069] 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.
[0070] 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.
[0071] S12. Perform image registration on the microscope image and gene image of the biological sample.
[0072] 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.
[0073] 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.
[0074] S13. Perform cell segmentation on the microscope image to obtain a cell segmentation result.
[0075] 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.
[0076] S14. Determine the gene image of the target cell based on the cell segmentation result and the image registration result of the gene image.
[0077] 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.
[0078] S15. Determine the initial molecular quantity of the gene molecules belonging to the target cell according to the gene image of the target cell.
[0079] In an optional embodiment, referring to FIG3 , the step of determining the initial contour area of the target region occupied by the gene molecules belonging to the target cells includes:
[0080] S16. Using a convex hull algorithm, the cell segmentation result is calculated to determine the initial contour area of the target region occupied by the gene molecules belonging to the target cell.
[0081] Among them, the convex hull algorithm (Convex Hull Algorithm) is to find the convex hull of N points in the target area according to the cell segmentation results. The area surrounded by the connection between these N points is the initial contour area.
[0082] S2. Determine the gene molecule density of the target cell based on the number of molecules and the initial contour area.
[0083] In an alternative embodiment, the ratio of the number of molecules to the initial contour area is determined as the gene molecule density of the target cell.
[0084] S3. Determine the number of background gene molecules within the preset region of the target region and the area of the preset region.
[0085] Background gene molecules are gene molecules whose cell of origin cannot be determined based on the gene image. The preset region is outside the target region but cannot exceed the area occupied by gene molecules belonging to neighboring cells. The preset region can be 5 pixels by 5 pixels.
[0086] In an optional embodiment, the preset area range can be an area determined based on the center point of the target cell. The center point of the target cell is determined by the gene image. The schematic diagram of the center point of the target cell is shown in FIG4.
[0087] S4. Determine the density of background gene molecules according to the number of background gene molecules and the area of the region.
[0088] In an optional embodiment, the ratio of the number of background gene molecules to the area of the region is determined as the background gene molecule density.
[0089] S5. Determine the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density.
[0090] In this embodiment, the aforementioned cell characteristics are utilized to determine the target cell's gene density based on the initial number of target cell genes and the target cell's initial outline area in the gene image. By comparing the target cell's gene density with the background gene density, the cell to which the background gene molecules belong is determined based on the gene density and background gene density. This significantly improves the efficiency and accuracy of correcting target cell genes.
[0091] In an optional embodiment, step S5 includes:
[0092] S51. If the difference between the gene molecule density and the background gene molecule density is within a preset range, the background gene molecules are corrected to the gene molecules belonging to the target cells.
[0093] If the difference between the gene molecule density and the background gene molecule density is not within a preset range, it is determined that the background gene molecule does not belong to the gene molecule of the target cell.
[0094] Because gene molecules are evenly distributed within the actual outline of the target cell in practice, the density of gene molecules within the initial outline of the target cell should be comparable to the density of background gene molecules outside the initial outline of the target cell and within the actual outline of the target cell.
[0095] In this embodiment, the aforementioned cell characteristics are utilized to determine the target cell's gene density based on the initial number of target cell genes in the gene image and the target cell's initial outline area. By comparing the target cell's gene density with the background gene density, background gene molecules whose difference between the two falls within a preset range are classified as target cell genes. This significantly improves the efficiency and accuracy of correcting target cell genes.
[0096] In an optional embodiment, referring to FIG5 , step S4 includes:
[0097] S41: Determine a plurality of preset area ranges of different sizes.
[0098] S42. Determine the background gene molecule density corresponding to each preset area range.
[0099] Referring to FIG5 , step S5 includes:
[0100] S52, correcting the background gene molecules within the preset region with the smallest difference in density from the gene molecules of the target cell to the gene molecules belonging to the target cell.
[0101] In this embodiment, the background gene molecules within the preset region with the smallest difference in density from the gene molecules of the target cells are corrected to the gene molecules belonging to the target cells, further improving the accuracy of correcting the gene molecules in the target cells.
[0102] In an optional embodiment, referring to FIG6 , step S5 includes:
[0103] S6. Generate target cell gene expression information based on the corrected results of the gene molecules belonging to the target cells.
[0104] The following are several sets of experimental effect diagrams to illustrate the beneficial effects of the gene image data correction method of this embodiment.
[0105] Figures 7a, 7b, and 7c show the spatial clustering of the mouse brain before correction; Figures 7d, 7e, and 7f show the clustering after correction. Comparison shows that the corrected spatial locations of cell clusters are clearer, with fewer noise points within clusters, and an increase in the average number of genes per cell (Average_Cell_UMI Count), indicating an increase in the value of the parameter (Params). The units of the horizontal and vertical axes in Figures 7a, 7b, 7c, 7d, 7e, and 7f are all pixels.
[0106] Figure 8a shows the image before correction using the UMAP algorithm (a dimensionality reduction algorithm) and the Leiden algorithm (a clustering algorithm); Figure 8b shows the image after correction using the UMAP algorithm (a dimensionality reduction algorithm) and the Leiden algorithm (a clustering algorithm). Compared to Figure 8a, the boundaries between the different classes in Figure 8b are clearer, indicating better classification results.
[0107] FIG9 is a diagram showing the morphological changes of several target cells after correction, where the black color represents the newly added gene molecules belonging to the target cells.
[0108] Example 2
[0109] This embodiment provides a gene image data correction system. Referring to FIG10 , the gene image data correction system includes:
[0110] Determination module 1 is used to determine the initial number of gene molecules belonging to the target cell in the gene image and the initial outline area of the target region occupied by the gene molecules belonging to the target cell; further used to determine the gene molecule density of the target cell based on the number of molecules and the initial outline area; further used to determine the number of background gene molecules within a preset region of the target region and the area of the preset region; further used to determine the background gene molecule density based on the number of background gene molecules and the area;
[0111] Correction module 2 is used to determine the cells to which the background gene molecules belong based on the gene molecule density and the background gene molecule density.
[0112] In an optional embodiment, correction module 2 is further configured to correct the background gene molecules to the gene molecules belonging to the target cell if the difference between the gene molecule density and the background gene molecule density is within a preset range. In an optional embodiment, determination module 1 is further configured to determine a plurality of preset regions of different sizes and to determine the background gene molecule density corresponding to each preset region.
[0113] The correction module 2 is further configured to correct the background gene molecules within a preset region having the smallest difference in density from the gene molecules of the target cells to the gene molecules belonging to the target cells.
[0114] In an optional embodiment, referring to FIG11 , the gene image data correction system further includes:
[0115] The acquisition module 3 is used to acquire microscope images and gene images of biological samples.
[0116] The registration module 4 is used to perform image registration on the microscope image and the gene image of the biological sample.
[0117] The acquisition module 3 is further used to perform cell segmentation on the microscope image to obtain a cell segmentation result.
[0118] 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 further used to determine the initial molecular number of the gene molecules belonging to the target cell based on the gene image of the target cell.
[0119] In an optional embodiment, the determination module 1 is further configured to use a convex hull algorithm to calculate the cell segmentation result to determine the initial contour area of the target region occupied by the gene molecules belonging to the target cell.
[0120] In an optional embodiment, the determination module 1 is further configured to determine the ratio of the number of molecules to the initial contour area as the gene molecule density of the target cell.
[0121] In an optional embodiment, the determination module 1 is further configured to determine the ratio of the number of background gene molecules to the area of the region as the background gene molecule density.
[0122] In an optional embodiment, referring to FIG11 , the gene image data correction system further includes:
[0123] The generating module 5 is used to generate target cell gene expression information according to the corrected results of the gene molecules belonging to the target cells.
[0124] It should be noted that the implementation principles and technical effects of each module in this embodiment can be found in the corresponding parts of Example 1 and will not be repeated here.
[0125] Example 3
[0126] This embodiment provides an electronic device. Figure 12 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 12 is merely an example and should not limit the functionality or scope of use of the embodiments of the present invention.
[0127] As shown in FIG12 , 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).
[0128] The bus 33 includes a data bus, an address bus, and a control bus.
[0129] 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 .
[0130] 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.
[0131] 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.
[0132] 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 FIG12 , 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.
[0133] 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.
[0134] Example 4
[0135] 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.
[0136] 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.
[0137] 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.
[0138] 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.
[0139] 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: Determining the initial number of gene molecules belonging to the target cell in the gene image and the initial contour area of the target region occupied by the gene molecules belonging to the target cell; determining the gene molecule density of the target cell according to the number of molecules and the initial contour area; Determine the number of background gene molecules within a preset region of the target region and the area of the preset region; Determining the background gene molecule density according to the number of background gene molecules and the area of the region; The cell to which the background gene molecule belongs is determined according to the gene molecule density and the background gene molecule density.
2. The gene image data correction method according to claim 1, wherein: The step of determining the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density comprises: If the difference between the gene molecule density and the background gene molecule density is within a preset range, the background gene molecules are corrected to the gene molecules belonging to the target cell.
3. The gene image data correction method according to claim 2, wherein: The step of determining the background gene molecule density according to the background gene molecules and the preset area includes: Determine multiple preset area ranges of different sizes; respectively determining the background gene molecule density corresponding to each preset region; The step of correcting the background gene molecules to the gene molecules belonging to the target cells comprises: The background gene molecules within a preset region with the smallest difference in density from the gene molecules of the target cell are corrected to the gene molecules belonging to the target cell.
4. The gene image data correction method according to claim 1, wherein: The steps of determining the initial number of gene molecules belonging to the target cell in the gene image include: 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 molecular quantity of the gene molecules belonging to the target cell is determined.
5. The gene image data correction method according to claim 4, wherein: The step of determining the initial contour area of the target region occupied by the gene molecule belonging to the target cell comprises: The convex hull algorithm is used to calculate the cell segmentation result to determine the initial contour area of the target region occupied by the gene molecules belonging to the target cells.
6. The gene image data correction method according to claim 1, wherein: The step of determining the gene molecule density of the target cell according to the number of molecules and the initial contour area comprises: The ratio of the number of molecules to the initial contour area is determined as the gene molecule density of the target cell.
7. The gene image data correction method according to claim 1, wherein: The step of determining the density of background gene molecules according to the number of background gene molecules and the area of the region comprises: The ratio of the number of background gene molecules to the area of the region is determined as the background gene molecule density.
8. The gene image data correction method according to claim 1, wherein: The step of determining the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density includes: Generate target cell gene expression information based on the corrected results of the gene molecules belonging to the target cell.
9. A gene image data correction system, characterized in that: The gene image data correction system includes: a determination module for determining an initial number of gene molecules belonging to a target cell in a gene image and an initial contour area of a target region occupied by the gene molecules belonging to the target cell; further for determining a gene molecule density of the target cell based on the number of molecules and the initial contour area; further for determining a number of background gene molecules within a preset region of the target region and an area of the preset region; further for determining a background gene molecule density based on the number of background gene molecules and the area; A correction module is used to determine the cell to which the background gene molecule belongs based on the gene molecule density and the background gene molecule density.
10. 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 8 is implemented.
11. 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 8 is implemented.