WO2024119321A1 - Procédé et appareil de traitement de segmentation de cellule, dispositif électronique - Google Patents

Procédé et appareil de traitement de segmentation de cellule, dispositif électronique Download PDF

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Publication number
WO2024119321A1
WO2024119321A1 PCT/CN2022/136657 CN2022136657W WO2024119321A1 WO 2024119321 A1 WO2024119321 A1 WO 2024119321A1 CN 2022136657 W CN2022136657 W CN 2022136657W WO 2024119321 A1 WO2024119321 A1 WO 2024119321A1
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gene expression
mask image
image
map
connected domain
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PCT/CN2022/136657
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English (en)
Chinese (zh)
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黄子睿
刘伟庆
李美
黎宇翔
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深圳华大生命科学研究院
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Priority to PCT/CN2022/136657 priority Critical patent/WO2024119321A1/fr
Publication of WO2024119321A1 publication Critical patent/WO2024119321A1/fr

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  • the present application relates to the field of data processing technology, and in particular to a method, device and electronic equipment for processing cell segmentation.
  • Cell segmentation is an important part of extracting intracellular gene expression in spatiotemporal omics technology. Segmenting cells to obtain the corresponding gene expression at the corresponding spatial position is an indispensable step in the analysis process.
  • the present application provides a cell segmentation processing method, device and electronic device, the main purpose of which is to improve the technical problems that the current existing cell segmentation methods will affect the efficiency and accuracy of cell segmentation processing and also increase the technical cost.
  • the present application provides a method for processing cell segmentation, comprising:
  • a watershed algorithm based on distance transformation is used to segment the connected domain where cell adhesion exists, so as to obtain a segmented mask image.
  • the present application provides a cell segmentation processing device, comprising:
  • an acquisition module configured to acquire a gene expression profile of a cell
  • a processing module is configured to preprocess the gene expression graph to obtain a preprocessed graph
  • the segmentation module is configured to perform binarization processing on the pre-processed image to obtain an initial mask image; based on the initial mask image, a watershed algorithm based on distance transformation is used to segment the connected domain with cell adhesion to obtain a segmented mask image.
  • the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the cell segmentation processing method described in the first aspect.
  • the present application provides an electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the cell segmentation processing method described in the first aspect when executing the computer program.
  • the present application provides a method, device and electronic device for processing cell segmentation.
  • the present application provides a solution for performing cell segmentation directly based on the gene expression map.
  • the gene expression map of the cell is first preprocessed to obtain a preprocessed map; then the preprocessed map is binarized to obtain an initial mask map; then, according to the initial mask map, a watershed algorithm based on distance transformation is used to segment the connected domains where cell adhesion exists to obtain a segmented mask map.
  • Cell segmentation does not rely on the image map, and does not require the additional introduction of technology for aligning the image map with the gene expression map, which eliminates the introduction of additional errors, while saving overall operation time and technical costs, and can improve the efficiency and accuracy of cell segmentation processing.
  • FIG1 is a schematic diagram showing a process flow of a cell segmentation processing method provided in an embodiment of the present application
  • FIG2 is a schematic diagram showing a flow chart of another cell segmentation processing method provided in an embodiment of the present application.
  • FIG3 shows a schematic diagram of an example process flow based on the method of this embodiment provided in an embodiment of the present application
  • FIG4 shows an example diagram of the effect of the gene expression graph provided in an embodiment of the present application.
  • FIG5 shows an example diagram of a sharpened image obtained through sharpening processing provided by an embodiment of the present application
  • FIG6 is a schematic diagram showing the effect of an initial mask image provided by an embodiment of the present application.
  • FIG7 is a schematic diagram showing the effect of an output mask image provided by an embodiment of the present application.
  • FIG8 shows a schematic structural diagram of a cell segmentation processing device provided in an embodiment of the present application.
  • this embodiment provides a cell segmentation processing method, as shown in FIG1 , the method includes:
  • Step 101 Obtain a gene expression map of a cell.
  • a gene expression map, or gene expression atlas, can be obtained based on gene expression data.
  • Step 102 preprocess the gene expression map of the cell to obtain a preprocessed map.
  • the gene expression map is a scatter plot, it is not convenient for cell segmentation, so preprocessing is required to process the gene expression map of the cell to obtain a preprocessed map with enhanced boundary effect.
  • Step 103 binarize the preprocessed image to obtain an initial mask image.
  • the Otsu method may be used to perform binarization processing on the preprocessed image to obtain an initial mask image.
  • Step 104 According to the initial mask image, a watershed algorithm based on distance transformation is used to segment the connected domain where the cells are adhered, so as to obtain a segmented mask image.
  • the watershed algorithm considers image segmentation based on the composition of the watershed.
  • this embodiment provides a solution for cell segmentation directly based on gene expression maps, which uses a combination of multiple image processing methods to provide more reliable cell segmentation results.
  • Cell segmentation does not rely on image maps, and does not require the introduction of additional technology to align image maps with gene expression maps, eliminating the introduction of additional errors, while saving overall operation time and technical costs, and can improve the efficiency and accuracy of cell segmentation processing.
  • this embodiment provides a specific method as shown in FIG. 2, which includes:
  • Step 201 Obtain a gene expression matrix including spatial positions.
  • a gene expression matrix including spatial positions is obtained from gene expression data of the cell.
  • the gene expression data may include gene identifiers, coordinate positions and total gene expression amounts of the corresponding coordinate positions of multiple genes.
  • Step 202 Generate a gene expression map of the cell based on the gene expression matrix.
  • step 202 may specifically include: first obtaining the coordinate position of the expressed gene in the gene expression matrix and the total gene expression amount at the corresponding coordinate position; then generating a gene expression map based on the coordinate position of the expressed gene and the total gene expression amount at the corresponding coordinate position, wherein the gene expression map is a grayscale map, and the grayscale value of the pixel point in the gene expression map is the total gene expression amount at the coordinate position corresponding to the pixel point.
  • the gene expression map of the cell can be accurately generated.
  • a gene expression matrix containing spatial positions is input, and an expression image is generated based on the coordinate positions of the expressed genes and the total gene expression amounts at the corresponding positions.
  • the specific form of the image is a grayscale image, and the grayscale value of the coordinate point is the total amount of gene expression at the coordinate.
  • step 202 may specifically include: drawing a gene expression map of the cell according to the gene expression matrix and the segmented mask map, wherein the spatial position in the gene expression matrix corresponds to the spatial position in the segmented mask map.
  • a cell-based expression map is drawn according to the gene expression matrix and the segmented mask map, and the spatial position in the gene expression matrix may correspond to the spatial position in the segmented mask map.
  • step 203 Since the generated gene expression image is a scatter plot, it is not easy to segment and needs to be processed. Specifically, the process shown in step 203 can be performed.
  • Step 203 pre-process the gene expression graph to obtain a median graph, and sharpen the median graph to obtain a sharpened graph.
  • the gene expression graph is a scatter plot, it is not convenient to perform cell segmentation, so preprocessing is required.
  • the gene expression graph of the cell is first processed into a median graph. Then, the median graph can be sharpened using a Laplacian operator to enhance the boundary effect of the median graph, thereby obtaining a sharpened graph.
  • the gene expression map is preprocessed to obtain a median map, which may specifically include: first, using a convolution kernel of a preset size (such as 13*13) to perform a convolution operation on the gene expression map so that the scattered points in the gene expression map are adhered to obtain a first convolution map; then detecting the local maximum point of the first convolution map according to the two-dimensional grayscale peak of the image; obtaining the pth percentile of the local maximum point, wherein p is a preset value, such as the pth percentile may be a 98% percentile value, or a 99% percentile value, etc.; if the pth percentile is within a preset range, using a first median filter to perform median filtering on the first convolution map to obtain a median map, wherein the filter size of the first median filter is determined according to the preset size.
  • a convolution kernel of a preset size such as 13*13
  • the method of this embodiment may also include: if the pth percentile is outside the preset range, determining the new size of the convolution kernel based on the pth percentile and the preset size; performing a convolution operation on the gene expression graph using the convolution kernel of the new size, so that the scattered points in the gene expression graph are adhered to obtain a second convolution graph; performing a median filtering on the second convolution graph using a second median filter to obtain a median graph, wherein the filter size of the second median filter is determined based on the new size.
  • a convolution kernel of size 13*13 (empirical value) to perform a convolution operation on the original image of the gene expression map to make the scatter plot stick together to obtain the first convolution map; then detect the local maximum point of the first convolution map according to the two-dimensional grayscale peak of the image, and take out the 99% quantile value R of all the local maximum points. If the R value is too different from a set empirical threshold (too high or too low, it will affect subsequent processing), it is considered that the 13*13 convolution kernel is not suitable for the original image.
  • the median filter used above is relatively large, and the grayscale boundary of the median image may be relatively blurred.
  • the laplacian operator is used to sharpen the median image to enhance the grayscale boundary, and a sharpened image is obtained to complete the preprocessing.
  • the cell segmentation process is performed, and specifically, the process shown in steps 204 to 207 can be executed.
  • Step 204 binarize the sharpened image to obtain an initial mask image.
  • the sharpened image obtained in the previous step is binarized using the Otsu method to obtain the initial mask image.
  • Step 205 Filter the connected domains in the initial mask image whose areas do not meet the preset conditions to obtain a filtered mask image.
  • step 205 may specifically include: filtering the connected domains in the initial mask image whose area is greater than a first preset threshold or the connected domains whose area is less than a second preset threshold to obtain a filtered mask image, wherein the first preset threshold is greater than the second preset threshold.
  • a first preset threshold For example, an empirical threshold is used to filter the connected domains in the initial mask whose area is too small or too large to obtain a filtered mask image.
  • Step 206 traverse each connected domain in the filter mask image, extract the area where the connected domain is located based on the minimum circumscribed rectangle of the connected domain, and use the watershed algorithm to segment the connected domain with cell adhesion to obtain a segmented mask image.
  • step 206 may specifically include: setting the grayscale value of each pixel in each connected domain in the filter mask image to a first value, and setting the grayscale value of each pixel outside the connected domain to a second value; for each target pixel in the connected domain whose grayscale value is the first value, remapping the grayscale value of the target pixel to the distance from the target pixel to the nearest pixel whose grayscale value is the second value, to obtain a distance map of the connected domain; binarizing the distance map of the connected domain to obtain a preset number of pixels in the connected domain that are farthest from the pixel whose grayscale value is the second value; using the preset number of pixels as injection points of the watershed algorithm, and using the watershed function to perform watershed segmentation on the original mask of the connected domain in the filter mask image to obtain a segmented target connected domain, and covering the target connected domain with the filter mask image to obtain a segmented mask image.
  • each connected domain in the filter mask image For example, traverse each connected domain in the filter mask image, extract the area where the connected domain is located based on the minimum circumscribed rectangle of the connected domain, and use the watershed algorithm based on distance transformation to further segment the connected domain where cell adhesion may exist.
  • the specific steps are as follows:
  • Step a For each connected domain area extracted, the grayscale value of the points in the target connected domain is set to 1, and the grayscale value of the points outside the connected domain is set to 0 (including background and non-target connected domains). A distance transformation is performed on each point with a value of 1, and its grayscale value is remapped to the distance from the point to the nearest point with a value of 0 (the distance between adjacent points is 1), thereby obtaining a distance map of the connected domain.
  • Step b Use an empirical threshold to binarize the distance map to obtain the farthest points from the point with a distance value of 0 in the connected domain (since each connected domain is different, the number of points is not fixed).
  • Step c Use the several points obtained in the previous step as water injection points of the watershed algorithm, use the watershed function in OpenCV to perform watershed segmentation on the original mask of the connected domain, obtain the segmented target connected domain, and overlay the result onto the filter mask image.
  • Step d perform the above steps on each connected domain traversed to obtain a segmented mask image.
  • Step 207 Perform a closure operation on the segmented mask image to obtain a mask image of the cell segmentation result.
  • the segmented mask is closed to obtain the final mask map. Finally, the final mask map can be output and saved for subsequent biological analysis at the cell level in combination with the original expression matrix.
  • FIG3 it is a schematic diagram of an example flow chart based on the method of this embodiment.
  • a gene expression matrix can be input, and a gene expression image can be generated based on the matrix, as shown in FIG4.
  • a 13*13 convolution kernel is used to perform a convolution operation on the gene expression map, so that the scattered points in the gene expression map are adhered to obtain a first convolution map.
  • a threshold that is, the local maximum point of the first convolution map is detected according to the two-dimensional grayscale peak of the image, and the 99% quantile value R of all the local maximum points is taken out.
  • the 13*13 convolution kernel is not suitable for the original image, and a new convolution kernel size is calculated using a ratio, and then the new convolution kernel is used to process the original image to obtain a second convolution map, and the median filter size is calculated using this ratio, and then the second convolution map is processed with a median filter of this size to obtain a median map. If the R value is within the allowable range, the first convolution map is used, and the first convolution map is processed with a median filter of size 35 to obtain a median map.
  • the laplacian operator is used to sharpen the median image to obtain a sharpened image, as shown in Figure 5.
  • the sharpened image is binarized using the large law method to obtain the initial mask image, as shown in Figure 6, and then the adhesion cells are segmented by area filtering and watershed algorithm. Finally, the cell mask image is output and saved, as shown in Figure 7.
  • this embodiment provides a solution for cell segmentation directly based on gene expression maps, which uses a combination of multiple image processing methods to provide more reliable cell segmentation results.
  • Cell segmentation does not rely on image maps, and does not require the introduction of additional technology to align image maps with gene expression maps, eliminating the introduction of additional errors, while saving overall operation time and technical costs, and can improve the efficiency and accuracy of cell segmentation processing.
  • this embodiment provides a cell segmentation processing device, as shown in FIG. 8 , the device includes: an acquisition module 31 , a processing module 32 , and a segmentation module 33 .
  • An acquisition module 31 is configured to acquire a gene expression profile of a cell
  • a processing module 32 is configured to preprocess the gene expression graph to obtain a preprocessed graph
  • the segmentation module 33 is configured to perform binarization processing on the pre-processed image to obtain an initial mask image; based on the initial mask image, use a watershed algorithm based on distance transformation to segment the connected domain with cell adhesion to obtain a segmented mask image.
  • the segmentation module 33 is specifically configured to filter out the connected domains in the initial mask image whose areas do not meet the preset conditions to obtain a filtered mask image; traverse each connected domain in the filtered mask image, extract the area where the connected domain is located based on the minimum circumscribed rectangle of the connected domain, and use the watershed algorithm to segment the connected domain with cell adhesion to obtain a segmented mask image.
  • the segmentation module 33 is further configured to set the grayscale value of each pixel in each connected domain in the filter mask image to a first value, and set the grayscale value of each pixel outside the connected domain to a second value; for each target pixel in the connected domain whose grayscale value is the first value, remap the grayscale value of the target pixel to the distance from the target pixel to the pixel whose grayscale value is the second value closest to it, to obtain a distance map of the connected domain; binarize the distance map of the connected domain to obtain a preset number of pixels in the connected domain that are farthest from the pixel whose grayscale value is the second value; use the preset number of pixels as injection points of the watershed algorithm, and use the watershed function to perform watershed segmentation on the original mask of the connected domain in the filter mask image to obtain a segmented target connected domain, and cover the target connected domain with the filter mask image to obtain a segmented mask image.
  • the segmentation module 33 is further configured to filter the connected domains in the initial mask image whose area is greater than a first preset threshold, or the connected domains whose area is less than a second preset threshold, to obtain the filtered mask image, wherein the first preset threshold is greater than the second preset threshold.
  • the processing module 32 is specifically configured to pre-process the gene expression graph to obtain a median graph; and perform sharpening processing on the median graph to obtain a sharpened graph.
  • the segmentation module 33 is specifically configured to perform binarization processing on the sharpening image to obtain an initial mask image.
  • the processing module 32 is further configured to perform a convolution operation on the gene expression map using a convolution kernel of a preset size, so that the scattered points in the gene expression map are adhered to obtain a first convolution map; detect the local maximum point of the first convolution map according to the two-dimensional grayscale peak of the image; obtain the pth percentile of the local maximum point, wherein p is a preset value; if the pth percentile is within a preset range, perform median filtering on the first convolution map using a first median filter to obtain the median map, wherein the filter size of the first median filter is determined according to the preset size.
  • the processing module 32 is further configured to, after obtaining the pth percentile in the local maximum point, if the pth percentile is outside a preset range, determine a new size of the convolution kernel according to the pth percentile and the preset size; perform a convolution operation on the gene expression map using the convolution kernel of the new size so that the scattered points in the gene expression map are adhered to obtain a second convolution map; perform a median filtering on the second convolution map using a second median filter to obtain the median map, wherein the filter size of the second median filter is determined based on the new size.
  • the acquisition module 31 is specifically configured to acquire a gene expression matrix including spatial positions; and generate the gene expression graph based on the gene expression matrix.
  • the acquisition module 31 is specifically configured to obtain the coordinate position of the expressed gene in the gene expression matrix and the total gene expression amount of the corresponding coordinate position; based on the coordinate position of the expressed gene and the total gene expression amount of the corresponding coordinate position, generate the gene expression map, wherein the gene expression map is a grayscale map, and the grayscale value of the pixel point in the gene expression map is the total gene expression amount of the coordinate position corresponding to the pixel point.
  • the acquisition module 31 is further configured to draw a gene expression map of the cell based on the gene expression matrix and the segmented mask map, wherein the spatial position in the gene expression matrix corresponds to the spatial position in the segmented mask map.
  • the segmentation module 33 is further configured to perform a closure operation on the segmented mask image to obtain a mask image of a cell segmentation result.
  • this embodiment further provides a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the above method as shown in FIG. 1 and FIG. 2 is implemented.
  • the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.
  • a non-volatile storage medium which can be a CD-ROM, USB flash drive, mobile hard disk, etc.
  • a computer device which can be a personal computer, server, or network device, etc.
  • the embodiment of the present application also provides an electronic device, which can be a personal computer, a laptop computer, etc., and the device includes a storage medium and a processor; the storage medium is used to store computer programs; the processor is used to execute the computer program to implement the above method shown in Figures 1 and 2.
  • the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc.
  • the user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc.
  • the network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.
  • the storage medium may also include an operating system and a network communication module.
  • the operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and/or programs.
  • the network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.
  • this embodiment provides a solution for cell segmentation directly based on gene expression maps, using a combination of multiple image processing methods to provide more reliable cell segmentation results.
  • Cell segmentation does not rely on image maps, and does not require the additional introduction of technology for aligning image maps with gene expression maps, eliminating the introduction of additional errors, while saving overall operation time and technical costs, and can improve the efficiency and accuracy of cell segmentation processing.

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Abstract

La présente demande se rapporte au domaine technique du traitement de données et concerne un procédé et un appareil de traitement de segmentation de cellule ainsi qu'un dispositif électronique. Le procédé consiste en : premièrement, le prétraitement d'une carte d'expression génique d'une cellule afin d'obtenir une carte prétraitée ; ensuite, la réalisation d'un traitement de binarisation sur la carte prétraitée pour obtenir un dessin de masque initial ; puis, selon le dessin de masque initial et à l'aide d'un algorithme de bassins versants sur la base d'une transformation de distance, la segmentation d'un domaine connecté où il y a une adhérence cellulaire pour obtenir un dessin de masque segmenté. En utilisant la solution technique de la présente demande, de multiples procédés de traitement d'image sont combinés, de sorte qu'un résultat de segmentation de cellule relativement fiable peut être obtenu. La segmentation cellulaire ne repose pas sur une carte d'image, et l'introduction supplémentaire d'une technique pour aligner une carte d'image avec une carte d'expression génique n'est pas requise, ce qui permet d'éliminer des erreurs supplémentaires introduites, et également d'économiser le temps de fonctionnement global et le coût technique.
PCT/CN2022/136657 2022-12-05 2022-12-05 Procédé et appareil de traitement de segmentation de cellule, dispositif électronique WO2024119321A1 (fr)

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