WO2024119321A1 - 细胞分割的处理方法、装置及电子设备 - Google Patents

细胞分割的处理方法、装置及电子设备 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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French (fr)
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黄子睿
刘伟庆
李美
黎宇翔
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BGI Shenzhen Co Ltd
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BGI Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs

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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

本申请涉及一种细胞分割的处理方法、装置及电子设备，涉及数据处理技术领域，其中方法包括：首先对细胞的基因表达图进行预处理，得到预处理图；再对所述预处理图进行二值化处理，得到初始掩模图；然后根据初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。通过应用本申请的技术方案，使用多种图像处理方法相结合，可提供较为可靠的细胞分割结果。细胞分割不依赖影像图，不需要额外引入将影像图与基因表达图进行配准的技术，排除了引入的额外误差，同时节省了整体操作时间和技术成本。

Description

细胞分割的处理方法、装置及电子设备 技术领域
本申请涉及数据处理技术领域，具体涉及一种细胞分割的处理方法、装置及电子设备。
背景技术
细胞分割是时空组学技术中提取细胞内基因表达的重要一环，通过分割细胞以获取对应空间位置上相应的基因表达是分析过程中不可或缺的一步。
目前，现有的细胞分割方式，通常借助相应生物组织切片的显微镜拍照影像或扫描影像进行细胞分割，辅以影像与基因表达图谱的配准以进行后续的基因提取分析。
然而，现有的细胞分割方式，对影像图质量要求较高，且为了后续分析，需要将影像图与基因表达图谱进行配准，引入了额外的技术需要和误差可能，进而会影响细胞分割处理的效率和准确性，而且还会增加技术成本。
发明内容
有鉴于此，本申请提供了一种细胞分割的处理方法、装置及电子设备，主要目的在于改善目前现有的细胞分割方式会影响细胞分割处理的效率和准确性，而且还会增加技术成本的技术问题。
第一方面，本申请提供了一种细胞分割的处理方法，包括：
获取细胞的基因表达图；
对所述基因表达图进行预处理，得到预处理图；
将所述预处理图进行二值化处理，得到初始掩模图；
根据所述初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
第二方面，本申请提供了一种细胞分割的处理装置，包括：
获取模块，被配置为获取细胞的基因表达图；
处理模块，被配置为对所述基因表达图进行预处理，得到预处理图；
分割模块，被配置为对所述预处理图进行二值化处理，得到初始掩模图；根据所述初始掩模图，使用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
第三方面，本申请提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现第一方面所述的细胞分割的处理方法。
第四方面，本申请提供了一种电子设备，包括存储介质、处理器及存储在存储介质上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现第一方面所述的细胞分割的处理方法。
借由上述技术方案，本申请提供的一种细胞分割的处理方法、装置及电子设备，与目前现有的细胞分割方式相比，本申请提供一种直接基于基因表达图进行细胞分割的方案，具体首先对细胞的基因表达图进行预处理，得到预处理图；再对所述预处理图进行二值化处理，得到初始掩模图；然后根据初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。通过应用本申请的技术方案，使用多种图像处理方法相结合，可提供较为可靠的细胞分割结果。细胞分割不依赖影像图，不需要额外引入将影像图与基因表达图进行配准的技术，排除了引入的额外误差，同时节省了整体操作时间和技术成本，可提高细胞分割处理的效率和准确性。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举本申请的具体实施方式。
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此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本申请的实施例，并与说明书一起用于解释本申请的原理。
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1示出了本申请实施例提供的一种细胞分割的处理方法的流程示意图；
图2示出了本申请实施例提供的另一种细胞分割的处理方法的流程示意图；
图3示出了本申请实施例提供的基于本实施例方法的示例流程示意图；
图4示出了本申请实施例提供的基因表达图的效果示例图；
图5示出了本申请实施例提供的经锐化处理得到锐化图的效果示例图；
图6示出了本申请实施例提供的初始掩模图的效果示意图；
图7示出了本申请实施例提供的输出掩模图的效果示意图；
图8示出了本申请实施例提供的一种细胞分割的处理装置的结构示意图。
具体实施方式
为了能够更清楚地理解本申请的上述目的、特征和优点，下面将对本申请的方案进行进一步描述。需要说明的是，在不冲突的情况下，本申请的实施例及实施例中的特征可以相互组合。
为了改善目前现有的细胞分割方式会影响细胞分割处理的效率和准确性，而且还会增加技术成本的技术问题。本实施例提供了一种细胞分割的处理方法，如图1所示，该方法包括：
步骤101、获取细胞的基因表达图。
基因表达图，或者称为基因表达图谱，具体可根据基因表达数据得到。
步骤102、对细胞的基因表达图进行预处理，得到预处理图。
由于基因表达图为散点图，不便于进行细胞分割，因此需要进行预处理，将细胞的基因表达图处理得到边界效果增强的预处理图。
步骤103、对预处理图进行二值化处理，得到初始掩模图。
本实施例可使用大津法对预处理图进行二值化处理，得到初始掩模图。
步骤104、根据初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
分水岭算法(Watershed Algorithm)，是根据分水岭的构成来考虑图像的分割。
与目前现有的细胞分割方式相比，本实施例提供一种直接基于基因表达图进行细胞分割的方案，使用多种图像处理方法相结合，可提供较为可靠的细胞分割结果。细胞分割不依赖影像图，不需要额外引入将影像图与基因表达图进行配准的技术，排除了引入的额外误差，同时节省了整体操作时间和技术成本，可提高细胞分割处理的效率和准确性。
进一步的，作为上述实施例的细化和扩展，为了完整说明本实施例方法的具体实现过程，本实施例提供了如图2所示的具体方法，该方法包括：
步骤201、获取包含空间位置的基因表达矩阵。
从细胞的基因表达数据中获取包含空间位置的基因表达矩阵。基因表达数据可以包括多个基因的基因标识符、坐标位置及相应坐标位置的总基因表达量。
步骤202、基于基因表达矩阵，生成细胞的基因表达图。
作为一种可选方式，步骤202具体可包括：首先获取基因表达矩阵中的表达基因的坐标位置及相应坐标位置的总基因表达量；再根据表达基因的坐标位置及相应坐标位置的总基因表达量，生成基因表达图，其中，基因表达图为灰度图，基因表达图中像素点的灰度值为与像素点对应坐标位置的总基因表达量。通过这种可选方式，可准确生成得到细胞的基因表达图。
例如，在具体的细胞分割使用中，输入含空间位置的基因表达矩阵，根据表达基因的坐标位置及相应位置的总基因表达量生成表达图像，该图像具体形式为灰度图，坐标点的灰度值为该坐标上基因表达的总量。
作为另一种可选方式，步骤202具体还可包括：根据基因表达矩阵和分割的掩码图，绘制细胞的基因表达图，其中，基因表达矩阵中的空间位置与分割的掩码图中的空间位置相对应。例如，根据基因表达矩阵和分割的掩码图绘制基于细胞的表达量图，该基因表达矩阵中的空间位置与分割的掩码图中的空间位置可以相对应。通过这种可选方式，可准确生成得到细胞的基因表达图。
由于生成的基因表达图像为散点图，不便于分割，需要进行处理，具体可执行步骤203所示的过程。
步骤203、对基因表达图进行预处理，得到中值图，并将中值图进行锐化处理，得到锐化图。
由于基因表达图为散点图，不便于进行细胞分割，因此需要进行预处理，在本实施例中，首先将细胞的基因表达图处理成中值图。然后可使用拉普拉斯算子(laplacian算子)对中值图进行锐化处理，以增强中值图的边界效果，进而得到锐化图。
可选的，对基因表达图进行预处理，得到中值图，具体可包括：首先利用预设尺寸(如13*13)的卷积核对基因表达图进行卷积操作，使得基因表达图中的散点粘连，得到第一卷积图；再根据图像二维灰度峰值检测第一卷积图的局部最大值点；获取局部最大值点中的第p百分位数，其中，p为预设数值，如第p百分位数可为98％分位值、或99％分位值等；若第p百分位数在预设范围内，则利用第一中值滤波器对第一卷积图进行中值过滤，得到中值图，其中，第一中值滤波器的滤波器大小是根据预设尺寸确定得到的。
进一步可选的，在获取局部最大值点中的第p百分位数之后，本实施例方法还可包括：若第p百分位数在预设范围外，则根据第p百分位数和预设尺寸，确定卷积核的新尺寸；利用新尺寸的卷积核对基因表达图进行卷积操作，使得基因表达图中的散点粘连，得到第二卷积图；利用第二中值滤波器对第二卷积图进行中值过滤，得到中值图，其中，第二中值滤波器的滤波器大小是根据新尺寸确定得到的。
示例性的，根据第p百分位数和预设尺寸，确定卷积核的新尺寸，具体可包括：按照公式K＝N*(N/R)，计算得到卷积核的新尺寸，其中，K*K表示卷积核的新尺寸，N*N表示预设尺寸，R表示第p百分位数。
例如，首先使用大小13*13(经验数值)的卷积核，对基因表达图的原图进行卷积操作使散点图粘连，得到第一卷积图；再根据图像二维灰度峰值检测第一卷积图的局部最大值点，取出所得所有局部最大值点中的99％分位值R，如R值与一个设定好的经验阈值差距过大(过高或过低，均会影响后续处理)，则认为13*13卷积核不适用于原图，此时计算K＝13*(13/R)，将原本13*13的卷积核改为K*K的卷积核，重新处理原图，得到第二卷积图；如R值在允许范围内，则沿用第一卷积图。卷积图表现为不同灰度的方块层叠的形式，灰度跨度较剧烈，且部分表达图像存在聚集点簇中心空洞的现象，为消除这一问题，使用13*2.7＝35(2.7可为经验数值，小数向下取整)为滤波器大小(如需要计算K，则此处核大小为K*2.7)对第二卷积图进行中值滤波，得到中值图。
为了填充点簇空洞，上述所用中值滤波器较大，中值图的灰度边界可能较为模糊，使用laplacian算子对中值图进行锐化以增强灰度边界，得到锐化图，完成预处理，下面执行细胞分割的过程，具体可执行步骤204至207所示的过程。
步骤204、将锐化图进行二值化处理，得到初始掩模图。
使用大津法对上一步得到的锐化图进行二值化，得到初始掩模图。
步骤205、过滤初始掩模图中面积不符合预设条件的连通域，得到过滤掩模图。
可选的，步骤205具体可包括：将初始掩模图中面积大于第一预设阈值的连通域、或面积小于第二预设阈值的连通域进行过滤，得到过滤掩模图，其中，第一预设阈值大于第二预设阈值。例如，使用经验阈值对初始掩模中面积过小或过大的连通域进行过滤，得到 过滤掩模图。
步骤206、遍历过滤掩模图中的每个连通域，基于连通域的最小外接矩形提取连通域所在区域，利用分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
可选的，步骤206具体可包括：将过滤掩模图中的每个连通域内的像素点的灰度值设置为第一数值，并将在每个连通域外的像素点的灰度值设置为第二数值；针对连通域内每个灰度值为第一数值的目标像素点，将目标像素点的灰度值重新映射为目标像素点到距其最近的灰度值为第二数值的像素点的距离，得到连通域的距离图谱；对连通域的距离图谱进行二值化处理，获得连通域中距离灰度值为第二数值的像素点最远的预设个数像素点；将预设个数像素点作为分水岭算法的注水点，利用分水岭函数对过滤掩模图中连通域的原始掩模进行分水岭分割，得到分割后的目标连通域，并将目标连通域覆盖到过滤掩模图，得到分割后的掩模图。
例如，遍历过滤掩模图中的每个连通域，基于连通域的最小外接矩形提取连通域所在区域，使用基于距离变换的分水岭算法对可能存在细胞粘连的连通域进行进一步分割，具体步骤如下：
步骤a、对每个提取出的连通域区域，目标连通域内的点灰度值设置为1，连通域外的点灰度值设置为0(包括背景和非目标连通域)，对每个值为1的点进行距离变换，将其灰度值重新映射为该点到距其最近的值为0的点的距离(相邻点距离为1)，得到该连通域的距离图谱。
步骤b、使用一个经验阈值对距离图谱进行二值化，得到连通域中距离值为0的点最远的数个点(由于每个连通域情况不同，各自数量不定)。
步骤c、将上一步得到的数个点作为分水岭算法的注水点，使用opencv中的分水岭函数对连通域的原始掩模进行分水岭分割，得到分割后的目标连通域，将该结果覆盖至过滤掩模图。
步骤d、对遍历的每个连通域进行上述步骤，得到分割后的掩模图。
步骤207、对分割后的掩模图进行闭包操作，得到细胞分割结果的掩模图。
为使细胞边界更规则，对分割后的掩模进行闭包操作，得到最终掩模图。最后可输出并保存最终掩模图，用于后续结合原始表达矩阵进行细胞层面的生物学分析。
例如，如图3所示，为基于本实施例方法的示例流程示意图。首先可输入基因表达矩阵，基于矩阵可生成基因表达图像，如图4所示。然后利用13*13卷积核对基因表达图进行卷积操作，使得基因表达图中的散点粘连，得到第一卷积图。此时需要通过阈值进行判断，即根据图像二维灰度峰值检测第一卷积图的局部最大值点，取出所得所有局部最大值点中的99％分位值R，如R值与一个设定好的经验阈值差距过大(过高或过低，均会影响后续处理)，则认为13*13卷积核不适用于原图，使用比例计算新的卷积核大小，然后使用新的卷积核处理原图得到第二卷积图，使用这一比例计算中值过滤器大小，然后用这个大小的中值过滤器处理第二卷积图得到中值图。而如果R值在允许范围内，则沿用第一卷积 图，使用大小为35的中值过滤器处理第一卷积图得到中值图。
在得到中值图之后，使用laplacian算子对中值图进行锐化处理，得到锐化图，如图5所示。采用大律法对锐化图进行二值化处理得到初始掩模图，如图6所示，然后通过面积过滤和分水岭算法分割粘连细胞。最后输出并保存细胞掩模图像，如图7所示。
与目前现有的细胞分割方式相比，本实施例提供一种直接基于基因表达图进行细胞分割的方案，使用多种图像处理方法相结合，可提供较为可靠的细胞分割结果。细胞分割不依赖影像图，不需要额外引入将影像图与基因表达图进行配准的技术，排除了引入的额外误差，同时节省了整体操作时间和技术成本，可提高细胞分割处理的效率和准确性。
进一步的，作为图1和图2所示方法的具体实现，本实施例提供了一种细胞分割的处理装置，如图8所示，该装置包括：获取模块31、处理模块32、分割模块33。
获取模块31，被配置为获取细胞的基因表达图；
处理模块32，被配置为对所述基因表达图进行预处理，得到预处理图；
分割模块33，被配置为对所述预处理图进行二值化处理，得到初始掩模图；根据所述初始掩模图，使用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
在具体的应用场景中，分割模块33，具体被配置为过滤所述初始掩模图中面积不符合预设条件的连通域，得到过滤掩模图；遍历所述过滤掩模图中的每个连通域，基于连通域的最小外接矩形提取连通域所在区域，利用所述分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
在具体的应用场景中，分割模块33，具体还被配置为将所述过滤掩模图中的每个连通域内的像素点的灰度值设置为第一数值，并将在每个连通域外的像素点的灰度值设置为第二数值；针对连通域内每个灰度值为所述第一数值的目标像素点，将所述目标像素点的灰度值重新映射为所述目标像素点到距其最近的灰度值为所述第二数值的像素点的距离，得到连通域的距离图谱；对连通域的距离图谱进行二值化处理，获得连通域中距离灰度值为所述第二数值的像素点最远的预设个数像素点；将所述预设个数像素点作为分水岭算法的注水点，利用分水岭函数对所述过滤掩模图中连通域的原始掩模进行分水岭分割，得到分割后的目标连通域，并将所述目标连通域覆盖到所述过滤掩模图，得到分割后的掩模图。
在具体的应用场景中，分割模块33，具体还被配置为将所述初始掩模图中面积大于第一预设阈值的连通域、或面积小于第二预设阈值的连通域进行过滤，得到所述过滤掩模图，其中，所述第一预设阈值大于所述第二预设阈值。
在具体的应用场景中，处理模块32，具体被配置为对所述基因表达图进行预处理，得到中值图；将所述中值图进行锐化处理，得到锐化图。
相应的，分割模块33，具体被配置为对所述锐化图进行二值化处理，得到初始掩模图。
在具体的应用场景中，处理模块32，具体还被配置为利用预设尺寸的卷积核对所述基因表达图进行卷积操作，使得所述基因表达图中的散点粘连，得到第一卷积图；根据图像 二维灰度峰值检测所述第一卷积图的局部最大值点；获取所述局部最大值点中的第p百分位数，其中，所述p为预设数值；若所述第p百分位数在预设范围内，则利用第一中值滤波器对所述第一卷积图进行中值过滤，得到所述中值图，其中，所述第一中值滤波器的滤波器大小是根据所述预设尺寸确定得到的。
在具体的应用场景中，处理模块32，具体还被配置为在所述获取所述局部最大值点中的第p百分位数之后，若所述第p百分位数在预设范围外，则根据所述第p百分位数和所述预设尺寸，确定卷积核的新尺寸；利用所述新尺寸的卷积核对所述基因表达图进行卷积操作，使得所述基因表达图中的散点粘连，得到第二卷积图；利用第二中值滤波器对所述第二卷积图进行中值过滤，得到所述中值图，其中，所述第二中值滤波器的滤波器大小是根据所述新尺寸确定得到的。
在具体的应用场景中，处理模块32，具体还被配置为按照公式K＝N*(N/R)，计算得到卷积核的新尺寸，其中，K*K表示卷积核的新尺寸，N*N表示所述预设尺寸，R表示所述第p百分位数。
在具体的应用场景中，获取模块31，具体被配置为获取包含空间位置的基因表达矩阵；基于所述基因表达矩阵，生成所述基因表达图。
在具体的应用场景中，获取模块31，具体还被配置为获取所述基因表达矩阵中的表达基因的坐标位置及相应坐标位置的总基因表达量；根据所述表达基因的坐标位置及相应坐标位置的总基因表达量，生成所述基因表达图，其中，所述基因表达图为灰度图，所述基因表达图中像素点的灰度值为与所述像素点对应坐标位置的总基因表达量。
在具体的应用场景中，获取模块31，具体还被配置为根据所述基因表达矩阵和分割的掩码图，绘制细胞的基因表达图，其中，所述基因表达矩阵中的空间位置与所述分割的掩码图中的空间位置相对应。
在具体的应用场景中，分割模块33，具体还被配置为对所述分割后的掩模图进行闭包操作，得到细胞分割结果的掩模图。
需要说明的是，本实施例提供的一种细胞分割的处理装置所涉及各功能单元的其它相应描述，可以参考图1和图2中的对应描述，在此不再赘述。
基于上述如图1和图2所示方法，相应的，本实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现上述如图1和图2所示的方法。
基于这样的理解，本申请的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施场景的方法。
基于上述如图1和图2所示的方法，以及图8所示的虚拟装置实施例，为了实现上述目的，本申请实施例还提供了一种电子设备，具体可为个人计算机、笔记本电脑等，该设 备包括存储介质和处理器；存储介质，用于存储计算机程序；处理器，用于执行计算机程序以实现上述如图1和图2所示的方法。
可选的，上述实体设备还可以包括用户接口、网络接口、摄像头、射频(Radio Frequency，RF)电路，传感器、音频电路、WI-FI模块等等。用户接口可以包括显示屏(Display)、输入单元比如键盘(Keyboard)等，可选用户接口还可以包括USB接口、读卡器接口等。网络接口可选的可以包括标准的有线接口、无线接口(如WI-FI接口)等。
本领域技术人员可以理解，本实施例提供的上述实体设备结构并不构成对该实体设备的限定，可以包括更多或更少的部件，或者组合某些部件，或者不同的部件布置。
存储介质中还可以包括操作系统、网络通信模块。操作系统是管理上述实体设备硬件和软件资源的程序，支持信息处理程序以及其它软件和/或程序的运行。网络通信模块用于实现存储介质内部各组件之间的通信，以及与信息处理实体设备中其它硬件和软件之间通信。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到本申请可以借助软件加必要的通用硬件平台的方式来实现，也可以通过硬件实现。通过应用本实施例的方案，与目前现有的细胞分割方式相比，本实施例提供一种直接基于基因表达图进行细胞分割的方案，使用多种图像处理方法相结合，可提供较为可靠的细胞分割结果。细胞分割不依赖影像图，不需要额外引入将影像图与基因表达图进行配准的技术，排除了引入的额外误差，同时节省了整体操作时间和技术成本，可提高细胞分割处理的效率和准确性。
需要说明的是，在本文中，诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述仅是本申请的具体实施方式，使本领域技术人员能够理解或实现本申请。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本申请的精神或范围的情况下，在其它实施例中实现。因此，本申请将不会被限制于本文所述的这些实施例，而是要符合与本文所申请的原理和新颖特点相一致的最宽的范围。

Claims (15)

  1. 一种细胞分割的处理方法，其特征在于，包括：
    获取细胞的基因表达图；
    对所述基因表达图进行预处理，得到预处理图；
    将所述预处理图进行二值化处理，得到初始掩模图；
    根据所述初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图，包括：
    过滤所述初始掩模图中面积不符合预设条件的连通域，得到过滤掩模图；
    遍历所述过滤掩模图中的每个连通域，基于连通域的最小外接矩形提取连通域所在区域，利用所述分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
  3. 根据权利要求2所述的方法，其特征在于，所述遍历所述过滤掩模图中的每个连通域，基于连通域的最小外接矩形提取连通域所在区域，利用所述分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图，包括：
    将所述过滤掩模图中的每个连通域内的像素点的灰度值设置为第一数值，并将在每个连通域外的像素点的灰度值设置为第二数值；
    针对连通域内每个灰度值为所述第一数值的目标像素点，将所述目标像素点的灰度值重新映射为所述目标像素点到距其最近的灰度值为所述第二数值的像素点的距离，得到连通域的距离图谱；
    对连通域的距离图谱进行二值化处理，获得连通域中距离灰度值为所述第二数值的像素点最远的预设个数像素点；
    将所述预设个数像素点作为分水岭算法的注水点，利用分水岭函数对所述过滤掩模图中连通域的原始掩模进行分水岭分割，得到分割后的目标连通域，并将所述目标连通域覆盖到所述过滤掩模图，得到分割后的掩模图。
  4. 根据权利要求2所述的方法，其特征在于，所述过滤所述初始掩模图中面积不符合预设条件的连通域，得到过滤掩模图，包括：
    将所述初始掩模图中面积大于第一预设阈值的连通域、或面积小于第二预设阈值的连通域进行过滤，得到所述过滤掩模图，其中，所述第一预设阈值大于所述第二预设阈值。
  5. 根据权利要求1所述的方法，其特征在于，所述对所述基因表达图进行预处理，得到预处理图，包括：
    对所述基因表达图进行预处理，得到中值图；
    将所述中值图进行锐化处理，得到锐化图；
    所述将所述预处理图进行二值化处理，得到初始掩模图，包括：
    将所述锐化图进行二值化处理，得到初始掩模图。
  6. 根据权利要求5所述的方法，其特征在于，所述对所述基因表达图进行预处理，得到中值图，包括：
    利用预设尺寸的卷积核对所述基因表达图进行卷积操作，使得所述基因表达图中的散点粘连，得到第一卷积图；
    根据图像二维灰度峰值检测所述第一卷积图的局部最大值点；
    获取所述局部最大值点中的第p百分位数，其中，所述p为预设数值；
    若所述第p百分位数在预设范围内，则利用第一中值滤波器对所述第一卷积图进行中值过滤，得到所述中值图，其中，所述第一中值滤波器的滤波器大小是根据所述预设尺寸确定得到的。
  7. 根据权利要求6所述的方法，其特征在于，在所述获取所述局部最大值点中的第p百分位数之后，所述方法还包括：
    若所述第p百分位数在预设范围外，则根据所述第p百分位数和所述预设尺寸，确定卷积核的新尺寸；
    利用所述新尺寸的卷积核对所述基因表达图进行卷积操作，使得所述基因表达图中的散点粘连，得到第二卷积图；
    利用第二中值滤波器对所述第二卷积图进行中值过滤，得到所述中值图，其中，所述第二中值滤波器的滤波器大小是根据所述新尺寸确定得到的。
  8. 根据权利要求7所述的方法，其特征在于，所述根据所述第p百分位数和所述预设尺寸，确定卷积核的新尺寸，包括：
    按照公式K＝N*(N/R)，计算得到卷积核的新尺寸，其中，K*K表示卷积核的新尺寸，N*N表示所述预设尺寸，R表示所述第p百分位数。
  9. 根据权利要求1所述的方法，其特征在于，所述获取细胞的基因表达图，包括：
    获取包含空间位置的基因表达矩阵；
    基于所述基因表达矩阵，生成所述基因表达图。
  10. 根据权利要求9所述的方法，其特征在于，所述基于所述基因表达矩阵，生成所述基因表达图，包括：
    获取所述基因表达矩阵中的表达基因的坐标位置及相应坐标位置的总基因表达量；
    根据所述表达基因的坐标位置及相应坐标位置的总基因表达量，生成所述基因表达图，其中，所述基因表达图为灰度图，所述基因表达图中像素点的灰度值为与所述像素点对应坐标位置的总基因表达量。
  11. 根据权利要求9所述的方法，其特征在于，所述基于所述基因表达矩阵，生成所述基因表达图，包括：
    根据所述基因表达矩阵和分割的掩码图，绘制细胞的基因表达图，其中，所述基因表达矩阵中的空间位置与所述分割的掩码图中的空间位置相对应。
  12. 根据权利要求1至11中任一项所述的方法，其特征在于，在所述根据所述初始掩模图，利用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图之后，所述方法还包括：
    对所述分割后的掩模图进行闭包操作，得到细胞分割结果的掩模图。
  13. 一种细胞分割的处理装置，其特征在于，包括：
    获取模块，被配置为获取细胞的基因表达图；
    处理模块，被配置为对所述基因表达图进行预处理，得到预处理图；
    分割模块，被配置为对所述预处理图进行二值化处理，得到初始掩模图；根据所述初始掩模图，使用基于距离变换的分水岭算法对存在细胞粘连的连通域进行分割，得到分割后的掩模图。
  14. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至12中任一项所述的方法。
  15. 一种电子设备，包括存储介质、处理器及存储在存储介质上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至12中任一项所述的方法。
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