WO2024119323A1 - Tissue segmentation method for sample image, system, device and medium - Google Patents

Abstract

Disclosed in the present invention are a tissue segmentation method for a sample image, a system, a device and a medium. The tissue segmentation method comprises: acquiring the gene expression level of a target tissue to be segmented in a sample image, and determining a gene expression matrix of the sample image, the gene expression matrix arranging the gene expression level according to pixel positions; assigning the gene expression level of each pixel position in the gene expression matrix to the corresponding pixel position in the sample image so as to determine image data; performing image processing on the image data to determine foreground pixel points of the image data; and, according to the foreground pixel points, marking the connected region of the image data so as to obtain a target tissue segmented image. By determining foreground pixel points to determine the connected region, the interference of the pixel values of background pixel points on a segmentation result is reduced, thereby enabling the segmentation result to be more complete and reducing artifacts.

Description

Method, system, device and medium for tissue segmentation of sample images Technical Field

The present invention relates to the field of image processing, and in particular to a tissue segmentation method, system, device and medium for sample images.

Background technique

Medical image tissue segmentation is one of the core links in medical image processing, involving digital image processing, pattern recognition, computer vision, biomedicine and other background disciplines. It is a multidisciplinary technology. This technology can highlight the tissue area of interest to the user, while simplifying the computational complexity of post-processing, bringing convenience to subsequent analysis.

When the tissue segmentation method in the prior art segments the target tissue, the target tissue needs to be photographed by a high-quality microscope for the tissue with a small image. When the image quality is poor, the segmentation effect of the target tissue in the image is poor.

Summary of the invention

The technical problem to be solved by the present invention is to overcome the defect in the prior art that when the image quality is poor, the segmentation effect of the target tissue in the image is poor, and to provide a tissue segmentation method, system, device and medium for a sample image.

The present invention solves the above technical problems through the following technical solutions:

In a first aspect, a tissue segmentation method for a sample image is provided, the tissue segmentation method comprising:

Acquiring gene expression amounts of a target tissue to be segmented in a sample image and determining a gene expression matrix of the sample image, wherein the gene expression matrix arranges the gene expression amounts according to pixel positions;

Determine image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image;

Determine foreground pixels of the image data by performing image processing on the image data;

The connected areas of the image data are marked according to the foreground pixels to obtain a target tissue segmentation image.

Optionally, determining the image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image includes:

Determining an image matrix of the same size as the gene expression matrix;

The gene expression amount at each pixel position in the gene expression matrix is assigned to the corresponding pixel position in the image matrix and all pixel positions are normalized to obtain image data of the sample image.

Optionally, the determining the foreground pixel points of the image data by performing image processing on the image data includes:

Sequentially using each pixel value as a pixel threshold to divide foreground pixels and background pixels from the image data;

Calculating the foreground average pixel value of the foreground pixel point and the background average pixel value of the background pixel point under each of the pixel threshold conditions, and determining the variance of the foreground pixel point and the background pixel point under each of the pixel threshold conditions according to the foreground average pixel value and the background average pixel value;

Traversing all pixel thresholds, and determining the pixel threshold corresponding to the maximum variance as the target threshold;

The foreground pixels of the image data are determined according to the target threshold value and the pixel values of the foreground pixels are unified.

Optionally, the tissue segmentation method further comprises:

The background pixels of the image data are determined according to the target threshold value and the pixel values of the background pixels are unified.

Optionally, the tissue segmentation method further comprises:

Performing convolution on the image data to extract low-level features of the image data;

The determining the foreground pixel points of the image data by performing image processing on the image data includes:

The foreground pixel points of the image data are determined by image processing on the image data after convolution.

Optionally, the step of marking the connected areas of the image data according to the foreground pixels to obtain the target tissue segmentation image includes:

Performing a morphological opening operation on the image data for connected region marking to smooth the edges of the connected regions of the image data;

and / or,

Performing a morphological closing operation on the image data for connected region marking to eliminate small holes in the connected regions of the image data;

and / or,

Holes are filled in the image data for which connected area marking is performed.

Optionally, the step of marking the connected areas of the image data according to the foreground pixels to obtain the target tissue segmentation image includes:

The foreground pixels with an adjacency relationship are determined as the pixels of the same connected region.

Optionally, after determining the foreground pixels having an adjacency relationship as pixels of the same connected region, the method further comprises:

Calculate the area of each connected region respectively, and calculate the standard deviation and mean of all connected regions;

Determine whether the area of each of the connected regions is between the standard deviation and the mean;

If so, retain the connected area;

If not, the connected region is discarded.

Optionally, after determining the image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image, the method further comprises:

downsampling the image data;

After the connected areas of the image data are marked according to the foreground pixels to obtain the target tissue segmentation image, the method includes:

The target tissue segmentation image is upsampled.

In a second aspect, a tissue segmentation system for a sample image is provided, wherein the tissue segmentation system comprises:

An acquisition module, used to acquire the gene expression amount of the target tissue to be segmented in the sample image and determine the gene expression matrix of the sample image, wherein the gene expression matrix arranges the gene expression amounts according to pixel positions;

An image data determination module, used to determine image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image;

a foreground pixel point determination module, configured to determine the foreground pixel points of the image data by performing image processing on the image data;

The segmented image determination module is used to mark the connected areas of the image data according to the foreground pixels to obtain a target tissue segmentation image.

According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the tissue segmentation method of the sample image according to the first aspect when executing the computer program.

According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the method for tissue segmentation of a sample image according to the first aspect is implemented.

The present invention generates a gene expression matrix by obtaining the gene expression amount of each pixel position, and converts the gene expression matrix into image data, thereby reducing the use of a microscope to shoot and correct tissue images and improving segmentation efficiency; the image data obtained by the gene expression amount is more accurate. The connected area is determined by determining the foreground pixel points to reduce the interference of the pixel values of the background pixel points in the spatial information on the segmentation result, making the segmentation result more complete and reducing artifacts.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a first flow chart of a tissue segmentation method of a sample image provided by an exemplary embodiment of the present invention;

FIG2 is a flow chart of determining image data of a method for tissue segmentation of a sample image provided by an exemplary embodiment of the present invention;

FIG3 is a schematic diagram of image data of a tissue segmentation method of a sample image provided by an exemplary embodiment of the present invention;

FIG4 is a schematic diagram of a 2D convolution of a tissue segmentation method for a sample image provided by an exemplary embodiment of the present invention;

FIG5 is a schematic diagram of low-level features of a tissue segmentation method for a sample image provided by an exemplary embodiment of the present invention;

FIG6 is a schematic diagram of binarization of a tissue segmentation method of a sample image provided by an exemplary embodiment of the present invention;

FIG7 is a schematic diagram of a morphological closing operation of a tissue segmentation method for a sample image provided by an exemplary embodiment of the present invention;

FIG8 is a schematic diagram of a morphological opening operation of a tissue segmentation method for a sample image provided by an exemplary embodiment of the present invention;

FIG9 is a schematic diagram of connected regions of a tissue segmentation method for a sample image provided by an exemplary embodiment of the present invention;

FIG10 is a schematic diagram of hole filling in a tissue segmentation method of a sample image provided by an exemplary embodiment of the present invention;

FIG11 is a module diagram of a tissue segmentation system for a sample image provided by an exemplary embodiment of the present invention;

FIG. 12 is a structural diagram of an electronic device provided by an exemplary embodiment of the present invention.

Detailed ways

The present invention is further described below by way of exemplary embodiments, but the present invention is not limited to the scope of the embodiments.

The embodiment of the present invention provides a tissue segmentation method for a sample image, and the tissue segmentation method includes the following types: a region-based segmentation method, a threshold-based segmentation method, a clustering-based segmentation method, and a manual segmentation method. The region-based segmentation method relies on the intensity uniformity of the image to detect the target boundary of the object region, and this method is likely to cause over-segmentation of the image; the threshold-based segmentation method compares the intensity value with one or more thresholds to segment the object region, and this method is likely to cause more artifacts in the segmentation of the object region, and the threshold needs to be determined through multiple experiments; the clustering-based segmentation method divides pixels into groups or clusters by using similarity metrics such as distance, connectivity, and intensity. This method does not consider spatial information and is sensitive to noise and grayscale unevenness.

FIG1 is a tissue segmentation method of a sample image provided by an exemplary embodiment of the present invention. Referring to FIG1 , the tissue segmentation method includes:

S101, obtaining the gene expression amount of the target tissue to be segmented in the sample image and determining the gene expression matrix of the sample image, wherein the gene expression matrix arranges the gene expression amounts according to pixel positions.

In one embodiment, the sample image may preferably be a medical image, which is not specifically limited in this embodiment and may be selected according to actual application scenarios.

In one embodiment, target tissue may include but is not limited to any one or more of sample tissue, biological tissue, muscle tissue, bone tissue, and cell tissue. Gene expression matrix characterizes the expression of genes in different positions (row and column positions of corresponding gene expression matrix). The data composition of each gene expression amount includes gene identifier, gene coordinates and gene expression amount, characterizing the expression amount of genes in a position. Taking cell tissue as an example, the gene expression amount of cell tissue can be determined by carrying out transcription processing to the DNA of cell tissue. In addition, gene expression amount is generally in utf-8 format.

In this embodiment, the gene expression amount at each pixel position in the sample image is obtained, and the gene expression amount is arranged according to the row coordinates and column coordinates of the pixel position to obtain a gene expression matrix. The gene expression matrix is consistent with the size of the sample image. When the gene expression amount at each pixel position is 0, it indicates that there is no gene of the target tissue at the pixel position. The gene expression amount at each pixel position in the gene expression matrix can represent the gene expression amount of one or more genes. If the size of the gene expression matrix is inconsistent with that of the sample image, the two images or one of the images can be scaled.

S102, determining image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image.

In one embodiment, step S102 specifically includes:

Determining an image matrix of the same size as the gene expression matrix;

The gene expression amount of each pixel position in the gene expression matrix is assigned to the corresponding pixel position in the image matrix and all pixel positions are normalized to obtain image data of the sample image.

Referring to FIG. 2, the gene expression amount at each pixel position in the gene expression matrix is stored in the dictionary with the row coordinates and column coordinates of each pixel position as the key and the gene expression amount as the value. Create an all-0 image matrix of the same size as the gene expression matrix, and assign the row coordinates and column coordinates of the gene expression amount stored in the dictionary to the same position in the image matrix, that is, the gene expression amount at the same pixel position in the image matrix is the same as the gene expression amount at the same pixel position in the gene expression matrix. The image matrix after assignment is normalized so that the pixel value of each pixel position is within the interval [0,255], and image data that is more accurate relative to the sample image is obtained, and the impact on subsequent tissue segmentation images is reduced. As shown in FIG. 3, the image data is preferably in TIFF format, and can also be in JPG format, etc. It is not limited to a specific form of expression and can be selected according to actual conditions.

In this embodiment, a gene expression matrix is generated by obtaining the gene expression amount at each pixel position, and the gene expression matrix is converted into image data, thereby reducing the process of repeatedly photographing and correcting tissue images through the original microscope, improving the segmentation efficiency, and making the image data more accurate in displaying the target tissue compared to the initial sample image, which can improve the accuracy of subsequent determination of the target tissue, thereby improving the accuracy of target tissue segmentation.

S103, determining foreground pixels of the image data by performing image processing on the image data;

In one embodiment, before step S103, the tissue segmentation method further includes:

Referring to FIG4 , the image data is convolved to extract low-level features of the image data. The convolution method preferably uses 2D convolution, and convolution methods such as dilated convolution and depthwise separable convolution can also be used to improve the computation accuracy and expand the perception field of view. The image data after convolution is shown in FIG5 .

The foreground pixels of the image data are determined by image processing on the convolved image data.

In one embodiment, step S103 specifically includes:

Each pixel value is used as a pixel threshold to divide the foreground pixel and the background pixel from the image data. The pixel value is in the interval [0, 255]. Taking the pixel value T ₀ as an example, the pixel with a pixel value less than T ₀ is the foreground pixel, and the pixel with a pixel value greater than T ₀ is the background pixel.

Calculate the foreground average pixel value of the foreground pixels and the background average pixel value of the background pixels under each pixel threshold condition.

The variance between the foreground pixel and the background pixel under each pixel threshold condition is determined according to the foreground average pixel value and the background average pixel value.

Traverse all pixel thresholds and determine the pixel threshold corresponding to the maximum variance as the target threshold;

The foreground pixels of the image data are determined according to the target threshold and the pixel values of the foreground pixels are unified to 255, and the pixel values of the background pixels are unified to 0, so as to obtain the binarized image data, as shown in FIG6 .

Optionally, foreground pixels and background pixels may be converted to each other by image inversion.

S104, marking the connected areas of the image data according to the foreground pixels to obtain a target tissue segmentation image.

In one embodiment, the connected region refers to an image region composed of foreground pixels having the same pixel value and adjacent positions in the image data, that is, a segmented region of the target tissue.

In one embodiment, step S104 specifically includes:

The foreground pixels with an adjacency relationship are determined as the pixels of the same connected region.

Among them, connected region analysis methods such as the two-pass method and the seed-filling method can be used.

In this embodiment, a two-pass method is preferably used. Specifically, during the first scanning process, all pixel points of the image data are traversed from left to right and from top to bottom starting from the first pixel point in the upper left corner of the image data, and a label is assigned to each foreground pixel point. If the pixel point adjacent to the upper or left side of the foreground pixel point currently being scanned is 0, the label value of the foreground pixel point currently being scanned is increased by one. If the pixel point adjacent to the upper or left side of the foreground pixel point currently being scanned is not 0, that is, there is a foreground pixel point adjacent to the foreground pixel point currently being scanned, then the label value of the foreground pixel point adjacent to the upper or left side is assigned to the foreground pixel point currently being scanned. Since the foreground pixels located in the same connected area may have one or more labels with different values during the first scanning process, it is necessary to merge the labels with different values. During the second scanning process, the label values of the foreground pixels with an adjacent relationship are unified, that is, assigned the same label, where the label is the minimum value of the label in the same connected area. It should be noted that the starting position of the scan in the two-pass method is not limited to a specific position. The upper left position in this embodiment is only used for example. In the process of assigning label values to foreground pixels, the positions of adjacent pixels are also determined according to the scanning direction, and are not specifically limited to the top or left.

Calculate the area of each connected region separately, and calculate the standard deviation and mean of all connected regions;

Determine whether the area of each connected region is between the standard deviation and the mean;

If so, keep the connected region;

If not, the connected region is discarded.

For the retained connected areas, the pixel values of all connected areas are set to 255 to obtain the image data after the connected areas are marked.

In one embodiment, step S104 specifically includes:

Referring to FIG. 7 , a morphological opening operation is performed on the image data for connected region marking, that is, the pixel points of all connected regions are traversed through the structural element, the pixel points of the connected regions are expanded, and then the expanded connected regions are eroded to smooth the edges of the connected regions of the image data;

Referring to FIG8 , the image data for connected region labeling is subjected to morphological closing operation, that is, all pixels of the connected regions are traversed through the structural element, the pixels of the connected regions are corroded, and then the corroded connected regions are expanded to eliminate small holes in the connected regions of the image data. The image data after morphological opening and morphological closing operations are shown in FIG9 .

In one embodiment, step S104 specifically includes:

Holes are filled in the image data for connected area marking. Specifically, the image data is copied, and the edges of the connected areas of the copied image data are expanded to prevent incomplete hole filling. The flood filling algorithm is used for filling. The principle is to fill from the first pixel point of the connected area until all the pixels in the connected area are filled with the same pixel value. The copied image data and the image data before copying are image ORed to obtain the image data after hole filling, as shown in Figure 10.

In one embodiment, the tissue segmentation method further includes: downsampling the image data and upsampling the target tissue segmentation image. The downsampling step is performed after step S102, and an interpolation algorithm can be used, preferably a nearest neighbor interpolation method, in order to reduce the feature dimension of the image data, facilitate subsequent operations, and reduce computing power. Upsampling is performed after step S104, and the interpolation algorithm of upsampling corresponds to the interpolation algorithm of downsampling, in order to restore the target tissue segmentation image to the same size as the gene expression matrix.

An exemplary embodiment of the present invention provides a tissue segmentation system for a sample image. Referring to FIG. 11 , the tissue segmentation system includes:

An acquisition module 21 is used to acquire a gene expression matrix of a target tissue, wherein the gene expression matrix includes gene expression amounts of the target tissue arranged according to pixel positions;

An image data determination module 22 determines image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image;

A foreground pixel point determination module 23, configured to determine foreground pixel points of the image data by performing image processing on the image data;

The segmented image determination module 24 is used to mark the connected areas of the image data according to the foreground pixels to obtain the target tissue segmentation image.

As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

An electronic device according to an exemplary embodiment of the present invention is shown in FIG12 , which shows a block diagram of an exemplary electronic device 30 suitable for implementing the embodiment of the present invention. The electronic device 30 shown in FIG12 is only an example and should not limit the functions and scope of use of the embodiment of the present invention.

As shown in Fig. 3, the electronic device 30 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including the memory 32 and the processor 31).

The bus 33 includes a data bus, an address bus, and a control bus.

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 .

The memory 32 may also include a program tool 325 (or utility) 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.

The processor 31 executes various functional applications and data processing by running the computer program stored in the memory 32, such as the method provided in any of the above embodiments.

The electronic device 30 may also communicate with one or more external devices 34. Such communication may be performed via an input/output (I/O) interface 35. Furthermore, the model-generated electronic device 30 may 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 37. As shown, the network adapter 37 communicates with other modules of the model-generated electronic device 30 via a bus 33. It should be understood that, although not shown in the figure, other hardware and/or software modules may be used in conjunction with the model-generated electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

It should be noted that although several units/modules or sub-units/modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units/modules described above can be embodied in one unit/module. Conversely, the features and functions of one unit/module described above can be further divided into multiple units/modules to be embodied.

An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method provided by any of the above embodiments is implemented.

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 of the above.

In a possible implementation manner, the embodiment of 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 a method for implementing any of the above embodiments.

Among them, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that this is only for illustration and the protection scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims (12)

A tissue segmentation method for a sample image, characterized in that the tissue segmentation method comprises: Acquiring gene expression amounts of a target tissue to be segmented in a sample image and determining a gene expression matrix of the sample image, wherein the gene expression matrix arranges the gene expression amounts according to pixel positions; Determine image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image; Determine foreground pixels of the image data by performing image processing on the image data; The connected areas of the image data are marked according to the foreground pixels to obtain a target tissue segmentation image. The tissue segmentation method according to claim 1, characterized in that the determining of image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image comprises: Determining an image matrix of the same size as the gene expression matrix; The gene expression amount of each pixel position in the gene expression matrix is assigned to the corresponding pixel position in the image matrix and all pixel positions are normalized to obtain image data of the sample image. The tissue segmentation method according to claim 1, characterized in that the step of performing image processing on the image data to determine the foreground pixels of the image data comprises: Sequentially using each pixel value as a pixel threshold to divide foreground pixels and background pixels from the image data; Calculating the foreground average pixel value of the foreground pixel and the background average pixel value of the background pixel under each pixel threshold condition, and determining the variance of the foreground pixel and the background pixel under each pixel threshold condition according to the foreground average pixel value and the background average pixel value; Traversing all pixel thresholds, and determining the pixel threshold corresponding to the maximum variance as the target threshold; The foreground pixels of the image data are determined according to the target threshold and the pixel values of the foreground pixels are unified. The tissue segmentation method according to claim 3, characterized in that the tissue segmentation method further comprises: The background pixels of the image data are determined according to the target threshold value and the pixel values of the background pixels are unified. The tissue segmentation method according to claim 1, characterized in that the tissue segmentation method further comprises: Performing convolution on the image data to extract low-level features of the image data; The determining the foreground pixel points of the image data by performing image processing on the image data includes: The foreground pixel points of the image data are determined by image processing on the image data after convolution. The tissue segmentation method according to claim 1, characterized in that the step of marking the connected areas of the image data according to the foreground pixels to obtain the target tissue segmentation image comprises: Performing a morphological opening operation on the image data for connected region marking to smooth the edges of the connected regions of the image data; and / or, Performing a morphological closing operation on the image data for connected region marking to eliminate small holes in the connected regions of the image data; and / or, Holes are filled in the image data for which connected area marking is performed. The tissue segmentation method according to claim 1, characterized in that the step of marking the connected areas of the image data according to the foreground pixels to obtain the target tissue segmentation image comprises: The foreground pixels with an adjacency relationship are determined as the pixels of the same connected region. The tissue segmentation method according to claim 7, characterized in that after determining the foreground pixels having an adjacency relationship as pixels of the same connected region, the method further comprises: Calculate the area of each connected region respectively, and calculate the standard deviation and mean of all connected regions; Determine whether the area of each of the connected regions is between the standard deviation and the mean; If yes, retain the connected area; If not, the connected region is discarded. The tissue segmentation method according to claim 1, characterized in that after determining the image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image, the method further comprises: downsampling the image data; After the connected areas of the image data are marked according to the foreground pixels to obtain the target tissue segmentation image, the method includes: The target tissue segmentation image is upsampled. A tissue segmentation system for a sample image, characterized in that the tissue segmentation system comprises: an acquisition module, used to acquire the gene expression amount of the target tissue to be segmented in the sample image and determine the gene expression matrix of the sample image, wherein the gene expression matrix arranges the gene expression amounts according to pixel positions; An image data determination module, used to determine image data by assigning the gene expression amount at each pixel position in the gene expression matrix to the corresponding pixel position in the sample image; a foreground pixel point determination module, configured to determine the foreground pixel points of the image data by performing image processing on the image data; The segmented image determination module is used to mark the connected areas of the image data according to the foreground pixels to obtain a target tissue segmentation image. An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the tissue segmentation method of a sample image according to any one of claims 1 to 9 when executing the computer program. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for tissue segmentation of a sample image according to any one of claims 1 to 9 is implemented.

Images