Tissue permeabilization time selection method, tissue permeabilization time selection device and electronic equipment

Abstract

The invention discloses a tissue permeabilization time selection method, a tissue permeabilization time selection device and electronic equipment, and relates to the technical field of biological experiments or other related technical fields, and the method comprises the following steps: carrying out permeabilization experiments with different time lengths on tissue slices to obtain a plurality of corresponding microscope images, carrying out chip identification on each microscope image, and carrying out chip identification on each microscope image; and positioning a chip area, counting the average brightness of the chip area, sorting different chips according to the average brightness, and selecting the experiment duration corresponding to the chip with the highest average brightness as the target permeabilization experiment duration. According to the invention, the technical problem that the operation is complicated when the permeabilization time is selected in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of biological experimental technology or other related fields, and more specifically, to a method, apparatus, and electronic device for selecting tissue permeation time. Background Technology

[0002] Spatial transcriptomics transfection (SRT) is a biological experimental technique that combines spatial transcriptomics and transfection technology to introduce exogenous RNA into tissues or cells to study the spatial distribution of gene expression and cell-cell interactions. This technique can provide detailed information about cell populations and gene expression within tissues, helping researchers better understand cell-cell interactions, cell type distribution, and biological processes in tissues. The choice of transfection time is crucial in transfection experiments as it can affect the results. The transfection time needs to be determined based on the specific experimental purpose, cell type, transfection reagent, and the properties of the exogenous DNA or RNA.

[0003] In related techniques, tissue optimization (TO) experiments are commonly used to determine the optimal permeation time for a particular tissue type. TO experiments are used to determine whether a sample is suitable for spatial transcription experiments and to identify the optimal permeation time. Different tissue types and slice thicknesses all affect the tissue permeation time. Only after determining the optimal permeation time can formal experiments on spatial gene expression begin.

[0004] The specific experimental procedure for tissue permeation is as follows: tissue mounting → fixation → staining → bright-field imaging with scanner → tissue permeation → fluorescent cDNA synthesis → tissue removal → fluorescence scanning. The optimal permeation time is determined based on the fluorescence intensity; the brighter the fluorescence, the better the permeation effect.

[0005] However, the tissue permeability test method has significant drawbacks: the judgment of tissue permeability mainly relies on tissue-optimized TO slides, and the cost of using a single TO slide is high and the operation is complicated.

[0006] There is currently no effective solution to the above problems. Summary of the Invention

[0007] This invention provides a method, apparatus, and electronic device for selecting tissue permeation time, to at least solve the technical problem of complex operation when selecting permeation time in related technologies.

[0008] According to one aspect of the present invention, a method for selecting tissue permeation time is provided, comprising: performing permeation experiments on tissue sections for different durations to obtain corresponding multiple microscopic images; performing chip identification on each microscopic image, locating the chip region, calculating the average brightness of the chip region, and sorting different chips according to the average brightness; and selecting the experimental duration corresponding to the chip with the highest average brightness as the target permeation experimental duration.

[0009] Optionally, the step of identifying the chip region for each of the microscope images includes: downsampling each of the microscope images; labeling the downsampled image into regions, including a background region and a target region, wherein the target region is used to locate the chip region of interest; generating a chip mask image based on the background region and the target region using a pre-trained image segmentation model; performing line detection on the mask image to obtain N intersection points of at least three lines, where N is an integer greater than or equal to 3; and cropping the chip region based on the N intersection points.

[0010] Optionally, the step of performing line detection on the mask image includes: mapping each edge point in the mask image to a parameter space, obtaining collinear points in the parameter space, and calculating the cumulative value of each point, wherein the parameter space includes a polar coordinate space; using a Hough accumulator to extract target points whose cumulative values ​​are greater than a preset cumulative threshold, and determining a detection line based on the extracted target points; and transforming the detection line in the parameter space to a Cartesian coordinate system to obtain the line in the mask image.

[0011] Optionally, after identifying the chip and locating the chip region in each microscope image, the method further includes: performing affine mapping on the associated image of the extracted chip region to obtain an associated image of a preset resolution; and performing image noise reduction processing on the associated image of the chip region.

[0012] Optionally, the step of performing image noise reduction processing on the associated image of the chip region includes: calculating the mean and standard deviation of the associated image, wherein the mean represents the image brightness and the standard deviation indicates the image contrast; defining a first enhancement factor and a second enhancement factor, wherein the first enhancement factor is used to enhance the image contrast and the second enhancement factor is used to enhance the image brightness; performing a linear transformation operation on the mean and corresponding second enhancement factor of the associated image, and on the standard deviation and corresponding first enhancement factor of the associated image, respectively, to enhance the image contrast and brightness; wherein the linear transformation operation is used to perform a linear transformation on each pixel value in the associated image.

[0013] Optionally, after performing the linear transformation operation, the method further includes: converting the floating-point image into an integer image using a pixel value conversion strategy, so that the pixel value range of the integer image is within a set range.

[0014] Optionally, the step of performing image noise reduction processing on the associated images of the chip regions includes: performing pixel value frequency statistics on the associated images of each chip region to obtain the pixel value with the highest frequency in each input image; collecting the pixel values ​​with the highest frequency of all chips to establish a pixel value range; and performing filtering operations on each associated image according to the established pixel value range.

[0015] Optionally, the step of sorting different chips according to the average brightness includes: calculating the average value of non-zero pixels in the associated image of each chip region; sorting the calculated average values ​​of non-zero pixels and outputting the sorting results.

[0016] According to another aspect of the present invention, a tissue permeation time selection device is also provided, comprising: an experimental unit for performing permeation experiments on tissue sections for different durations to obtain corresponding multiple microscope images; a chip identification unit for identifying chips in each microscope image, locating chip regions, calculating the average brightness of the chip regions, and sorting different chips according to the average brightness; and a time selection unit for selecting the experimental duration corresponding to the chip with the highest average brightness as the target permeation experimental duration.

[0017] Optionally, the chip recognition unit includes: a downsampling module for downsampling each of the microscope images; an interval labeling module for labeling the downsampled image into intervals, including a background area and a target area, wherein the target area is used to locate the chip region of interest; an image generation module for generating a chip mask image based on the background area and the target area using a pre-trained image segmentation model; an intersection point acquisition module for performing line detection on the mask image to obtain N intersection points of at least three lines, where N is an integer greater than or equal to 3; and a chip localization module for cropping the chip region based on the N intersection points.

[0018] Optionally, the intersection point acquisition module includes: a mapping submodule, used to map each edge point in the mask image to a parameter space, obtain collinear points in the parameter space, and calculate the cumulative value of each point, wherein the parameter space includes: a polar coordinate space; an extraction submodule, used to extract target points with cumulative values ​​greater than a preset cumulative threshold using a Hough accumulator, and determine a detection line based on the extracted target points; and a coordinate transformation submodule, used to transform the detection line in the parameter space to a Cartesian coordinate system to obtain the line in the mask image.

[0019] Optionally, the tissue permeation time selection device further includes: an affine mapping unit, used to perform affine mapping on the associated image of the chip region after chip identification and chip region positioning in each microscope image, to obtain an associated image of a preset resolution; and a noise reduction unit, used to perform image noise reduction processing on the associated image of the chip region.

[0020] Optionally, the enhancement and filtering unit includes: a calculation module for calculating the mean and standard deviation of the associated image, wherein the mean represents image brightness and the standard deviation indicates image contrast; an enhancement factor definition module for defining a first enhancement factor and a second enhancement factor, wherein the first enhancement factor is used to enhance the contrast of the image and the second enhancement factor is used to enhance the brightness of the image; and a formula input module for performing linear transformation operations on the mean and corresponding second enhancement factor of the associated image, and the standard deviation and corresponding first enhancement factor of the associated image, respectively, to enhance the contrast and brightness of the image; wherein the linear transformation operation is used to perform a linear transformation on each pixel value in the associated image.

[0021] Optionally, the tissue permeation time selection device further includes: an image conversion module, used to convert the floating-point image into an integer image using a pixel value conversion strategy after performing a linear transformation operation, so that the pixel value range of the integer image is within a set range.

[0022] Optionally, the enhancement and filtering unit includes: a statistics module, used to perform pixel value frequency statistics on the associated images of each chip region to obtain the most frequent pixel value in each input image; a pixel value range establishment module, used to collect the most frequent pixel values ​​of all chips and establish a pixel value range; and a filtering module, used to perform filtering operations on each associated image according to the established pixel value range.

[0023] Optionally, the tissue permeation time selection device further includes: an average value calculation unit, used to sort different chips according to the average brightness and calculate the average value of non-zero pixels in the associated image of each chip region; and a sorting unit, used to sort the calculated average values ​​of non-zero pixels and output the sorting results.

[0024] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the tissue permeation time selection method described above.

[0025] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the tissue permeation time selection method described above.

[0026] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the tissue permeation time selection method described in any one of the above embodiments.

[0027] In this disclosure, permeation experiments of different durations are first performed on tissue sections to obtain multiple corresponding microscopic images. Chip identification is performed on each microscopic image to locate the chip region, the average brightness of the chip region is calculated, and different chips are sorted according to the average brightness. The experimental duration corresponding to the chip with the highest average brightness is selected as the target permeation experiment duration.

[0028] Based on the above disclosure, this invention provides a method for automatically selecting the optimal permeabilization time point for different tissues or cells, optimizing experimental results, improving experimental efficiency, reducing costs, and simplifying operations. This makes it easier for researchers to obtain high-quality spatiotemporal omics data and solves the technical problem of complex operation when selecting permeabilization time in related technologies. Attached Figure Description

[0029] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0030] Figure 1 This is a flowchart of an optional method for selecting tissue permeation time according to an embodiment of the present invention;

[0031] Figure 2 This is a flowchart of an optional permeation time selection algorithm based on fluorescence staining clarity according to an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of an optional tissue permeation time selection device according to an embodiment of the present invention;

[0033] Figure 4 This is a hardware structure block diagram of an electronic device (or mobile device) for performing a tissue permeation time selection method according to an embodiment of the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] To facilitate understanding of the present invention by those skilled in the art, some terms or nouns involved in the present invention are explained below:

[0037] The Segmentation Aspecting model is a pre-trained model used for image segmentation tasks. This model can effectively segment objects or regions in an image, achieving tasks such as semantic segmentation and instance segmentation.

[0038] The `cv2.convertScaleAbs` function multiplies each element of the input array by a scaling factor and converts the result to an unsigned 8-bit integer. In this invention, this function is used to convert a floating-point image to an integer image.

[0039] The Hough Transform is used to detect lines, circles, or other shapes in an image. It detects specific shapes in an image by mapping each point in the image to a parameter space and then searching the parameter space to find a set of points that are collinear or colcircular.

[0040] The following embodiments of the present invention can be applied to various systems / applications / devices for selecting tissue permeation time. The present invention solves the problem of permeation time for different tissues by selecting the time point with the strongest fluorescence signal, minimal RNA diffusion, and clearest internal tissue structure, thus determining the optimal permeation time point. Using the optimal permeation time to begin formal experiments on spatial gene expression can significantly improve the quality of subsequent spatiotemporal omics data, ensuring more accurate and reliable results in research.

[0041] The present invention will now be described in detail with reference to various embodiments.

[0042] Example 1

[0043] According to an embodiment of the present invention, an embodiment of a method for selecting tissue permeation time is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0044] Figure 1 This is a flowchart of an optional tissue permeation time selection method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0045] Step S101: Perform permeation experiments on tissue sections for different durations to obtain corresponding multiple microscopic images.

[0046] It should be noted that the tissue permeation time selection method provided in this embodiment is based on the tissue-optimized TO experiment. The TO experiment uses tissue-optimized slides to explore the optimal tissue permeation time. The tissue-optimized slides can contain multiple capture regions (e.g., 8 capture regions, 8mm x 8mm in size). Each capture region is covered with oligonucleotides for capturing genes. Six regions can be set with 6 different permeation times, and the other 2 regions are the negative control and the positive control, respectively. The negative control contains tissue sections but no permeation agent, and the positive control does not contain tissue sections but directly adds RNA.

[0047] In the case of permeabilization experiments on tissue sections at different time points, a series of sections can be obtained by following the standard operating procedure of the customized chip permeabilization reagent kit. Each section represents a tissue sample under different permeabilization times.

[0048] The standard operating procedure for the customized chip permeation reagent kit includes: selecting a tissue permeation time range of 0-30 min, for example, setting up multiple gradient experimental time periods of 6 min, 12 min, 18 min, 24 min, and a positive control, for testing in 5 groups. The chip with the longest permeation time is added first, starting from the four corners of the chip, with permeation reagent added sequentially at regular intervals (e.g., 6 min); then reverse transcription reaction, tissue removal, fluorescence imaging, and obtaining microscopic images are performed.

[0049] After obtaining the microscopic image, chip identification and chip region can be performed.

[0050] Step S102: For each microscope image, chip identification is performed, the chip area is located, the average brightness of the chip area is calculated, and different chips are sorted according to the average brightness.

[0051] Optionally, the step of identifying the chip region for each microscope image includes: downsampling each microscope image; labeling the downsampled image into regions, including background and target regions, where the target region is used to locate the chip region of interest; generating a mask image of the chip based on the background and target regions using a pre-trained image segmentation model; performing line detection on the mask image to obtain N intersection points of at least three lines, where N is an integer greater than or equal to 3; and cropping the chip region based on the N intersection points.

[0052] It should be noted that, in this embodiment, when performing interval labeling on the downsampled image, image background and chip region labeling can be performed. Specifically, this can include setting background cue points at the four corners of the downsampled image and setting foreground cue points within the image organization. The background cue points are used to determine the background in the downsampled image, and the foreground cue points are used to determine the target area, i.e., to locate the chip region of interest.

[0053] In the chip recognition stage, this embodiment can utilize the guidance function of the Segment Anything pre-trained model to first perform image downsampling and region labeling. For example, image downsampling includes downsampling a high-resolution image captured at 10x magnification to obtain a 2048*2048 pixel image. During region labeling, for example, setting background and foreground cue points on the downsampled image includes setting background cue points at the four corners of the image and foreground cue points within the tissue. These background and foreground cue points guide the model to identify chip regions. Background cue points represent the background in the image, while foreground cue points indicate the chip region of interest. These cue points are key elements in training the model.

[0054] Then, a mask image of the chip can be generated based on the background area and the target area using an image segmentation model. The image segmentation model can be selected by the user. For example, a Segment Aspecting pre-trained model can be used. In this embodiment, a mask image of the chip can be generated based on the background area and the target area after the interval is labeled. This mask image will help this embodiment accurately locate the chip area, making it easier to process in subsequent analysis.

[0055] For further analysis, this embodiment requires line detection on the mask image, for example, using Hough transform to detect lines in the image to obtain three or more intersection points on at least three sides. The presence of these intersection points allows this embodiment to capture the chip region.

[0056] Optionally, the step of performing line detection on the mask image includes: mapping each edge point in the mask image to a parameter space, obtaining collinear points in the parameter space, and calculating the cumulative value of each point, wherein the parameter space includes: polar coordinate space; using a Hough accumulator to extract target points whose cumulative values ​​are greater than a preset cumulative threshold, and determining the detection line based on the extracted target points; transforming the detection line in the parameter space to a Cartesian coordinate system to obtain the line in the mask image.

[0057] For line detection, this embodiment typically uses polar coordinates to represent the line, where the line is described by two parameters: the polar radius (distance from the origin) and the polar angle (angle between the line and the horizontal axis). The general polar coordinate form of the Hough transform is as follows: ρ = x*cos(θ) + y*s in(θ), where (ρ,θ) are the parameters of the line, (x,y) are the coordinates of the edge points in the image, θ is the direction angle of the line, and ρ is the distance of the line from the origin.

[0058] After mapping, the accumulation table in the parameter space is called a Hough accumulator. In a Hough accumulator, accumulation becomes stronger when multiple edge points correspond to the same straight line. Therefore, strong accumulation regions in the accumulator represent possible straight lines. In the parameter space, accumulation is performed for each point to obtain an accumulation value, which represents the number of straight lines passing through that point. Then, line detection is performed: by setting an accumulation value threshold, this embodiment can determine the detected straight lines. Finally, this embodiment can find the detected straight lines in the image by transforming the lines in the parameter space back to the Cartesian coordinate system.

[0059] It should be noted that after identifying the chip and locating the chip region in each microscope image, affine mapping is performed on the associated image of the extracted chip region to obtain an associated image at a preset resolution. Image noise reduction processing is also required for the associated image of the chip region.

[0060] In this embodiment, after obtaining the associated image of the chip region, an affine mapping is performed on the associated image to obtain an associated image of a preset resolution, for example, converting it into a chip region of 2048*2048 pixels.

[0061] To improve the visual clarity of the image, this embodiment performs contrast and brightness enhancement processing. This step aims to make the tissue area clearer and reduce noise in the image for subsequent analysis and identification. Optionally, the step of image denoising processing on the associated image of the chip area includes: calculating the mean and standard deviation of the associated image, where the mean represents the image brightness and the standard deviation indicates the image contrast (the mean represents the brightness of the input image, and the standard deviation indicates the dispersion of pixel values ​​in the input image); defining a first enhancement factor and a second enhancement factor, where the first enhancement factor is used to enhance the image contrast and the second enhancement factor is used to enhance the image brightness; performing a linear transformation operation on the mean and corresponding second enhancement factor of the associated image, and on the standard deviation and corresponding first enhancement factor of the associated image, respectively, to enhance the contrast and brightness of the image; wherein the linear transformation operation is used to perform a linear transformation on each pixel value in the associated image.

[0062] This embodiment defines two enhancement factors, alpha and beta, which control the degree of enhancement of image contrast and brightness, respectively.

[0063] It should be noted that this embodiment uses the `cv2.convertScaleAbs` function to perform a linear transformation operation. The formula for the linear transformation is as follows: `enhanced_pixel_value = alpha * origina_pixel_value + beta`, where `alpha` controls the enhancement of contrast, `beta` controls the enhancement of brightness, and `origina_pixel_value` refers to the pixel value of the input image. Through this linear transformation formula, a linear transformation is performed on each pixel value, thereby enhancing the contrast and brightness of the image.

[0064] Optionally, after performing the linear transformation operation, it is also necessary to perform image data type conversion, including: using a pixel value conversion strategy to convert the floating-point image into an integer image, so that the pixel value range of the integer image is within a set range. Specifically, the pixel value conversion strategy is used to multiply each pixel value in the input matrix obtained after the linear transformation by a scaling factor, and then take the absolute value of the multiplication result.

[0065] After completing the linear transformation operation, this embodiment mainly uses the cv2.convertScaleAbs function to convert the floating-point image to an integer image, and ensures that the pixel values ​​are within a suitable range, thus realizing the data type conversion of the image.

[0066] In this embodiment, the intensity of the enhancement effect can be controlled by adjusting the values ​​of two enhancement factors, alpha and beta. Enhancement is a common technique in image processing used to improve image quality and adapt to different application scenarios.

[0067] Optionally, the step of performing image noise reduction processing on the associated images of the chip regions further includes: performing pixel value frequency statistics on the associated images of each chip region to obtain the most frequent pixel value in each input image; collecting the most frequent pixel values ​​of all chips and establishing a pixel value range; and performing filtering operations on each associated image according to the established pixel value range.

[0068] To further optimize the image analysis in this embodiment, 50 pixels are first cropped from the edges of the chip area. Then, band-stop filtering is used to remove any noise that may exist within the chip itself. Next, the average brightness of the entire chip area after denoising is calculated, and different chips are ranked based on this average brightness. This helps to better understand the characteristics and brightness attributes of each chip, providing foundational data for subsequent research.

[0069] When using band-stop filtering to filter the chip area, the process includes: 1. Pixel frequency statistics: Performing pixel frequency statistics on each chip image to determine which pixel value appears most frequently in each chip. 2. Identifying the most frequent pixel value in each chip image. 3. Establishing a pixel value range: Next, this embodiment collects the most frequent pixel values ​​from all chips to establish a pixel value range. This range will include the most frequent pixel values ​​from all chips, thereby capturing the main features of the entire dataset. 4. Image filtering: Using the established pixel value range, this embodiment can perform filtering operations on each chip image. The purpose of filtering is to retain only pixels whose pixel values ​​fall within this range and exclude other pixel values.

[0070] Optionally, the step of sorting different chips based on average brightness includes: calculating the average value of non-zero pixels in the associated image of each chip region; sorting the calculated average values ​​of non-zero pixels and outputting the sorting results.

[0071] After image filtering, this embodiment calculates the average value of non-zero pixels in each chip image to help understand the average performance of the main features of each chip. Finally, this embodiment sorts these calculated average values ​​of non-zero pixels to determine which chips have higher average feature performance and their ranking in the entire dataset.

[0072] Step S103: Select the experimental duration corresponding to the chip with the highest average brightness as the target transparency experimental duration.

[0073] The present invention provides a method for selecting tissue permeation time, which involves first performing permeation experiments on tissue sections for different durations to obtain multiple corresponding microscopic images, identifying chips in each microscopic image, locating chip regions, calculating the average brightness of chip regions, sorting different chips according to the average brightness, and selecting the experimental duration corresponding to the chip with the highest average brightness as the target permeation experimental duration.

[0074] In this embodiment, a method is provided for different tissues or cells to automatically select the optimal permeabilization time point, optimize experimental results, improve experimental efficiency, reduce costs, and reduce operational complexity, making it easier for researchers to obtain high-quality spatiotemporal omics data and solving the technical problem of complex operation when selecting permeabilization time in related technologies.

[0075] The invention will now be described in conjunction with another alternative embodiment.

[0076] Figure 2 This is a flowchart of an optional permeation time selection algorithm based on fluorescence staining clarity according to an embodiment of the present invention, such as... Figure 2 As shown, the permeation time selection algorithm mainly includes: a permeation experiment part, a part for calculating the clarity of the tissue region, and a part for outputting results.

[0077] The permeation experiment section includes: the slicing process, the permeation experiment process, and the microscope imaging section.

[0078] The part for calculating tissue region sharpness includes: selecting the chip region, tissue region enhancement processing, and calculating image sharpness.

[0079] Taking the selection of the optimal permeabilization time based on hippocampal embryo microarray images as an example:

[0080] The first step involved performing permeation experiments on serially sliced ​​hippocampal embryo samples for 6 min, 12 min, 18 min, and 24 min, respectively.

[0081] The second step is to perform chip identification on the microscope image, extract the chip area and perform affine mapping to obtain a 2048*2048 chip area.

[0082] Here, the chip recognition process relies on the guidance function of the Segment Anything pre-trained model. This includes: 1. Image downsampling: Downsampling the high-resolution image captured at 10x magnification to obtain a 2048*2048 pixel image. 2. Background and foreground cue points: Setting background cue points at the four corners of the image, and foreground cue points within the tissue. This step guides the model to recognize the chip region. 3. Generating a chip mask image: Using the Segment Anything pre-trained model, a chip mask image can be generated based on the cue points. 4. Using Hough transform to detect lines in the image to obtain three or more intersection points on at least three sides. The existence of these intersection points allows us to crop the chip region and perform affine mapping, converting it into a 2048*2048 pixel chip region.

[0083] The third step, to improve the visual clarity of the image, involves enhancing contrast and brightness. This enhancement is based on the following principles and formulas: 1. Linear Transformation Principle: Enhancing contrast and brightness is a linear transformation that changes the appearance of the image by stretching or compressing the range of pixel values. 2. Mean and Standard Deviation: The mean and standard deviation of the input image are first calculated. 3. Enhancement Factors: Two enhancement factors, alpha and beta, are defined to control the degree of contrast and brightness enhancement. 4. Linear Transformation Formula: The key part is the `cv2.convertScaleAbs` function, which performs a linear transformation operation. 5. Data Type Conversion: The `cv2.convertScaleAbs` function is used to convert the floating-point image to an integer image, ensuring that the pixel values ​​are within the appropriate range.

[0084] The fourth step involves using band-stop filtering to remove any noise that may be present in the chip itself. The average brightness of the entire chip area after noise reduction is then calculated, and the chips are ranked based on this average brightness.

[0085] The specific principle of band-stop filtering technology is as follows: 1. Pixel frequency statistics: Perform pixel frequency statistics on each chip image. 2. Highest frequency pixel value: Once the pixel frequency statistics are completed, the highest frequency pixel value in each chip image can be determined. 3. Establish pixel value range: Next, collect the highest frequency pixel values ​​of all chips to establish a pixel value range. 4. Image filtering: Using the established pixel value range, filtering operations can be performed on each chip image. 5. Calculation of non-zero pixel average: After filtering, we can calculate the average value of non-zero pixels in each chip image. 6. Ranking: Ranking these calculated non-zero pixel average values ​​allows us to determine which chips have higher average feature performance and their ranking in the entire dataset.

[0086] The permeabilization time selection scheme described above provides an effective method for choosing the optimal permeabilization time point, thereby optimizing experimental results. This not only improves experimental efficiency and reduces costs but also lowers operational complexity, making it easier for users to obtain high-quality spatiotemporal omics data. With the continuous development of spatiotemporal omics analysis theory and technology, the practicality and applicability of this algorithm will continue to expand, providing researchers with more powerful tools to explore the spatial expression characteristics of cells and tissues in greater depth.

[0087] The following is a detailed description with reference to another embodiment.

[0088] Example 2

[0089] The tissue permeation time selection device provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above.

[0090] Figure 3 This is a schematic diagram of an optional tissue permeation time selection device according to an embodiment of the present invention, such as... Figure 3 As shown, the tissue permeation time selection device may include: experimental unit 31, chip recognition unit 32, and time selection unit 33.

[0091] Among them, experimental unit 31 is used to perform permeation experiments on tissue sections for different durations to obtain corresponding multiple microscope images.

[0092] The chip identification unit 32 is used to identify chips in each microscope image, locate chip areas, calculate the average brightness of chip areas, and sort different chips according to the average brightness.

[0093] The time selection unit 33 is used to select the experimental duration corresponding to the chip with the highest average brightness as the target transparency experimental duration.

[0094] The aforementioned tissue permeation time selection device allows for the following steps: Experimental unit 31 performs permeation experiments on tissue sections for different durations, generating multiple corresponding microscopic images. Chip recognition unit 32 identifies the chip in each microscopic image, locates the chip region, calculates the average brightness of the chip region, and sorts the chips based on average brightness. Time selection unit 33 then selects the experimental duration corresponding to the chip with the highest average brightness as the target permeation experiment duration. This embodiment provides an automated method for selecting the optimal permeation time for different tissues or cells, optimizing experimental results. This not only improves experimental efficiency and reduces costs but also lowers operational complexity, making it easier for researchers to obtain high-quality spatiotemporal omics data and solving the technical problem of complex permeation time selection in related technologies.

[0095] Optionally, the chip recognition unit includes: a downsampling module for downsampling each of the microscope images; an interval labeling module for labeling the downsampled image into intervals, including a background area and a target area, wherein the target area is used to locate the chip region of interest; an image generation module for generating a chip mask image based on the background area and the target area using a pre-trained image segmentation model; an intersection point acquisition module for performing line detection on the mask image to obtain N intersection points of at least three lines, where N is an integer greater than or equal to 3; and a chip localization module for cropping the chip region based on the N intersection points.

[0096] Optionally, the intersection point acquisition module includes: a mapping submodule, used to map each edge point in the mask image to a parameter space, obtain collinear points in the parameter space, and calculate the cumulative value of each point, wherein the parameter space includes: a polar coordinate space; an extraction submodule, used to extract target points with cumulative values ​​greater than a preset cumulative threshold using a Hough accumulator, and determine a detection line based on the extracted target points; and a coordinate transformation submodule, used to transform the detection line in the parameter space to a Cartesian coordinate system to obtain the line in the mask image.

[0097] Optionally, the tissue permeation time selection device further includes: an affine mapping unit, used to perform affine mapping on the associated image of the chip region after chip identification and chip region positioning in each microscope image, to obtain an associated image of a preset resolution; and a noise reduction unit, used to perform image noise reduction processing on the associated image of the chip region.

[0098] Optionally, the enhancement and filtering unit includes: a calculation module for calculating the mean and standard deviation of the associated image, wherein the mean represents the image brightness and the standard deviation indicates the image contrast; an enhancement factor definition module for defining a first enhancement factor and a second enhancement factor, wherein the first enhancement factor is used to enhance the image contrast and the second enhancement factor is used to enhance the image brightness; and a formula input module for performing linear transformation operations on the mean and corresponding second enhancement factor of the associated image, and the standard deviation and corresponding first enhancement factor of the associated image, respectively, to enhance the contrast and brightness of the image; wherein the linear transformation operation is used to perform a linear transformation on each pixel value in the associated image.

[0099] Optionally, the tissue permeation time selection device further includes: an image conversion module, used to convert the floating-point image into an integer image using a pixel value conversion strategy after performing a linear transformation operation, so that the pixel value range of the integer image is within a set range, wherein the pixel value conversion strategy is used to multiply each pixel value in the input matrix obtained after the linear transformation by a scaling factor, and take the absolute value of the multiplication result.

[0100] Optionally, the enhancement and filtering unit includes: a statistics module for performing pixel value frequency statistics on the associated images of each chip region to obtain the most frequent pixel value in each input image; a pixel value range establishment module for collecting the most frequent pixel values ​​of all chips and establishing a pixel value range; and a filtering module for performing filtering operations on each associated image according to the established pixel value range.

[0101] Optionally, the tissue permeation time selection device further includes: an average value calculation unit, used to sort different chips according to average brightness and calculate the average value of non-zero pixels in the associated image of each chip region; and a sorting unit, used to sort the calculated average values ​​of non-zero pixels and output the sorting results.

[0102] The aforementioned tissue permeation time selection device may also include a processor and a memory. The aforementioned experimental unit 31, chip identification unit 32, time selection unit 33, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0103] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and the permeabilization time points for spatial transcriptome permeabilization experiments can be selected by adjusting kernel parameters.

[0104] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory, and the memory includes at least one memory chip.

[0105] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the tissue permeation time selection method of any one of the above embodiments.

[0106] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the tissue permeation time selection method of any one of the above embodiments.

[0107] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the tissue permeation time selection method described in various embodiments of this application.

[0108] This application also provides a computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the tissue permeation time selection method described in various embodiments of this application.

[0109] Figure 4 This is a hardware structure block diagram of an electronic device (or mobile device) for performing a tissue permeation time selection method according to an embodiment of the present invention. Figure 4 As shown, an electronic device may include one or more ( Figure 4 The processor 402 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and the memory 404 for storing data may also be included. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 4 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown.

[0110] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0111] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0116] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for selecting tissue permeation time, characterized in that, include: Permeation experiments were performed on tissue sections for different durations to obtain multiple corresponding microscopic images; For each of the aforementioned microscope images, chip identification is performed, chip regions are located, the average brightness of the chip regions is calculated, and different chips are sorted according to the average brightness; The experimental duration corresponding to the chip with the highest average brightness was selected as the target transparency experimental duration.

2. The method for selecting tissue permeation time according to claim 1, characterized in that, The steps of performing chip identification and locating the chip region for each of the aforementioned microscope images include: Each of the aforementioned microscope images is downsampled; The downsampled image is labeled with regions, including background regions and target regions, wherein the target regions are used to locate chip regions of interest; A mask image of the chip is generated based on the background region and the target region using a pre-trained image segmentation model. Perform line detection on the mask image to obtain at least three lines with N intersection points, where N is an integer greater than or equal to 3; The chip region is extracted based on the N intersection points.

3. The method for selecting tissue permeation time according to claim 2, characterized in that, The step of performing line detection on the mask image includes: Map each edge point in the mask image to a parameter space, obtain the collinear points in the parameter space, and calculate the cumulative value of each point, wherein the parameter space includes: polar coordinate space; Hough accumulator is used to extract target points whose accumulated value is greater than a preset accumulated threshold, and a detection line is determined based on the extracted target points; The detected straight lines in the parameter space are transformed to the Cartesian coordinate system to obtain the straight lines in the mask image.

4. The method for selecting tissue permeation time according to claim 1, characterized in that, After performing chip identification and locating the chip region on each of the aforementioned microscope images, the process further includes: Affine mapping is performed on the associated image of the extracted chip region to obtain an associated image of a preset resolution; Image noise reduction processing is performed on the associated image of the chip region.

5. The method for selecting tissue permeation time according to claim 4, characterized in that, The step of performing image noise reduction processing on the associated image of the chip region includes: Calculate the mean and standard deviation of the associated images, wherein the mean represents the image brightness and the standard deviation indicates the image contrast; Define a first enhancement factor and a second enhancement factor, wherein the first enhancement factor is used to enhance the contrast of the image, and the second enhancement factor is used to enhance the brightness of the image; A linear transformation operation is performed on the mean and corresponding second enhancement factor of the associated image, and the standard deviation and corresponding first enhancement factor of the associated image, respectively, to enhance the contrast and brightness of the image; wherein the linear transformation operation is used to perform a linear transformation on each pixel value in the associated image.

6. The method for selecting tissue permeation time according to claim 4, characterized in that, After performing the linear transformation operation, the following is also included: A pixel value conversion strategy is used to convert a floating-point image into an integer image so that the pixel value range of the integer image is within a set range.

7. The method for selecting tissue permeation time according to claim 4, characterized in that, The step of performing image noise reduction processing on the associated image of the chip region includes: Pixel frequency statistics are performed on the associated images of each chip region to obtain the pixel value with the highest frequency in each input image; Collect the most frequent pixel values ​​from all chips and establish a range of pixel values; Based on the established pixel value range, filtering operations are performed on each of the associated images.

8. The method for selecting tissue permeation time according to claim 1, characterized in that, The step of sorting different chips according to the average brightness includes: The average value of non-zero pixels in the associated image of each chip region is calculated. Sort the calculated non-zero pixel average values ​​and output the sorting results.

9. A tissue permeation time selection device, characterized in that, include: The experimental unit is used to perform permeation experiments on tissue sections for different durations to obtain multiple corresponding microscopic images. The chip identification unit is used to identify chips in each microscope image, locate chip regions, calculate the average brightness of the chip regions, and sort different chips according to the average brightness. The time selection unit is used to select the experimental duration corresponding to the chip with the highest average brightness as the target transparency experimental duration.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the tissue permeation time selection method according to any one of claims 1 to 8.

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