Method for acquiring scanning image of biological sample

Abstract

The embodiment of the invention provides a scanning image acquisition method of a biological sample. The method comprises the following steps: acquiring a section sample of a biological sample to be scanned; the scanning electron microscope is controlled to conduct scanning on the section sample based on the multiple sets of focusing parameters, a composite detector is adopted in the scanning process to collect a current signal set of a focusing surface generated by the section sample under each set of focusing parameters, and the current signal set comprises multiple current signals; image processing is carried out on the current signal group under each focusing surface to obtain a plurality of signal images under each focusing surface, and the image processing comprises multi-stage multiplication amplification, signal conversion, sampling, algorithm operation and AI image extraction; image filtering is carried out on the signal images under each focusing surface through an image filtering algorithm, a plurality of layered scanning images are obtained, and the total number of the layered scanning images is different from the total number of the signal images.

Description

Technical Field

[0001] The present application relates to the field of electron microscopic imaging technology, and in particular to a method for acquiring a scanning image of a biological sample. Background Art

[0002] The acquisition of internal structural information of biological samples is highly dependent on the quality of scanning electron microscope (SEM) images (scanned images), especially their axial resolution, which is the key to ensuring the complete presentation of internal structural information. The axial resolution of the scanned image is closely related to the slice thickness of the biological sample: the thinner the slice, the higher the axial resolution, which can reveal the internal features of the biological sample more finely.

[0003] However, current methods for reducing slice thickness mainly focus on physical slice technology, but these traditional technologies face significant limitations. Although they perform well in processing millimeter-scale samples, they are unable to keep up when pursuing nanometer-scale slice thickness. Therefore, current physical slice technology is obviously insufficient in meeting the needs of high-precision and high-resolution imaging of the internal structure of biological samples. How to provide a method for acquiring axial high-resolution scanning images that supports nanometer-scale has become an urgent problem to be solved. Summary of the invention

[0004] The present application provides a method for acquiring a scanning image of a biological sample to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present application, a method for acquiring a scanning image of a biological sample is provided, the method comprising: Obtaining a cross-section sample of a biological sample to be scanned; Controlling the scanning electron microscope to scan the section sample based on multiple sets of focusing parameters, and using a composite detector to collect a current signal group of the focusing plane generated by the section sample under each set of focusing parameters during the scanning process, wherein the current signal group includes multiple current signals; Performing image processing on the current signal group under each focusing plane to obtain multiple signal images under each focusing plane, wherein the image processing includes multi-stage multiplication and amplification, signal conversion, sampling, algorithm operation and AI image extraction; An image filtering algorithm is used to perform image filtering on multiple signal images under each focus plane to obtain multiple layered scanning images, and the total number of the layered scanning images is different from the total number of the signal images.

[0006] According to an embodiment of the present application, before controlling the scanning electron microscope to scan the section sample based on multiple sets of focusing parameters, the method further includes acquiring multiple sets of focusing parameters, and accordingly, The acquiring of multiple sets of focusing parameters comprises: Determine the scaled size of the section sample according to the original field size of the scanning electron microscope and the set limit size, and adjust the first gain and the second gain of the scanning electron microscope according to the scaled size; Obtaining the required number of groups of focusing parameters, and determining multiple layers of the section sample according to the required number of groups; For each layer, a parameter optimization algorithm is used to obtain the focus parameters of each layer, and multiple groups of focus parameters are obtained. The focus parameters include coarse adjustment parameters and fine adjustment parameters.

[0007] According to one embodiment of the present application, a cross-section sample of a biological sample to be scanned is obtained; Controlling the scanning electron microscope to scan the section sample based on multiple sets of focusing parameters, and using a composite detector to collect a current signal group of the focusing plane generated by the section sample under each set of focusing parameters during the scanning process, wherein the current signal group includes multiple current signals; The current signal group under each focusing plane is image processed to obtain multiple layered scanning images of the cross-section sample, and the image processing includes multi-stage multiplication and amplification, signal conversion, sampling, algorithm operation and AI image extraction.

[0008] According to an embodiment of the present application, the point-by-point and line-by-line scanning includes: The point-by-point and line-by-line scanning includes: The converging lens of the scanning electron microscope focuses the high-voltage focused incident electron beam into an electron beam probe, and performs point-by-point scanning on the section sample based on the electron beam probe; The deflector of the scanning electron microscope deflects the electron beam probe so that the electron beam probe scans the section sample line by line; The point-by-point scanning and line-by-line scanning are performed at a continuous large angle.

[0009] According to an embodiment of the present application, the image filtering algorithm is used to perform image filtering on multiple signal images under each focus plane to obtain multiple layered scanning images, including: Adaptively filter and de-noise all signal images of each focal plane and perform spatial registration to obtain aligned images corresponding to all signal images; Calculate the quality score of each aligned image based on its clarity, noise level and structural similarity, and select valid images according to the quality score of each aligned image; Taking the valid image as input, the point spread function is initialized according to the preset kernel function, and the image is reconstructed through the iterative deconvolution algorithm guided by the noise confidence; Screening a plurality of layered scanning images to be processed according to the gradient characteristics and noise confidence of the reconstructed image; Performing brightness equalization and contrast enhancement on a plurality of layered scanned images to be processed to obtain a plurality of layered scanned images; Among them, the noise confidence is obtained through local variance and global variance; During image reconstruction, the point spread function parameters are adjusted according to the structural similarity gradient, and the update intensity is controlled by the noise weight matrix.

[0010] According to one embodiment of the present application, the multiple current signals include at least two of the following: secondary electrons, backscattered electrons, Auger electrons, X-ray energy spectrum, bright field information, and dark field information.

[0011] According to one embodiment of the present application, the slice thickness of the cross-section sample is 30~100nm.

[0012] According to one embodiment of the present application, the composite detector includes a secondary electron detector, a backscattered electron detector, a bright field detector and a dark field detector. The secondary electron detector is arranged directly above the cut sample, the backscattered electron detector is arranged on the side above the cut sample, the bright field detector is arranged directly below the cut sample, and the dark field detector is arranged on the side below the cut sample.

[0013] According to an embodiment of the present application, the number of the multiple groups of focusing parameters is determined according to the axial resolution requirement of the scanned image; The axial resolution requirement of the scanning image is determined according to the nanometer level requirement.

[0014] According to an embodiment of the present application, the section sample of the biological sample to be scanned is obtained from a sample processing device, wherein the sample processing device includes a slicing device, a plasma processing device and a coating device; accordingly, The section sample of the biological sample to be scanned is obtained by the sample processing device through the following operations: The slicing device is used to continuously slice the biological sample to be scanned to obtain a plurality of sample slices to be processed; The plasma processing device and the coating device are used to perform plasma thinning and significant treatment and surface conductive film coating treatment on multiple sample slices to obtain multiple sample slices; The section sample is determined from the plurality of sample slices.

[0015] According to a second aspect of the present application, a scanning image acquisition device for a biological sample is provided, the device comprising: An acquisition module, used for acquiring a cross-section sample of a biological sample to be scanned; A collection module, used to control the scanning electron microscope to scan the section sample based on multiple sets of focusing parameters, and use a composite detector to collect a current signal group of the focus plane generated by the section sample under each set of focusing parameters during the scanning process, wherein the current signal group includes multiple current signals; An image processing module is used to perform image processing on the current signal group under each focusing plane to obtain multiple signal images under each focusing plane, wherein the image processing includes multi-stage multiplication and amplification, signal conversion, sampling, algorithm operation and AI image extraction; The image filtering module is used to perform image filtering on multiple signal images under each focus plane using an image filtering algorithm to obtain multiple layered scanning images, and the total number of the layered scanning images is different from the total number of the signal images.

[0016] According to a third aspect of the present application, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present application.

[0017] According to a fourth aspect of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method described in the present application.

[0018] The present application embodiment provides a method for acquiring scanning images of biological samples, which obtains a section sample of the biological sample to be scanned; controls a scanning electron microscope to scan the section sample based on multiple sets of focusing parameters, and uses a composite detector to collect the current signal group of the focusing plane generated by the section sample under each set of focusing parameters during the scanning process, wherein the current signal group includes multiple current signals; image processing is performed on the current signal group under each focusing plane to obtain multiple layered scanning images of the section sample, wherein the image processing includes multi-level multiplication and amplification, signal conversion, sampling, algorithm operation, and AI image extraction. The sample slices are finely divided into focus planes by multiple sets of focusing parameters, thereby achieving "secondary slicing" of the sample slices, which greatly improves the axial resolution and nanometer level of the scanning image.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become readily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, wherein: In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0021] Figure 1 A schematic diagram of the implementation process of the method for acquiring a scanning image of a biological sample provided in an embodiment of the present application is shown; Figure 2 An example diagram showing a composite detector arrangement of a method for acquiring a scanning image of a biological sample provided by an embodiment of the present application; Figure 3 A schematic diagram showing the composition and structure of a scanning image acquisition device for a biological sample provided in an embodiment of the present application is shown; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] In order to make the purpose, features, and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0023] Figure 1 A schematic diagram of the implementation flow of the method for acquiring a scanning image of a biological sample provided in an embodiment of the present application is shown.

[0024] refer to Figure 1 , the embodiment of the present application provides a method for acquiring a scanning image of a biological sample, the method comprising: Operation 101 : obtaining a cross-section sample of a biological sample to be scanned.

[0025] In order to obtain a thinner biological sample scanning image, the biological sample to be scanned needs to be sliced. When the biological sample scanning image needs to be generated, a cross-sectional sample obtained after slicing the biological sample to be scanned is obtained.

[0026] In one embodiment of the present application, obtaining a section sample of a biological sample to be scanned can be regarded as sending an acquisition signal to a device for slicing the biological sample, so that the slicing device transmits the section sample of the biological sample to a corresponding processing position. In order to ensure the randomness of the acquisition of the scanned image, obtaining a section sample of the biological sample can also be regarded as obtaining a section sample from a user, that is, receiving a section sample placed by the user at a corresponding processing position when receiving a scan image acquisition instruction sent by the user.

[0027] In one embodiment of the present application, obtaining a cross-sectional sample of a biological sample to be scanned is to obtain a cross-sectional sample of the biological sample to be scanned from a sample processing device, and the sample processing device includes a slicing device, a plasma processing device and a coating device; accordingly, the cross-sectional sample of the biological sample to be scanned is obtained by the sample processing device using the following operations: using the slicing device to continuously cut the biological sample to be scanned to obtain a plurality of sample slices to be processed; using the plasma processing device and the coating device to perform plasma thinning and significant treatment and surface conductive film coating treatment on the plurality of sample slices to obtain a plurality of sample slices; and determining the cross-sectional sample from the plurality of sample slices.

[0028] The cross-section sample can be obtained by slicing the biological sample to be scanned by the sample processing device, which controls its own slicing device, plasma processing device and coating device to continuously cut the biological sample, plasma thinning and significant treatment and surface conductive film coating to obtain multiple sample slices. Then, one of the sample slices is selected as the cross-section sample.

[0029] In one embodiment of the present application, the slice thickness of the cut sample is 30-100 nm.

[0030] The slice thickness of the sectioned sample is the minimum cross-sectional thickness. The minimum cross-sectional thickness is limited by the diamond knife, slicing device and process. The currently available minimum cross-sectional thickness is 30~100nm.

[0031] Operation 102 is to control a scanning electron microscope to scan a section sample based on multiple sets of focusing parameters, and during the scanning process, use a composite detector to collect a current signal group of a focusing plane generated by the section sample under each set of focusing parameters, wherein the current signal group includes multiple current signals.

[0032] Scanning images mainly rely on electron microscopes to obtain. Scanning electron microscopes (SEMs) generally use secondary electron detectors (SEDs) and backscattered electron detectors (BSEDs), but SEMs can only obtain information such as the morphology and composition of the sample surface, and cannot detect the internal information of the sample. Transmission electron microscopes (TEMs) generally use bright field detectors (BFDs) and dark field detectors (DFDs), which have higher resolution than SEMs and can observe information inside the sample to obtain elemental and spatial structure information of the sample. Therefore, in order to obtain more internal structure information of biological samples, the embodiment of the present application uses a scanning electron microscope (STEM) with the comprehensive characteristics of SEM and TEM to scan the sample slices to obtain a variety of internal information of the sample and obtain the three-dimensional structure of the sample slices. Among them, scanning means scanning and transmission.

[0033] The slice thickness of the section sample obtained by slicing using physical technology may not meet the nanometer level requirements. Therefore, the embodiment of the present application needs to perform a secondary cutting of the section sample by a scanning electron microscope to cut the section sample into a sample that meets the nanometer level. Specifically, by setting multiple sets of focusing parameters and adjusting the scanning electron microscope based on the multiple sets of focusing parameters, the scanning electron microscope can be controlled to divide the section sample into multiple focusing planes at different depths, thereby reducing the slice thickness of the sample slice. Each set of focusing parameters corresponds to a depth of the sample slice.

[0034] In order to obtain various current signals generated at each focal plane during the scanning process of the section sample, a composite detector is arranged around the sample slice. The composite detector is composed of multiple detectors, and each detector can obtain a type of current signal. During the scanning process, the current signal group of the section sample at each focal plane is continuously obtained based on the composite detector. Among them, the current signal group includes multiple current signals.

[0035] In one embodiment of the present application, the number of the multiple groups of focusing parameters is determined according to the axial resolution requirement of the scanning image; and the axial resolution requirement of the scanning image is determined according to the nanometer level requirement.

[0036] The axial resolution is the minimum spacing between the scanned images corresponding to the two focal planes. If the spacing is equal, the numerical value is the original slice thickness of the sectioned sample. The more focal planes there are, the more layers of the sectioned sample there are, and the higher the axial resolution is. Therefore, the axial resolution can be controlled by controlling the amount of data on the focal plane to achieve a scanned image with an axial resolution of a few nanometers. Among them, each group of focusing parameters in the multiple groups of focusing parameters is arranged in size, and the spacing between each two adjacent groups of focusing parameters is the same.

[0037] For example, when the slice thickness of the cross-sectioned sample is 30 nanometers, by setting four sets of equidistant focusing parameters, an axial resolution of a few nanometers can be obtained, that is, 30 nanometers / 4.

[0038] In one embodiment of the present application, it is also necessary to obtain multiple sets of focusing parameters, which can be obtained through the following operations: determining the scaled size of the section sample according to the original field of view size and the set limit size of the scanning electron microscope, and adjusting the first gain and the second gain of the scanning electron microscope according to the scaled size; obtaining the required number of groups of focusing parameters, and determining multiple layers of the section sample according to the required number of groups; for each layer, using a parameter optimization algorithm to obtain the focusing parameters of each layer, and obtaining multiple sets of focusing parameters, the focusing parameters including coarse adjustment parameters and fine adjustment parameters.

[0039] First, it is necessary to determine the number of layers for cutting the section sample. Each layer requires a set of focusing parameters. Therefore, obtaining the number of layers for cutting the section sample can also be understood as obtaining the number of groups of focusing parameters. After determining the layers, it is necessary to determine the focusing parameters of each layer. The embodiment of the present application preferably uses a parameter optimization algorithm to determine the focusing parameters of each layer.

[0040] Before using the parameter optimization algorithm to obtain the focusing parameters, in order to make the algorithm usable, the field of view of the scanning electron microscope is also preprocessed, that is, the image displayed by the scanning electron microscope is scaled. The image scaling process is a gain adjustment process. The specific process of field of view preprocessing includes: When the original field size satisfy When the first gain To perform scaling:

[0041] Among them, s is the scaling ratio, that is, the scaling size.

[0042] After preprocessing the field of view, the focusing parameters of each layer are determined. The process of obtaining the focusing parameters of each layer based on the parameter optimization algorithm may include: 1) Coarse-grained search ( Indicates coarse, coarse adjustment parameters, represents fine, fine adjustment parameter), where the coarse adjustment parameter represents the coarse adjustment value of the objective lens, and the fine adjustment parameter represents the fine adjustment value of the objective lens: initialization: ; Traversal: ; renew: ,in is the current sharpness index; Boundary detection: If ,like ; Repeat the traversal, update, and boundary detection steps until

[0043] 2) Medium-granularity search; Step Length: ; Search domains: ; renew: ; 3) Fine-grained search; Step Length: ; Search domains: ; renew: ; 4) Sub-pixel interpolation optimization (quadratic curve interpolation Initialized to 0); like Calculate the quadratic curve difference not at the search domain boundary :

[0044]

[0045] 5) Output the focusing parameters of each layer Will Determine the final focusing parameters and restore the first gain is the initial value.

[0046] Operation 103 , performing image processing on the current signal group under each focusing plane to obtain multiple signal images under each focusing plane, wherein the image processing includes multi-stage multiplication and amplification, signal conversion, sampling, algorithm operation and AI image extraction.

[0047] The current signal group of each focusing plane is subjected to multi-stage amplification, signal conversion, sampling, algorithm operation and AI (Artificial Intelligence) extraction processing to obtain the signal image of each focusing plane.

[0048] The current signal generated in a scanning electron microscope is often very weak, so it needs to be amplified at multiple levels to ensure that the signal is strong enough for subsequent processing. In order to ensure that the amplified scanning current signal meets the requirements of subsequent sampling and algorithm operation, the amplified current signal needs to be converted. The signal conversion may include but is not limited to filtering, modulation and other processing. The purpose of signal conversion is to remove noise, improve the signal-to-noise ratio or change the shape of the current signal. In order to facilitate subsequent processing, the continuous scanning current signal is also converted into a discrete signal by sampling. The purpose of sampling is to convert the scanning current signal into a digital signal.

[0049] The sampled digital signal needs to be processed by various algorithms to extract useful information. The algorithmic operations may include statistical analysis of the signal, frequency domain analysis, and image reconstruction, with the purpose of extracting information reflecting the characteristics of the sample from the original digital signal.

[0050] The last step is image extraction, which is to convert the signal processed by the algorithm into a visual image. Image extraction can include image enhancement, filtering, segmentation and other processing. Image extraction can be performed using conventional AI image extraction methods or image conversion algorithms, which will not be described here.

[0051] In operation 104, an image filtering algorithm is used to perform image filtering on the multiple signal images under each focus plane to obtain multiple layered scanning images, wherein the total number of the layered scanning images is different from the total number of the signal images.

[0052] Due to the lack of current signals, the image obtained after processing may have defects. In the case that the image obtained after processing has defects, the defective image needs to be filtered out. Therefore, all signal images are filtered based on the image filtering algorithm to obtain the final multiple layered scanning images.

[0053] In one embodiment of the present application, since the plurality of layered scanning images are obtained by filtering all signal images, the number of the layered scanning images is less than the number of the signal images.

[0054] In one embodiment of the present application, an image filtering algorithm is used to perform image filtering on multiple signal images under each focusing plane to obtain multiple layered scanning images of multiple cross-section samples, including: adaptive filtering denoising and spatial registration of all signal images of each focusing plane to obtain aligned images corresponding to all signal images; calculating the quality score of each aligned image based on the clarity, noise level and structural similarity of each aligned image, and screening valid images according to the quality score of each aligned image; taking the valid image as input, initializing the point spread function according to a preset kernel function, and reconstructing the image through an iterative deconvolution algorithm guided by noise confidence; screening multiple layered scanning images to be processed according to the gradient characteristics and noise confidence of the reconstructed image; performing brightness equalization and contrast enhancement on multiple layered scanning images to be processed to obtain multiple layered scanning images; wherein the noise confidence is obtained by local variance and global variance; in the process of reconstructing the image, adjusting the point spread function parameters according to the structural similarity gradient, and controlling the update intensity through the noise weight matrix.

[0055] For example, taking n focusing planes, each of which includes k signal images, and image filtering to obtain m (m≤k×n) layered scanning images, the process of image filtering the k×n signal images includes: 1) Adaptive filtering denoising For image denoising, Adaptive Non-local Mean Denoising (Adaptive NLM) is used to suppress noise while retaining the detailed features of biological samples as much as possible. The specific design of Adaptive Non-local Mean Denoising is as follows: For image I, the filtered value of pixel x

[0056] Among them, the weight function :

[0057] and is the center point coordinates in pixels Adaptive blocks (side length , for local standard deviation of the domain), The horizontal coordinates of all pixels in the image contain of A collection of .

[0058] Distance Metrics Use Gaussian-weighted Euclidean distance:

[0059] Among them, the standard deviation of the Gaussian function is: , is the index variable, is the Gaussian kernel function, which is related to k and Positive correlation.

[0060] Adaptive smoothing parameters : , It is a custom weight, usually 0.8.

[0061] is the normalization factor:

[0062] 2) Registration The images are registered based on the Anisotropic Guided Registration algorithm to obtain aligned images. The design of the Anisotropic Guided Registration algorithm is as follows: Let the reference image be , the image to be registered is , solve the transformation matrix by minimizing the anisotropic loss function :

[0063] Gradient direction constraints :

[0064] in, From the reference image The main gradient direction of a point determines:

[0065] The convolution kernel represents the horizontal and vertical coordinates; Anisotropy Weight :

[0066] Higher weights are given to marginal areas; Transformation Model : Affine transformation parameterization:

[0067] in, , , , , , is the affine matrix internal parameter, is the horizontal and vertical coordinates of the pixel point; 3) Image quality score calculation The image is screened to see if it is defective by designing an image quality assessment function for calculating the quality score.

[0068] Image quality assessment function:

[0069] Among them, Tenengrad gradient (clarity): , , is the Sobel operator; Noise level: median of local variance ; SSIM: structural similarity; Weight .

[0070] For each focus layer of signal images, keep Before value indivual( ) or remove those below the threshold The image is obtained by taking the effective image, where and threshold To be configured according to actual needs; 5) Reconstruct the image The noise-adaptive Richardson-Lucy deconvolution algorithm (noise confidence-guided iterative deconvolution algorithm) is used for fuzzy tomography. The blurred image is reconstructed by Iterative optimization layered screening Layer images, point spread function (PSF) estimation for each layer ,in, Indicates the depth of focus, that is, for each layer, the process is as follows: a. Initialization Input blurred image and initialize the PSF estimate (preset kernel function), calculate the global noise variance , the calculation method of noise variance is: perform 3-layer wavelet decomposition on the image to obtain high-frequency sub-band ,variance:

[0071] Noise Confidence: , is the attenuation coefficient, the default value is 10, for Local variance of the sliding window.

[0072] b. Iterative Optimization Dynamic PSF correction (updated every 5 times ), is the learning rate ， Among them, the loss function :

[0073] is the total variation regularization of the PSF, is the balance coefficient, the default value is 0.01; Noise weight update: weights calculated based on local variance Dynamically adjust the iteration step size at the pixel level:

[0074] in, It is an empirical parameter, with a default value of 0.1. The greater the noise, the smaller the number of iterations, thus suppressing noise amplification.

[0075] Regularization term RL iteration: ,

[0076] Among them, the gradient term : Gradient descent direction based on the structural similarity index, coefficient Adaptive adjustment according to noise level: , To prevent small constants (such as 10-6) from dividing by zero; c. Layered screening Calculate the gradient of each layer result and noise confidence ,reserve and Layer , and is the gradient and noise confidence threshold, which can be configured according to actual needs.

[0077] Therefore, the embodiment of the present application uses multiple sets of focusing parameters to finely divide the focusing surface of the sample slice, thereby achieving "secondary slicing" of the sample slice, greatly improving the axial resolution and nanometer level of the scanned image.

[0078] In one embodiment of the present application, a scanning electron microscope is controlled to scan a cross-section sample based on multiple sets of focusing parameters, including: based on the multiple sets of focusing parameters, the focusing parameters of the scanning electron microscope are adjusted in sequence, and the scanning electron microscope is controlled to generate a high-voltage focused incident electron beam corresponding to each set of focusing parameters, and the focusing plane generated by the cross-section sample under each set of focusing parameters is scanned point by point and line by line.

[0079] In one embodiment of the present application, scanning point by point and line by line includes: a converging lens of a scanning electron microscope focuses a high-voltage focused incident electron beam into an electron beam probe, and performs point by point scanning on a section sample based on the electron beam probe; a deflector of a scanning electron microscope deflects the electron beam probe so that the electron beam probe performs line by line scanning on the section sample. The point by point scanning and line by line scanning are performed at a continuous large angle.

[0080] By adjusting the focusing parameters of the high-voltage focused incident electron beam, n-layer focal planes of the sample slice can be obtained, and the n-layer focal planes are scanned through, and then the signal of each layer of the focal plane is collected based on the composite detector.

[0081] In the scanning process, the sample slice needs to be scanned point by point and line by line. The process of point-by-point scanning is: focusing the incident electron beam into an electron beam "probe" based on a converging lens, and scanning it point by point on the sample. The process of line-by-line scanning includes: using a deflector to deflect the electron beam so that the electron beam scans the sample line by line. The point-by-point and line-by-line scanning is performed in a continuous large angle manner.

[0082] In one embodiment of the present application, the multiple current signals include at least two of the following: secondary electrons (SE), backscattered electrons (BSE), Auger electrons (AE), X-ray energy spectrum (EDX), bright field information (BF), and dark field information (DF).

[0083] Figure 2 An example diagram showing a composite detector arrangement for a method for acquiring a scanning image of a biological sample provided in an embodiment of the present application.

[0084] refer to Figure 2In one embodiment of the present application, the composite detector includes a secondary electron detector, a backscattered electron detector, a bright field detector and a dark field detector. The secondary electron detector is arranged directly above the cut sample, the backscattered electron detector is arranged on the upper side of the cut sample, the bright field detector is arranged below the cut sample, and the dark field detector is arranged on the lower side of the cut sample.

[0085] Figure 3 A schematic diagram of the structure of a scanning image acquisition device for biological samples provided in an embodiment of the present application is shown.

[0086] refer to Figure 3 Based on the above-mentioned scanning image acquisition method of biological samples, an embodiment of the present application also provides a scanning image acquisition device for biological samples, which includes: an acquisition module 301, used to acquire a cross-section sample of the biological sample to be scanned; an acquisition module 302, used to control the scanning electron microscope to scan the cross-section sample based on multiple sets of focusing parameters, and use a composite detector to collect the current signal group of the focusing plane generated by the cross-section sample under each set of focusing parameters during the scanning process, and the current signal group includes multiple current signals; an image processing module 303, used to perform image processing on the current signal group under each focusing plane to obtain multiple signal images under each focusing plane, and the image processing includes multi-stage multiplication and amplification, signal transformation, sampling, algorithm operation and AI image extraction; an image filtering module 304, used to use an image filtering algorithm to perform image filtering on the multiple signal images under each focusing plane to obtain multiple layered scanning images, and the total number of layered scanning images is different from the total number of signal images.

[0087] It should be noted that the description of the device in the embodiment of the present application is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment, so it will not be repeated. Figure 1 to Figure 2 The present invention can be understood by referring to the description of any one of the accompanying drawings.

[0088] According to an embodiment of the present application, the present application also provides an electronic device and a non-transitory computer-readable storage medium.

[0089] Figure 4A schematic block diagram of an example electronic device 40 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0090] like Figure 4 As shown, the electronic device 40 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 40 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0091] A number of components in the electronic device 40 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as a scanning image acquisition method for a biological sample. For example, in some embodiments, the scanning image acquisition method for a biological sample may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the scanning image acquisition method for the biological sample described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the scanning image acquisition method of the biological sample in any other appropriate manner (eg, by means of firmware).

[0093] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0095] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0098] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0099] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0100] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for acquiring a scanning image of a biological sample, characterized in that: The method comprises: Obtaining a cross-section sample of a biological sample to be scanned; Controlling the scanning electron microscope to scan the section sample based on multiple sets of focusing parameters, and using a composite detector to collect a current signal group of the focusing plane generated by the section sample under each set of focusing parameters during the scanning process, wherein the current signal group includes multiple current signals; Performing image processing on the current signal group under each focusing plane to obtain multiple signal images under each focusing plane, wherein the image processing includes multi-stage multiplication and amplification, signal conversion, sampling, algorithm operation and AI image extraction; An image filtering algorithm is used to perform image filtering on multiple signal images under each focus plane to obtain multiple layered scanning images, and the total number of the layered scanning images is different from the total number of the signal images.

2. The method according to claim 1, characterized in that Before controlling the scanning electron microscope to scan the section sample based on the multiple sets of focusing parameters, the method further includes acquiring the multiple sets of focusing parameters, and accordingly, The acquiring of multiple sets of focusing parameters comprises: Determine the scaled size of the section sample according to the original field size of the scanning electron microscope and the set limit size, and adjust the first gain and the second gain of the scanning electron microscope according to the scaled size; Obtaining the required number of groups of focusing parameters, and determining multiple layers of the section sample according to the required number of groups; For each layer, a parameter optimization algorithm is used to obtain the focus parameters of each layer, and multiple groups of focus parameters are obtained. The focus parameters include coarse adjustment parameters and fine adjustment parameters.

3. The method according to claim 1, characterized in that Controlled scanning electron microscope to scan the sectioned sample based on multiple sets of focusing parameters, including: Based on multiple sets of focusing parameters, the focusing parameters of the scanning electron microscope are adjusted in sequence, and the scanning electron microscope is controlled to generate a high-voltage focused incident electron beam corresponding to each set of focusing parameters, and the focusing plane generated by the section sample under each set of focusing parameters is scanned point by point and line by line.

4. The method according to claim 3, characterized in that The point-by-point and line-by-line scanning includes: The converging lens of the scanning electron microscope focuses the high-voltage incident electron beam into an electron beam probe, and performs point-by-point scanning on the sectioned sample based on the electron beam probe; The deflector of the scanning electron microscope deflects the electron beam probe so that the electron beam probe scans the section sample line by line; The point-by-point scanning and line-by-line scanning are performed at a continuous large angle.

5. The method according to claim 1, characterized in that The image filtering algorithm is used to filter the multiple signal images under each focus plane to obtain multiple layered scanning images, including: Adaptively filter and de-noise all signal images of each focal plane and perform spatial registration to obtain aligned images corresponding to all signal images; Calculate the quality score of each aligned image based on its clarity, noise level and structural similarity, and select valid images according to the quality score of each aligned image; Taking the valid image as input, the point spread function is initialized according to the preset kernel function, and the image is reconstructed through the iterative deconvolution algorithm guided by the noise confidence; Screening a plurality of layered scanning images to be processed according to the gradient characteristics and noise confidence of the reconstructed image; Performing brightness equalization and contrast enhancement on a plurality of layered scanned images to be processed to obtain a plurality of layered scanned images; Among them, the noise confidence is obtained through local variance and global variance; During image reconstruction, the point spread function parameters are adjusted according to the structural similarity gradient, and the update intensity is controlled by the noise weight matrix.

6. The method according to claim 1, characterized in that The multiple current signals include at least two of the following: secondary electrons, backscattered electrons, Auger electrons, X-ray energy spectrum, bright field information, and dark field information.

7. The method according to claim 1, characterized in that The slice thickness of the cross-section sample is 30-100 nm.

8. The method according to claim 1, characterized in that The composite detector includes a secondary electron detector, a backscattered electron detector, a bright field detector and a dark field detector. The secondary electron detector is arranged directly above the cut sample, the backscattered electron detector is arranged on the side above the cut sample, the bright field detector is arranged directly below the cut sample, and the dark field detector is arranged on the side below the cut sample.

9. The method according to claim 1, characterized in that: The number of the multiple groups of focusing parameters is determined according to the axial resolution requirement of the scanned image; The axial resolution requirement of the scanning image is determined according to the nanometer level requirement.

10. The method according to claim 1, characterized in that The step of obtaining the section sample of the biological sample to be scanned is obtaining the section sample of the biological sample to be scanned from a sample processing device, wherein the sample processing device includes a slicing device, a plasma processing device and a coating device; accordingly, The section sample of the biological sample to be scanned is obtained by the sample processing device through the following operations: The slicing device is used to continuously slice the biological sample to be scanned to obtain a plurality of sample slices to be processed; The plasma processing device and the coating device are used to perform plasma thinning and significant treatment and surface conductive film coating treatment on multiple sample slices to obtain multiple sample slices; The section sample is determined from the plurality of sample slices.

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