Real-time matching system for refractive index of transparent reagent and microscopic light path parameters

Abstract

The present application relates to the field of microscopic imaging technology, and particularly relates to a real-time matching system of refractive index of transparent reagent and microscopic light path parameters, comprising: a historical matching data acquisition module, used for acquiring historical microscopic light path parameters and historical microscopic images corresponding to a target transparent reagent; an initial microscopic light path parameter determination module, used for analyzing microscopic effects corresponding to the historical microscopic light path parameters according to the historical microscopic images, and determining initial microscopic light path parameters of the target transparent reagent in a current matching stage; a comparison module, used for acquiring an initial microscopic image, comparing the initial microscopic image with the historical microscopic images, and obtaining a parameter adjustment iteration step and a parameter adjustment curve; and a matching module, used for iteratively adjusting the initial microscopic light path parameters according to the parameter adjustment iteration step and the parameter adjustment curve, so as to make the target microscopic light path parameters real-time match the refractive index of the target transparent reagent, and improve the matching effect of the microscopic light path parameters and the refractive index of the transparent reagent.

Description

Technical Field

[0001] This invention relates to the field of microscopic imaging technology, specifically to a real-time matching system for the refractive index of a transparentizing reagent and the parameters of a microscopic optical path. Background Technology

[0002] In deep imaging of biological tissues, two-photon microscopy and clearing agents can be combined to observe detailed internal structures at high resolution without cutting the sample. Two-photon microscopy works based on the two-photon absorption phenomenon: when two photons are absorbed by a molecule almost simultaneously, a higher-energy electronic state is generated. This electronic state rapidly returns to the ground state and releases energy, producing a fluorescence signal. Because the probability of two-photon absorption is relatively low, a high-intensity laser source is required. Simultaneously, two-photon microscopy typically employs confocal microscopy, precisely controlling the focal point of the laser beam to achieve deep imaging of living tissues. Although two-photon microscopy has significantly improved imaging depth, its effectiveness is still affected by light scattering within the biological tissue. Clearing agents alter the tissue's optical properties by making it transparent, particularly reducing refractive index mismatch and removing lipid components, thereby reducing light scattering and allowing light to penetrate deeper into the tissue.

[0003] The core objective of tissue clearing is to eliminate refractive index (RI) differences within the sample and at its interfaces. For example, the difference between traditional clearing agents (RI=1.52) and biological tissue (RI=1.33-1.46) can lead to light scattering and spherical aberration, reducing imaging resolution. The refractive index of the clearing agent needs to match that of the sample and the microscope objective to reduce light scattering and spherical aberration, thereby improving image quality. If the refractive index of the clearing agent does not match that of the sample or objective, light will refract at the interface, creating spherical aberration and reducing imaging resolution.

[0004] When matching the microscopic optical path parameters with the refractive index of the clearing reagent, the refractive index of the reagent will change due to esterification and other effects during the clearing process, which will reduce the matching effect between the microscopic optical path parameters and the refractive index of the clearing reagent, and thus reduce the imaging quality of biological tissues.

[0005] Therefore, improving the matching effect between the microscopic optical path parameters and the refractive index of the transparentizing reagent is an urgent problem to be solved. Summary of the Invention

[0006] To address the technical problem of improving the matching effect between microscopic optical path parameters and the refractive index of transparentizing reagents, the present invention aims to provide a real-time matching system for the refractive index of transparentizing reagents and microscopic optical path parameters. The specific technical solution adopted is as follows:

[0007] In one embodiment of this application, a real-time matching system for the refractive index of a transparentizing reagent and microscopic optical path parameters is provided. The system includes:

[0008] The historical matching data acquisition module is used to acquire historical matching data corresponding to the target transparentizing reagent. The historical matching data includes historical microscopic optical path parameters of various tissues and historical microscopic images corresponding to the historical microscopic optical path parameters.

[0009] The initial microscopic optical path parameter determination module is used to analyze the microscopic effect corresponding to the historical microscopic image based on the historical microscopic image, and determine the initial microscopic optical path parameters of the target transparentizing reagent in the current matching stage based on the analysis result of the microscopic effect.

[0010] The comparison module is used to obtain the initial microscopic image corresponding to the initial microscopic optical path parameters in the current matching stage, and compare the initial microscopic image with the historical microscopic image to obtain the parameter adjustment iteration step size and parameter adjustment curve.

[0011] The matching module is used to adjust the iteration step size and the parameter adjustment curve according to the parameters, and iteratively adjust the initial microscopic optical path parameters to obtain the target microscopic optical path parameters, so that the target microscopic optical path parameters are matched with the refractive index of the target transparentizing reagent in real time.

[0012] In one embodiment of this application, the initial microscopic optical path parameter determination module includes:

[0013] The microscopic effect analysis unit is used to analyze the image features of the historical microscopic images and determine the image quality information corresponding to the historical microscopic images;

[0014] The parameter interval analysis unit is used to divide the historical microscopic optical path parameters into multiple parameter intervals and determine the preferred situation corresponding to each parameter interval based on the image quality information.

[0015] The parameter determination unit is used to determine a target parameter interval from the plurality of parameter intervals according to the preferred conditions corresponding to each parameter interval, and to select the initial microscopic optical path parameters from the target parameter interval.

[0016] In one embodiment of this application, the image features include the point light source imaging diffusion degree and contrast, and the microscopic effect analysis unit includes:

[0017] A segmentation subunit is used to perform threshold segmentation on the historical microscopic image to obtain the bright area in the historical microscopic image, and to use the bright area as the target analysis area.

[0018] The first image feature analysis subunit is used to obtain the point spread function corresponding to the target analysis region, and determine the point light source imaging spread degree corresponding to the target analysis region based on the point spread function;

[0019] The second image feature analysis subunit is used to determine the contrast between the target analysis region and the background in the historical microscopic image.

[0020] The first image quality information determination subunit is used to obtain the number of target analysis regions and the area of ​​each target analysis region, and to determine the image quality information corresponding to the historical microscopic image based on the number, the area, the point light source imaging diffusion degree and the contrast. The image quality information is used to indicate the imaging quality of the microscopic image.

[0021] In one embodiment of this application, the parameter interval analysis unit includes:

[0022] The image quality curve construction subunit is used to construct image quality curves corresponding to various tissues based on the image quality information corresponding to the historical microscopic images, wherein the horizontal axis of the image quality curve is the historical microscopic optical path parameter, and the vertical axis of the image quality curve is the image quality information.

[0023] The interval division subunit is used to place the image quality curves corresponding to various tissues in a coordinate space, and divide the horizontal coordinate of the coordinate space into multiple parameter intervals according to a preset interval range.

[0024] The preferred case determination subunit is used to determine the preferred case based on the microscopic optical path parameters and image quality information within each parameter range. The preferred case is used to indicate the preferred selection of microscopic optical path parameters within the parameter range.

[0025] In one embodiment of this application, the parameter determination unit includes:

[0026] The target parameter range determination subunit is used to select the parameter range with the largest preferred condition as the target parameter range;

[0027] The initial microscopic optical path parameter selection sub-unit is used to select the minimum value within the target parameter range as the initial microscopic optical path parameter.

[0028] In one embodiment of this application, the comparison module includes:

[0029] The second image quality information determination subunit is used to analyze the image features of the initial microscopic image and determine the image quality information corresponding to the initial microscopic image.

[0030] The comparison subunit is used to compare the image quality information corresponding to the initial microscopic image with the image quality curves corresponding to various tissues, and determine the distance based on the result of the distance comparison.

[0031] The candidate parameter determination subunit is used to determine candidate microscopic optical path parameters from the image quality curve corresponding to the minimum distance case;

[0032] The step size determination subunit is used to determine the parameter adjustment iteration step size based on the preferred situation of the target parameter range when the candidate microscopic optical path parameter is within the target parameter range;

[0033] The parameter adjustment curve determination subunit is used to determine the parameter adjustment curve based on the image quality curve that provides the maximum image quality information corresponding to the initial microscopic optical path parameters in the coordinate space.

[0034] In one embodiment of this application, the step of comparing the image quality information corresponding to the initial microscopic image with the image quality curves corresponding to various tissues, and determining the distance based on the result of the distance comparison, includes:

[0035] Obtain the image quality information corresponding to the initial microscopic image and the Euclidean distance between it and the image quality curves corresponding to various tissues;

[0036] The distance is determined based on the Euclidean distance and the number of curve overlaps corresponding to the initial microscopic optical path parameters.

[0037] In one embodiment of this application, determining the parameter adjustment curve based on the image quality curve that maximizes the image quality information corresponding to the initial microscopic optical path parameters in the coordinate space includes:

[0038] Obtain multiple peaks from the image quality curve that maximizes the image quality information corresponding to the initial microscopic optical path parameters in the coordinate space;

[0039] The parameter adjustment curve is obtained by curve fitting of the multiple peaks using the least squares method.

[0040] In one embodiment of this application, after iteratively adjusting the initial microscopic optical path parameters according to the parameter adjustment step size and the parameter adjustment curve to obtain the target microscopic optical path parameters, the method further includes:

[0041] If the number of iterations is greater than the preset threshold for the number of iterations, and the image quality information of the target microscopic image corresponding to the target microscopic optical path parameters is less than the preset threshold for image quality information, then the initial microscopic optical path parameters of the target transparentizing reagent are redefined, the initial microscopic image of the initial microscopic optical path parameters is obtained, and the initial microscopic image and the historical microscopic image are compared to obtain a new parameter adjustment iteration step size and a new parameter adjustment curve.

[0042] In one embodiment of this application, adjusting the initial microscopic optical path parameters includes:

[0043] The thickness of the coverslip is adjusted by using a calibration ring, which in turn adjusts the numerical aperture of the excitation light.

[0044] The present invention has the following beneficial effects:

[0045] First, the historical matching data acquisition module acquires historical matching data corresponding to the target clearing reagent. This historical matching data includes historical microscopic optical path parameters for various tissues, as well as historical microscopic images corresponding to these parameters. Then, the initial microscopic optical path parameter determination module analyzes the microscopic effects corresponding to the historical microscopic optical path parameters based on the historical microscopic images, and determines the initial microscopic optical path parameters of the target clearing reagent at the current matching stage based on the analysis results. Next, the comparison module acquires the initial microscopic image corresponding to the initial microscopic optical path parameters at the current matching stage and compares the initial microscopic image with the historical microscopic image to obtain the parameter adjustment iteration step size and parameter adjustment curve. Finally, the matching module iteratively adjusts the initial microscopic optical path parameters according to the parameter adjustment iteration step size and the parameter adjustment curve to obtain the target microscopic optical path parameters, ensuring that the target microscopic optical path parameters match the refractive index of the target clearing reagent in real time. In this invention, historical data contains a wealth of imaging experience obtained under different tissue samples and clearing reagents. This data reflects the actual matching effect of different combinations of microscopic optical path parameters on specific clearing reagents, providing a scientific basis for subsequent parameter selection, avoiding blindly setting initial parameters, and improving the matching accuracy at system startup. By analyzing the quality of historical microscopic images, the imaging effect of different historical microscopic optical path parameters is evaluated, and the optimal initial parameters for the current stage are determined accordingly. Using image quality as the core indicator to judge the quality of parameters gives the system visual feedback capability. It does not rely on human experience and can automatically select the initial parameters closest to the optimal state through algorithms, significantly improving the accuracy of initial matching. By identifying the degree to which current parameters deviate from their optimal state through the similarity or difference between images, and combining this with the trend of image quality changes, a suitable iteration step size and optimization path can be obtained, avoiding blind adjustments, improving convergence efficiency, and achieving a leap from static matching to dynamic adaptive adjustment. Based on the parameter adjustment curve and step size, the initial parameters are iteratively optimized in multiple rounds to finally obtain the target microscopic optical path parameters, achieving high-precision matching with the refractive index of the current transparentizing reagent. A closed-loop control system is constructed to continuously monitor imaging quality and provide feedback for parameter adjustment. It has good robustness and real-time response capability, and can cope with the refractive index changes caused by esterification reactions and other processes during the use of the transparentizing reagent, maintaining the long-term stability and matching accuracy of the system. Attached Figure Description

[0046] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the implementation environment of a real-time matching system for the refractive index of a transparent reagent and microscopic optical path parameters provided in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the structure of a real-time matching system for the refractive index of a transparent reagent and microscopic optical path parameters provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of an image quality curve provided in one embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the refractive index change curve of a transparentizing reagent provided in one embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram illustrating the determination of candidate microscopic optical path parameters according to an embodiment of the present invention;

[0052] Figure 6 This is a schematic flowchart illustrating a method for real-time matching of the refractive index of a transparentizing reagent with microscopic optical path parameters, provided as an embodiment of the present invention. Detailed Implementation

[0053] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a real-time matching system for the refractive index of a transparent reagent and microscopic optical path parameters proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0054] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application 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 this application 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 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.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0056] The following description, in conjunction with the accompanying drawings, details the specific scheme of a real-time matching system for the refractive index of a transparent reagent and microscopic optical path parameters provided by the present invention.

[0057] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation environment of a real-time matching system for the refractive index of a transparentizing reagent and microscopic optical path parameters, as provided in one embodiment of the present invention. Figure 1 As shown, the implementation environment includes a two-photon microscope 101 and a matching terminal 102. The matching terminal 102 can be a terminal device configured with a target client, including but not limited to laptops, tablets, handheld computers, PADs, desktop computers, etc., with local computing capabilities. The target client can be a video client, instant messaging client, browser client, etc., with a real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters installed. The matching terminal 102 can communicate with the two-photon microscope 101 via a network, including but not limited to wired networks and wireless networks. The wired network includes local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs). The wireless network includes Bluetooth, Wi-Fi, and other networks that enable wireless communication. The matching terminal 102 may include, but is not limited to, a human-computer interaction screen, a processor, and a memory. The human-computer interaction screen can be used to display microscopic images. The processor can be used, but is not limited to, to respond to human-computer interaction operations, execute corresponding operations, or generate corresponding instructions.

[0058] As an alternative, the microscopic optical path parameters and microscopic images of the two-photon microscope 101 can be acquired through the matching terminal 102.

[0059] As an alternative, the matching terminal 102 can also be a server, which can be a single server, a server cluster consisting of multiple servers, or a cloud server. The above is merely an example, and this embodiment does not impose any limitations on it.

[0060] As an alternative approach, a real-time matching system for the refractive index of the transparent reagent and the microscopic optical path parameters can be mounted on the matching terminal 102. The specific structure of this real-time matching system for the refractive index of the transparent reagent and the microscopic optical path parameters is as follows:

[0061] The historical matching data acquisition module is used to acquire historical matching data corresponding to the target transparentizing reagent. The historical matching data includes historical microscopic optical path parameters of various tissues and historical microscopic images corresponding to the historical microscopic optical path parameters.

[0062] The initial microscopic optical path parameter determination module is used to analyze the microscopic effect corresponding to the historical microscopic image based on the historical microscopic image, and determine the initial microscopic optical path parameters of the target transparentizing reagent in the current matching stage based on the analysis result of the microscopic effect.

[0063] The comparison module is used to obtain the initial microscopic image corresponding to the initial microscopic optical path parameters in the current matching stage, and compare the initial microscopic image with the historical microscopic image to obtain the parameter adjustment iteration step size and parameter adjustment curve.

[0064] The matching module is used to adjust the iteration step size and the parameter adjustment curve according to the parameters, and iteratively adjust the initial microscopic optical path parameters to obtain the target microscopic optical path parameters, so that the target microscopic optical path parameters are matched with the refractive index of the target transparentizing reagent in real time.

[0065] Based on the above approach, historical data contains a wealth of imaging experience obtained under different tissue samples and different clearing reagents. This data reflects the actual matching effect of different combinations of microscopic optical path parameters on specific clearing reagents, providing a scientific basis for subsequent parameter selection, avoiding blindly setting initial parameters, and improving the matching accuracy at system startup. By analyzing the quality of historical microscopic images, the imaging effect of different historical microscopic optical path parameters is evaluated, and the optimal initial parameters for the current stage are determined accordingly. Using image quality as the core indicator to judge the quality of parameters gives the system visual feedback capabilities. It does not rely on human experience and can automatically select the initial parameters closest to the optimal state through algorithms, significantly improving the accuracy of initial matching. By analyzing the similarities or differences between images, the degree to which the current parameters deviate from their optimal state can be identified. Combined with the trend of image quality changes, an appropriate iteration step size and optimization path can be obtained, avoiding blind adjustments and improving convergence efficiency. This achieves a leap from static matching to dynamic adaptive adjustment. Based on the parameter adjustment curve and step size, the initial parameters are iteratively optimized in multiple rounds to finally obtain the target microscopic optical path parameters, achieving high-precision matching with the refractive index of the current transparentizing reagent. A closed-loop control system is constructed, which continuously monitors the imaging quality and provides feedback for parameter adjustment. It has good robustness and real-time response capabilities, and can cope with refractive index changes caused by esterification reactions during the use of the transparentizing reagent, maintaining the long-term stability and matching accuracy of the system.

[0066] As an optional example, this embodiment does not limit the mounting subject of the real-time matching system for the refractive index of the transparent reagent and the microscopic optical path parameters. The real-time matching system for the refractive index of the transparent reagent and the microscopic optical path parameters can be mounted on the matching terminal 102. For example, if the matching terminal 102 is a desktop computer, part or all of the real-time matching system for the refractive index of the transparent reagent and the microscopic optical path parameters can be mounted on the desktop computer.

[0067] In one embodiment of this application, a real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters is provided. Figure 2 This is a schematic diagram of a real-time matching system for the refractive index of a transparentizing reagent and microscopic optical path parameters according to an embodiment of the present invention. (See attached diagram.) Figure 2 The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters includes the modules described in 210 to 240 below:

[0068] The historical matching data acquisition module 210 is used to acquire historical matching data corresponding to the target transparentizing reagent. The historical matching data includes historical microscopic optical path parameters of various tissues and historical microscopic images corresponding to the historical microscopic optical path parameters.

[0069] Among them, target-transparent reagents are currently used chemical reagents for tissue transparency. Target-transparent reagents reduce light scattering and improve imaging depth and clarity by changing the refractive index of biological tissues and removing lipids.

[0070] The historical matching data consists of microscopic parameters and image data recorded in the past when various clearing reagents were used. The microscopic optical path parameters used may include objective lens numerical aperture, excitation wavelength, laser power, etc.; the image data are historical microscopic images acquired under the corresponding conditions; and image quality assessment results may also be included.

[0071] Among them, tissues are biological samples that require deep imaging. Different tissues have different optical properties (such as refractive index, density, and structural complexity), so the requirements for microscopic optical path parameters are also different.

[0072] Among them, the historical microscopic optical path parameters are the microscope settings used in the past under different combinations of tissues and clearing reagents, which can include objective magnification, numerical aperture (NA), depth of focus, excitation light intensity, scanning speed, etc.

[0073] Among them, historical microscopic images are images captured under specific microscopic optical path parameters and transparent reagent combinations, which are used for subsequent image quality analysis, comparison and optimization judgment.

[0074] As an alternative example, a clearing agent can be injected into the tissue structure to obtain microscopic images. The clearing agent serves as a medium between the tissue to be examined and the objective lens, while a spatial light modulator generates a tiled light sheet to obtain image information at different resolutions. When acquiring microscopic images of cleared tissue using two-photon microscopy, a clearing agent needs to be injected between the tissue to be examined and the objective lens to reduce the impact of RI mismatch. Matching the refractive index of the clearing agent with the microscopic optical path parameters requires adjusting the parameters of the microscopic optical path.

[0075] The initial microscopic optical path parameter determination module 220 is used to analyze the microscopic effect corresponding to the historical microscopic image based on the historical microscopic image, and determine the initial microscopic optical path parameters of the target transparentizing reagent in the current matching stage based on the analysis result of the microscopic effect.

[0076] Among them, microscopic effects refer to the imaging quality of microscopic images, including sharpness, contrast, signal-to-noise ratio, and resolution. Microscopic effects can be analyzed through point spread function analysis, contrast calculation, image sharpness / blur level detection, and fluorescence signal intensity.

[0077] Among them, the initial microscopic optical path parameters are a set of starting microscopic parameters recommended by the system for the current matching stage after analyzing historical data. This can avoid trial and error from scratch and improve the system startup efficiency and accuracy.

[0078] The comparison module 230 is used to obtain the initial microscopic image corresponding to the initial microscopic optical path parameters in the current matching stage, and compare the initial microscopic image with the historical microscopic image to obtain the parameter adjustment iteration step size and parameter adjustment curve.

[0079] The parameter adjustment iteration step size is the magnitude of each adjustment to the microscopic optical path parameters. It can control the speed and accuracy of the optimization process. For example, if the current numerical aperture is 0.9, it is recommended to increase it by 0.05 next time, then the step size is 0.05.

[0080] Among them, the parameter adjustment curve is a trend curve describing the change of microscopic image quality with a certain microscopic optical path parameter. It can be fitted based on historical data or real-time image quality feedback and is used to guide the system on how to adjust parameters to approach the optimal matching point.

[0081] When matching the RI of the same clearing reagent, logically the parameters should be the same. However, during the operation of the clearing reagent, the tissue to be tested and the clearing reagent will undergo degreasing, causing the RI to decrease by 0.1-0.2. The reaction of different tissues within the action zone of the same clearing reagent is also different, and the matching process is also diverse. Therefore, it is necessary to analyze the same / similar and different parts of the matching process for subsequent real-time parameter matching.

[0082] The matching module 240 is used to adjust the iteration step size and the parameter adjustment curve according to the parameters, and iteratively adjust the initial microscopic optical path parameters to obtain the target microscopic optical path parameters, so that the target microscopic optical path parameters are matched with the refractive index of the target transparentizing reagent in real time.

[0083] In this process, when iteratively adjusting the initial microscopic optical path parameters according to the parameter adjustment step size and parameter adjustment curve, the system automatically adjusts the microscopic parameters based on image quality feedback, gradually approaching the optimal matching state. During adjustment, various intelligent optimization strategies such as gradient descent, genetic algorithm, and neural network can be used to ensure that the microscopic optical path parameters (such as objective lens parameters, cover glass thickness, laser focusing mode, etc.) always adapt to the refractive index characteristics of the current clearing reagent. Even if the refractive index of the reagent changes during the clearing process, it can be dynamically adjusted to maintain high-resolution imaging.

[0084] For example, assuming a clearing reagent A (refractive index 1.45) is used for two-photon microscopy imaging of tissue, the system calls the database to search for past imaging data on tissue using reagent A or other reagents with similar refractive indices (e.g., 1.42~1.48); it acquires the corresponding microscopic optical path parameters (e.g., NA=1.0, excitation wavelength 1040nm, depth of focus 2μm) and their corresponding images. The system analyzes the sharpness, contrast, etc., of these historical images; it finds that the image quality is highest when NA=0.9 and depth of focus 1.8μm; the initial parameters are set to NA=0.9 and depth of focus 1.8μm. The current tissue image is acquired using these initial parameters; it is compared with historical images, and the current image is found to be slightly blurry; the parameter adjustment direction (e.g., increasing NA) and step size (+0.05) are calculated, and a parameter adjustment curve is generated. Multiple adjustments are made based on the curve and step size; finally, the image is found to be clearest when NA=0.95, at which point the system considers it to have achieved refractive index matching with reagent A; the final parameters are output and applied to subsequent imaging.

[0085] In one embodiment of this application, the initial microscopic optical path parameter determination module includes:

[0086] The microscopic effect analysis unit is used to analyze the image features of the historical microscopic images and determine the image quality information corresponding to the historical microscopic images;

[0087] The parameter interval analysis unit is used to divide the historical microscopic optical path parameters into multiple parameter intervals and determine the preferred situation corresponding to each parameter interval based on the image quality information.

[0088] The parameter determination unit is used to determine a target parameter interval from the plurality of parameter intervals according to the preferred conditions corresponding to each parameter interval, and to select the initial microscopic optical path parameters from the target parameter interval.

[0089] Image features are various quantifiable visual attributes or patterns extracted from historical microscopic images. Examples include: point light source imaging diffusion (e.g., point spread function PSF); image contrast (grayscale difference between the target analysis area and the background); image sharpness / clarity; fluorescence signal intensity distribution; and the number and area of ​​highlight regions. Image features serve as fundamental data for evaluating image quality and are used to construct subsequent image quality information.

[0090] Image quality information is an indicator reflecting the overall quality of an image, generated by integrating multiple image features. It can be a single numerical value (such as an image sharpness score) or a multi-dimensional vector (such as [contrast score, blur score, resolution score]). When determining image quality information, different weights can be assigned to different image features using a weighted summation method; alternatively, it can be derived from the output of a machine learning model, using a trained model to predict image quality and provide a basis for subsequent parameter evaluation.

[0091] The parameter range is a continuous range into which historical microscopic optical path parameters (such as numerical aperture NA, excitation wavelength, depth of focus, etc.) are divided. It can be divided uniformly, such as dividing 0.8~1.2NA into [0.8, 0.9), [0.9, 1.0), [1.0, 1.1), [1.1, 1.2]; or it can be divided adaptively, dynamically adjusting the interval boundaries according to the trend of image quality changes.

[0092] The preferred selection for each parameter interval refers to the overall performance of the parameters within that interval based on image quality information, indicating the degree of priority for selection. The preferred selection can be represented by: numerical scoring (e.g., 1-5 points); ranking; or probability distribution (e.g., an 80% probability of producing a high-quality image within a certain parameter interval). The preferred selection is influenced by average image quality, image stability (small fluctuations), and imaging success rate (percentage of clear images), guiding the system to select the optimal candidate interval from numerous parameter intervals.

[0093] The target parameter range is the range of parameters most likely to produce high-quality images, selected by the system after optimization analysis. When determining the target parameter range, the range with the highest average image quality can be chosen; alternatively, a comprehensive score can be calculated combining stability and success rate.

[0094] In this embodiment, image quality information is used as the core evaluation standard to avoid subjective experience errors. A parameter range and optimal case mechanism are introduced to enhance the system's automated decision-making capabilities. The parameter search space is narrowed, allowing for rapid location of the target parameter range and reducing debugging costs. Based on historical data, it is applicable to different tissues and combinations of clearing reagents. Even in the face of image quality fluctuations or abnormal data, stable judgments can be maintained through range statistics.

[0095] In one embodiment of this application, the image features include point light source imaging diffusion and contrast, and the microscopic effect analysis unit includes:

[0096] A segmentation subunit is used to perform threshold segmentation on the historical microscopic image to obtain the bright area in the historical microscopic image, and to use the bright area as the target analysis area.

[0097] The first image feature analysis subunit is used to obtain the point spread function corresponding to the target analysis region, and determine the point light source imaging spread degree corresponding to the target analysis region based on the point spread function;

[0098] The second image feature analysis subunit is used to determine the contrast between the target analysis region and the background in the historical microscopic image.

[0099] The first image quality information determination subunit is used to obtain the number of target analysis regions and the area of ​​each target analysis region, and to determine the image quality information corresponding to the historical microscopic image based on the number, the area, the point light source imaging diffusion degree and the contrast. The image quality information is used to indicate the imaging quality of the microscopic image.

[0100] Point spread imaging diffusion refers to the degree to which an ideal point structure (such as a fluorescently labeled point) is imaged as a blurry spot during microscopic imaging. Smaller diffusion results in higher resolution; larger diffusion results in a blurrier image. Commonly used metrics include the full width at half maximum (FWHM), diameter, or area of ​​the point spread function (PSF).

[0101] Contrast ratio refers to the difference in brightness between the target analysis area and the background. Higher contrast ratio results in a clearer image; lower contrast ratio indicates greater noise interference or a poor signal-to-noise ratio.

[0102] Thresholding segmentation of the historical microscopic images involves dividing the pixels in the image into foreground (target) and background categories based on their grayscale values. Global thresholding or local adaptive thresholding can be used to extract regions with high brightness (such as cell nuclei, fluorescent markers, etc.) for subsequent analysis.

[0103] The highlighted area is the brighter region in the image after thresholding and segmentation. It may include highly expressed regions of fluorescently labeled target structures, cells, or subcellular structures.

[0104] The point spread function (PSF) is the response distribution of an optical system to an ideal point light source. It is a core indicator for measuring microscope resolution and can be used to quantitatively analyze the degree of image blur. To obtain the PSF corresponding to the target analysis region, multiple point structures can be selected in the highlighted area, and their average intensity distribution can be fitted to the PSF. Gaussian fitting and other methods can be used for modeling.

[0105] When determining the diffusion degree of the point light source imaging corresponding to the target analysis area based on the point spread function, the FWHM (full width at half maximum) of the PSF can be calculated, or the two-dimensional Gaussian standard deviation σ of the PSF can be calculated; the diffusion degree value of each bright area is obtained; and used for subsequent image quality scoring.

[0106] For example, analyzing the acquired historical matching data reveals that the refractive index (RI) is dynamically changing due to the reaction between the clearing agent and the tissue. Consequently, the optimal matching optical path parameters corresponding to the clearing agent also change based on this dynamic RI. In subsequent matching of refractive index and microscopic optical path parameters, historical data needs to be referenced to improve current matching accuracy and reduce matching time, thus enhancing real-time performance. The quality of the image acquired after each adjustment of the microscopic optical path parameters can be used to assess the effectiveness of the adjustment. Due to the diversity of the tissues to be detected, image quality cannot be directly determined by the characteristics of the tissues. Analyzing imaging features in the acquired images, such as the point surface area (PSF), reflects the diffusion degree of the point source imaging. The image quality information of the microscopic images acquired after each parameter adjustment is calculated: for the acquired microscopic images, threshold segmentation is used to obtain the bright areas in the image, which are the target analysis areas. The PSF of the target analysis area is obtained, and the contrast between the target analysis area and the background is also acquired.

[0107] For example, by analyzing the performance of each target analysis region in a microscopic image, the image quality information corresponding to the microscopic image can be obtained. The image quality information can be represented as follows:

[0108]

[0109] in, Indicates the first Sub-deep imaging Image quality information of the microscopic image obtained after adjusting the parameters; Indicates the number of target analysis regions in the image; Indicates the first The area of ​​each target analysis region; For the first The diffusion degree of point light source imaging in the target analysis area; For the first The contrast between the target analysis area and the background in historical micrographs.

[0110] in, For depth-weighted regions, the greater the imaging depth, the greater the signal attenuation due to light scattering, and the larger the area. The smaller the size (e.g., deep cells may only appear as small bright spots; the greater the depth, the higher the weight). The larger the value, the more it emphasizes the impact of the imaging quality of deep structures on the overall score.

[0111] in, To suppress light scattering, The larger the value, the more severe the light scattering (larger diffusion range in point light source imaging). The optimization goal is to reduce... Therefore, its reciprocal directly reflects the scattering suppression effect.

[0112] in, Image quality is quantified by weighting the depth sensitivity, scattering suppression capability, and contrast of the target analysis region; the higher the value, the higher the image quality.

[0113] In this embodiment, quantitative indicators are used instead of subjective experience to improve the consistency of judgment. Point spread function and contrast analysis are introduced to reveal differences in image quality in detail. Threshold segmentation is used to locate key regions, reducing interference from invalid regions and improving processing efficiency. Image quality information is calculated based on real data, making the selection of initial parameters more scientific. This method does not depend on specific devices or sample types and has good versatility and scalability.

[0114] In one embodiment of this application, the parameter interval analysis unit includes:

[0115] The image quality curve construction subunit is used to construct image quality curves corresponding to various tissues based on the image quality information corresponding to the historical microscopic images, wherein the horizontal axis of the image quality curve is the historical microscopic optical path parameter, and the vertical axis of the image quality curve is the image quality information.

[0116] The interval division subunit is used to place the image quality curves corresponding to various tissues in a coordinate space, and divide the horizontal coordinate of the coordinate space into multiple parameter intervals according to a preset interval range.

[0117] The preferred case determination subunit is used to determine the preferred case based on the microscopic optical path parameters and image quality information within each parameter range. The preferred case is used to indicate the preferred selection of microscopic optical path parameters within the parameter range.

[0118] The image quality curve is a graph plotted based on the relationship between the imaging conditions of historical microscopic images (such as optical path parameters, refractive index of the transparentizing agent, etc.) and the corresponding image quality information. It visually demonstrates the trend of image quality variation under different microscopic optical path parameters. The horizontal axis of the image quality curve represents historical microscopic optical path parameters, such as magnification, numerical aperture, and light source wavelength; the vertical axis represents image quality information, including but not limited to quantitative indicators such as point light source imaging diffusion and contrast.

[0119] In constructing image quality curves for various tissues based on the image quality information corresponding to the historical microscopic images, it is first necessary to obtain a series of historical microscopic images taken under different conditions, along with their corresponding microscopic optical path parameters and image quality evaluation results. Based on the previously mentioned technical solution, quality analysis is performed on each image to obtain specific image quality information. The analysis results are then plotted on a coordinate system with image quality as the vertical axis and microscopic optical path parameters as the horizontal axis, and these points are connected using interpolation or fitting methods to form a smooth curve.

[0120] For example, see Figure 3 , Figure 3 This is a schematic diagram of an image quality curve provided in an embodiment of the present invention, obtained according to the above operations for the first... Image quality information from deep tissue imaging was used to construct... Figure 3 The image quality curve shown. Figure 3 In the image quality curve, the horizontal axis represents the historical microscopic optical path parameters, and the vertical axis represents the acquired image quality information (parameter changes and image quality are definitely non-linear).

[0121] The process involves analyzing historical matching data from multiple organizations using the aforementioned methods, resulting in multiple curves, one for each organization. The similarity in variation among these curves is calculated; similarity indicates that the parameters in this portion reflect the influence of most organizations on the RI (Relationship Tolerance), making this portion highly relevant for subsequent matching analyses. The horizontal coordinate (x) of overlapping curves is calculated, with each x corresponding to an overlap count (n). For example, the number of identical y values ​​when x is the same represents the overlap count. However, simply analyzing similarity is insufficient for obtaining optimal parameters; the range of the optimal parameters for each interval must also be considered. Placing all curves in a coordinate space and setting the horizontal coordinate values ​​allows for the division into different interval ranges. For example, a preset interval range of 10 could be used, with the first interval being 0-10 and the second interval being 10-20.

[0122] For example, the first The preferred cases corresponding to the parameter range can be represented as follows:

[0123]

[0124] in, Indicates the first The optimal scenario corresponding to the parameter range; This represents the total number of times deep imaging of the tissue was performed in the historical matching data; Indicates the first The number of data points within the parameter range; This indicates the time interval for parameter adjustment within the parameter range. When the value is 1, this time interval is the specific time from the initial state to the first parameter adjustment. When it is greater than 1, for example when When the value is 2, the time after the first parameter adjustment is obtained 1 minute after adding the solution, and the time after the second parameter adjustment is obtained 2 minutes after adding the solution. Therefore, the time interval is 1 minute. For example, when... When the value is 3, the time interval is the interval between the time after the second parameter adjustment and the time after the third parameter adjustment; Indicates the first The organization corresponding to the first The slope of the change in image quality information after the adjustment; Indicates the first The organization corresponding to the first Image quality information after parameter adjustment.

[0125] In this embodiment, by pre-constructing image quality curves, researchers can quickly identify which microscopic optical path parameters are best suited for specific types of tissue samples, significantly saving time and resources. This provides a scientific basis for guiding the selection of microscope configurations, ensuring the highest possible image quality for each observation. This method is not only applicable to specific sample types but can also be adjusted according to different types of tissues or clearing agents, demonstrating strong flexibility and broad application prospects. It helps to promote the development of the field of microscopic imaging towards greater standardization and reduces the variability in results caused by differences in individual experience.

[0126] In one embodiment of this application, the parameter determination unit includes:

[0127] The target parameter range determination subunit is used to select the parameter range with the largest preferred condition as the target parameter range;

[0128] The initial microscopic optical path parameter selection sub-unit is used to select the minimum value within the target parameter range as the initial microscopic optical path parameter.

[0129] Among them, see Figure 4 , Figure 4 This is a schematic diagram of the refractive index change curve of a transparentizing reagent provided in one embodiment of the present invention. Figure 4 The x-axis represents time, and the y-axis represents the refractive index of the clearing agent. When matching new tissues, parameters need to be set in advance to acquire a set of microscopic images. The subsequent parameter iterations are then analyzed based on the effectiveness of the acquired microscopic images. However, the analysis of the RI (refractive index) of the clearing agent may reveal issues such as… Figure 4 As shown, this is a changing process, so the acquired parameters also change. Therefore, different RIs correspond to different parameter settings. During matching, the required parameters can be predicted based on the image's effect. For example, if the acquired image effect is similar to the image effect in a historical matching process, the preferred parameters corresponding to that matching process can be used as the preferred parameters for the currently acquired image effect, or in other words, the preferred parameters corresponding to that matching process are close to the preferred parameters for the currently acquired image effect.

[0130] For example, suppose we are using a clearing agent to perform microscopic observation of a specific type of biological tissue sample and want to obtain the best image quality by optimizing the microscopic optical path parameters. An image quality curve for this tissue has been constructed, and different parameter intervals have been defined, each with its corresponding optimal condition (i.e., the best image quality obtainable within that interval). Analysis reveals that the numerical aperture (NA) range from 0.8 to 1.4 is divided into three parameter intervals: [0.8, 1.0), [1.0, 1.2), and [1.2, 1.4]. Among these three intervals, the second interval, [1.0, 1.2), is determined to have the highest optimal condition, meaning that the highest quality microscopic images can be obtained within this range. Therefore, the system uses this interval [1.0, 1.2) as the target parameter interval. Next, to begin actual microscopic observation, an initial microscopic optical path parameter needs to be selected. Since the target parameter interval is [1.0, 1.2), the initial microscopic optical path parameter (numerical aperture) is set to 1.0.

[0131] In this embodiment, by selecting the minimum value within the target parameter range as the initial microscopic optical path parameter, it is ensured that the experiment starts within the range where high-quality images are likely to be obtained. This not only improves work efficiency but also increases the likelihood of obtaining satisfactory results on the first attempt. Once the initial parameters are set, they can be gradually fine-tuned to a more precise position according to specific needs or actual conditions. Compared to aimless blind searching, this method greatly simplifies the process of finding the optimal microscopic optical path parameter. Adopting this standardized process helps reduce the variability of results caused by differences in individual experience, making experimental results more consistent and comparable across different times, locations, and even different laboratories. Although the minimum value within the target parameter range is selected as the starting point, it does not limit further exploration of other parameter settings within this range. This ensures both rapid startup and flexibility to respond to changes in needs under special circumstances.

[0132] In one embodiment of this application, the comparison module includes:

[0133] The second image quality information determination subunit is used to analyze the image features of the initial microscopic image and determine the image quality information corresponding to the initial microscopic image.

[0134] The comparison subunit is used to compare the image quality information corresponding to the initial microscopic image with the image quality curves corresponding to various tissues, and determine the distance based on the result of the distance comparison.

[0135] The candidate parameter determination subunit is used to determine candidate microscopic optical path parameters from the image quality curve corresponding to the minimum distance case;

[0136] The step size determination subunit is used to determine the parameter adjustment iteration step size based on the preferred situation of the target parameter range when the candidate microscopic optical path parameter is within the target parameter range;

[0137] The parameter adjustment curve determination subunit is used to determine the parameter adjustment curve based on the image quality curve that provides the maximum image quality information corresponding to the initial microscopic optical path parameters in the coordinate space.

[0138] Distance comparison involves comparing the image quality information (such as dot diffusion and contrast) of the current initial microscopic image with the historically constructed image quality curves on a numerical basis. Euclidean distance, Mahalanobis distance, or correlation coefficient can be used to compare the degree of difference between the current image quality and the image quality corresponding to different parameters under each tissue type.

[0139] When determining the distance based on the distance comparison results, the "distance" between the current image quality information and each image quality curve is calculated, and these distance values ​​are sorted to obtain a "distance list" that represents the degree of matching between the current image and various tissues under different parameters; the smaller the distance, the closer the current image is to the imaging performance of that type of tissue under a certain parameter.

[0140] In determining candidate microscopic optical path parameters from the image quality curve corresponding to the minimum distance case, the historical image quality curve most similar to the current image can be found, and the corresponding optimal microscopic optical path parameter can be extracted from it as a candidate parameter.

[0141] Specifically, when determining the parameter adjustment curve based on the image quality curve with the largest image quality information corresponding to the initial microscopic optical path parameters in the coordinate space, one of the multiple image quality curves is selected as the path basis for subsequent iterative optimization, which is the curve with the highest image quality near the current initial parameters.

[0142] In this embodiment, a distance comparison mechanism is used to quickly identify the closest historical imaging pattern, improving the scientific rigor and accuracy of candidate parameters. Instead of relying on fixed rules, the strategy is dynamically adjusted based on the actual image quality, adapting to various tissue and reagent combinations. Blindly setting parameters is avoided; existing data is directly used to recommend optimal candidate parameters, significantly shortening debugging time. A clear optimization direction is established through parameter adjustment curves, making the iteration process more targeted and controllable. The system can continuously compare the quality of new images with historical curves to achieve continuous optimization and address the impact of refractive index changes during the transparency process.

[0143] In one embodiment of this application, the step of comparing the image quality information corresponding to the initial microscopic image with the image quality curves corresponding to various tissues, and determining the distance based on the result of the distance comparison, includes:

[0144] Obtain the image quality information corresponding to the initial microscopic image and the Euclidean distance between it and the image quality curves corresponding to various tissues;

[0145] The distance is determined based on the Euclidean distance and the number of curve overlaps corresponding to the initial microscopic optical path parameters.

[0146] Euclidean distance is a mathematical method that measures the "straight-line distance" between two points in a multidimensional space. It is used to quantify the similarity between current image quality information and historical image quality curves. The smaller the distance, the closer the two are.

[0147] Specifically, when obtaining the image quality information corresponding to the initial microscopic image and the Euclidean distance between it and the image quality curves corresponding to various tissues, the image quality information of the current image (e.g., point spread = 0.85 μm, contrast = 2.3) is treated as a vector; at the same time, each parameter point on each historical image quality curve is also regarded as an image quality vector; the Euclidean distance between the current image vector and each point of these historical curves is calculated; and the curve with the smallest minimum distance or the smallest average distance is identified as the best matching candidate.

[0148] The curve overlap number is the frequency or number of times an image acquired under the current initial microscopic optical path parameters appears on a certain image quality curve.

[0149] For example, the distance during the first iteration adjustment can be represented as follows: where

[0150]

[0151] in, This indicates the distance status during the first iteration adjustment, where 1 represents the first iteration adjustment; The Euclidean distance between the image quality information corresponding to the initial microscopic image and the image quality curves corresponding to various tissues is the Euclidean distance between parameter x and the image quality curves corresponding to various tissues. This indicates the number of overlaps corresponding to parameter x. The greater the number of overlaps, the closer the distance is to the target.

[0152] For example, candidate microscopic optical path parameters are determined from the image quality curve corresponding to the minimum distance case. At this location, multiple curves may overlap (a point may appear on multiple curves). See also... Figure 5 , Figure 5This is a schematic diagram illustrating the determination of candidate microscopic optical path parameters according to an embodiment of the present invention. Figure 5 In the diagram, the horizontal axis represents the microscopic optical path parameters, and the vertical axis represents image quality information. For example... Figure 5 As shown, there are multiple overlapping curves at point X. When determining candidate microscopic optical path parameters, it is necessary to analyze the image quality information in the overlapping curves. The parameter value corresponding to the first D value in the subsequent curve (i.e., the "expected adjustment parameter value" in the figure) is used as the pre-candidate microscopic optical path parameter.

[0153] In this embodiment, Euclidean distance is introduced as a quantification standard, making image quality comparison more scientific and objective. Combining the curve overlap count avoids mismatches caused by accidental errors or abnormal data. It supports automatic identification and matching of multiple tissue types, enhancing the system's versatility and intelligence. It quickly locks onto the optimal matching curve, reducing invalid searches and improving parameter adjustment efficiency. It continuously compares newly acquired images with historical curves, achieving dynamic adaptive adjustment.

[0154] In one embodiment of this application, determining the parameter adjustment curve based on the image quality curve that maximizes the image quality information corresponding to the initial microscopic optical path parameters in the coordinate space includes:

[0155] Obtain multiple peaks from the image quality curve that maximizes the image quality information corresponding to the initial microscopic optical path parameters in the coordinate space;

[0156] The parameter adjustment curve is obtained by curve fitting of the multiple peaks using the least squares method.

[0157] A peak is a point on the image quality curve where the image quality information reaches its local or global maximum. A peak represents the best imaging effect obtained under a certain microscopic optical path parameter; multiple peaks can reflect the trend of image quality as the parameter changes. For example, if the image is clearest when NA=0.95, then NA=0.95 is a peak; if the image becomes clear again when NA=1.1, then NA=1.1 is another peak.

[0158] Specifically, multiple peaks in the image quality curve that corresponds to the initial microscopic optical path parameters in the coordinate space with the largest image quality information can be obtained. Among all historically constructed image quality curves, the curve with the highest image quality near the current initial microscopic optical path parameters (e.g., NA=1.0) can be selected. Then, multiple points where the image quality reaches the peak value (i.e., peaks) are extracted on this curve. These peak points reflect the imaging optimization trend of this tissue type under different parameters.

[0159] The least squares method is a mathematical optimization technique that finds the best function match for data by minimizing the sum of squared errors between observed values ​​and model predictions. Multiple extracted peak points are used as input data; a smooth curve is fitted to represent the trend of image quality as parameters change.

[0160] Curve fitting involves finding a continuous mathematical function (curve) to approximate the variation of a set of discrete data points (such as peak points). In this application, the least squares method can be used to fit the peak points, resulting in a smooth curve describing the variation of image quality with the microscopic optical path parameters. This curve is the "parameter adjustment curve" upon which subsequent parameter iterative optimization depends.

[0161] For example, if the obtained parameters fall within the aforementioned parameter range, the parameter adjustments may be relatively minor. Therefore, parameter iteration can be performed within this range, with a preset iteration step size of 1. Adjust step size , The larger the value, the more refined the adjustment is needed to achieve better quality.

[0162] For example, the RI (Residual Intensity) of the clearing reagent is a dynamically changing process. The above is only a feedback adjustment process for the image acquired in the current state, satisfying only the parameters corresponding to the current RI. When the acquired reagent RI data fluctuates, it may cause the matched parameters to fail to acquire image data well, requiring the above operation to be repeated. However, repeating the parameters may have a certain delay, which cannot meet the real-time adjustment of the device. Therefore, it is necessary to determine a parameter adjustment curve based on the initial microscopic optical path parameters, and to make subsequent adjustments based on the parameter adjustment curve. Analyzing historical data, due to the changes in the RI of the clearing reagent, the curve between the acquired image quality and the parameters may have multiple peaks corresponding to the RI changes. After the first matching, the curve with the maximum image quality information in the historical curve corresponding to the parameters can be obtained. This curve has multiple peaks, and the parameters corresponding to these multiple peaks are the parameter values ​​that may be predicted later.

[0163] In this embodiment, peak extraction and curve fitting accurately capture the trend of image quality changes, avoiding blind searching. It does not rely on fixed rules but dynamically constructs an optimization path based on the actual image quality. The fitted curve provides a clear optimization direction, enabling parameter iteration to converge to the optimal state more quickly. It can handle situations where the image quality curve has multiple peaks and is suitable for complex optical conditions. The least squares method is a mature and widely used mathematical tool, easy to implement and deploy.

[0164] In one embodiment of this application, after iteratively adjusting the initial microscopic optical path parameters according to the parameter adjustment step size and the parameter adjustment curve to obtain the target microscopic optical path parameters, the method further includes:

[0165] If the number of iterations is greater than the preset threshold for the number of iterations, and the image quality information of the target microscopic image corresponding to the target microscopic optical path parameters is less than the preset threshold for image quality information, then the initial microscopic optical path parameters of the target transparentizing reagent are redefined, the initial microscopic image of the initial microscopic optical path parameters is obtained, and the initial microscopic image and the historical microscopic image are compared to obtain a new parameter adjustment iteration step size and a new parameter adjustment curve.

[0166] The preset iteration adjustment threshold is a maximum allowed number of iterations set by the system (e.g., a maximum of 5 attempts), used to control the computational cost and time of the parameter optimization process. This avoids infinite loop optimization. If the image quality cannot be improved even after exceeding this threshold, it indicates that the current path may be invalid or has a deviation; triggering the system to perform a "reset" operation and reselect initial parameters.

[0167] The preset image quality information threshold is the minimum image quality standard set according to imaging requirements (e.g., image sharpness score ≥ 0.8). The preset image quality information threshold serves as the standard for judging whether the current imaging is qualified; if the final image quality is lower than this threshold, it indicates that the current parameters have not achieved effective matching.

[0168] For example, the above operation can obtain a parameter adjustment curve. During matching, the parameter value can be determined through the parameter adjustment curve for real-time adjustment. If the obtained curve fails to obtain good image data for 5 consecutive times during the adjustment process, the parameter adjustment curve may have some error. The above operation can then be repeated to obtain a new parameter adjustment curve.

[0169] In this embodiment, even if an initial path error or environmental changes cause optimization failure, the optimization capability can be restored through a restart mechanism. An upper limit on the number of iterations is set to prevent meaningless repeated attempts; a lower limit on image quality is set to ensure the validity of the results. Failure detection and path switching can be completed without manual intervention, truly achieving "intelligent matching." It can handle challenging scenarios such as changes in the refractive index of transparent reagents over time and strong tissue heterogeneity. It ensures that the final output microscopic images always meet preset quality requirements, enhancing experimental repeatability and reliability.

[0170] In one embodiment of this application, adjusting the initial microscopic optical path parameters includes:

[0171] The thickness of the coverslip is adjusted by using a calibration ring, which in turn adjusts the numerical aperture of the excitation light.

[0172] Microscopes are typically equipped with a correction ring to compensate for differences in optical paths caused by coverslips of varying thicknesses. Variations in coverslip thickness affect the refraction of light as it passes through the sample, thus impacting image sharpness. The correction ring adjusts the distance between the objective lens and the coverslip to ensure that light is correctly focused on the sample; this is particularly important for high-resolution microscopy, especially when using oil immersion objectives.

[0173] Numerical aperture is an important indicator of a microscope objective's ability to collect light, determining the microscope's maximum resolution and contrast. A higher numerical aperture means more light can be collected, thus improving image resolution and brightness. Adjusting the numerical aperture of the excitation beam can optimize image quality based on sample characteristics and the refractive index of the clearing agent. In two-photon microscopy, appropriately adjusting the numerical aperture of the excitation beam can also reduce background noise and enhance the target signal.

[0174] For example, suppose a novel clearing agent is being used to treat tissue samples, and the goal is to obtain optimal microscopic images. Initially, the microscope's optical path parameters are set to NA=1.0, and the coverslip thickness is 0.17 mm (standard thickness). The calibration ring is used with default settings and no special adjustments are made. The excitation beam numerical aperture is set to the system's recommended value. After initial imaging, the image edges are found to be blurry, and while the central portion is clear, the overall contrast is poor. Analysis suggests this may be due to a slight deviation in the coverslip thickness (actual thickness approximately 0.18 mm) and an unsatisfactory excitation beam numerical aperture setting. Adjusting the coverslip thickness: Using the calibration ring on the microscope, gradually fine-tune until the optimal position for the current coverslip thickness is found. This may require multiple attempts, with each fine-tuning followed by re-image capture and evaluation of the effect. Finally, determine the calibration ring position to achieve optimal image clarity across the entire field of view. Depending on the sample characteristics and the refractive index of the transparentizing reagent, the numerical aperture of the excitation light can be appropriately increased or decreased. If the background noise is high and the target signal is weak, the numerical aperture can be appropriately reduced to reduce non-specific signals. If the details of the target structure are difficult to distinguish, the numerical aperture can be slightly increased to improve the resolution. In the experiment, it was found that after adjusting the numerical aperture of the excitation light from 1.0 to 1.05, the image contrast and detail performance were significantly improved.

[0175] In this embodiment, by precisely adjusting the coverslip thickness and the numerical aperture of the excitation light, the light is accurately focused on the sample, improving image clarity, contrast, and resolution. Different combinations of samples and clearing reagents may require different optical settings; flexible parameter adjustment helps to meet complex and varied practical needs. The microscope's built-in correction ring design allows for a rapid response even with non-standard coverslips, ensuring that imaging quality is not affected.

[0176] As can be seen from the above embodiments, historical data contains a wealth of imaging experience obtained under different tissue samples and different clearing reagents. This data reflects the actual matching effect of different combinations of microscopic optical path parameters on specific clearing reagents, providing a scientific basis for subsequent parameter selection, avoiding blindly setting initial parameters, and improving the matching accuracy at system startup. By analyzing the quality of historical microscopic images, the imaging effect of different historical microscopic optical path parameters is evaluated, and the optimal initial parameters for the current stage are determined accordingly. Using image quality as the core indicator to judge the quality of parameters gives the system visual feedback capability. It does not rely on human experience and can automatically select the initial parameters closest to the optimal state through algorithms, significantly improving the accuracy of initial matching. Accuracy: By identifying the degree to which current parameters deviate from their optimal state through the similarity or difference between images, and combining this with the trend of image quality changes, a suitable iteration step size and optimization path can be obtained, avoiding blind adjustments, improving convergence efficiency, and realizing a leap from static matching to dynamic adaptive adjustment. Based on the parameter adjustment curve and step size, the initial parameters are iteratively optimized in multiple rounds to finally obtain the target microscopic optical path parameters, achieving high-precision matching with the refractive index of the current transparentizing reagent. A closed-loop control system is constructed, which continuously monitors the imaging quality and provides feedback for parameter adjustment. It has good robustness and real-time response capability, and can cope with the refractive index changes caused by esterification reactions during the use of the transparentizing reagent, maintaining the long-term stability and matching accuracy of the system.

[0177] In one embodiment of this application, a method for real-time matching of the refractive index of a transparentizing reagent with microscopic optical path parameters is also provided. Figure 6 This is a flowchart illustrating a method for real-time matching of the refractive index of a transparentizing reagent with microscopic optical path parameters according to an embodiment of the present invention. (See attached diagram.) Figure 6 The method includes the following steps:

[0178] S610. Obtain historical matching data corresponding to the target clearing reagent. The historical matching data includes historical microscopic optical path parameters of various tissues and historical microscopic images corresponding to the historical microscopic optical path parameters.

[0179] S620. Based on the historical microscopic images, analyze the microscopic effects corresponding to the historical microscopic optical path parameters, and based on the analysis results of the microscopic effects, determine the initial microscopic optical path parameters of the target transparentizing reagent in the current matching stage.

[0180] S630. Obtain the initial microscopic image corresponding to the initial microscopic optical path parameters in the current matching stage, and compare the initial microscopic image with the historical microscopic image to obtain the parameter adjustment iteration step size and parameter adjustment curve.

[0181] S640. Adjust the iteration step size and the parameter adjustment curve according to the parameters, and iteratively adjust the initial microscopic optical path parameters to obtain the target microscopic optical path parameters so that the target microscopic optical path parameters match the refractive index of the target transparentizing reagent in real time.

[0182] The real-time matching method between the refractive index of the clearing reagent and the microscopic optical path parameters in this embodiment of the application utilizes historical data containing a wealth of imaging experience obtained under different tissue samples and clearing reagent conditions. This data reflects the actual matching effect of different combinations of microscopic optical path parameters on specific clearing reagents, providing a scientific basis for subsequent parameter selection, avoiding blindly setting initial parameters, and improving the matching accuracy at system startup. By analyzing the quality of historical microscopic images, the imaging effect of different historical microscopic optical path parameters is evaluated, and the optimal initial parameters for the current stage are determined accordingly. Image quality is used as the core indicator to judge the quality of parameters, giving the system visual feedback capabilities. It does not rely on human experience and can automatically select the initial parameters closest to the optimal state through algorithms. It significantly improves the accuracy of initial matching; by identifying the degree to which the current parameters deviate from the optimal state through the similarity or difference between images, and by combining the trend of image quality changes, it obtains a suitable iteration step size and optimization path, avoiding blind adjustments, improving convergence efficiency, and realizing the leap from static matching to dynamic adaptive adjustment; based on the parameter adjustment curve and step size, the initial parameters are iteratively optimized in multiple rounds, and finally the target microscopic optical path parameters are obtained, achieving high-precision matching with the refractive index of the current transparentizing reagent. A closed-loop control system is constructed, which continuously monitors the imaging quality and feeds back the adjustment parameters. It has good robustness and real-time response capability, and can cope with the refractive index changes caused by esterification reactions and other processes during the use of the transparentizing reagent, maintaining the long-term stability and matching accuracy of the system.

[0183] Specific embodiments of the real-time matching method between the refractive index of the transparentizing reagent and the microscopic optical path parameters in this application can be found in the examples shown in the above-described real-time matching system between the refractive index of the transparentizing reagent and the microscopic optical path parameters, and will not be repeated here.

[0184] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0185] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A real-time matching system for the refractive index of a transparent reagent and microscopic optical path parameters, characterized in that, The system includes: The historical matching data acquisition module is used to acquire historical matching data corresponding to the target transparentizing reagent. The historical matching data includes historical microscopic optical path parameters of various tissues and historical microscopic images corresponding to the historical microscopic optical path parameters. The initial microscopic optical path parameter determination module is used to analyze the microscopic effect corresponding to the historical microscopic image based on the historical microscopic image, and determine the initial microscopic optical path parameters of the target transparentizing reagent in the current matching stage based on the analysis result of the microscopic effect. The comparison module is used to obtain the initial microscopic image corresponding to the initial microscopic optical path parameters in the current matching stage, and compare the initial microscopic image with the historical microscopic image to obtain the parameter adjustment iteration step size and parameter adjustment curve. The matching module is used to adjust the iteration step size and the parameter adjustment curve according to the parameters, and iteratively adjust the initial microscopic optical path parameters to obtain the target microscopic optical path parameters, so that the target microscopic optical path parameters are matched with the refractive index of the target transparentizing reagent in real time.

2. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 1, characterized in that, The initial microscopic optical path parameter determination module includes: The microscopic effect analysis unit is used to analyze the image features of the historical microscopic images and determine the image quality information corresponding to the historical microscopic images; The parameter interval analysis unit is used to divide the historical microscopic optical path parameters into multiple parameter intervals and determine the preferred situation corresponding to each parameter interval based on the image quality information. The parameter determination unit is used to determine a target parameter interval from the plurality of parameter intervals according to the preferred conditions corresponding to each parameter interval, and to select the initial microscopic optical path parameters from the target parameter interval.

3. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 2, characterized in that, The image features include the diffusion degree and contrast of the point light source imaging, and the microscopic effect analysis unit includes: A segmentation subunit is used to perform threshold segmentation on the historical microscopic image to obtain the bright area in the historical microscopic image, and to use the bright area as the target analysis area. The first image feature analysis subunit is used to obtain the point spread function corresponding to the target analysis region, and determine the point light source imaging spread degree corresponding to the target analysis region based on the point spread function; The second image feature analysis subunit is used to determine the contrast between the target analysis region and the background in the historical microscopic image. The first image quality information determination subunit is used to obtain the number of target analysis regions and the area of ​​each target analysis region, and to determine the image quality information corresponding to the historical microscopic image based on the number, the area, the point light source imaging diffusion degree and the contrast. The image quality information is used to indicate the imaging quality of the microscopic image.

4. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 2, characterized in that, The parameter range analysis unit includes: The image quality curve construction subunit is used to construct image quality curves corresponding to various tissues based on the image quality information corresponding to the historical microscopic images, wherein the horizontal axis of the image quality curve is the historical microscopic optical path parameter, and the vertical axis of the image quality curve is the image quality information. The interval division subunit is used to place the image quality curves corresponding to various tissues in a coordinate space, and divide the horizontal coordinate of the coordinate space into multiple parameter intervals according to a preset interval range. The preferred case determination subunit is used to determine the preferred case based on the microscopic optical path parameters and image quality information within each parameter range. The preferred case is used to indicate the preferred selection of microscopic optical path parameters within the parameter range.

5. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 4, characterized in that, The parameter determination unit includes: The target parameter range determination subunit is used to select the parameter range with the largest preferred condition as the target parameter range; The initial microscopic optical path parameter selection sub-unit is used to select the minimum value within the target parameter range as the initial microscopic optical path parameter.

6. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 5, characterized in that, The comparison module includes: The second image quality information determination subunit is used to analyze the image features of the initial microscopic image and determine the image quality information corresponding to the initial microscopic image. The comparison subunit is used to compare the image quality information corresponding to the initial microscopic image with the image quality curves corresponding to various tissues, and determine the distance based on the result of the distance comparison. The candidate parameter determination subunit is used to determine candidate microscopic optical path parameters from the image quality curve corresponding to the minimum distance case. The step size determination subunit is used to determine the parameter adjustment iteration step size based on the preferred situation of the target parameter range when the candidate microscopic optical path parameter is within the target parameter range; The parameter adjustment curve determination subunit is used to determine the parameter adjustment curve based on the image quality curve that provides the maximum image quality information corresponding to the initial microscopic optical path parameters in the coordinate space.

7. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 6, characterized in that, The step of comparing the image quality information corresponding to the initial microscopic image with the image quality curves corresponding to various tissues, and determining the distance based on the comparison result, includes: Obtain the image quality information corresponding to the initial microscopic image and the Euclidean distance between it and the image quality curves corresponding to various tissues; The distance is determined based on the Euclidean distance and the number of curve overlaps corresponding to the initial microscopic optical path parameters.

8. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 6, characterized in that, The step of determining the parameter adjustment curve based on the image quality curve that maximizes the image quality information corresponding to the initial microscopic optical path parameters in the coordinate space includes: Obtain multiple peaks from the image quality curve that maximizes the image quality information corresponding to the initial microscopic optical path parameters in the coordinate space; The parameter adjustment curve is obtained by curve fitting of the multiple peaks using the least squares method.

9. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 2, characterized in that, After iteratively adjusting the initial microscopic optical path parameters according to the parameters and the parameter adjustment curve to obtain the target microscopic optical path parameters, the method further includes: If the number of iterations is greater than the preset threshold for the number of iterations, and the image quality information of the target microscopic image corresponding to the target microscopic optical path parameters is less than the preset threshold for image quality information, then the initial microscopic optical path parameters of the target transparentizing reagent are redefined, the initial microscopic image of the initial microscopic optical path parameters is obtained, and the initial microscopic image and the historical microscopic image are compared to obtain a new parameter adjustment iteration step size and a new parameter adjustment curve.

10. The real-time matching system for the refractive index of the transparentizing reagent and the microscopic optical path parameters as described in claim 1, characterized in that, Adjusting the initial microscopic optical path parameters includes: The thickness of the coverslip is adjusted by using a calibration ring, which in turn adjusts the numerical aperture of the excitation light.

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