Fluorescence microscope calibration system based on machine learning

Abstract

The invention relates to the technical field of optical instrument calibration, in particular to a fluorescence microscope calibration system based on machine learning, which comprises an imaging data acquisition module, an equipment data acquisition module, a fluorescence microscope calibration module and a data processing module, the system comprises an imaging environment data acquisition module used for acquiring imaging environment data and equipment data of a fluorescence microscope, a sample data processing module used for processing original imaging data of a standard sample and target imaging data of the standard sample, a sample data analysis module used for analyzing the imaging state of the target imaging data, and an imaging quality analysis module used for analyzing the imaging state of the target imaging data. The imaging quality analysis module is used for analyzing imaging quality, the equipment abnormity analysis module is used for analyzing equipment abnormity, and the calibration module is used for generating a calibration scheme of the fluorescence microscope. According to the invention, the calibration efficiency and the calibration accuracy of the fluorescence microscope are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical instrument calibration, and in particular to a fluorescence microscope calibration system based on machine learning. Background Art

[0002] Fluorescence microscopes are an indispensable and important tool in modern biomedical research. However, due to factors such as equipment aging and environmental changes, fluorescence microscopes may experience problems such as reduced resolution and color distortion during long-term use. Traditional manual calibration methods are not only time-consuming and labor-intensive, but also difficult to ensure the consistency and accuracy of each calibration. Therefore, the development of a system that can achieve automated and intelligent calibration is of great significance to improving the working efficiency and imaging accuracy of fluorescence microscopes.

[0003] Chinese patent publication number CN116957943A discloses a microscope stitching calibration method based on image fusion, comprising the following steps: collecting microscopic images between two adjacent frames at equal intervals; determining the blur variation of several consecutive adjacent microscopic images along the same axis; obtaining the position coordinates of key position points on the surface to be observed of the object to be measured in the coordinate system of the measuring platform; fitting the plane where the surface to be observed is located; analyzing the rotation angle and controlling the measuring platform to rotate; stitching and fusing the overlapping areas of two adjacent microscopic images; it can be seen that the calibration process of the microscope in this invention is a digital calibration of the microscopic image, and it does not analyze the imaging process of the microscope, nor calibrate the imaging process of the microscope, and there are problems of low efficiency and inaccurate calibration of the microscope. Summary of the invention

[0004] The object of the present invention is to provide a fluorescence microscope calibration system based on machine learning to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A fluorescence microscope calibration system based on machine learning, characterized by comprising: An imaging data acquisition module, used to acquire original imaging data of the standard sample and target imaging data of the standard sample; Equipment data acquisition module, used to collect imaging environment data and equipment data of fluorescence microscope; A sample data processing module is used to process the original imaging data of the standard sample and the target imaging data of the standard sample to obtain original standard data, target image parameters, original processed images and target processed images; A sample data analysis module is used to analyze the imaging state of the target imaging data according to the target image parameters; An imaging quality analysis module is used to analyze the clarity and light intensity abnormality of the target imaging data according to the imaging state analysis results of the target imaging data and the target processed image, and to analyze the imaging quality of the target imaging data according to the analysis results; Equipment abnormality analysis module, used to analyze equipment abnormality based on equipment data from the fluorescence microscope; The calibration module is used to generate a calibration scheme for the fluorescence microscope according to an imaging quality analysis result of the target imaging data and an equipment abnormality analysis result of the fluorescence microscope.

[0006] Further, the sample data processing module includes a raw data processing unit, which is used to identify the stained pixel position coordinates of the stained object in the raw imaging data of the standard sample, and eliminate background pixels from the raw imaging data according to the stained pixel position coordinates of the stained object to obtain an original processed image; The image data processing unit is also used to calculate the standard uniformity α1 according to the original processed image; The image data processing unit is further used to obtain a standard contrast β1 of the original processed image, and use the standard contrast β1 and the standard uniformity α1 as original standard data.

[0007] Furthermore, the sample data analysis module is used to analyze the imaging state of the target imaging data according to the target image parameters, and send an imaging result signal to the user according to the analysis result: if α2<α1×η1 and β1×η2≤β2<β1×η3, the sample data analysis module determines that the imaging state of the target imaging data is normal, and sends an imaging success signal to the user; if α2≥α1×η1 or β2 [β1×η2, β1×η3), the sample data analysis module determines that the imaging state of the target imaging data is abnormal, and sends an imaging failure signal to the user; Wherein, η1 is a preset fluorescence imaging uniformity, η2 is a first preset contrast, η3 is a second preset contrast, and η2<η3.

[0008] Furthermore, the imaging quality analysis module includes an image light intensity analysis unit, which is used to grayscale the original processed image and the target processed image, and calculate the light intensity difference index gz when the imaging state of the target imaging data is normal. At this time, if gz exceeds the preset light intensity difference index GZ, the image light intensity analysis unit determines that the light intensity of the target imaging data is abnormal; otherwise, it determines that the light intensity of the target imaging data is normal.

[0009] Furthermore, the imaging quality analysis module also includes an ambient light intensity analysis unit, which is used to analyze the ambient light abnormality based on the imaging environment data and the equipment data of the fluorescence microscope. The ambient light abnormality analysis results include normal and abnormal, and when the ambient light is abnormal, the preset light intensity difference index is adjusted to GZ'.

[0010] Furthermore, the imaging quality analysis module also includes an exposure analysis unit, which is used to analyze exposure abnormality according to the exposure time t of the target imaging data. The analysis results of the exposure abnormality include normal, first abnormality and second abnormality. When the first abnormality occurs, the preset interference light intensity is optimized to YQ'; when the second abnormality occurs, the preset interference light intensity is optimized to YQ".

[0011] Furthermore, the imaging quality analysis module is also provided with a clarity analysis unit, which is used to calculate the clarity index ▽T of the target processed image when the imaging state of the target imaging data is normal, and analyze the clarity of the target imaging data according to the clarity index ▽T of the target processed image. The clarity analysis results of the target imaging data include abnormal clarity and normal clarity.

[0012] Furthermore, the imaging quality analysis module is also provided with an imaging quality analysis unit, which is used to analyze the imaging quality of the target imaging data according to the clarity analysis results and the light intensity abnormality analysis results of the target imaging data, and the imaging quality analysis results of the target imaging data include good, unqualified and qualified.

[0013] Furthermore, the device abnormality analysis module is used to calculate the device abnormality index γ according to the device data of the fluorescence microscope, and analyze the device abnormality according to the device abnormality index. The analysis result of the device abnormality includes normal device and abnormal device.

[0014] Furthermore, the calibration module includes a calibration unit, which is used to generate a calibration scheme for the fluorescence microscope according to an imaging quality analysis result of the target imaging data and an equipment abnormality analysis result of the fluorescence microscope: When the imaging quality of the target imaging data is good, the calibration unit does not calibrate the fluorescence microscope; When the imaging quality is qualified and the device is normal, if a1×(▽T-YT) / YT+a2×(gz-GZ) / GZ<H, the calibration unit determines that the light source stability of the fluorescence microscope is abnormal, and calibrates the excitation power of the excitation light source to P'; wherein H is a preset light source stability index, a1 is a light intensity weight, a2 is a clarity weight, and a1+a2=1; When the imaging quality is unqualified, the calibration unit determines that the fluorescence microscope device is faulty and recommends that the user replace the filter and the excitation light source; When the imaging quality is qualified and the device is abnormal, the calibration unit determines the filter error of the fluorescence microscope and adjusts the excitation wavelength range to DG'; The calibration unit also uses the adjusted excitation wavelength range and the calibrated excitation power as a calibration scheme.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: by analyzing the imaging state of the current fluorescence microscope by analyzing the standard imaging image of the fluorescence microscope and the currently acquired target imaging image, and then by analyzing the light source state of the target imaging, the state of the light source structure of the fluorescence microscope is analyzed, and the light source and equipment of the fluorescence microscope are calibrated in combination with the equipment state analysis result of the fluorescence microscope, thereby effectively improving the calibration efficiency and calibration accuracy of the fluorescence microscope. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the structure of the fluorescence microscope calibration system based on machine learning in this embodiment.

[0018] Figure 2 Schematic diagram of the structure of the sample data processing module of this embodiment.

[0019] Figure 3 Schematic diagram of the structure of the imaging quality analysis module of this embodiment.

[0020] Figure 4 Schematic diagram of the structure of the calibration module of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0022] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0023] Specifically, the fluorescence microscope calibration system based on machine learning described in this embodiment is applied to the monochrome imaging calibration of the fluorescence microscope; the fluorescence microscope described in this embodiment is specifically a wide-field fluorescence microscope used for cell staining; the system described in this embodiment acts in a computer connected to the fluorescence microscope, and is data-connected to the fluorescence microscope through a circuit.

[0024] See also Figure 1 As shown, it is a schematic diagram of the structure of the fluorescence microscope calibration system based on machine learning described in this embodiment, including: An imaging data acquisition module is used to acquire original imaging data of a standard sample and target imaging data of the standard sample; the standard sample is a fixed detection object used for calibrating a fluorescence microscope, which is specifically a biological cell staining sample, and this implementation does not specifically limit the method for producing the biological cell staining sample; the original imaging data of the standard sample is the best historical imaging image of the standard sample under a fluorescence microscope, and is acquired by user interactive input; the target imaging data of the standard sample is the image currently obtained by imaging the standard sample through a fluorescence microscope; the target imaging data of the standard sample in this embodiment is acquired by taking a fluorescence microscope in the form of a digital signal.

[0025] It can be understood that when the raw imaging data and target imaging data are collected by a fluorescence microscope in this embodiment, the imaging equipment and operations such as shooting angle, objective lens height, eyepiece and objective lens model and magnification are the same, that is, the collection process of the raw imaging data and the target imaging data is the same.

[0026] Please continue reading Figure 1 As shown, the system also includes: The device data acquisition module is used to collect imaging environment data and device data of the fluorescence microscope; the imaging environment data includes the imaging environment light intensity, The equipment data of the fluorescence microscope includes the excitation wavelength range, the excitation light intensity, the emission wavelength range, the fluorescence intensity, and the service life of the filter.

[0027] Specifically, the excitation wavelength range, excitation light intensity, emission wavelength range, and fluorescence intensity in the equipment data of the fluorescence microscope described in this embodiment are all collected by a spectrometer, and the filter service life is collected through user interactive input; the imaging environment data is collected by setting various intelligent sensors at the edge of the fluorescence microscope.

[0028] Please continue reading Figure 1 As shown, the system also includes: The sample data processing module is used to process the original imaging data of the standard sample and the target imaging data of the standard sample.

[0029] See also Figure 2 As shown, the sample data processing module includes a raw data processing unit, which is used to process the raw imaging data of the standard sample to obtain raw standard data of the raw imaging data, and the raw standard data refers to the optimal imaging parameters of the raw imaging data; The image data processing unit identifies the stained pixel position coordinates of the stained object in the original imaging data of the standard sample, and eliminates the background pixels of the original imaging data according to the stained pixel position coordinates of the stained object to obtain an original processed image; The image data processing unit is also used to calculate the standard uniformity α1 according to the original processed image, and set ; Wherein, YN represents the number of pixels in the original imaging data, Qyn represents the fluorescence intensity of the ynth pixel, and QJ represents the mean fluorescence intensity of each pixel in the original imaging data; The image data processing unit is also used to obtain the standard contrast β1 of the original processed image, and use the standard contrast β1 and the standard uniformity α1 as the original standard data; by calculating the original standard data to extract the quality parameters represented in the original imaging data, and using this original standard data as a digital basis for judging the imaging state, the accuracy of the imaging state judgment is improved.

[0030] Specifically, this embodiment does not specifically limit the process of identifying the stained objects in the original imaging data of the sample standard. In this embodiment, the professional identification software (BD FACSChorus™ software) in the computer connected to the fluorescence microscope is used for identification. Those skilled in the art may use other methods to identify the stained objects. At the same time, the process of obtaining the standard contrast in this embodiment is a public prior art known to those skilled in the art, and this embodiment does not specifically limit it. In this embodiment, the standard contrast is referred to by calculating the contrast ratio.

[0031] Please continue reading Figure 2 As shown, the system further includes a target data processing unit, which is connected to the raw data processing unit, and is used to process the target imaging data of the standard sample to obtain target image parameters of the target imaging data; The target data processing unit obtains a target processed image according to the target imaging data, and calculates target image parameters according to the target processed image; The target image data has the same format as the original standard data, and the target image data includes a target uniformity α2 and a target contrast β2.

[0032] Specifically, the process of "obtaining a target processed image according to target imaging data, and calculating target image parameters according to the target processed image" in this embodiment is the same as the analysis and calculation process of the original processed image and the original standard data, and is not repeated in this embodiment.

[0033] Please continue reading Figure 1 As shown, the system also includes a sample data analysis module, which is connected to the sample data processing module and is used to analyze the imaging state of the target imaging data according to the target image parameters, and send an imaging result signal to the user according to the analysis result: if α2<α1×η1 and β1×η2≤β2<β1×η3, the sample data analysis module determines that the imaging state of the target imaging data is normal, and sends an imaging success signal to the user; if α2≥α1×η1 or β2 [β1×η2,β1×η3), the sample data analysis module determines that the imaging state of the target imaging data is abnormal, and sends an imaging failure signal to the user; the imaging state of the target image parameters of the target sample data is judged by the original standard data to exclude the target imaging data that obviously does not meet the calibration standard, thereby improving the calibration efficiency of the fluorescence microscope; Wherein, η1 is a preset fluorescence imaging uniformity, η2 is a first preset contrast, η3 is a second preset contrast, and η2<η3.

[0034] Specifically, this embodiment does not impose any specific restrictions on the values ​​of the preset fluorescence imaging uniformity η1, the first preset contrast η2, and the second preset contrast η3. It is only necessary to meet the value requirements of the preset fluorescence imaging uniformity η1, the first preset contrast η2, and the second preset contrast η3. In this embodiment, the preset fluorescence imaging uniformity η1 is set to 0.5, the first preset contrast η2 is set to 0.8, and the second preset contrast η3 is set to 1.2.

[0035] Please continue reading Figure 1 As shown, the system also includes an imaging quality analysis module, which is connected to the sample data analysis module and the device data acquisition module. The imaging quality analysis module is used to analyze the imaging quality according to the imaging state analysis results of the target imaging data and the target processed image.

[0036] See also Figure 3 As shown, the imaging quality analysis module includes an image light intensity analysis unit, which is used to analyze the light intensity abnormality of the target imaging data according to the imaging state analysis result of the target imaging data, the original processed image and the target processed image, and the light intensity abnormality refers to the light leakage phenomenon of the background in the target imaging data; The image light intensity analysis unit is used to grayscale the original processed image and the target processed image, and when the imaging state of the target imaging data is normal, calculate the light intensity difference index gz, set gz=Σgc(i), wherein gc(i) is the light difference of the i-th pixel, set gc(i)=|ys(i)-mb(i)| / N, N is the number of pixels of the original processed image, ys(i) is the grayscale value of the i-th pixel in the original processed image, mb(i) is the grayscale value of the i-th pixel in the target processed image, at this time, if gz<GZ, the image light intensity analysis unit determines that the light intensity of the target imaging data is normal; if gz≥GZ, the image light intensity analysis unit determines that the light intensity of the target imaging data is abnormal; wherein GZ is a preset light intensity difference index; by calculating the light intensity difference of the target processed image and using it to represent the proportion of background light intensity in the image brightness, and then analyzing the light intensity abnormality, accurate judgment of the light leakage phenomenon of the target imaging data is achieved.

[0037] Specifically, this embodiment does not impose any specific limitation on the value of the preset light intensity difference index GZ. Technical personnel in this field can set it freely as long as the value requirement of the preset light intensity difference index GZ is met. In this embodiment, the optimal value of the preset light intensity difference index GZ is 10.

[0038] Please continue reading Figure 3 As shown, the imaging quality analysis module also includes an ambient light intensity analysis unit, which is connected to the image light intensity analysis unit. The ambient light intensity analysis unit is used to analyze the abnormality of ambient light according to the imaging environment data and the equipment data of the fluorescence microscope, and adjust the light intensity abnormality analysis process of the target imaging data according to the abnormality analysis result of ambient light: if QHG<YQ, the ambient light intensity analysis unit determines that the ambient light is normal and does not make any adjustments; if QHG≥YQ, the ambient light intensity analysis unit determines that the ambient light is abnormal, and adjusts the preset light intensity difference value. The number is adjusted to GZ', and GZ'=GZ×[1-(QHG-YQ) / QHG] is set; wherein, QHG∈DG, QHG is the light intensity in the ambient light belonging to the excitation wavelength range DG, DG is the excitation wavelength range and DG={BC1≤LX≤BC2}, BC1 and BC2 are the left and right values ​​of the excitation wavelength range, respectively, and YQ is the preset interference light intensity; by analyzing the abnormality of the ambient light intensity, the interference of the ambient natural light on the fluorescence imaging is eliminated, and the analysis result of the light intensity abnormality is adjusted according to the analysis result, so that the analysis result of the light intensity abnormality is more accurate.

[0039] Specifically, this embodiment does not specifically limit the value of the preset interference light intensity YQ, and those skilled in the art can freely set it as long as the value requirement of the preset interference light intensity YQ is met. In this implementation, the optimal value of the preset interference light intensity YQ is 50lx.

[0040] Please continue reading Figure 3 As shown, the imaging quality analysis module further includes an exposure analysis unit, which is connected to the ambient light intensity analysis unit. The exposure analysis unit is used to analyze exposure abnormality according to the exposure duration of the target imaging data, and optimize the light intensity abnormality adjustment process of the target imaging data according to the analysis result. Exposure abnormality refers to the light leakage caused by excessive exposure when the light intensity is abnormal: When the light intensity of the target imaging data is normal, if t<T1, the exposure analysis unit determines that the exposure of the target imaging data is normal and does not perform optimization; if t≥T1, the exposure analysis unit determines that the exposure of the target imaging data is the first abnormality, and optimizes the preset interference light intensity to YQ', setting YQ'=YQ×ln[e-(t-T1) / T1], where e is a natural logarithm; When the light intensity of the target imaging data is abnormal, if t<T2, the exposure analysis unit determines that the exposure of the target imaging data is normal and does not perform optimization; if t≥T2, the exposure analysis unit determines that the exposure of the target imaging data is the second abnormality, and optimizes the preset interference light intensity to YQ", and sets YQ"=YQ×[1-(t-T2) / T2]; by comparing and analyzing the exposure time, an accurate analysis of the light leakage caused by the exposure is achieved, and then the strong abnormality adjustment process is optimized, so that the analysis result of the light intensity abnormality is more accurate; Wherein, t is the exposure time of the target imaging data, T1 is the first preset exposure time, T2 is the second preset exposure time, and T1>T2.

[0041] Specifically, this embodiment does not impose any specific restrictions on the values ​​of the first preset exposure time T1 and the second preset exposure time T2. Technical personnel in this field can set them freely as long as the value requirements of the first preset exposure time T1 and the second preset exposure time T2 are met. In this embodiment, the optimal value of the first preset exposure time T1 is 100ms, and the optimal value of the second preset exposure time T2 is 60ms.

[0042] It can be understood that the exposure time described in this embodiment is an imaging parameter controlled by the fluorescence microscope itself.

[0043] Please continue reading Figure 3 As shown, the imaging quality analysis module is further provided with a clarity analysis unit, which is used to analyze the clarity of the target imaging data according to the target processing image; The clarity analysis unit calculates the clarity index ▽T of the target processed image when the imaging state of the target imaging data is normal, and sets , where D represents the pixel area of ​​the target processing image, D={(x,y)|0≤x≤X, 0≤y≤Y} is set, X is the maximum number of horizontal pixels of the target processing image, Y is the maximum number of vertical pixels of the target processing image, Gx represents the gray gradient of the pixel with coordinates (x,y) on the horizontal coordinate axis, Gy represents the gray gradient of the pixel with coordinates (x,y) on the vertical coordinate axis, and ZG represents the total gray value of the target processing image; the clarity analysis unit quantifies the clarity state presented by the target imaging data with a clarity index ▽T; The clarity analysis unit analyzes the clarity of the target imaging data according to the clarity index ▽T of the target processed image: if ▽T<YT, the clarity analysis unit determines that the clarity of the target imaging data is abnormal; if ▽T≥YT, the clarity analysis unit determines that the clarity of the target imaging data is normal; wherein YT is the preset image clarity; by judging the clarity of the target imaging data and thereby reflecting the stability of the light source of the fluorescence microscope (when the light source is unstable, the wavelength of the excitation light emitted by the light source will also be unstable, resulting in blurred imaging), accurate judgment of the clarity is achieved.

[0044] Specifically, this embodiment does not impose any specific limitation on the value of the preset image clarity YT, and those skilled in the art can freely set it as long as the value requirement of the preset image clarity YT is met. In this embodiment, the optimal value of the preset image clarity YT is 0.2.

[0045] It can be understood that in this embodiment, the imaging blur phenomenon caused by focusing is not specifically analyzed, and in this embodiment, it is assumed that the focusing of the target imaging data is accurate; at the same time, the calculation process of Gx and Gy in this embodiment is Gx=gray(x+1,y)-gray(x-1,y), Gy=gray(x,y+1)-gray(x,y-1), wherein gray(x+1,y) represents the grayscale value of the pixel point with coordinates (x+1,y), and the meanings of gray(x,y+1), gray(x,y-1) and gray(x-1,y) are similar to gray(x+1,y), which are the grayscale values ​​of pixels with different coordinates, respectively, and will not be elaborated in this embodiment.

[0046] Please continue reading Figure 3As shown, the imaging quality analysis module is also provided with an imaging quality analysis unit, which is connected to the clarity analysis unit and the image light intensity analysis unit, and the imaging quality analysis unit is used to analyze the imaging quality of the target imaging data according to the clarity analysis result and the light intensity abnormality analysis result of the target imaging data: if the light intensity and clarity of the target imaging data are normal, the imaging quality analysis unit determines that the imaging quality of the target imaging data is good; if the clarity of the target imaging data is abnormal, the imaging quality analysis unit determines that the imaging quality of the target imaging data is unqualified; if the light intensity of the target imaging data is abnormal and the clarity is normal, the imaging quality analysis unit determines that the imaging quality of the target imaging data is qualified.

[0047] Specifically, the good described in this embodiment is a level of imaging quality above the qualified level, and the qualified level in this embodiment does not include good.

[0048] Please continue reading Figure 1 As shown, the system further includes an equipment abnormality analysis module, which is connected to the equipment data acquisition module, and the equipment abnormality analysis module is used to analyze equipment abnormality according to equipment data of the fluorescence microscope; The device abnormality analysis module calculates the device abnormality index γ according to the device data of the fluorescence microscope, and sets γ=(Ww) / W×(mM) / M; wherein W is the set excitation wavelength of the fluorescent material, w is the actual excitation wavelength of the target imaging data, m is the remaining service life of the fluorescence microscope filter, and M is the total service life of the fluorescence microscope; The device abnormality analysis module analyzes the device abnormality according to the device abnormality index: if γ<K, the device abnormality analysis module determines that the device of the fluorescence microscope is normal; if γ≥K, the device abnormality analysis module determines that the device of the fluorescence microscope is abnormal; wherein K is a preset device abnormality index; the device abnormality analysis module indirectly analyzes the aging degree of the consumable parts of the fluorescence microscope and directly analyzes the direct wavelength error, thereby achieving accurate judgment of the device status.

[0049] Specifically, this embodiment does not impose any specific limitation on the value of the preset device abnormality index K, and those skilled in the art can freely set it as long as the value requirement of the preset device abnormality index K is met. In this embodiment, the optimal value of the preset device abnormality index K is 0.5.

[0050] Please continue reading Figure 1As shown, the system also includes a calibration module, which is connected to the device abnormality analysis module and the data status analysis module. The calibration module is used to generate a calibration scheme for the fluorescence microscope based on the data status analysis results of the target imaging data and the device abnormality analysis results of the fluorescence microscope, and output the calibration scheme to the user.

[0051] See also Figure 4 As shown, the calibration module includes a calibration unit, and the calibration unit is used to generate a calibration scheme for the fluorescence microscope according to an imaging quality analysis result of the target imaging data and an equipment abnormality analysis result of the fluorescence microscope: When the imaging quality of the target imaging data is good, the calibration unit does not calibrate the fluorescence microscope; When the imaging quality is qualified and the equipment is normal, if a1×(▽T-YT) / YT+a2×(gz-GZ) / GZ<H, the calibration unit determines that the light source stability of the fluorescence microscope is abnormal, and calibrates the excitation power of the excitation light source to P', setting P'=P×ln{e-[a1×(▽T-YT) / YT+a2×(gz-GZ) / GZ-K] / K}; wherein H is a preset light source stability index, P is the actual excitation power of the excitation light source, a1 is a light intensity weight, a2 is a clarity weight, and a1+a2=1; When the imaging quality is unqualified, the calibration unit determines that the fluorescence microscope device is faulty and recommends that the user replace the filter and the excitation light source; When the imaging quality is qualified and the device is abnormal, the calibration unit determines the filter error of the fluorescence microscope and adjusts the excitation wavelength range to DG', setting DG'={BC1'≤LX≤BC2'}, where BC1'=BC1×exp[(γ-K) / K], BC2'=BC2×exp[(K-γ) / K]; The calibration unit also uses the adjusted excitation wavelength range and the calibrated excitation power as a calibration scheme; the calibration unit combines the analysis results of the device status and the analysis results of the imaging quality to specifically analyze the actual error components of the fluorescence microscope reflected by the two, and calibrates them, thereby effectively improving the calibration efficiency and calibration accuracy of the fluorescence microscope.

[0052] Specifically, this embodiment does not impose any specific limitation on the value of the preset light source stability index H. Those skilled in the art can set it freely as long as the value requirement of the preset light source stability index H is met. In this embodiment, the optimal value of the preset light source stability index H is 0.3. In this embodiment, the actual excitation power P of the excitation light source is obtained by connecting the fluorescence microscope to a computer.

[0053] Please continue reading Figure 4As shown, the calibration module further includes an output unit, which is connected to the calibration unit, and is used to output the calibration scheme of the fluorescence microscope to the user.

[0054] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A fluorescence microscope calibration system based on machine learning, characterized in that: include: An imaging data acquisition module, used to acquire original imaging data of the standard sample and target imaging data of the standard sample; Equipment data acquisition module, used to collect imaging environment data and equipment data of fluorescence microscope; A sample data processing module is used to process the original imaging data of the standard sample and the target imaging data of the standard sample to obtain original standard data, target image parameters, original processed images and target processed images; A sample data analysis module is used to analyze the imaging state of the target imaging data according to the target image parameters; An imaging quality analysis module is used to analyze the clarity and light intensity abnormality of the target imaging data according to the imaging state analysis results of the target imaging data and the target processed image, and to analyze the imaging quality of the target imaging data according to the analysis results; Equipment abnormality analysis module, used to analyze equipment abnormality based on equipment data from the fluorescence microscope; The calibration module is used to generate a calibration scheme for the fluorescence microscope according to an imaging quality analysis result of the target imaging data and an equipment abnormality analysis result of the fluorescence microscope.

2. The fluorescence microscope calibration system based on machine learning according to claim 1, characterized in that: The sample data processing module includes a raw data processing unit, which is used to identify the stained pixel position coordinates of the stained object in the raw imaging data of the standard sample, and eliminate the background pixels of the raw imaging data according to the stained pixel position coordinates of the stained object to obtain an original processed image; The image data processing unit is also used to calculate the standard uniformity α1 according to the original processed image; The image data processing unit is further used to obtain a standard contrast β1 of the original processed image, and use the standard contrast β1 and the standard uniformity α1 as original standard data.

3. The fluorescence microscope calibration system based on machine learning according to claim 2, characterized in that: The sample data analysis module is used to analyze the imaging state of the target imaging data according to the target image parameters, and send an imaging result signal to the user according to the analysis result: if α2<α1×η1 and β1×η2≤β2<β1×η3, the sample data analysis module determines that the imaging state of the target imaging data is normal, and sends an imaging success signal to the user; if α2≥α1×η1 or β2 [β1×η2, β1×η3), the sample data analysis module determines that the imaging state of the target imaging data is abnormal, and sends an imaging failure signal to the user; Wherein, η1 is a preset fluorescence imaging uniformity, η2 is a first preset contrast, η3 is a second preset contrast, and η2<η3.

4. The fluorescence microscope calibration system based on machine learning according to claim 3, characterized in that: The imaging quality analysis module includes an image light intensity analysis unit, which is used to grayscale the original processed image and the target processed image, and calculate the light intensity difference index gz when the imaging state of the target imaging data is normal. At this time, if gz exceeds the preset light intensity difference index GZ, the image light intensity analysis unit determines that the light intensity of the target imaging data is abnormal; Otherwise, it is determined that the light intensity of the target imaging data is normal.

5. The fluorescence microscope calibration system based on machine learning according to claim 4, characterized in that: The imaging quality analysis module also includes an ambient light intensity analysis unit, which is used to analyze the ambient light abnormality based on the imaging environment data and the equipment data of the fluorescence microscope. The ambient light abnormality analysis results include normal and abnormal, and when the ambient light is abnormal, the preset light intensity difference index is adjusted to GZ'.

6. The fluorescence microscope calibration system based on machine learning according to claim 5, characterized in that: The imaging quality analysis module also includes an exposure analysis unit, which is used to analyze exposure abnormality according to the exposure time t of the target imaging data. The analysis results of the exposure abnormality include normal, first abnormality and second abnormality. When the first abnormality occurs, the preset interference light intensity is optimized to YQ'; when the second abnormality occurs, the preset interference light intensity is optimized to YQ".

7. The fluorescence microscope calibration system based on machine learning according to claim 6, characterized in that: The imaging quality analysis module is also provided with a clarity analysis unit, which is used to calculate the clarity index ▽T of the target processed image when the imaging state of the target imaging data is normal, and analyze the clarity of the target imaging data according to the clarity index ▽T of the target processed image. The clarity analysis results of the target imaging data include abnormal clarity and normal clarity.

8. The machine learning-based fluorescence microscope calibration system according to claim 7, characterized in that: The imaging quality analysis module is also provided with an imaging quality analysis unit, which is used to analyze the imaging quality of the target imaging data according to the clarity analysis results and the light intensity abnormality analysis results of the target imaging data. The imaging quality analysis results of the target imaging data include good, unqualified and qualified.

9. The machine learning-based fluorescence microscope calibration system according to claim 8, characterized in that: The device abnormality analysis module is used to calculate the device abnormality index γ according to the device data of the fluorescence microscope, and analyze the device abnormality according to the device abnormality index. The analysis result of the device abnormality includes the device being normal and the device being abnormal.

10. The fluorescence microscope calibration system based on machine learning according to claim 9, characterized in that: The calibration module includes a calibration unit, which is used to generate a calibration scheme for the fluorescence microscope according to an imaging quality analysis result of the target imaging data and an equipment abnormality analysis result of the fluorescence microscope: When the imaging quality of the target imaging data is good, the calibration unit does not calibrate the fluorescence microscope; When the imaging quality is qualified and the device is normal, if a1×(▽T-YT) / YT+a2×(gz-GZ) / GZ<H, the calibration unit determines that the light source stability of the fluorescence microscope is abnormal, and calibrates the excitation power of the excitation light source to P'; wherein H is a preset light source stability index, a1 is a light intensity weight, a2 is a clarity weight, and a1+a2=1; When the imaging quality is unqualified, the calibration unit determines that the fluorescence microscope device is faulty and recommends that the user replace the filter and the excitation light source; When the imaging quality is qualified and the device is abnormal, the calibration unit determines the filter error of the fluorescence microscope and adjusts the excitation wavelength range to DG'; The calibration unit also uses the adjusted excitation wavelength range and the calibrated excitation power as a calibration scheme.

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