Hyperspectral microscope and HE staining combined section identification method

Abstract

The invention relates to the technical field of biomedical engineering, and discloses a hyperspectral microscope combined HE staining section identification method, which comprises a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module, the data analysis module calculates an automatic dyeing control index Q1, a microscope imaging stability coefficient S1 and an image quality evaluation score G1, and the abnormal data processing module identifies dyeing abnormal data in an automatic dyeing process and abnormal data in a microscope imaging process according to calculated numerical values and judges whether the final imaging quality reaches the standard or not; the data management module carries out data correction and fault diagnosis on dyeing abnormal data in the automatic dyeing process and abnormal data in the microscope imaging process according to the abnormal data automatically recognized by the abnormal data processing module and sends corrected parameters and fault diagnosis information to the control module at the same time, and the control module issues a repair instruction.

Description

Technical Field

[0001] The invention relates to the technical field of biomedical engineering, and in particular to a method for identifying slices in combination with a hyperspectral microscope and HE staining. Background Art

[0002] In traditional medical pathology, H&E staining allows pathologists to clearly observe the morphological structure of tissue samples. Although this traditional method is of great value, it relies on the effect of chemical staining and is easily affected by multiple factors such as the type of fixative, staining reagents, and slicer performance, resulting in unstable imaging quality and complex operation. In order to solve these problems, hyperspectral microscopy (HMI) technology came into being, and the application of multispectral analysis on HE-stained sections provided a new solution. HMI technology can capture more spectral information for more detailed analysis and identification of tissue types, realize automatic staining control and image processing technology, and significantly improve recognition efficiency and consistency of results. The introduction of this technology not only retains the advantages of traditional HE staining, but also strengthens the in-depth understanding of pathological tissue characteristics, bringing new perspectives and tools to pathological diagnosis and research. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the prior art, the present invention provides a method for identifying slices using a hyperspectral microscope combined with HE staining, which has the advantages of automatic staining control and stable imaging quality, and solves the problem of reliance on chemical staining and unstable imaging quality in traditional medical pathology.

[0005] (II) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solution: a method for identifying a section by combining a hyperspectral microscope with HE staining, comprising the following steps:

[0007] Step 1: Establish a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module;

[0008] Step 2, the data acquisition module is divided into an automatic staining data capture unit, a microscope imaging data receiving unit and a staining slice data output unit;

[0009] Step 3: The data analysis module is divided into an HE staining slice recognition unit and a microscope imaging stabilization unit, and calculates the automatic staining control index Ql, the microscope imaging stability coefficient Sl and the image quality assessment score Gl;

[0010] Step 4: The abnormal data processing module identifies abnormal staining data during the automatic staining process and abnormal data during the microscope imaging process according to the automatic staining control index Ql and the microscope imaging stability coefficient Sl, and determines whether the quality of the final imaging meets the standard according to the image quality evaluation score Gl.

[0011] Preferably, the method comprises a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module;

[0012] The data acquisition module includes an automatic staining data capturing unit, a microscope imaging data receiving unit and a staining slice data output unit, wherein the automatic staining data unit collects automatic staining data through a hyperspectral imaging system, the microscope imaging data receiving unit collects microscope imaging data through a CCD camera, and the staining slice data output unit collects staining slice data through an imaging spectrometer, and the spectrum data capturing unit, the microscope imaging data receiving unit and the staining slice data output unit are connected to the data analysis module through a network;

[0013] The data analysis module includes a HE-stained slice identification unit and a microscope imaging stabilization unit, wherein the HE-stained slice identification unit calculates an automatic staining control index Ql according to the automatic staining data, the microscope imaging stabilization unit calculates a microscope imaging stability coefficient Sl according to the microscope imaging data, and the microscope imaging stabilization unit calculates an image quality assessment score Gl according to the stained slice data, and the HE-stained slice identification unit and the microscope imaging stabilization unit are connected to the abnormal data processing module through a network;

[0014] The abnormal data processing module identifies abnormal staining data in the automatic staining process and abnormal data in the microscope imaging process according to the automatic staining control index Ql and the microscope imaging stability coefficient Sl, and determines whether the quality of the final imaging meets the standard according to the image quality evaluation score Gl. The abnormal data processing module is connected to the data management module through a network.

[0015] Preferably, the automatic staining data unit performs data numbering on the initially set staining time, the initial concentration of the staining solution, the background staining value, the staining environment temperature influence value and the staining instrument correction factor according to the automatic staining data characteristics, and the initially set staining time, the initial concentration of the staining solution, the background staining value, the staining environment temperature influence value and the staining instrument correction factor are numbered as T, C, B, μ, ε, and the HE staining section recognition unit calculates the automatic staining control index Ql according to the automatic staining data, and the calculation formula is:

[0016]

[0017] In the formula, Ql represents the automatic dyeing control index, T, C, B, μ, and ε represent the initial set dyeing time, the initial concentration of the dyeing solution, the background dyeing value, the dyeing environment temperature influence value, and the dyeing instrument correction factor, respectively, and (1-μ) represents the constant value that excludes the temperature influence value.

[0018] Preferably, the microscope imaging data receiving unit numbers the grayscale values ​​of all images according to the characteristics of the microscope imaging data, and the grayscale values ​​of the images are numbered as f1, f2, f3, ... f n .

[0019] Preferably, the microscope imaging stabilization unit calculates the microscope imaging stabilization coefficient S1 according to the microscope imaging data, and the calculation formula is:

[0020]

[0021] In the formula, Sl represents the microscope imaging stability coefficient, f1, f2, f3, ... f n represents the gray value of the image, n represents the number of images, f i represents the gray value of the i-th image, Represents the average gray value of all images.

[0022] Preferably, the stained slice data output unit numbers the signal intensity and background noise level of the stained area according to the stained slice data characteristics, and the signal intensity numbers of the stained area are E1, E2, E3, ... E n , the background noise levels are numbered R1, R2, R3, ...R m .

[0023] Preferably, the microscope imaging stabilization unit calculates the image quality evaluation score Gl according to the stained slice data, and the calculation formula is:

[0024]

[0025] In the formula, G1 represents the image quality assessment score, E1, E2, E3, ... E n Indicates the signal intensity of the stained area, R1, R2, R3, ... R m represents the background noise level, represents the average signal intensity of the stained area, n represents the number of statistically stained areas, represents the average level of background noise, and m represents the number of statistical background noise levels.

[0026] Preferably, the abnormal data processing module automatically identifies the initial concentration abnormal value or background staining abnormal value of the staining solution during the automatic staining process according to the automatic staining control index Ql, and automatically identifies the sharpening abnormal value or contrast abnormal value of the imaging system according to the microscope imaging stability coefficient Sl.

[0027] Preferably, the abnormal data processing module determines whether the quality of the final imaging meets the standard based on the image quality assessment score Gl. When the image quality assessment score Gl ≥ 95 points, the imaging quality is excellent; when the image quality assessment score Gl < 90 points, the imaging quality is good; when the image quality assessment score Gl < 80 points, the imaging quality meets the standard.

[0028] Preferably, the data management module performs data correction and fault diagnosis on the abnormal data in the automatic staining process and the abnormal data in the microscope imaging process according to the abnormal data automatically identified by the abnormal data processing module, and sends the corrected parameters and fault diagnosis information to the control module, which issues a repair instruction. The control module is connected to the data acquisition module, the data analysis module, the abnormal data processing module, and the data management module through the network.

[0029] Compared with the prior art, the present invention provides a method for identifying slices using a hyperspectral microscope combined with HE staining, which has the following beneficial effects:

[0030] 1. The present invention calculates the automatic staining control index Ql, and the abnormal data processing module compares the automatic staining control index Ql with the historical preset standard range. When the automatic staining control index Ql exceeds the preset range, it means that the initial concentration of the staining solution is too high, resulting in too deep staining. The abnormal data is transmitted to the data management module, and the data management module automatically reduces the concentration of the staining solution to adjust it to an appropriate staining depth. When the automatic staining control index Ql is lower than the preset range, it means that the background staining value is too low, resulting in insufficient staining. The data management module will correspondingly increase the background staining value to ensure sufficient staining. The adjustment process helps to achieve quality control of the staining process to ensure that each stained section can achieve the best effect, thereby improving the accuracy of pathological diagnosis.

[0031] 2. The present invention calculates the microscope imaging stability coefficient Sl. When the microscope imaging stability coefficient Sl is less than 1, it indicates that there is fluctuation or blur in the imaging system, which leads to a decrease in image quality. When the abnormal data processing module recognizes an abnormal situation, it will automatically check the stability of the imaging system (including the light source, the microscope optical system and the sample position) to ensure that all components are in a stable state. When the microscope imaging stability coefficient Sl is greater than 1, it indicates that the imaging system is over-sharpened or the contrast is too high, and image details will be lost. The data management module will automatically adjust the focus or optical settings of the microscope to obtain a more balanced image effect, thereby solving the problem of reliance on chemical staining and unstable imaging quality in traditional medical pathology. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1 , a method for identifying slices using a hyperspectral microscope combined with HE staining, comprising the following steps:

[0035] Step 1: Establish a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module;

[0036] Step 2, the data acquisition module is divided into an automatic staining data capture unit, a microscope imaging data receiving unit and a staining slice data output unit;

[0037] Step 3: The data analysis module is divided into an HE staining slice recognition unit and a microscope imaging stabilization unit, and calculates the automatic staining control index Ql, the microscope imaging stability coefficient Sl and the image quality assessment score Gl;

[0038] Step 4: The abnormal data processing module identifies abnormal staining data during the automatic staining process and abnormal data during the microscope imaging process according to the automatic staining control index Ql and the microscope imaging stability coefficient Sl, and determines whether the quality of the final imaging meets the standard according to the image quality evaluation score Gl.

[0039] The method includes a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module;

[0040] The data acquisition module includes an automatic staining data capturing unit, a microscope imaging data receiving unit and a staining slice data output unit. The automatic staining data unit collects automatic staining data through a hyperspectral imaging system, the microscope imaging data receiving unit collects microscope imaging data through a CCD camera, and the staining slice data output unit collects staining slice data through an imaging spectrometer. The spectral data capturing unit, the microscope imaging data receiving unit and the staining slice data output unit are connected to the data analysis module through a network.

[0041] The data analysis module includes a HE-stained slice recognition unit and a microscope imaging stabilization unit. The HE-stained slice recognition unit calculates an automatic staining control index Ql according to the automatic staining data. The microscope imaging stabilization unit calculates a microscope imaging stability coefficient Sl according to the microscope imaging data. The microscope imaging stabilization unit calculates an image quality assessment score Gl according to the stained slice data. The HE-stained slice recognition unit and the microscope imaging stabilization unit are connected to the abnormal data processing module through a network.

[0042] The abnormal data processing module identifies abnormal dyeing data in the automatic dyeing process and abnormal data in the microscope imaging process according to the automatic dyeing control index Ql and the microscope imaging stability coefficient Sl, and determines whether the quality of the final imaging meets the standard according to the image quality evaluation score Gl. The abnormal data processing module is connected to the data management module through the network;

[0043] The automatic staining data unit numbers the initially set staining time, the initial concentration of the staining solution, the background staining value, the staining environment temperature influence value and the staining instrument correction factor according to the automatic staining data characteristics. The initially set staining time, the initial concentration of the staining solution, the background staining value, the staining environment temperature influence value and the staining instrument correction factor are numbered as T, C, B, μ, ε. The HE staining slice recognition unit calculates the automatic staining control index Ql according to the automatic staining data, and the calculation formula is:

[0044]

[0045] In the formula, Ql represents the automatic dyeing control index, T, C, B, μ, and ε represent the initial set dyeing time, the initial concentration of the dyeing solution, the background dyeing value, the dyeing environment temperature influence value, and the dyeing instrument correction factor, respectively, and (1-μ) represents the constant value that excludes the temperature influence value;

[0046] The advantages are: by calculating the automatic staining control index Ql, the abnormal data processing module compares the automatic staining control index Ql with the historical preset standard range. When the automatic staining control index Ql exceeds the preset range, it means that the initial concentration of the staining solution is too high, resulting in excessive staining. The abnormal data is transmitted to the data management module, and the data management module will automatically reduce the concentration of the staining solution to adjust it to an appropriate staining depth. When the automatic staining control index Ql is lower than the preset range, it means that the background staining value is too low, resulting in insufficient staining. The data management module will increase the background staining value accordingly to ensure sufficient staining. This adjustment process helps to achieve quality control of the staining process to ensure that each stained section can achieve the best effect, thereby improving the accuracy of pathological diagnosis.

[0047] The microscope imaging data receiving unit numbers the grayscale values ​​of all images according to the characteristics of the microscope imaging data. The grayscale values ​​of the images are numbered as f1, f2, f3, ... f n .

[0048] The microscope imaging stabilization unit calculates the microscope imaging stabilization coefficient Sl according to the microscope imaging data, and the calculation formula is:

[0049]

[0050] In the formula, Sl represents the microscope imaging stability coefficient, f1, f2, f3, ... f n represents the gray value of the image, n represents the number of images, f i represents the gray value of the i-th image, Represents the average gray value of all images;

[0051] The advantages are: by calculating the microscope imaging stability coefficient Sl, when the microscope imaging stability coefficient Sl is less than 1, it means that there is fluctuation or blur in the imaging system, which leads to a decrease in image quality. When the abnormal data processing module recognizes the abnormal situation, it will automatically check the stability of the imaging system (including the light source, microscope optical system and sample position) to ensure that all components are in a stable state. When the microscope imaging stability coefficient Sl is greater than 1, it means that the imaging system is over-sharpened or the contrast is too high, and the image details will be lost. The data management module will automatically adjust the focus or optical settings of the microscope to obtain a more balanced image effect.

[0052] The stained slice data output unit numbers the signal intensity and background noise level of the stained area according to the stained slice data characteristics. The signal intensity of the stained area is numbered as E1, E2, E3, ... E n , the background noise levels are numbered R1, R2, R3, ... R m .

[0053] The microscope imaging stabilization unit calculates the image quality evaluation score Gl based on the stained slice data, and the calculation formula is:

[0054]

[0055] In the formula, G1 represents the image quality assessment score, E1, E2, E3, ... E n Indicates the signal intensity of the stained area, R1, R2, R3, ... R m represents the background noise level, represents the average signal intensity of the stained area, n represents the number of statistically stained areas, represents the average level of background noise, and m represents the number of statistical background noise levels;

[0056] The advantages are: by calculating the image quality assessment score Gl, it is helpful to evaluate the quality level of the image, so that it can be applied to the subsequent pathological analysis required by the pathology, which can reduce the risk of misdiagnosis and missed diagnosis. The parameters in the formula help to positively understand the relationship between signal intensity and background noise in the imaging process, so that the experimental conditions of sample preparation, staining process and microscope setting can be adjusted according to actual needs to obtain higher quality images. The abnormal data processing module can automatically perform image quality assessment based on the image quality assessment score Gl, and identify abnormal data at the same time to reduce manual intervention and improve data processing speed.

[0057] The abnormal data processing module automatically identifies the abnormal initial concentration value or background staining value of the staining solution during the automatic staining process according to the automatic staining control index Ql, and automatically identifies the abnormal sharpness value or contrast value of the imaging system according to the microscope imaging stability coefficient Sl.

[0058] The abnormal data processing module determines whether the quality of the final imaging meets the standard based on the image quality assessment score Gl. When the image quality assessment score Gl ≥ 95 points, the imaging quality is excellent; when the image quality assessment score Gl < 95 points, the imaging quality is good; when the image quality assessment score Gl < 80 points, the imaging quality meets the standard.

[0059] The data management module performs data correction and fault diagnosis on the abnormal data in the automatic staining process and the abnormal data in the microscope imaging process according to the abnormal data automatically identified by the abnormal data processing module, and sends the corrected parameters and fault diagnosis information to the control module, which issues repair instructions. The control module is connected to the data acquisition module, the data analysis module, the abnormal data processing module, and the data management module through the network.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying slices using a hyperspectral microscope combined with HE staining, characterized in that: The following steps are involved: Step 1: Establish a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module; Step 2, the data acquisition module is divided into an automatic staining data capture unit, a microscope imaging data receiving unit and a staining slice data output unit; Step 3: The data analysis module is divided into an HE staining slice recognition unit and a microscope imaging stabilization unit, and calculates the automatic staining control index Ql, the microscope imaging stability coefficient Sl and the image quality assessment score Gl; Step 4: The abnormal data processing module identifies abnormal staining data during the automatic staining process and abnormal data during the microscope imaging process according to the automatic staining control index Ql and the microscope imaging stability coefficient Sl, and determines whether the quality of the final imaging meets the standard according to the image quality evaluation score Gl.

2. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 1, characterized in that: The method comprises a data acquisition module, a data analysis module, an abnormal data processing module, a data management module and a control module; The data acquisition module includes an automatic staining data capturing unit, a microscope imaging data receiving unit and a staining slice data output unit, wherein the automatic staining data unit collects automatic staining data through a hyperspectral imaging system, the microscope imaging data receiving unit collects microscope imaging data through a CCD camera, and the staining slice data output unit collects staining slice data through an imaging spectrometer, and the spectrum data capturing unit, the microscope imaging data receiving unit and the staining slice data output unit are connected to the data analysis module through a network; The data analysis module includes a HE-stained slice identification unit and a microscope imaging stabilization unit, wherein the HE-stained slice identification unit calculates an automatic staining control index Ql according to the automatic staining data, the microscope imaging stabilization unit calculates a microscope imaging stability coefficient Sl according to the microscope imaging data, and the microscope imaging stabilization unit calculates an image quality assessment score Gl according to the stained slice data, and the HE-stained slice identification unit and the microscope imaging stabilization unit are connected to the abnormal data processing module through a network; The abnormal data processing module identifies abnormal staining data in the automatic staining process and abnormal data in the microscope imaging process according to the automatic staining control index Ql and the microscope imaging stability coefficient Sl, and determines whether the quality of the final imaging meets the standard according to the image quality evaluation score Gl. The abnormal data processing module is connected to the data management module through a network.

3. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 2, characterized in that: The automatic staining data unit numbers the initially set staining time, the initial concentration of the staining solution, the background staining value, the staining environment temperature influence value and the staining instrument correction factor according to the automatic staining data characteristics. The initially set staining time, the initial concentration of the staining solution, the background staining value, the staining environment temperature influence value and the staining instrument correction factor are numbered as T, C, B, μ, ε. The HE staining section recognition unit calculates the automatic staining control index Ql according to the automatic staining data, and the calculation formula is: In the formula, Ql represents the automatic dyeing control index, T, C, B, μ, and ε represent the initial set dyeing time, the initial concentration of the dyeing solution, the background dyeing value, the dyeing environment temperature influence value, and the dyeing instrument correction factor, respectively, and (1-μ) represents the constant value that excludes the temperature influence value.

4. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 1, characterized in that: The microscope imaging data receiving unit performs data numbering on the grayscale values ​​of all images according to the characteristics of the microscope imaging data. The grayscale values ​​of the images are numbered as f1, f2, f3, ... f n .

5. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 4, characterized in that: The microscope imaging stabilization unit calculates the microscope imaging stabilization coefficient S1 according to the microscope imaging data, and the calculation formula is: In the formula, Sl represents the microscope imaging stability coefficient, f1, f2, f3, ... f n represents the gray value of the image, n represents the number of images, f i represents the gray value of the i-th image, Represents the average gray value of all images.

6. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 1, characterized in that: The stained slice data output unit performs data numbering on the signal intensity and background noise level of the stained area according to the stained slice data characteristics, and the signal intensity numbering of the stained area is E1, E2, E3, ... E n , the background noise levels are numbered R1, R2, R3, ...R m .

7. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 6, characterized in that: The microscope imaging stabilization unit calculates the image quality evaluation score Gl according to the stained slice data, and the calculation formula is: In the formula, G1 represents the image quality assessment score, E1, E2, E3, ... E n Indicates the signal intensity of the stained area, R1, R2, R3, ... R m represents the background noise level, represents the average signal intensity of the stained area, n represents the number of statistically stained areas, represents the average level of background noise, and m represents the number of statistical background noise levels.

8. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 7, characterized in that: The abnormal data processing module automatically identifies the initial concentration abnormal value or background staining abnormal value of the staining solution during the automatic staining process according to the automatic staining control index Ql, and automatically identifies the sharpening abnormal value or contrast abnormal value of the imaging system according to the microscope imaging stability coefficient Sl.

9. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 7, characterized in that: The abnormal data processing module determines whether the quality of the final imaging meets the standard based on the image quality assessment score Gl. When the image quality assessment score Gl ≥ 95 points, the imaging quality is excellent; when the image quality assessment score Gl < 95 points, the imaging quality is good; when the image quality assessment score Gl < 80 points, the imaging quality meets the standard.

10. The method for identifying a hyperspectral microscope combined with HE-stained sections according to claim 9, characterized in that: The data management module performs data correction and fault diagnosis on abnormal staining data during automatic staining and abnormal data during microscope imaging according to the abnormal data automatically identified by the abnormal data processing module, and sends the corrected parameters and fault diagnosis information to the control module, which issues a repair instruction. The control module is connected to the data acquisition module, the data analysis module, the abnormal data processing module, and the data management module through the network.

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