Raman spectrum automatic focusing system and method for detecting pathological tissue sample

Abstract

The invention discloses a Raman spectrum automatic focusing system and method for detecting a pathological tissue sample, and the system comprises a light source module which is used for generating laser with a preset wavelength so as to irradiate the pathological tissue sample; the automatic focusing module integrates a mechanical structure and an image recognition algorithm, and performs automatic focusing on the surface of the sample through a low-pass spectroscope, an objective lens, a focusing camera, a controller and a focusing execution mechanism; the spectrum analysis module is used for collecting and analyzing Raman scattering light generated by interaction of the focused exciting light and the pathological tissue sample on the basis of a Raman spectrum analyzer, and by integrating the stable light source, the automatic focusing module, the spectrum analysis module and the data processing module, rapid, accurate and automatic detection of the pathological tissue sample is achieved; the method has the advantages of simplicity and convenience in operation, high detection speed, high resolution, high automation degree and the like, is widely applied to the fields of clinical pathological detection, biomedical research and the like, and provides powerful support for disease diagnosis.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical equipment, and in particular relates to a Raman spectrum automatic focusing system and method for detecting pathological tissue samples. Background Art

[0002] Accurate detection of pathological tissue samples plays a vital role in the early diagnosis of diseases, the selection of treatment options, and the evaluation of prognosis. Although traditional pathological detection methods, such as hematoxylin-eosin (H&E) staining and immunohistochemistry, have been widely used in clinical practice and have achieved certain results, their inherent limitations cannot be ignored. These methods are not only cumbersome and time-consuming in operation, but also have high reagent costs, which has brought a considerable economic burden to medical institutions and patients. More importantly, their shortcomings in detection sensitivity and specificity limit their application potential in high-precision and rapid detection of pathological tissue samples. In contrast, Raman spectroscopy technology has emerged in the detection of pathological tissue samples with its unique advantages. By detecting the vibration and rotation of specific bonds in molecules, this technology can reveal detailed information about the molecular composition structure and intermolecular interactions of samples, like an accurate "molecular fingerprint". This non-destructive detection method is not only highly sensitive, but also simple and fast to operate. It has shown broad application prospects in the fields of biological macromolecule analysis, pathogenic microorganism detection, and precise molecular diagnosis of tumors.

[0003] In the detection of pathological tissue samples, Raman spectroscopy technology has shown its unique value. It can quickly and accurately identify key biological macromolecules such as nucleic acids, proteins, and lipids in samples, providing strong technical support for the early detection of diseases. However, despite the many advantages of Raman spectroscopy technology, it still faces some challenges in practical applications.

[0004] Among them, the focusing problem is one of the key factors that restrict the widespread application of Raman spectroscopy technology in the detection of pathological tissue samples. At present, the focusing method of most Raman spectrometers still relies on manual operation. Whether it is holding the sample or adjusting the distance between the objective lens and the sample through a mechanical mechanism, the operator needs to judge the size and position of the laser spot with the naked eye. The final judgment of whether the focus is accurate depends on the Raman spectrum obtained by manual analysis after the CCD in the spectrometer collects the image. This mode of operation is not only inefficient, but also prone to inaccurate focusing due to human factors, which in turn affects the accuracy and reliability of the test results. Therefore, a Raman spectroscopy automatic focusing system and method for detecting pathological tissue samples are proposed to solve the above problems. Summary of the invention

[0005] The object of the present invention is to provide a Raman spectrum automatic focusing system and method for detecting pathological tissue samples, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a Raman spectroscopy automatic focusing system for detecting pathological tissue samples, comprising:

[0007] The light source module uses a stable and efficient laser light source to generate laser light of a specific wavelength, which is suitable for exciting Raman scattered light in pathological tissue samples. The design of the laser light source ensures the stability and excitation efficiency of the light source, providing a reliable excitation source for subsequent Raman spectroscopy analysis;

[0008] The autofocus module integrates high-precision mechanical structure and advanced image recognition algorithm, including low-pass spectroscope, objective lens, focus camera, controller and focus actuator. Through the collaborative work of these components, the system can automatically capture the sample image, and use the image recognition algorithm to determine the focus state, and drive the focus actuator to achieve fast and accurate autofocus. In addition, the focus camera and the controller transmit image data through a data connection line to ensure real-time processing and feedback;

[0009] The spectrum analysis module uses a high-resolution Raman spectrometer to collect and analyze the Raman scattered light generated by the interaction between the focused excitation light and the pathological tissue sample. This module can accurately analyze the Raman spectrum characteristics of the sample as a "fingerprint" to identify the sample type, and achieve accurate detection of pathological tissue samples;

[0010] The data processing module is responsible for receiving the spectral data transmitted by the spectral analysis module and identifying specific components or pathological tissues in the sample by comparing it with the preset standard Raman spectral database. This module also has a statistical analysis function, which can further analyze the test results and provide a scientific basis for pathological diagnosis. The data processing module is connected to the standard Raman spectral database through a data interface, supports real-time or offline comparison of spectral data, and outputs test results and statistical analysis reports.

[0011] Preferably, the light source module adopts a laser light source, and the laser wavelength generated by the laser light source is suitable for exciting Raman scattered light in the pathological tissue sample.

[0012] Preferably, the automatic focusing module further comprises a data connection line between the focusing camera and the controller, which is used to transmit the sample image captured by the focusing camera to the controller for processing.

[0013] Preferably, the data processing module further comprises a data interface connected to a standard Raman spectrum database for real-time or offline comparison of spectrum data and extraction of characteristic spectra using the formula:

[0014] ;

[0015] Calculate the spectral deviation parameters, normalize them and output the test results and statistical analysis report;

[0016] in, represents the spectral deviation parameter, Representative spectral measurements, represents the arithmetic mean of the spectral measurements, represents the total number of spectral measurements, Representative Spectral measurements.

[0017] Preferably, the system further comprises a signal processing module, which processes the Raman scattering signal by a preset processing method to eliminate signal errors and converts the signal into a corresponding electrical signal.

[0018] Preferably, the signal error is eliminated by using the formula:

[0019] ;

[0020] Calculate the error correction value, perform error correction, and convert it into a corresponding electrical signal;

[0021] in, represents the error correction value, Representative The raw data value of the Raman scattering signal, represents the average value of all Raman scattering signal data, Representative The temperature measurement value at a moment, represents the average value of all temperature measurements, represents the total number of data points of the Raman scattering signal, Represents the total number of data points for temperature measurement, Represents a nonzero constant used to adjust the stability of the calculation.

[0022] Preferably, the signal processing module further includes an electrical signal optimization unit, which processes the electrical signal by a preset optimization method to eliminate interference and improve data accuracy.

[0023] Preferably, the preset optimization method includes smoothing, automatic peak analysis and least squares method.

[0024] Preferably, the light source module further comprises a wavelength-tunable laser source for adjusting the wavelength of the excitation light according to the requirements of the pathological tissue sample.

[0025] A method for detecting pathological tissue samples using the above system, the method comprising the following steps:

[0026] S1: Use the light source module to generate excitation light of a specific wavelength to irradiate the pathological tissue sample;

[0027] S2: Automatically focus the sample through the autofocus module;

[0028] S3: using the spectrum analysis module to collect and analyze Raman scattered light to obtain Raman spectrum data of the sample;

[0029] S4: The data processing module compares the acquired spectral data with the standard Raman spectral database, identifies specific components or diseased tissues in the sample, and outputs the test results and statistical analysis report.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention realizes rapid, accurate and automated detection of pathological tissue samples by integrating a stable light source, automatic focusing, spectral analysis and data processing modules. It has the advantages of simple operation, fast detection speed, high resolution and high degree of automation. It is widely used in clinical pathology detection, biomedical research and other fields, providing strong support for disease diagnosis.

[0032] (2) The present invention has the advantages of fast response speed, strong noise reduction ability, and high signal-to-noise ratio by smoothing the data; the Savitzky-Golay filter fitting method determines the appropriate filter parameters according to the average trend of the NDVI time series curve, and uses polynomials to realize the least squares fitting within the sliding window; the Savitzky-Golay filter method is used for iterative calculation to simulate the entire NDVI time series data to obtain the long-term change trend; the asymmetric Gaussian function fitting method uses a combination of piecewise Gaussian functions (curves) to simulate the seasonal growth (phenology) law of vegetation, and one combination represents a vegetation prosperity and decline process. Finally, the time series reconstruction is realized by smoothly connecting each Gaussian fitting curve. The automatic peak search analysis model based on the comparison method uses the advantages of fast, accurate and simple peak search of the simple comparison method to realize qualitative molecular identification of spectral data. The Raman single detection band has a wide detection range; the Raman spectrum has a steep peak profile and obvious characteristics, which is more suitable for qualitative analysis and quantitative analysis; confocal Raman can focus the laser beam to a smaller band and has a wider application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a system block diagram of the present invention;

[0034] Figure 2 Schematic diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] See also Figure 1-Figure 2 As shown, the present invention provides the following technical solutions:

[0037] Implementation Method 1

[0038] In this embodiment, a total reflector 1, a low-pass beam splitter 2, an objective lens 3, a focus camera 4, a controller 5 and a focus actuator 6 are provided; the green light beam G of the green light LED passes through the total reflector 1 and is reflected by the low-pass beam splitter 2, the reflected light of the low-pass beam splitter 2 passes through the objective lens 3 and shines on the sample A, the reflected light of the sample A passes through the objective lens 3 and is reflected by the low-pass beam splitter 2, the reflected light of the low-pass beam splitter 2 is reflected by the total reflector 1 to the focus camera 4, and an image is formed on the focus camera 4, the focus camera 4 collects the sample image and transmits it to the controller 5, the control signal output terminal of the controller 5 is connected to the focus actuator 6, and the driving signal of the focus actuator 6 controls the movement of the objective lens 3 until the focus is completed. Figure 2 In FIG. 1 , the symbol P represents a Raman spectrometer.

[0039] Combining the above:

[0040] Assuming that the wavelength of the Raman spectrometer laser used is 785nm, the fill light LED is designed to be green light (the wavelength of green light is less than 785nm of the spectrometer Raman laser), and the low-pass spectroscope can separate the green light from the Raman laser and completely reflect the green light. The controller contains a motion control core, the CPU runs an embedded operating system, executes an image recognition algorithm, and determines whether the focus state is accurate. The focus actuator includes a motor and a motor drive circuit to control the movement of the objective lens.

[0041] Before the spectrometer collects data, the green LED is turned on. The green light path passes through the total reflector, is reflected by the low-pass beam splitter, passes through the objective lens and shines on the sample. The image light path reflected by the sample passes through the objective lens, is reflected by the low-pass beam splitter, and then reflects through the total reflector to reach the focus camera, where it is imaged. The focus camera collects the sample image and transmits it to the controller. The controller determines whether the current focal length is correct through the corresponding image algorithm. If it is not correct, the controller controls the movement of the objective lens through the focus actuator, and then continuously determines the current focal length state through the focus camera and adjusts the objective lens until the focus is completed. After the focus is completed, the green light is turned off and the Raman spectrum is collected.

[0042] The Raman spectrum automatic focusing system of the present invention is mainly composed of a light source module, an automatic focusing module, a spectrum analysis module, a data processing module, and necessary connection and control circuits. The hardware layout is as follows:

[0043] The light source module is placed at the top of the system and uses a stable and tunable laser to ensure the output of laser light of a specific wavelength (such as 785nm), which is accurately guided to the autofocus module through an optical fiber or light guide;

[0044] The autofocus module is located below the light source module and includes a low-pass beam splitter (used to separate the excitation light and scattered light), a high-precision objective lens (used to focus the laser and collect scattered light), a focus camera (installed next to the objective lens and used to capture the sample surface image in real time), a controller (responsible for algorithm operation and command sending) and a focus actuator (such as an electric stepper motor, which drives the objective lens to move up and down to achieve autofocus);

[0045] The spectrum analysis module receives the Raman scattered light from the autofocus module and performs spectrum separation and detection through a high-resolution Raman spectrum analyzer. The analyzer integrates components such as filters, gratings, and photomultiplier tubes.

[0046] The data processing module is equipped with a high-performance computer or embedded system, with a built-in standard Raman spectroscopy database and data analysis software, and is responsible for the reception, processing, comparison and result output of spectral data.

[0047] The specific operation process is as follows:

[0048] Step 1: Initialization and setup: start the system, set the laser wavelength to be detected, and load or update the standard Raman spectrum database;

[0049] Step 2: Sample placement: Place the pathological tissue sample to be tested on the sample stage of the autofocus module, ensuring that the sample surface is flat and within the laser irradiation range;

[0050] Step 3: Automatic focusing, the focus camera takes an initial image of the sample surface and transmits it to the controller. The controller runs the image recognition algorithm, analyzes the image clarity or feature points, and calculates the focus position. The controller sends instructions to the focus actuator to drive the objective lens to move to the calculated focus position to achieve automatic focusing. The above steps can be repeated for fine-tuning until the optimal focus state is achieved;

[0051] Step 4: Spectral acquisition: the laser light source emits a laser of a specific wavelength, which is focused by a low-pass spectroscope and an objective lens and then irradiates the sample. The generated Raman scattered light is collected by the objective lens and introduced into the spectral analysis module.

[0052] Step 5: Spectral analysis: the spectral analysis module performs spectral separation and detection on the collected Raman scattered light to generate Raman spectrum data of the sample;

[0053] Step 6: Data processing and diagnosis: The data processing module receives the spectral data and compares it with the standard Raman spectral database. It identifies specific components or diseased tissues in the sample and outputs the test results. Optionally, it performs statistical analysis and generates diagnostic reports or visual charts.

[0054] Implementation method 2:

[0055] Based on the original system, this implementation further enhances the flexibility of the light source module to support laser output of multiple wavelengths. By introducing a wavelength selector or tunable laser, the user can select or adjust the wavelength of the laser according to actual needs to excite specific chemical bonds or biomolecules in different pathological tissue samples.

[0056] In actual operation, after placing the pathological tissue sample on the sample stage, the user can select the required laser wavelength through the system's user interface. The system then automatically adjusts the light source module to match the selected wavelength. Then, the system performs initialization settings, autofocus, spectrum acquisition and analysis, and final data processing and diagnostic output according to the normal process.

[0057] This multi-wavelength detection capability enables the system to be more widely used in different research fields and clinical practice, especially in scenarios where specific wavelength excitation is required to identify specific chemical bonds or biomolecules.

[0058] Implementation method three:

[0059] This embodiment focuses on improving the performance and accuracy of the autofocus module. To achieve this goal, we use a more advanced image recognition algorithm and a more precise focus actuator.

[0060] The new image recognition algorithm can more accurately analyze the image features of the sample surface, thereby calculating a more precise focus position. At the same time, the focus actuator also uses a higher-precision stepper motor and more precise displacement control to ensure that the objective lens can accurately move to the calculated focus position.

[0061] In practical applications, this enhanced autofocus algorithm can significantly improve the focusing speed and accuracy of the system, thereby further improving the quality and efficiency of spectral acquisition.

[0062] Implementation method 4:

[0063] This embodiment integrates remote monitoring and data analysis functions into the system, allowing users to remotely access and control the system through the Internet and view and analyze detection data in real time.

[0064] In order to achieve this function, we added a remote communication module and a data processing server to the system. The remote communication module is responsible for transmitting the system status information and detection data to the data processing server in real time, while the data processing server is responsible for storing, analyzing and visualizing these data.

[0065] Users can remotely access the data processing server through any Internet-enabled device (such as a computer, mobile phone or tablet) to view real-time test data, historical records and analysis reports. This remote monitoring and data analysis function enables users to grasp the system's operating status and test results anytime and anywhere, thereby more flexibly responding to various research and clinical practice needs.

[0066] Implementation method five:

[0067] This implementation emphasizes the modular design and scalability of the system so that users can easily add or replace various modules of the system as needed.

[0068] Each module of the system (such as light source module, autofocus module, spectrum analysis module and data processing module) adopts standardized interfaces and connection methods, making them easy to disassemble and replace. In addition, we also provide a variety of module options and accessories to meet the different needs of users.

[0069] This modular design and scalability enables the system to adapt more flexibly to different application scenarios and user needs, while also facilitating system maintenance and upgrades.

[0070] Implementation method six:

[0071] This embodiment integrates a sample processing and transmission module into the system to improve the efficiency and accuracy of sample detection. The module includes functions such as sample cutting, grinding, loading and transmission, and can automatically complete the entire process from sample preparation to detection.

[0072] In actual operation, users only need to put the original pathological tissue sample into the sample processing module, and the system will automatically cut, grind and load the sample, and then transfer it to the automatic focusing module for detection. This integrated design not only reduces the steps and time of manual operation, but also reduces the risk of sample contamination or damage during processing.

[0073] Implementation method seven:

[0074] This implementation adds an intelligent recognition and classification module to the system, which can automatically recognize and classify pathological tissue samples using advanced technologies such as deep learning.

[0075] During the detection process, the system first uses the autofocus module to obtain a clear image of the sample, and then transmits it to the intelligent recognition and classification module for processing. By comparing the sample image with the image features in the preset database, the module can accurately identify the type and degree of lesions of the sample and classify it into the corresponding category. This intelligent recognition and classification function enables the system to provide test results more quickly and assist doctors in making more accurate diagnoses and treatments.

[0076] Implementation method eight:

[0077] This embodiment adopts multi-channel parallel detection technology to further improve the detection speed and throughput of the system. By introducing multiple spectral analysis modules and data processing modules, the system can simultaneously handle the detection tasks of multiple pathological tissue samples.

[0078] In actual applications, users can place multiple samples on the sample stage at the same time, and the system can automatically assign a detection channel to each sample, and perform autofocus, spectrum acquisition and analysis, and data processing in parallel. This multi-channel parallel detection technology not only significantly improves the detection speed of the system, but also enables the system to handle the detection needs of a large number of samples more efficiently.

[0079] Implementation method nine:

[0080] This embodiment adds an environmental control and protection module to the system to ensure that the system can still operate stably in harsh or complex environments and protect samples from contamination or damage.

[0081] The module includes functions such as temperature control, humidity control, dust and vibration prevention, and can monitor and adjust the environmental parameters inside the system in real time to keep them within an appropriate range. At the same time, the module also adopts a sealed design and high-efficiency filtering technology to prevent external pollutants from entering the system and affecting the test results of the samples. This environmental control and protection system enables the system to provide accurate and reliable test results in various environments.

[0082] Implementation method ten:

[0083] This implementation method focuses on user experience and interactive design, providing users with an intuitive, easy-to-use, and feature-rich user interface and operation method.

[0084] The system's user interface adopts a graphical design, which can clearly display the system's status information, test results, analysis reports, etc. At the same time, the system also provides a wealth of operation options and setting parameters to meet the different needs and preferences of users. In addition, the system also supports interactive methods such as voice control and gesture recognition, allowing users to operate and control the system more conveniently. This user-friendly interface and interactive design not only improves the ease of use and operability of the system, but also enables users to complete detection tasks more efficiently and obtain accurate results.

[0085] Implementation method eleven:

[0086] This embodiment integrates remote monitoring and diagnosis functions into the system, allowing doctors or experts to view and analyze test data in real time at different locations and provide remote medical consultation and diagnosis.

[0087] The system transmits the test data to the cloud server in real time through cloud computing and Internet of Things technology, and doctors or experts can access the data remotely through dedicated mobile devices or computers. This remote monitoring and diagnosis system not only improves the utilization efficiency of medical resources, but also enables patients in remote areas or areas with scarce medical resources to obtain high-quality medical services.

[0088] Implementation method 12:

[0089] This embodiment introduces an adaptive focusing algorithm to further improve the automatic focusing speed and accuracy of the system. The algorithm can automatically adjust the focusing parameters according to the characteristics of the sample and the detection conditions to achieve a faster and more stable focusing process.

[0090] In practical applications, the system first uses preliminary spectral information or image features to pre-classify samples, and then selects the corresponding focusing algorithm and parameters based on the classification results. This adaptive focusing algorithm not only improves the focusing speed and accuracy of the system, but also enables the system to more flexibly respond to different types of pathological tissue samples.

[0091] Implementation method thirteen:

[0092] This implementation adds a multi-dimensional data analysis and visualization module to the system to provide richer and more intuitive detection results and analysis reports.

[0093] This module can use a variety of data analysis techniques, such as cluster analysis and principal component analysis, to conduct in-depth mining and analysis of test data. At the same time, the module also supports a variety of visualization methods, such as three-dimensional scatter plots and heat maps, to intuitively display the distribution and characteristics of data. This multi-dimensional data analysis and visualization function not only helps doctors understand the test results more accurately, but also provides them with more diagnostic basis and decision support.

[0094] Implementation method 14:

[0095] This embodiment adopts a modular design, so that the system can be easily expanded and upgraded. Each module is connected and communicated through a standardized interface, and modules can be added or replaced as needed to meet different application scenarios and requirements.

[0096] For example, new spectral analysis modules can be added to support more detection technologies and wavelength ranges; new data processing modules can be added to introduce more advanced algorithms and analysis methods; and new sample processing modules can be added to support different types of samples. This modular design not only improves the flexibility and scalability of the system, but also enables the system to keep up with the pace of technological development and maintain long-term competitiveness.

[0097] Implementation method 15:

[0098] This implementation adds security authentication and data protection mechanisms to the system to ensure the security and privacy of the detection data.

[0099] The system uses encrypted communication technology and security authentication protocols to ensure the security of test data during transmission and storage. At the same time, the system also provides user authority management and data access control functions, and only authorized users can access and operate test data. In addition, the system also supports data backup and recovery functions to prevent data loss or damage. This security authentication and data protection mechanism not only protects the privacy rights of patients, but also improves the reliability and stability of the system.

[0100] Implementation method 16:

[0101] This implementation combines artificial intelligence (AI) technology, especially deep learning algorithms, to achieve automatic identification and location of pathological tissue samples. The system first acquires a high-resolution image of the sample through Raman spectroscopy automatic focusing, and then uses the AI ​​model to analyze the image and automatically identify pathological tissue areas, such as cancer cells, inflammatory areas, etc.

[0102] In actual operation, users only need to put the sample into the system, and the system will automatically complete the whole process of focusing, spectrum acquisition, image analysis, and pathological tissue identification and positioning. This AI-based automatic identification and positioning function not only improves the detection efficiency, but also reduces manual intervention and reduces the misjudgment rate.

[0103] Implementation method seventeen:

[0104] This embodiment introduces multimodal fusion technology, combining Raman spectroscopy with other imaging techniques (such as fluorescence imaging, optical coherence tomography, etc.) to provide more comprehensive and accurate pathological tissue information.

[0105] The system collects data from multiple modalities at the same time and integrates these data using advanced fusion algorithms to form multimodal images or data sets. The system then conducts comprehensive analysis of these multimodal data to reveal more details and characteristics of pathological tissues. This multimodal fusion detection and analysis technology helps doctors gain a deeper understanding of the structure and function of pathological tissues, thereby making more accurate diagnosis and treatment decisions.

[0106] Implementation 18:

[0107] This implementation adds real-time feedback and guidance functions to the system to assist doctors in real-time analysis and decision-making during the testing process.

[0108] The system provides doctors with real-time feedback and guidance by monitoring and analyzing Raman spectral data in real time, combined with the prediction results of the AI ​​model. For example, when the system detects an abnormal spectral signal, it can immediately notify the doctor and provide information such as the possible pathological type and degree of the lesion. This real-time feedback and guidance system not only improves the accuracy and efficiency of detection, but also enables doctors to make timely adjustments and optimizations during the detection process.

[0109] Implementation method 19:

[0110] This embodiment provides customized detection schemes and reporting functions to meet the individual needs of different hospitals, departments or patients.

[0111] The system allows users to select specific detection parameters, algorithms, and report formats according to actual needs. For example, for different types of pathological tissue samples, users can select different spectral ranges, resolutions, acquisition speeds, and other parameters; for different diagnostic needs, users can select different analysis algorithms and report templates. This customized detection scheme and reporting function enables the system to adapt to different application scenarios and needs more flexibly, improving the pertinence and practicality of detection.

[0112] Implementation method 20:

[0113] This implementation constructs a remote collaboration and knowledge sharing platform to promote communication and cooperation among doctors, experts, and researchers.

[0114] The platform uses cloud computing and big data technologies to achieve remote access, sharing and analysis of test data. Doctors or experts can upload their own test data and analysis results on the platform and communicate and discuss with other users. At the same time, the platform also provides a wealth of resources such as knowledge bases and case libraries for users to learn and refer to. This remote collaboration and knowledge sharing platform not only promotes the optimal allocation and shared use of medical resources, but also promotes the continuous development and innovation of Raman spectroscopy automatic focusing systems in the field of pathological tissue sample detection.

[0115] Implementation method 21:

[0116] Compare spectral data in real time or offline and extract characteristic spectra using the formula:

[0117] ;

[0118] Calculate the spectral deviation parameters, normalize them and output the test results and statistical analysis report;

[0119] in, represents the spectral deviation parameter, Representative spectral measurements, represents the arithmetic mean of the spectral measurements, represents the total number of spectral measurements, Representative Spectral measurements.

[0120] Real-time or offline comparison of spectral data requires data preprocessing. First, the collected spectral data is standardized to eliminate the errors that may be caused by different light sources, different equipment or different measurement environments. Specifically, the set of spectral measurement values ​​is The total number of measurement points is , for each measurement value Normalization is performed to convert it into a relative numerical range. After normalization, the spectral data needs to be denoised. In signal processing, sliding average, detrending or filtering methods are usually used to reduce data noise. For example, a sliding window smoothing method is applied to the spectral sequence, that is, a weighted average is performed on continuous measurement points. For example, if the window size is set to , then The smoothed value of a measurement point can be expressed as:

[0121] ;

[0122] in, Represents the smoothed spectral value. This processing can reduce the impact of random errors on the analysis results. After the preprocessing is completed, the spectral deviation parameter needs to be calculated. , first find the arithmetic mean of the spectral data , calculate the deviation of each measured value relative to the average value, sum up the absolute values ​​of the deviations and take the average, and at the same time calculate the sum of the squares of the change amplitudes between adjacent measured values ​​of the spectral data, and then take the root mean square to measure the change trend of the spectral data. Finally, the spectral deviation parameters are calculated and normalized to output the test results and statistical analysis report.

[0123] formula:

[0124] ;

[0125] Specific parameter values:

[0126] Assume that the spectrum measurement value sequence measured in an experiment is as follows (unit: W / m2):

[0127] , , , , ;

[0128] Total number of spectral measurements: ;

[0129] Arithmetic mean of spectral data:

[0130] ;

[0131] Compute the mean of the first absolute deviations:

[0132] ;

[0133] Calculate the mean squared variation of adjacent measurements:

[0134] ;

[0135] ;

[0136] Final calculation :

[0137] ;

[0138] The benefit of the formula is that by calculating the average absolute deviation of the spectral data and the variation range of the measured values, it can comprehensively reflect the stability and change trend of the spectral data and avoid the information loss that may be caused by a single statistical feature.

[0139] Analysis of numerical results:

[0140] Calculated spectral deviation parameters , which can be compared with the set spectral stability threshold. For example, if the set deviation threshold is , then the current measured spectral data has a large deviation, indicating that the data has a large fluctuation range, and further analysis of the characteristic changes of the spectral data is needed.

[0141] Implementation method 22:

[0142] To eliminate signal errors, use the formula:

[0143] ;

[0144] Calculate the error correction value, perform error correction, and convert it into a corresponding electrical signal;

[0145] in, represents the error correction value, Representative The raw data value of the Raman scattering signal, represents the average value of all Raman scattering signal data, Representative The temperature measurement value at a moment, represents the average value of all temperature measurements, represents the total number of data points of the Raman scattering signal, Represents the total number of data points for temperature measurement, Represents a nonzero constant used to adjust the stability of the calculation.

[0146] After processing the Raman scattering signal, error correction is required after obtaining the original data. First, for the original signal data set , perform benchmark statistics on it and calculate its mean , and then get the deviation of each data point from the mean , the total error can be obtained by accumulating all deviations In practical applications, laser Raman spectroscopy can be used to measure multiple locations of a sample. For example, the spectral signals of different points on a silicon wafer can be measured under certain temperature conditions to obtain the raw data of multiple Raman scattering signals. The deviation value of each point can be calculated to reflect the uniformity inside the sample. Then, the temperature measurement data is introduced. , calculate its mean , calculate the temperature deviation of all measurement points And accumulate to obtain the temperature influence factor Considering the signal stability under different measurement environments, the adjustment parameters are increased. To correct the calculation, the final error correction value calculation formula is:

[0147] ;

[0148] In an actual example, if the Raman scattering signal of a material at five measurement points is: , then the signal mean is , calculate the deviation and sum it up to get At the same time, assuming that there are five temperature measurement points, the data are: K, then the mean temperature K, calculate the sum of squares of deviations at each point, and get , if the adjustment parameter is set , then the error correction value is calculated as follows:

[0149] ;

[0150] The final error correction value is used to compensate for the measurement error of the Raman scattering signal, thereby eliminating the signal error and converting it into a corresponding electrical signal.

[0151] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0153] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0155] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0156] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A Raman spectroscopy automatic focusing system for detecting pathological tissue samples, characterized in that: include: A light source module, used to generate laser light of a preset wavelength to irradiate a pathological tissue sample; The autofocus module integrates mechanical structure and image recognition algorithm to automatically focus on the sample surface through a low-pass spectroscope, objective lens, focus camera, controller and focus actuator; The spectrum analysis module, based on a Raman spectrometer, collects and analyzes the Raman scattered light generated by the interaction between the focused excitation light and the pathological tissue sample to obtain the Raman spectrum data of the sample; The data processing module is used to receive the spectral data from the spectral analysis module and compare it with the preset standard Raman spectral database to identify specific components or diseased tissues in the sample, perform statistical analysis on the test results, and output the analysis results.

2. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 1, characterized in that: The light source module adopts a laser light source, and the laser wavelength generated by the laser light source is suitable for exciting Raman scattered light in the pathological tissue sample.

3. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 1, characterized in that: The automatic focusing module also includes a data connection line between the focusing camera and the controller, which is used to transmit the sample image collected by the focusing camera to the controller for processing.

4. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 1, characterized in that: The data processing module also includes a data interface connected to a standard Raman spectrum database for real-time or offline comparison of spectrum data and extraction of characteristic spectra using the formula: ； Calculate the spectral deviation parameters, normalize them and output the test results and statistical analysis report; in, represents the spectral deviation parameter, Representative Spectral measurements, represents the arithmetic mean of the spectral measurements, represents the total number of spectral measurements, Representative Spectral measurements.

5. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to any one of claims 1 to 4, characterized in that: It also includes a signal processing module, which processes the Raman scattering signal through a preset processing method to eliminate signal errors and converts it into a corresponding electrical signal.

6. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 5, characterized in that: The signal error is eliminated by using the formula: ； Calculate the error correction value, perform error correction, and convert it into a corresponding electrical signal; in, represents the error correction value, Representative The raw data value of the Raman scattering signal, represents the average value of all Raman scattering signal data, Representative The temperature measurement value at a moment, represents the average value of all temperature measurements, represents the total number of data points of the Raman scattering signal, Represents the total number of data points for temperature measurement, Represents a nonzero constant used to adjust the stability of the calculation.

7. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 5, characterized in that: The signal processing module further includes an electrical signal optimization unit, which processes the electrical signal through a preset optimization method to eliminate interference and improve data accuracy.

8. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 7, characterized in that: The preset optimization method includes smoothing, automatic peak analysis and least squares method.

9. The Raman spectroscopy automatic focusing system for detecting pathological tissue samples according to claim 1, characterized in that: The light source module also includes a wavelength-tunable laser source for adjusting the wavelength of the excitation light according to the requirements of the pathological tissue sample.

10. A method for detecting a pathological tissue sample using the system according to any one of claims 1 to 9, the method comprising the following steps: S1: Use the light source module to generate excitation light of a specific wavelength to irradiate the pathological tissue sample; S2: Automatically focus the sample through the autofocus module; S3: using the spectrum analysis module to collect and analyze Raman scattered light to obtain Raman spectrum data of the sample; S4: The data processing module compares the acquired spectral data with the standard Raman spectral database, identifies specific components or diseased tissues in the sample, and outputs the test results and statistical analysis report.

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