Full-automatic pathological section quality detection method and system based on OCT (Optical Coherence Tomography) technology

Abstract

The invention discloses a pathological section full-automatic quality detection method and system based on an OCT technology, and relates to the technical field of pathological detection. The system comprises an OCT imaging module, a sample bearing and positioning module, a data processing module and a quality evaluation module, according to the method, automatic flattening and positioning of a pathological section are achieved through a sample bearing and positioning module, three-dimensional scanning is conducted on the pathological section through an OCT imaging module to obtain an original interference signal, signal noise reduction, wave number resampling, Fourier transform and other processing are completed through a data processing module, and a three-dimensional microscopic image is constructed. And finally, the quality evaluation module automatically identifies slice defects, quantifies defect levels and outputs a quality detection result. According to the invention, full-process automation of pathological section quality detection is realized, manual intervention is not needed, the detection precision can reach a microscopic scale, the problems of low efficiency, high omission ratio and dependence on experience judgment in traditional manual sampling inspection are effectively solved, and an objective and efficient technical scheme is provided for pathological section quality control.

Description

Technical Field

[0001] This invention relates to the field of pathological testing technology, specifically to a fully automated method and system for quality testing of pathological slides based on OCT (Optical Coherence Tomography) technology. Background Technology

[0002] Pathological sections are an important basis for disease diagnosis, and their quality directly affects the accuracy of the diagnostic results. Currently, the quality inspection of pathological sections in clinical practice mainly adopts the method of random sampling under a light microscope combined with H&E counterstaining: technicians randomly select some sections under a microscope and observe macroscopic defects such as knife marks, wrinkles, and air bubbles with the naked eye. If abnormal staining or blurred structure is found, the tissue needs to be resectioned or the original sections need to be degummed and counterstained for comparison and confirmation.

[0003] However, existing technologies have significant drawbacks: First, they rely heavily on manual labor, with testing procedures and evaluation standards heavily influenced by experience and lacking objective quantitative indicators; second, their detection accuracy is limited, with the naked eye only able to identify obvious defects larger than 50μm, easily missing microscopic defects such as micro-wrinkles and shallow knife marks; third, the sampling rate is limited, making full-volume testing impossible and posing quality risks; and fourth, the restaining process for substandard sections is complex and time-consuming, and the tissue cannot be traced back, resulting in resource waste. Therefore, there is an urgent clinical need for an automated, high-precision pathological section quality testing technology to address these issues. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a fully automated quality inspection method and system for pathological slides based on OCT technology, which automates the entire process of pathological slide quality inspection, improves detection accuracy and efficiency, and reduces reliance on manual labor.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An automated quality inspection system for pathological slides based on OCT technology includes: an OCT imaging module for emitting light signals and receiving reflected light signals from pathological slides to generate raw spectral interference signals; a sample carrying and positioning module for placing pathological slides and automatically flattening, positioning, and adjusting the scanning area of ​​the slides; a data processing module, communicatively connected to the OCT imaging module, for processing the raw spectral interference signals to construct a three-dimensional microscopic image of the pathological slides; and a quality assessment module, communicatively connected to the data processing module, for identifying defects and assessing the quality level of the three-dimensional microscopic image, and outputting the quality inspection results.

[0007] Furthermore, the OCT imaging module includes a light source, an optical fiber isolator, an optical fiber coupler, a reference arm, a sample arm, and a spectrometer; the light signal emitted by the light source is split into two paths by the optical fiber coupler, which enter the reference arm and the sample arm respectively. The two reflected light signals are coupled to form the original spectral interference signal and transmitted to the spectrometer.

[0008] Furthermore, the reference arm includes a collimating lens, a focusing objective, a reflecting mirror, and a displacement stage, the displacement stage being used to adjust the position of the reflecting mirror to match the optical path; the sample arm includes a collimating lens, an XY scanning galvanometer, and a focusing objective, the XY scanning galvanometer being used to control the beam to cover the scanning area of ​​the pathological slide.

[0009] Furthermore, the sample carrying and positioning module includes an electric stage and an image positioning unit. The electric stage can move in the X, Y, and Z directions, and the image positioning unit is used to acquire slice position information and feed it back to the electric stage to drive the slice to flatten on the imaging window surface.

[0010] Furthermore, the data processing module includes a signal preprocessing unit and an image reconstruction unit; the signal preprocessing unit is used to remove background noise, resample and interpolate wavenumbers, and perform dispersion compensation on the original spectral interference signal; the image reconstruction unit is used to perform Fourier transform, amplitude extraction, and logarithmic compression on the preprocessed signal to construct a three-dimensional microscopic stereoscopic image.

[0011] This invention also provides a fully automated quality inspection method for pathological slides based on OCT technology, comprising the following steps: S1: placing the pathological slide in the sample carrying and positioning module, and using an automatic positioning and flattening mechanism to fit the slide into the imaging window; S2: the OCT imaging module scans the scanning area of ​​the pathological slide point by point, acquiring the original spectral interference signal; S3: the data processing module processes the original spectral interference signal to generate a three-dimensional microscopic image of the pathological slide; S4: the quality assessment module identifies defects in the three-dimensional microscopic image, quantifies defect parameters, assesses the quality level, and outputs the detection results.

[0012] Furthermore, in step S2, the scanning area is determined in the following way: the image positioning unit of the sample carrying and positioning module acquires the slice sub-field of view image, and after segmentation and stitching, the complete sample area is determined, and then it is divided into multiple sub-regions for sequential scanning.

[0013] Further, in step S3, the wavenumber resampling and interpolation process specifically involves mapping the original signal sampled at equal intervals according to wavelength λ to data based on equal intervals of wavenumber k through nonlinear interpolation, wherein the relationship between wavenumber k and wavelength λ is k=2π / λ;

[0014] The Fourier Transform (FFT) is the inverse Fourier transform of the k-space interference signal I(k) to obtain the depth domain signal I(z); it is expressed as: ;

[0015] Where z is the depth coordinate, and I(z) contains the reflectivity distribution information of the sample.

[0016] Amplitude extraction and logarithmic compression involve taking the modulus of the complex result and performing a logarithmic transformation to enhance image contrast, as shown below:

[0017] Image(z) = 20log10|I(z)|

[0018] Image(z) is the data of the final OCT structure image used for display;

[0019] For data noise reduction, the main focus is on background noise, pattern noise, etc., in order to improve the signal-to-noise ratio of the data;

[0020] For data segmentation, the main process is to divide the one-dimensional bitstream signal into three-dimensional (X,Y,Z) signals in order to extract the imaging data of the BScan image at a certain X coordinate and the imaging data of the enFace image at a certain Z coordinate.

[0021] Furthermore, in step S4, defect identification includes the detection of knife marks, wrinkles, folds, bubbles, and missing adhesive layers. Defect localization is achieved by extracting grayscale abrupt change areas, abnormal morphology areas, and interlayer separation areas from the image.

[0022] Furthermore, in step S4, the quality level assessment adopts a defect severity scoring mechanism, which quantifies and scores defects based on their size, quantity, and distribution density. The scoring results correspond to three levels: qualified, awaiting re-inspection, and unqualified.

[0023] This fully automated quality inspection method and system for pathological slides based on OCT technology has the following beneficial effects:

[0024] This invention combines OCT technology with automated control, data processing, and artificial intelligence algorithms to construct a complete fully automated quality inspection solution for pathological slides. This solution overcomes the limitations of traditional manual inspection, achieving automation of the inspection process and quantification and precision of the results. It can be widely applied in hospital pathology departments, third-party testing institutions, and other scenarios, providing reliable quality assurance for pathological diagnosis and possessing significant clinical application value and market prospects. Attached Figure Description

[0025] Figure 1 Hardware composition diagram of OCT imaging in this invention;

[0026] Figure 2A schematic diagram of the functional components and data flow of the OCT imaging software of the present invention;

[0027] Figure 3 This invention Figure 1 Detailed image of the mid-scan galvanometer;

[0028] Figure 4 The final functional composition diagram of the OCT imaging software of this invention is shown.

[0029] Figure 5 The flowchart of pathological slide quality analysis of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0031] Example 1

[0032] This fully automated quality inspection system for pathological slides based on OCT technology:

[0033] One: such as Figure 1 As shown, the hardware components and optical path of OCT imaging are detailed:

[0034] Component 1: Light source, optical isolator, fiber optic coupler;

[0035] Component 2: Reference arm, including collimating lens, focusing objective, reflecting mirror and displacement stage;

[0036] Component 3: Sample arm, including collimating lens, scanning galvanometer (X and Y scanning directions), focusing objective, and sample (pathological section);

[0037] Component 4: Spectrometer, including collimating lens, grating focusing lens, and CCD camera;

[0038] Component 5: Image processing.

[0039] Second: In component 1, the light source emits a light signal, which is then split into two paths by an optical fiber coupler. These paths enter the reference arm of component 2 and the sample arm of component 3, respectively. The light isolator isolates the reflected light to prevent the light source from being burned.

[0040] A beam of light enters the reference arm (component 2), is collimated by a collimating lens to form a parallel beam, and then passes through a focusing lens to form a focused beam that enters the reflector. The beam is reflected back along the same path, meaning it re-enters the fiber coupler after passing through the focusing and collimating lenses. The translation stage can adjust the position of the reflector, facilitating the adjustment of the focused beam's focal point to fall on the plane mirror and the optical path of the reference beam.

[0041] Another beam of light enters the three sample arms, is collimated into a parallel beam by a collimating lens, and then focused by a focusing lens before entering the sample (pathological slide). The beam is reflected by the sample, returning along the same path, passing through the focusing and collimating lenses again before entering the fiber coupler. Two reflective mirrors, capable of X and Y directional displacement (X and Y galvanometers), are installed between the collimating and focusing lenses. These mirrors control the beam's movement in the X and Y directions, allowing it to sequentially cover the sample. Simultaneously, the sample is placed on a stage that can move in the X, Y, and Z directions. This allows the stage to adjust the position of the focused beam entering the sample in conjunction with the galvanometers and to align with the reference optical path, achieving coherent light formation.

[0042] Two beams of light, after reflection, enter the fiber optic coupler together and are coupled to form coherent light, which then enters the spectrometer. The beam passes through a collimating lens to form a parallel beam that enters a grating. The grating splits the beam, and the interference light is dispersed at specific angles according to wavenumber before being focused onto individual pixels of the CCD camera by a focusing lens. The CCD camera acquires the optical signal and converts it into an electrical signal, which is then transmitted to the data processing module.

[0043] Three: such as Figure 2 As shown, the system imaging software functions and data processing flow are illustrated.

[0044] The system imaging software mainly consists of two parts: data communication and OCT imaging.

[0045] Data communication section: Reference to main communication objects Figure 1 The scanning galvanometer and CCD camera are configured in the software to trigger the scanning galvanometer and CCD camera with consistent trigger signals.

[0046] For the scanning galvanometer, after receiving the start signal, the drive unit performs a three-dimensional scan of the sample according to the system's planned path. For a given (X,Y) coordinate determined by the galvanometer, the coupled beam from the reference arm and sample arm contains depth information in the Z direction, used for generating subsequent AScan data at (X,Y). For a given coordinate X, the galvanometer moves to its maximum field of view in the Y direction to obtain two-dimensional scan data for that coordinate X, used for generating subsequent BScan images. By controlling the galvanometer drive unit to move the field of view in the Y direction at each coordinate X, the three-dimensional (X,Y,Z) structural data of the sample obtained by stitching together the two-dimensional scan data, i.e., the BScan images, can be obtained. Details of the scanning galvanometer are shown below. Figure 3 .

[0047] For CCD cameras, after receiving the start signal, the driving device converts the light signal of each pixel into an electrical signal and transmits the data to the system data receiving interface in a bitstream manner, prioritizing horizontal transmission pixel by pixel.

[0048] The OCT imaging component mainly consists of spectral data processing, data noise reduction, data segmentation, and data imaging.

[0049] For spectral data processing, the system performs background noise removal, wavenumber resampling and interpolation, dispersion compensation, Fourier transform (FFT), amplitude extraction, and logarithmic compression on the photoelectric bitstream signal acquired from the CCD camera, i.e., the original spectral interferometric signal. Wavenumber resampling and interpolation reconstructs the original spectral interferometric signal data sampled at equal intervals according to wavelength λ into data based on equal wavenumber intervals. Due to the nonlinear relationship between wavelength and wavenumber, the system performs nonlinear interpolation to map the wavelength λ-domain data to k-space. The relationship between wavelength λ and wavenumber k is expressed as:

[0050] k=2π / λ

[0051] The Fourier Transform (FFT) is the inverse Fourier transform of the k-space interference signal I(k) to obtain the depth domain signal I(z). It is represented as:

[0052]

[0053] Where z is the depth coordinate, and I(z) contains the reflectivity distribution information of the sample.

[0054] Amplitude extraction and logarithmic compression involve taking the modulus of the complex result and performing a logarithmic transformation to enhance image contrast, as shown below:

[0055] Image(z) = 20log10|I(z)|

[0056] Image(z) is the data of the final OCT structure image used for display.

[0057] For data noise reduction, the main focus is on background noise, pattern noise, etc., in order to improve the signal-to-noise ratio of the data.

[0058] For data segmentation, the main process is to divide the one-dimensional bitstream signal into three-dimensional (X,Y,Z) signals in order to extract the imaging data of the BScan image at a certain X coordinate and the imaging data of the enFace image at a certain Z coordinate.

[0059] For data imaging, it refers to the real-time presentation of the final OCT structural image data Image(z), with an effect such as... Figure 4 As shown, it mainly includes the real-time effects of AScan, Bscan, and enface images.

[0060] 4. For the quality analysis of pathological sections, the system scans the pathological sections using OCT imaging software to obtain three-dimensional microscopic data. The system performs defect analysis on the images, identifies various artifacts, their severity, and distribution, scores the defects, and provides the quality analysis results for the sections. The workflow is shown below. Figure 5 .

[0061] Based on this fully automated quality inspection method and system for pathological slides using OCT technology, the technological innovation and practical application value are precisely aligned:

[0062] Fully automated process, significantly reducing costs and improving efficiency: From automatic positioning, flattening, and scanning of pathological slides to signal processing, defect identification, and quality rating, the entire process requires no manual intervention, completely eliminating reliance on the experience of testing personnel. This reduces labor costs and avoids human error. The testing efficiency is 5-10 times higher than traditional sampling inspection, enabling full-volume slide testing and solving the problem of incomplete coverage in traditional sampling inspection.

[0063] High precision in microscopic detection and significantly reduced false negative rate: With the help of the three-dimensional microscopic imaging capability of OCT technology, microscopic defects such as micro-wrinkles and shallow knife marks smaller than 50μm can be accurately identified. This breaks through the limitation that the traditional naked eye can only identify obvious defects larger than 50μm. The detection precision reaches the microscopic scale, greatly reducing the risk of false negatives and ensuring the reliability of pathological slide quality.

[0064] The testing standards are quantified, and the results are objective and impartial: quantitative indicators such as defect size, quantity, and distribution density are established, along with a clear quality scoring model (three-level assessment: qualified / pending re-inspection / unqualified), to replace traditional subjective experience judgments, ensuring the consistency of test results across different batches and institutions, and providing an objective basis for the quality control of pathological slides.

[0065] Non-contact and non-destructive, with traceable slides: OCT technology is a non-contact imaging method. The detection process does not damage pathological slides or affect subsequent diagnostic use. It avoids the problem of tissue non-traceability caused by traditional counterstaining procedures, reduces resource waste, and ensures the feasibility of secondary verification of slides.

[0066] Wide adaptability and diverse application scenarios: It is compatible with common clinical pathological slide types such as paraffin sections, and is suitable for various scenarios such as hospital pathology departments, third-party testing institutions, and biobanks. It not only meets the quality control needs of clinical diagnosis, but also supports the standardized testing of large-scale samples, making it highly practical.

[0067] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A fully automated quality inspection system for pathological slides based on OCT technology, characterized in that, include: The OCT imaging module is used to emit light signals and receive reflected light signals from pathological sections to generate raw spectral interference signals. The sample carrying and positioning module is used to place pathological slides and realize automatic flattening, positioning and scanning area adjustment of the slides; the data processing module is connected in communication with the OCT imaging module and is used to process the original spectral interference signal to construct a three-dimensional microscopic image of the pathological slide; the quality assessment module is connected in communication with the data processing module and is used to identify defects and assess the level of the three-dimensional microscopic image and output the quality inspection results.

2. The fully automated quality inspection system for pathological slides based on OCT technology according to claim 1, characterized in that, The OCT imaging module includes a light source, an optical fiber isolator, an optical fiber coupler, a reference arm, a sample arm, and a spectrometer. The light signal emitted by the light source is split into two paths by the optical fiber coupler, which enter the reference arm and the sample arm respectively. The two reflected light signals are coupled to form the original spectral interference signal and transmitted to the spectrometer.

3. The fully automated quality inspection system for pathological slides based on OCT technology according to claim 2, characterized in that, The reference arm includes a collimating lens, a focusing objective, a reflecting mirror, and a displacement stage. The displacement stage is used to adjust the position of the reflecting mirror to match the optical path. The sample arm includes a collimating lens, an XY scanning galvanometer, and a focusing objective. The XY scanning galvanometer is used to control the light beam to cover the scanning area of ​​the pathological slide.

4. The fully automated quality inspection system for pathological slides based on OCT technology according to claim 1, characterized in that, The sample carrying and positioning module includes an electric stage and an image positioning unit. The electric stage can move in the X, Y, and Z directions. The image positioning unit is used to acquire slice position information and feed it back to the electric stage, driving the slice to flatten on the surface of the imaging window.

5. The fully automated quality inspection system for pathological slides based on OCT technology according to claim 1, characterized in that, The data processing module includes a signal preprocessing unit and an image reconstruction unit. The signal preprocessing unit is used to remove background noise, resample and interpolate wavenumbers, and perform dispersion compensation on the original spectral interference signal. The image reconstruction unit is used to perform Fourier transform, amplitude extraction, and logarithmic compression on the preprocessed signal to construct a three-dimensional microscopic image.

6. A fully automated quality inspection method for pathological slides based on OCT technology, characterized in that, Includes the following steps: S1: The pathological slide is placed in the sample carrying and positioning module, and the slide is fitted into the imaging window through an automatic positioning and flattening mechanism; S2: The OCT imaging module scans the scanning area of ​​the pathological slide point by point and acquires the original spectral interference signal; S3: The data processing module processes the original spectral interference signal to generate a three-dimensional microscopic image of the pathological slide; S4: The quality assessment module identifies defects in the three-dimensional microscopic image, quantifies defect parameters, evaluates the quality level, and outputs the detection results.

7. The fully automated quality inspection method for pathological slides based on OCT technology according to claim 6, characterized in that, In step S2, the scanning area is determined as follows: the image positioning unit of the sample carrying and positioning module acquires the slice sub-field of view image, and after segmentation and stitching, the complete sample area is determined, and then it is divided into multiple sub-regions for sequential scanning.

8. The fully automated quality inspection method for pathological slides based on OCT technology according to claim 6, characterized in that, In step S3, the wavenumber resampling and interpolation process is as follows: the original signal sampled at equal intervals according to wavelength λ is mapped to data based on equal intervals of wavenumber k through nonlinear interpolation, where the relationship between wavenumber k and wavelength λ is k=2π / λ; The Fourier Transform (FFT) is the inverse Fourier transform of the k-space interference signal I(k) to obtain the depth domain signal I(z); it is expressed as: ; Where z is the depth coordinate, and I(z) contains the reflectance distribution information of the sample; Amplitude extraction and logarithmic compression involve taking the modulus of the complex result and performing a logarithmic transformation to enhance image contrast, as shown below: Image(z) = 20log10|I(z)| Image(z) is the data of the final OCT structure image used for display; For data noise reduction, the main focus is on background noise, pattern noise, etc., in order to improve the signal-to-noise ratio of the data; For data segmentation, the main process is to divide the one-dimensional bitstream signal into three-dimensional (X,Y,Z) signals in order to extract the imaging data of the BScan image at a certain X coordinate and the imaging data of the enFace image at a certain Z coordinate.

9. The fully automated quality inspection method for pathological slides based on OCT technology according to claim 6, characterized in that, In step S4, defect identification includes the detection of knife marks, wrinkles, folds, bubbles, and missing adhesive layers. Defect localization is achieved by extracting grayscale abrupt change areas, abnormal morphology areas, and interlayer separation areas from the image.

10. The fully automated quality inspection method for pathological slides based on OCT technology according to claim 6, characterized in that, In step S4, the quality level assessment adopts a defect severity scoring mechanism, which quantifies and scores defects based on their size, quantity, and distribution density. The scoring results correspond to three levels: qualified, awaiting re-inspection, and unqualified.

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