Method for identifying meningioma infiltrating dura mater and delimiting infiltrating position boundary based on multi-photon technology

Abstract

The invention relates to a method for identifying a meningioma infiltrating dura mater and delimiting an infiltrating position boundary based on a multi-photon technology, and belongs to the field of computer vision and biomedical optics. The method comprises the following steps: acquiring endogenous multi-photon optical signals of meningioma tissues and normal dura mater tissues around the meningioma tissues; in a multi-channel mode, high-resolution and high-contrast microstructure imaging of meningioma and normal dura mater tissues around the meningioma is obtained; the method comprises the following steps: acquiring certain quantitative morphological optical diagnosis parameters for identifying epidural tissue infiltration at the periphery of meningioma, and acquiring spectral images of tumors at the infiltration part and peripheral normal epidural tissues in a spectral mode; therefore, the infiltration of peripheral tissues of meningioma can be quantitatively and qualitatively identified in morphology and spectrum. And finally, determining the boundary of the tumor tissue at the infiltration position according to different texture analysis on the multiphoton microscopic images of the tumor tissue at the infiltration position and the surrounding tissue. The method disclosed by the invention is beneficial to quickly and automatically identifying the infiltrated boundary of the peripheral dura mater tissue.

Description

Technical Field

[0001] The invention belongs to the fields of computer vision and biomedical optics, and in particular relates to a method for identifying meningioma infiltrating the dura mater and defining the boundary of the infiltrated area based on multiphoton technology. Background Art

[0002] Meningioma is one of the most common brain tumors, accounting for more than 30% of primary brain tumors. Although resection is considered the preferred treatment for patients with meningioma, more than 30% of patients who undergo resection will experience recurrence within 15 years and require reoperation. Residual meningioma in the peripheral dura mater is one of the most important factors leading to meningioma recurrence. Therefore, it is of great significance to identify whether the meningioma has infiltrated the peripheral dura mater and accurately determine the boundaries of the infiltration. The existing method mainly relies on the experience of the surgeon to judge, and there is a large degree of uncertainty. Although the surgical effect can be verified by pathological examination after surgery, the traditional pathological examination method requires cumbersome steps such as sectioning and staining, and cannot provide surgeons with surgical guidance in a timely manner.

[0003] Multiphoton microscopy has been widely used in biomedical research. This technology can obtain high-resolution images for tissue pathology diagnosis in real time without labeling, and has the potential to provide intraoperative guidance for surgeons. However, direct identification of multiphoton images by human eyes not only requires a lot of training for clinicians, but also has certain subjectivity and uncertainty. Based on this, image analysis technology can provide quantitative data by analyzing multiphoton images, realize automatic identification of meningioma infiltration of the dura mater in multiphoton images and accurately determine the boundaries of the infiltration, effectively improving the efficiency of complete resection of meningiomas. Summary of the invention

[0004] The purpose of the present invention is to provide a method for identifying meningioma infiltration of the dura mater and delineating the boundary of the infiltration site based on multiphoton technology. The method is conducive to quickly and automatically determining whether meningioma dura mater infiltration occurs and accurately determining the boundary of the infiltration site.

[0005] To achieve the above object, the technical solution of the present invention is: a method for identifying meningioma infiltrating the dura mater and delineating the boundary of the infiltration site based on multiphoton technology, comprising the following steps:

[0006] Step S1, identification of endogenous multiphoton signals of meningioma and surrounding normal dura mater tissue;

[0007] Step S2: microscopic imaging of meningioma and surrounding normal dura mater tissue;

[0008] Step S3, identifying the infiltration of dura mater tissue around the meningioma;

[0009] Step S4: Delineate the tumor tissue boundary at the site of dura mater tissue infiltration surrounding the meningioma.

[0010] In one embodiment of the present invention, step S1 specifically includes the following steps:

[0011] Step S11, based on the multiphoton optical imaging system, using the highly sensitive Mate spectrum detector equipped in the system, in the spectrum detection mode, obtaining the spectrum imaging image of the fresh meningioma tissue and the surrounding normal dura mater tissue;

[0012] Step S12, using a spectrum-induced analysis method to obtain a multiphoton spectrum and peak values ​​of fresh meningioma tissue and its surrounding normal tissue, and identifying and extracting endogenous optical signals of the meningioma and its surrounding normal tissue;

[0013] Step S13, using traditional pathological histological diagnosis as the "gold standard", analyze the components of meningioma and its surrounding normal tissues through H&E staining and special staining; comprehensively analyze the spectral overlay image (spectral imaging image obtained in step S11), emission spectrum (spectral overlay image obtained by spectral induced analysis method) and pathological picture analysis results, and finally determine the specific tissue component source of endogenous multiphoton signals of meningioma tissue and its surrounding normal tissues.

[0014] In one embodiment of the present invention, step S2 specifically includes the following steps:

[0015] Step S21, in a multi-channel mode, continuously detecting and imaging endogenous signals in different emission signal bands to obtain high-contrast microstructure imaging of meningioma and its surrounding normal tissue components in different emission spectral bands;

[0016] Step S22, based on the spectral imaging diagram and imaging obtained in step S11, determining the parameters of the multiphoton optical microscopic imaging system required for obtaining high-contrast imaging of different components of the meningioma and its surrounding normal tissues, including the optimal laser wavelength, excitation light power range, pulse repetition frequency, detection gain, and detection emission light band range;

[0017] Step S23, taking different subtypes of meningiomas and their surrounding normal dura mater tissues as research objects, obtaining high-resolution, high-contrast multiphoton optical microscopic images of the microstructures of different subtypes of meningiomas and their surrounding normal dura mater tissues.

[0018] In one embodiment of the present invention, in step S21, dual-channel second harmonic and two-photon excitation fluorescence or multi-channel second harmonic and multiple two-photon excitation fluorescence continuous detection imaging is performed on endogenous signals in different emission signal bands.

[0019] In one embodiment of the present invention, step S3 specifically includes the following steps:

[0020] Step S31, in a multi-channel mode, obtaining optical diagnostic features for identifying dural tissue infiltration around meningiomas;

[0021] Step S32, developing Matlab programs and open source programs to calculate the content, direction angle, and spacing of collagen in the tissue, and obtain certain quantitative morphological optical diagnostic parameters for identifying the infiltration of the dura mater tissue around the meningioma;

[0022] Step S33, in spectral mode, obtaining spectral images of the infiltrated tumor and its surrounding normal tissues, analyzing the differences in the spectral images of the infiltrated tumor and its surrounding normal tissues, including spectral peaks and spectral intensities; and extracting certain quantitative spectral difference data as spectral diagnostic parameters, including nicotinamide adenine dinucleotide NADH, flavin adenine dinucleotide FAD and second harmonic SHG intensity, redox rate, and the ratio of the maximum intensity of two-photon excited fluorescence TPEF to the maximum intensity of SHG, so as to quantitatively and qualitatively identify the infiltration of tissues surrounding meningioma in the spectrum;

[0023] Step S34, using SPSS statistical analysis software, further quantify and statistically analyze the various morphological optical diagnostic parameter indicators extracted for diagnosing peripheral dural tissue infiltration of meningiomas, and use this as a standard, combined with optical diagnostic features, to establish an optical diagnostic standard for identifying peripheral dural tissue infiltration of meningiomas.

[0024] In one embodiment of the present invention, in step S31, the optical diagnostic features include the direction, morphology and content of the collagen fibers after infiltration.

[0025] In one embodiment of the present invention, in step S32, the developed Matlab program and the open source program are Otsu threshold segmentation algorithm programs.

[0026] In one embodiment of the present invention, in step S4, a program based on Matlab language is developed, which relies on the difference in texture analysis on multiphoton microscopic images of the infiltrated tumor tissue and its surrounding tissues, and automatically distinguishes the infiltrated tumor tissue and its surrounding tissues through a clustering method and automatically delineates the boundary of the infiltrated tumor tissue; finally, it is compared with the H&E staining image of the frozen section to verify the accuracy of the boundary delineation.

[0027] In one embodiment of the present invention, the clustering method includes K-means clustering method and FCM fuzzy clustering.

[0028] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for identifying meningioma infiltration of the dura mater and delineating the boundaries of the infiltration site based on multiphoton technology. The method combines multiphoton technology and image analysis technology to diagnose meningioma tissue sections, which solves the limitation of previous methods that require a lot of manpower and time costs. It can automatically identify meningioma infiltration of the dura mater in multiphoton images and accurately determine the boundaries of the infiltration site, effectively improving the efficiency of complete resection of meningioma. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of dural infiltration around meningioma.

[0031] Figure 2 The present invention is a flowchart of a method implementation according to an embodiment of the present invention.

[0032] Figure 3 This is the recognition result of the boundary of the dura mater infiltration around the meningioma in the embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0034] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0035] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0036] The present invention provides a method for identifying meningioma infiltration into the dura mater and delineating the boundary of the infiltration site based on multiphoton technology, comprising the following steps:

[0037] Step S1, identification of endogenous multiphoton signals of meningioma and surrounding normal dura mater tissue;

[0038] Step S2: microscopic imaging of meningioma and surrounding normal dura mater tissue;

[0039] Step S3, identifying the infiltration of dura mater tissue around the meningioma;

[0040] Step S4: Delineate the tumor tissue boundary at the site of dura mater tissue infiltration surrounding the meningioma.

[0041] The following is the specific implementation process of the present invention.

[0042] Figure 1 Schematic diagram of dural infiltration around meningioma.

[0043] like Figure 2 As shown, this embodiment provides a method for identifying meningioma infiltration into the dura mater and delineating the boundary of the infiltration site based on multiphoton technology, comprising the following steps:

[0044] Step S1: Identification of endogenous multiphoton signals in meningioma and surrounding normal dura mater tissue

[0045] Step S2: Microscopic imaging of meningioma and surrounding normal dura mater tissue

[0046] Step S3: Identification of dural tissue infiltration around meningioma

[0047] Step S4: Delineation of tumor tissue boundaries at the site of dura mater tissue infiltration surrounding the meningioma

[0048] In this embodiment, step S1 specifically includes the following steps:

[0049] Step S11, the multiphoton microscopic imaging system is built on a commercial laser scanning microscope platform (LSM 510 Zeiss, Germany), using a mode-locked femtosecond Ti: Sapphire laser as a light source, and the Mate detector obtains the backscattered signal of the tissue sample through a reflection grating and a photomultiplier tube array. In the spectral detection mode, the spectral imaging of fresh meningioma tissue and surrounding normal dura mater tissue is obtained.

[0050] Step S12: using a spectrum-induced analysis method to obtain multiphoton spectra and peak values ​​of fresh meningioma tissue and surrounding normal tissue, and identifying and extracting endogenous optical signals of meningioma and surrounding normal tissue.

[0051] Step S13, using traditional pathological histological diagnosis as the "gold standard", analyze the composition of meningioma and surrounding normal tissues through H&E staining and special staining. Combining the above spectral overlay images, emission spectra and pathological picture analysis results, the specific tissue component sources of endogenous multiphoton signals of meningioma tissue and surrounding normal tissues are finally determined. (The spectral overlay images are all obtained from the spectral mode of the multiphoton system (multiphoton spectroscopy technology), and the emission spectrum is obtained through the spectral overlay image and further obtained through the spectral induced analysis method.)

[0052] In this embodiment, step S2 specifically includes the following steps:

[0053] Step S21, in a multi-channel mode, performing dual-channel detection imaging of second harmonic and two-photon excitation fluorescence or multi-channel continuous detection imaging of second harmonic and multiple two-photon excitation fluorescence on endogenous signals in different emission signal bands to obtain high-contrast microstructure imaging of meningioma and its surrounding normal tissue components with different emission spectral bands.

[0054] Step S22: Based on the spectral imaging diagram and imaging obtained in step S11, further determine the optimal parameters of the multiphoton optical microscopic imaging system required for obtaining high-contrast imaging of different components of the meningioma and its surrounding normal tissues as shown in Table 1:

[0055] Table 1: Optimal parameters for multiphoton optical microscopy systems

[0056]

[0057] Step S23: On this basis, different subtypes of meningiomas and their surrounding normal dura mater tissues are taken as research objects to obtain high-resolution, high-contrast multiphoton optical microscopic images of their microstructures.

[0058] In this embodiment, step S3 specifically includes the following steps:

[0059] Step S31: In multi-channel mode, optical diagnostic features for identifying the infiltration of dura mater tissue around meningioma are obtained, mainly focusing on the changes in the direction, morphology and content of collagen fibers after infiltration.

[0060] Step S32, further develop Matlab program, Otsu threshold segmentation to automatically calculate the content of collagen in tissue, use Fiberfit open source program to automatically calculate the direction of collagen, and obtain some quantitative morphological optical diagnostic parameters for identifying the infiltration of dura mater tissue around meningioma.

[0061] Step S33, in spectral mode, obtain spectral images of the infiltrated tumor and surrounding normal tissue. Develop a peak separation program based on Matlab language to fit and analyze the proportion of tissue components of the infiltrated tumor and surrounding normal tissue. At the same time, extract some quantitative spectral difference data as spectral diagnostic parameters, including NADH, FAD and SHG intensity, redox rate, ratio of TPEF maximum intensity to SHG maximum intensity, etc., so as to quantitatively and qualitatively identify the infiltration of tissues around meningioma in the spectrum.

[0062] Step S34: SPSS statistical analysis software is used to further quantify and statistically analyze the various diagnostic parameter indicators extracted for diagnosing the infiltration of the dura mater around meningioma. Based on this standard and combined with the optical diagnostic features, an optical diagnostic standard for identifying the infiltration of the dura mater around meningioma is established.

[0063] In this embodiment, step S3 specifically includes the following steps:

[0064] Step S41, image preprocessing: scaling the image to 1000×1000 pixels according to the image size, and converting it into a grayscale image.

[0065] Step S42, image segmentation: Use the fuzzy C-means clustering algorithm (FCM) to perform cluster analysis on the grayscale image to determine the central area in the image. Then, through morphological operations and connected area marking, select the largest connected area as a rough segmentation template and display it.

[0066] Step S43, fine segmentation and contour extraction: perform erosion and dilation operations on the rough segmentation template, apply the GrowCut algorithm to perform fine segmentation, and display the fine segmentation template. Adjust the size of the fine segmentation template back to the original image size, and draw the contour on the original image. Display the image with the contour.

[0067] The above program design scheme provided in this embodiment can be stored in a computer-readable storage medium in a coded form and implemented in the form of a computer program, and the basic parameter information required for calculation is input through computer hardware, and the calculation result is output.

[0068] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0069] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0070] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0071] 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 comprising 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.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions 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.

[0073] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology, characterized in that: The steps include: Step S1, identification of endogenous multiphoton signals of meningioma and surrounding normal dura mater tissue; Step S2: microscopic imaging of meningioma and surrounding normal dura mater tissue; Step S3, identifying the infiltration of dura mater tissue around the meningioma; Step S4: Delineate the tumor tissue boundary at the site of dura mater tissue infiltration surrounding the meningioma.

2. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 1, characterized in that: Step S1 specifically includes the following steps: Step S11, based on the multiphoton optical imaging system, using the highly sensitive Mate spectrum detector equipped in the system, in the spectrum detection mode, obtaining the spectrum imaging image of the fresh meningioma tissue and the surrounding normal dura mater tissue; Step S12, using a spectrum-induced analysis method to obtain a multiphoton spectrum and peak values ​​of fresh meningioma tissue and its surrounding normal tissue, and identifying and extracting endogenous optical signals of the meningioma and its surrounding normal tissue; Step S13: Using traditional histopathological diagnosis as the "gold standard", the components of meningioma and its surrounding normal tissues are analyzed by H&E staining and special staining; the results of spectral overlay image, emission spectrum and pathological picture analysis are integrated to finally determine the specific tissue component sources of endogenous multiphoton signals of meningioma tissue and its surrounding normal tissues.

3. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 2, characterized in that: Step S2 specifically includes the following steps: Step S21, in a multi-channel mode, continuously detecting and imaging endogenous signals in different emission signal bands to obtain high-contrast microstructure imaging of meningioma and its surrounding normal tissue components in different emission spectral bands; Step S22, based on the spectral imaging diagram and imaging obtained in step S11, determining the parameters of the multiphoton optical microscopic imaging system required for obtaining high-contrast imaging of different components of the meningioma and its surrounding normal tissues, including the optimal laser wavelength, excitation light power range, pulse repetition frequency, detection gain, and detection emission light band range; Step S23, taking different subtypes of meningiomas and their surrounding normal dura mater tissues as research objects, obtaining high-resolution, high-contrast multiphoton optical microscopic images of the microstructures of different subtypes of meningiomas and their surrounding normal dura mater tissues.

4. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 3, characterized in that: In step S21, dual-channel second harmonic and two-photon excitation fluorescence or multi-channel second harmonic and multiple two-photon excitation fluorescence continuous detection imaging is performed on endogenous signals in different emission signal bands.

5. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step S31, in a multi-channel mode, obtaining optical diagnostic features for identifying dural tissue infiltration around meningiomas; Step S32, developing Matlab programs and open source programs to calculate the content, direction angle, and spacing of collagen in the dura mater tissue, and obtaining certain quantitative morphological optical diagnostic parameters for identifying the infiltration of the dura mater tissue around the meningioma; Step S33, in spectral mode, obtaining spectral images of the infiltrated tumor and its surrounding normal tissues, analyzing the differences in the spectral images of the infiltrated tumor and its surrounding normal tissues, including spectral peaks and spectral intensities; and extracting certain quantitative spectral difference data as spectral diagnostic parameters, including nicotinamide adenine dinucleotide NADH, flavin adenine dinucleotide FAD and second harmonic SHG intensities, redox ratios, and the ratio of the maximum intensity of two-photon excited fluorescence TPEF to the maximum intensity of SHG, so as to quantitatively and qualitatively identify the infiltration of tissues surrounding meningioma in the spectrum; Step S34, using SPSS statistical analysis software, further quantify and statistically analyze the various morphological optical diagnostic parameter indicators extracted for diagnosing peripheral dural tissue infiltration of meningiomas, and use this as a standard, combined with optical diagnostic features, to establish an optical diagnostic standard for identifying peripheral dural tissue infiltration of meningiomas.

6. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 5, characterized in that: In step S31, the optical diagnostic features include the direction, morphology and content of the collagen fibers after infiltration.

7. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 5, characterized in that: In step S32, the developed Matlab program and the open source program are Otsu threshold segmentation algorithm programs.

8. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 1, characterized in that: In step S4, a program based on Matlab language is developed. Relying on the difference in texture analysis on multiphoton microscopic images of the invasive tumor tissue and its surrounding tissue, the invasive tumor tissue and its surrounding tissue are automatically distinguished and the boundary of the invasive tumor tissue is automatically delineated through a clustering method; finally, the boundary is compared with the H&E staining image of the frozen section to verify the accuracy of the boundary delineation.

9. The method for identifying meningioma infiltration into the dura mater and defining the boundary of the infiltration site based on multiphoton technology according to claim 8, characterized in that: Clustering methods include K-means clustering method and FCM fuzzy clustering.

10. A computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, and when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 9 can be implemented.

Images