Spectral modulation endoscopic method and device for rapidly displaying in-vivo component distribution

Abstract

The invention discloses a spectral modulation endoscopic method and device for rapidly displaying in-vivo component distribution, and belongs to the field of spectral analysis. The method comprises the following steps: determining a wavelength range of spectrum modulation according to an absorption spectrum curve of a target component; in the wavelength range, broadband spectrum modulation is carried out on illumination light of the endoscope; calibrating a full-view-field illumination spectrum curve and a detection response spectrum curve of the endoscope in various illumination modulation states; imaging the in-vivo biological tissue based on the calibrated endoscope, and acquiring a plurality of images in each illumination modulation state; and developing a deep learning algorithm based on Monte Carlo modeling and a calibration result to calculate the acquired image to obtain the content distribution of the target component in the biological tissue. The endoscope device is good in compatibility with an existing endoscope device, compression coding of spectrum information is achieved through illumination spectrum modulation, therefore, the number of times and time of image collection are reduced, and the endoscope device is more suitable for dynamic scenes.

Description

Technical Field

[0001] The present invention belongs to the fields of medical imaging, computational imaging, and spectral analysis, and particularly relates to a spectral modulation endoscopy method and device for rapidly displaying in-vivo component distribution. Background Art

[0002] The distribution of biological tissue components is directly related to its health status. Obtaining the tissue component distribution without damage for auxiliary diagnosis and efficacy evaluation is of great significance in life science research and clinical medical applications. Hyperspectral images carry more information related to tissue component categories and contents compared to black-and-white intensity imaging and color imaging, and are widely used for component identification and analysis in fields such as healthcare, industrial inspection, and remote sensing. The traditional method for obtaining tissue component distribution images based on hyperspectral information is to first obtain hyperspectral three-dimensional cube data by performing temporal scanning in the wavelength dimension or spatial dimension, or by setting a mosaic hyperspectral filter array in front of the imaging camera detector; then, it is calculated in combination with the absorption and scattering spectral characteristics of the components. The temporal scanning method results in long imaging time and slow speed, making it difficult to apply to dynamic targets; the mosaic method can perform snapshot imaging, but sacrifices spatial resolution proportionally. In the application scenario of in-vivo detection, especially in-lumen detection, biological tissues are affected by breathing and heartbeat activities and belong to dynamic targets; at the same time, doctors have relatively high requirements for the spatial resolution of imaging, often reaching a spatial resolution of 2K or even 4K in order to detect early lesions. Therefore, it is difficult to use traditional solutions to achieve rapid high-resolution component distribution calculation and analysis of dynamic tissues during in-vivo endoscopy.

[0003] For example, the TIVITA® Mini endoscope hyperspectral imaging product provided by DIASPECTIVE VISION company can obtain complete hyperspectral images of static tissues based on scanning in the spatial dimension, and calculate the distribution of various components such as lipids, water, hemoglobin, and oxygen saturation from the hyperspectral images. However, the acquisition time is on the order of seconds, and when used in parts severely affected by breathing and heartbeat movements (such as the esophagus), motion blur and image misalignment may occur, making it impossible to clearly present the tissue component distribution. Another example is that the narrow-band light imaging technology in Olympus endoscope products selects two narrow-band lights with central wavelengths located at 415 nm and 540 nm (the spectral half-width of the light source is often less than 50 nanometers) to illuminate biological tissues; although the application of characteristic spectral bands greatly increases the contrast of blood vessels in the image, the absorption and scattering information of tissues outside the wavelength range covered by the narrow-band light is lost, limiting the accuracy of quantitative analysis of the contents of various components.

[0004] Therefore, how to rapidly and with high spatial resolution use the hyperspectral information of in-vivo tissues in a wide wavelength range for its component distribution calculation and analysis has become an important issue. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a spectral modulation endoscopy method and device for rapidly displaying in-vivo component distribution.

[0006] To achieve the above object, the present invention provides the following technical solutions: A spectral modulation endoscopy method for rapidly displaying in-vivo component distribution, the method comprising the following steps:

[0007] According to the absorption spectral curves of M target components, determine the wavelength range of spectral modulation so that the wavelength range includes the bands where the target components have high absorption rates; the high absorption rate specifically means that the absorption rate of the target component to light is not less than 1% compared to the total absorption rate of all components in the sample;

[0008] Within the wavelength range, perform spectral wide-band modulation on the illumination light of the endoscope, and the number of modulations is N, where N>1;

[0009] Calibrate the full-field illumination spectral curve I i (λ) of the endoscope in N illumination modulation states, where λ represents the wavelength; the subscript i represents the i-th illumination modulation state, and its value ranges from 1 to N; calibrate the detection response spectral curve D j (λ) of the endoscope. When the endoscope camera is a grayscale camera, j takes the value of 1 and is calibrated once. When the endoscope camera is a color camera, j takes values from 1 to 3 and calibrates the three color channels separately;

[0010] Use the calibrated endoscope for in-vivo biological tissue imaging, and collect 1 image or several images in each illumination modulation state and take the average;

[0011] Based on a deep learning algorithm, calculate the images collected in N illumination modulation states to obtain the content distribution of M target components in the biological tissue.

[0012] Further, the spectral wide-band modulation specifically is: after the modulation illumination light is normalized by the maximum intensity within the band range, the wavelength range where the spectral intensity is greater than 50% exceeds 50 nanometers.

[0013] Further, the wavelength calibration range of the illumination spectral curve and the detection response spectral curve covers the band where the product of the two in the wavelength dimension and after normalization is greater than 1%.

[0014] Further, the calculation of the images collected based on the deep learning algorithm to obtain the content distribution of the target components in the biological tissue includes:

[0015] Configure a series of preset values for the content of M target components within an operable range, and set the scattering spectrum of the biological tissue and the total absorption spectrum of non-target components according to the type of biological tissue; perform Monte Carlo modeling on the photon propagation in the biological tissue to obtain the scattering reflection spectral curve S(λ, α1, α2…α M ), where λ represents the wavelength, and α1, α2…α M represent the content of M target components;

[0016] Multiply the calibrated full-field illumination spectral curve I i (λ) and the detection response spectral curve D j (λ) by the scattering reflection spectral curve S(λ, α1, α2…α M ) and integrate in the wavelength dimension to obtain the detection intensity values corresponding to the preset values of the content of M components , and construct a data set on the corresponding relationship between the content of M components and the intensity value R ij ; among them, when using a grayscale camera, the number of intensity values R ij is N, and when using a color camera, the number of intensity values R ij is 3N;

[0017] Construct a deep learning neural network. When using a grayscale camera, the number of input values of the network is N; when using a color camera, the number of input values of the network is 3N; the number of output values of the network is M, corresponding to the content of M components; based on the data set, train the deep learning neural network;

[0018] Extract the intensity values corresponding to the same pixel point in the images collected under N illumination modulation states, input them into the trained deep learning neural network, and obtain M output values, each output value corresponding to the content of M components at that pixel point; repeat this process for all pixel points in each image to obtain the content distribution images of M components.

[0019] The present invention also provides a spectral modulation endoscope device for quickly displaying the in-vivo component distribution, including: an electrically controlled wide-spectrum tunable illumination device, an illumination coupling device, an endoscope body, an image acquisition device, a synchronous signal transmission cable, an image transmission cable, and a computer;

[0020] The illumination coupling device includes a coupling lens and an optical fiber bundle; the coupling lens is connected to the electrically controlled wide-spectrum tunable illumination device, and the optical fiber bundle is connected to the coupling lens;

[0021] The image output end of the endoscope body is connected to the image acquisition device, and its illumination input end is connected to the optical fiber bundle;

[0022] The computer is connected to the electronically controlled wide-spectrum tunable lighting device through a synchronization signal transmission cable, and is connected to the image acquisition device through the synchronization signal transmission cable and the image transmission cable; the computer is provided with a synchronization control device and a tissue component content distribution calculation device.

[0023] Further, the electronically controlled wide-spectrum tunable lighting device includes a wide-spectrum light source, a fast electronically controlled filter wheel, and N filters with different transmittance curves; the N filters are placed on the fast electronically controlled filter wheel for spectral adjustment of the output light of the wide-spectrum light source.

[0024] Further, the electronically controlled wide-spectrum tunable lighting device includes a wide-spectrum light source and a metasurface element with electronically controllable transmission / reflection spectrum, and the metasurface element is used for spectral adjustment of the output light of the wide-spectrum light source.

[0025] Further, after the spectrum of the output light of the electronically controlled wide-spectrum tunable lighting device is normalized by the intensity maximum value, the wavelength range with a spectral intensity greater than 50% exceeds 50 nanometers.

[0026] Further, the image acquisition device is a grayscale camera or a color camera.

[0027] Further, the synchronization control device is used to control the electronically controlled wide-spectrum tunable lighting device and the image acquisition device, for realizing synchronous acquisition of images in each lighting light modulation state; the tissue component content distribution calculation device has a built-in deep learning algorithm, processes the images acquired by the image acquisition device, and outputs the distribution result of the biological tissue components.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) The present invention does not need to modify the endoscope body of the existing endoscope, and has good compatibility with the existing endoscope device. The electronically controlled wide-spectrum tunable lighting device and the lighting coupling device can be designed as a module to be connected to the existing endoscope body, and the synchronization control device can be designed to be connected to the image acquisition device of the existing endoscope, so as to realize lighting modulation and synchronous acquisition. At the same time, a commercial computer can be provided with a tissue component content distribution calculation device. Therefore, it is convenient for clinical staff to use.

[0030] (2) Compared with using a spatial dimension or wavelength dimension time-sequential scanning scheme to collect uncompressed hyperspectral information and then perform component analysis, the present invention realizes compressed coding of spectral information through N times of wide-band modulation of the lighting spectrum, thereby reducing the number of image acquisitions and time, and is more suitable for dynamic scenes.

[0031] (3) The present invention can quantitatively output the content distribution images of multiple main components of biological tissues, and the spatial resolution is equivalent to that of classical white light endoscopy imaging. Description of the Drawings

[0032] Figure 1 Schematic diagram of an example of a hard mirror for illumination modulation;

[0033] Figure 2 Structural diagram of a filter;

[0034] Figure 3 Schematic diagram of the transmittance of a filter. Among them, (a) is a curve graph, and (b) is a correlation coefficient graph;

[0035] Figure 4 Spectral calibration result graph of the illumination channel;

[0036] Figure 5 Spectral response calibration result graph of the detection channel;

[0037] Figure 6 Scattering reflection spectral curve graph of a mouse liver calculated by Monte Carlo modeling under two groups of blood oxygen and blood volume ratios;

[0038] Figure 7 Experimental result graph. Among them, (a) is a schematic diagram of the blood volume ratio output when observing the live liver of a mouse, and (b) is a graph of the blood oxygen saturation result output when observing the live liver of a mouse;

[0039] In the figure, 1. White light LED, 2. Collimating lens, 3. Fast electronically controlled filter wheel, 4. Filter, 5. Coupling lens, 6. Fiber optic bundle, 7. Endoscope body, 8. Color camera, 9. Image transmission cable, 10. Synchronous signal transmission cable, 11. Computer, 12. Monitor. Detailed implementation mode

[0040] Here, exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0041] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the", and "said" used in the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0042] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0043] The present invention will be described in detail below with reference to the accompanying drawings. Without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0044] An embodiment of the present invention provides a spectral modulation endoscopy method for quickly displaying in-vivo component distribution, including the following steps:

[0045] (1) The target components are selected as oxyhemoglobin and deoxyhemoglobin, and both have high absorption rates in the visible light band. Therefore, the illumination coding band is set to 420 nanometers to 750 nanometers.

[0046] (2) Spectral modulation is performed on the illumination light of the endoscope between 420 nanometers and 750 nanometers, and the number of modulations is 3. Filter films made of titanium oxide and silicon oxide materials are used to implement filters with different transmittance functions between 420 nanometers and 750 nanometers. By designing the thicknesses of the titanium oxide and silicon oxide films, the illumination light modulated by 3 filters is achieved, such that the cross-correlation coefficient between any two of them is less than 0.5.

[0047] (3) Calibration of the illumination spectral curve: Use a spectrometer to calibrate the illumination spectral curve I i (λ) of the endoscope in 3 illumination modulation states, where λ represents the wavelength, and its value range is 420 nanometers to 750 nanometers; the subscript i represents the i-th illumination modulation state, and its value ranges from 1 to 3. Calibration of the detection channel: Install a black-and-white / color camera on the endoscope, and a band-pass filter with a wavelength range of 420 nanometers to 750 nanometers is installed in front of the camera target surface. Use the endoscope to image a single-wavelength standard sample to obtain the response rate of the detection channel at this wavelength. In the range of 420 nanometers to 750 nanometers, scan the wavelength of the standard sample to obtain the response spectral curve D j (λ) of the camera, where λ represents the wavelength, and its value range is 420 nanometers to 750 nanometers; when the camera is a grayscale camera, j takes the value of 1; when the endoscope camera is a color camera, calibration is performed on the three color channels respectively, and the value of j ranges from 1 to 3.

[0048] (4) Use the calibrated endoscope for imaging biological tissues, and collect 1 image in each illumination modulation state, for a total of 3 images collected.

[0049] (5) Develop a deep learning algorithm to calculate the 3 images collected in step (4) to obtain the content distribution of oxygenated and deoxygenated hemoglobin in biological tissues.

[0050] Among them, the specific implementation of the deep learning algorithm described in step (5) is discussed as follows:

[0051] (5.1) Set a series of preset values for the content of oxygenated and deoxygenated hemoglobin in biological tissues within a reasonable range, and set its scattering spectrum and the total absorption spectrum of non-target components according to the type of biological tissue. Conduct Monte Carlo modeling on the photon propagation in biological tissues in the range of 420 nm to 750 nm to establish the scattering reflection spectrum curve S(λ, α1, α2) in the range of 420 nm to 750 nm, where λ represents the wavelength, and α1 and α2 represent the contents of oxygenated and deoxygenated hemoglobin respectively.

[0052] (5.2) Multiply the calibrated illumination spectrum curve I i (λ) and the detection response spectrum curve D j (λ) established in (5.1) in the wavelength dimension and then integrate to obtain the intensity value displayed in the collected image when the contents of oxygenated and deoxygenated hemoglobin are α1 and α2 respectively, that is . Construct the corresponding relationship between the content of oxygenated and deoxygenated hemoglobin and the intensity value R ij as a data set. When using a grayscale camera, the number of R ij is 3, and when using a color camera, the number of R ij is 9.

[0053] (5.3) Construct a deep learning neural network. When using a grayscale camera, the number of input values of the network is 3; when using a color camera, the number of input values of the network is 9; the number of output values of the network is 2, corresponding to the content of oxygenated and deoxygenated hemoglobin. Based on the data set established in (5.2), conduct network training.

[0054] Among them, the calculation method for calculating the content distribution of target components in biological tissues from the collected images in step (5) is to extract all the intensity values (the number is 3 or 9) corresponding to the same pixel point in the 3 grayscale or color images collected in step (4), input them into the neural network trained in (5.3), and obtain 2 output values, corresponding to the content of oxygenated and deoxygenated hemoglobin at this pixel point. Apply the above calculation method to all pixel points in the collected images to obtain the content of oxygenated and deoxygenated hemoglobin.

[0055] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a spectral modulation endoscope device for rapidly displaying in-vivo component distribution, which comprises: an electronically controlled wide-spectrum tunable lighting device, a lighting coupling device, an endoscope body 7, an image acquisition device, an image transmission cable 9, a synchronization signal transmission cable 10, and a computer 11.

[0056] The electronically controlled wide-spectrum tunable lighting device includes a white light LED 1, a collimating lens 2, a fast electronically controlled filter wheel 3, and three filter films 4. The white light LED 1 is connected to the collimating lens 2, and the collimating lens 2 is connected to the fast electronically controlled filter wheel 3; the filter films 4 are placed on the fast electronically controlled filter wheel 3; the power of the white light LED 1 is greater than 90W, and the filter films 4 are composed of five layers of titanium oxide and silicon oxide thin films, as Figure 2 shown. The transmittance curves of the three filter films 4 are as shown in Figure 3 Figure (a) below, and the pairwise correlation coefficient matrix is as shown in Figure 3 Figure (b) below, and all the correlation coefficients are less than 0.5.

[0057] The lighting coupling device includes a coupling lens 5 and an optical fiber bundle 6; the coupling lens 5 is connected to the fast electronically controlled filter wheel 3, and the optical fiber bundle 6 is connected to the coupling lens 5; the lighting coupling device couples the output light of the electronically controlled wide-spectrum tunable lighting device into the lighting channel of the endoscope body.

[0058] The image output end of the endoscope body 7 is connected to the image acquisition device, and the lighting input end is connected to the optical fiber bundle 6;

[0059] The computer 11 is connected to the fast electronically controlled filter wheel 3 through the synchronization signal transmission cable 10, and is connected to the image acquisition device through the synchronization signal transmission cable 10 and the image transmission cable 9. The image acquisition device selects a color camera 8; the computer 11 is provided with a synchronization control device and a tissue component content distribution calculation device, and is externally connected to a display 12.

[0060] When the three filter films 4 modulate the white light LED 1 respectively, the spectral curves at the output end of the lighting channel of the endoscope body 7 are as shown in Figure 4 the figure below. The total detection response spectral curve corresponding to the superposition of the detection optical path and the color camera 8 is as shown in Figure 5 the figure below.

[0061] A series of scattering and reflection spectra under a series of blood oxygen and blood volume ratios are simulated by Monte Carlo, as shown in Figure 6As shown in the figure. Let α1 and α2 represent the contents of oxygenated and deoxygenated hemoglobin (in mol / L), respectively. Then the blood oxygen saturation = α1 / (α1 + α2), and the blood volume ratio = (α1 + α2) * MW / C, where MW is the molar mass of hemoglobin, in g / mol; C is the mass of hemoglobin in a unit volume of pure blood, with an average of 150 g / L. For each set of scattered reflection spectra, integrate the product of the spectra with the spectral curves I i (λ) (i = 1, 2, 3) and the detection response spectral curves D j (λ) (j = 1, 2, 3) in the wavelength dimension to obtain 9 values. Construct a deep learning neural network to establish the mapping relationship from the 9 values to the blood oxygen value and the blood volume ratio value. Incorporate the developed deep learning neural network into the tissue component content distribution calculation device in the computer 11.

[0062] The control signal sent by the synchronization control device in the computer 11 is transmitted to the electro - controlled filter wheel 3 and the color camera 8 through the synchronization signal transmission cable 10, so that the 3 filter plates 4 modulate the output light of the white - light LED 1 in sequence. When each filter plate 4 is in the modulation position, the color camera 8 simultaneously acquires 1 image, and a total of 3 color images are acquired. The 3 color images are transmitted to the tissue component content distribution calculation device in the computer 11 through the image transmission cable 9 for processing. The calculated blood oxygen and blood volume ratio distribution maps are displayed on the display 12.

[0063] Application Example

[0064] Use the Figure 1 spectral modulation endoscope device for rapidly displaying in - vivo component distribution shown in the figure to observe the liver of a live mouse. Figure 7 Figures (a) and (b) show the blood volume ratio and blood oxygen saturation distributions of the mouse liver, respectively.

[0065] After considering the specification and practicing the content disclosed herein, those skilled in the art will readily think of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary.

[0066] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A spectral modulation endoscopy method for rapidly displaying body composition distribution, characterized in that: Comprising: According to the absorption spectral curves of M target components, determine the wavelength range of spectral modulation so that the wavelength range includes the bands where the target components have high absorption rates; the high absorption rate specifically means that the absorption rate of the target component to light is not less than 1% compared to the total absorption rate of all components in the sample; Within the wavelength range, perform spectral broadband modulation on the illumination light of the endoscope, and the number of modulations is N, where N>1; Calibrate the full-field illumination spectral curves of the endoscope in N illumination modulation states; calibrate the detection response spectral curve of the endoscope. When the endoscope camera is a grayscale camera, perform calibration once. When the endoscope camera is a color camera, calibrate the three color channels separately; Use the calibrated endoscope for in-vivo biological tissue imaging, and collect 1 image or several images and take the average under each illumination modulation state; Based on a deep learning algorithm, calculate the images collected in N illumination modulation states to obtain the content distributions of M target components in the biological tissue.

2. The method according to claim 1, wherein The spectral broadband modulation specifically is: after the modulated illumination light is normalized by the maximum intensity within the band range, the wavelength range where the spectral intensity is greater than 50% exceeds 50 nanometers.

3. The method according to claim 1, characterized in that, The wavelength calibration ranges of the illumination spectral curve and the detection response spectral curve cover the bands where the product of the two in the wavelength dimension is normalized and the value is greater than 1%.

4. The method according to claim 1, characterized in that, The calculation of the images collected based on the deep learning algorithm to obtain the content distributions of the target components in the biological tissue includes: Configure a series of preset values for the contents of M target components in the operable interval, and set its scattering spectrum and the total absorption spectrum of non-target components according to the type of biological tissue; perform Monte Carlo modeling on the photon propagation in the biological tissue to obtain the scattering and reflection spectral curves within the wavelength range; Multiply and integrate the calibrated full-field illumination spectral curve, the detection response spectral curve, and the scattering and reflection spectral curve in the wavelength dimension to obtain the detection intensity values corresponding to the contents of M components under the preset values, and construct a data set about the corresponding relationship between the contents of M components and the intensity values; among them, when using a grayscale camera, the number of intensity values is N, and when using a color camera, the number of intensity values is 3N; Construct a deep learning neural network. When using a grayscale camera, the number of input values of the network is N; when using a color camera, the number of input values of the network is 3N; the number of output values of the network is M, corresponding to the contents of M components; based on the data set, train the deep learning neural network; Extract the intensity values corresponding to the same pixel point in the images collected in N illumination modulation states, input them into the trained deep learning neural network, and obtain M output values. Each output value corresponds to the contents of M components at this pixel point; repeat this process for all pixel points in each image to obtain the content distribution images of M components.

5. A spectral modulation endoscope device for rapidly displaying in-vivo component distribution, characterized in that, Comprising: An electronically controlled broadband tunable illumination device, an illumination coupling device, an endoscope body, an image acquisition device, a synchronous signal transmission cable, an image transmission cable, and a computer; The illumination coupling device includes a coupling lens and an optical fiber bundle; The coupling lens is connected to the electrically controlled wide-spectrum adjustable lighting device, and the optical fiber bundle is connected to the coupling lens; The image output end of the endoscope body is connected to the image acquisition device, and the illumination input end thereof is connected to the optical fiber bundle; The computer is connected to the electrically controlled wide-spectrum adjustable lighting device via a synchronous signal transmission cable, and is connected to the image acquisition device via a synchronous signal transmission cable and an image transmission cable; the computer is provided with a synchronous control device and a tissue component content distribution calculation device.

6. The device according to claim 5, characterized in that The electrically controlled wide-spectrum adjustable lighting device includes a wide-spectrum light source, a fast electrically controlled filter wheel, and N filters with different transmittance curves; the N filters are placed on the fast electrically controlled filter wheel and are used to adjust the spectrum of the output light of the wide-spectrum light source.

7. The device according to claim 5, characterized in that, The electrically controlled wide-spectrum adjustable lighting device includes a wide-spectrum light source and a metasurface element with electrically controllable transmittance / reflection spectrum, and the metasurface element is used to adjust the spectrum of output light of the wide-spectrum light source.

8. The device according to claim 5, characterized in that, After the spectrum of the output light of the electrically controlled wide-spectrum adjustable lighting device is normalized according to the maximum intensity, the wavelength range in which the spectrum intensity is greater than 50% exceeds 50 nanometers.

9. The device according to claim 5, characterized in that The image acquisition device is a grayscale camera or a color camera.

10. The device according to claim 5, characterized in that The synchronous control device is used to control the electrically controlled wide-spectrum adjustable lighting device and the image acquisition device to achieve synchronous acquisition of images under each illumination light modulation state; the tissue component content distribution calculation device has a built-in deep learning algorithm to process the images collected by the image acquisition device and output the distribution results of biological tissue components.

Images