A spectral modulation endoscopy method and device for rapidly displaying body composition distribution

Abstract

The present invention discloses a spectral modulation endoscopy method and device for rapidly displaying the distribution of in-body components, belonging to the field of spectral analysis. The method comprises: determining the wavelength range of spectral modulation according to the absorption spectrum curve of the target component; performing wide-band spectral modulation on the illumination light of the endoscope within the wavelength range; calibrating the full-field illumination spectrum curve and detection response spectrum curve of the endoscope under multiple illumination modulation states; imaging in-body biological tissues based on the calibrated endoscope, and collecting several images under each illumination modulation state; developing a deep learning algorithm based on Monte Carlo modeling and calibration results to calculate the collected images and obtain the content distribution of the target component in the biological tissue. The present invention is not only compatible with existing endoscope devices, but also realizes compressed encoding of spectral information through illumination spectrum modulation, thereby reducing the number and time of image acquisition, and 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 body component distribution. Background Art

[0002] The compositional distribution of biological tissues is directly correlated with their health status. Obtaining tissue composition distribution non-destructively, and thereby aiding diagnosis and therapeutic evaluation, is of great significance in life science research and clinical medicine. Compared to black-and-white intensity and color imaging, hyperspectral images carry more information about tissue composition types and content, and are widely used for component identification and analysis in healthcare, industrial testing, remote sensing, and other fields. Traditional methods for acquiring tissue composition distribution images based on hyperspectral information involve first performing time-series scanning in the wavelength or spatial dimensions, or placing a mosaic hyperspectral filter array in front of the imaging camera to obtain three-dimensional hyperspectral cube data. This data is then inferred based on the absorption and scattering spectral characteristics of the components. Time-series scanning methods result in long imaging times and slow speeds, making them difficult to apply to dynamic targets. Mosaic methods allow for snapshot imaging, but at the expense of spatial resolution. In in vivo testing, especially in intracavitary applications, biological tissues are subject to the influence of respiratory and cardiac activity, making them dynamic. Furthermore, to detect early lesions, doctors demand high spatial resolution, often reaching 2K or even 4K. Therefore, during in vivo observation, it is difficult to use traditional methods to achieve rapid and high-resolution calculation and analysis of component distribution of dynamic tissues.

[0003] For example, the TIVITA® Mini endoscopic hyperspectral imaging product from DIASPECTIVE VISION uses spatial scanning to acquire complete hyperspectral images of static tissues. It can then infer the distribution of various components, such as lipids, water, hemoglobin, and oxygen saturation, from these hyperspectral images. However, acquisition time is measured in seconds, and when used in areas severely affected by respiratory and cardiac motion (such as the esophagus), motion blur and image misalignment may occur, preventing a clear visualization of tissue composition distribution. For another example, the narrowband imaging technology used in Olympus endoscopes uses two narrowband light beams (often with a spectral half-width less than 50 nanometers) with central wavelengths of 415nm and 540nm to illuminate biological tissue. While the use of this characteristic spectral band significantly increases vascular contrast in the image, tissue absorption and scattering information outside the wavelength range covered by the narrowband light is lost, limiting the accuracy of quantitative analysis of the content of various components.

[0004] Therefore, how to quickly and spatially resolve the hyperspectral information of in vivo tissues over a wide wavelength range for the calculation and analysis of their component distribution has become an important issue. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the present invention aims to provide a spectral modulation endoscopy method and device for rapidly displaying body composition distribution.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a spectral modulation endoscopy method for rapidly displaying body composition distribution, the method comprising the following steps:

[0007] Determining a wavelength range for spectral modulation based on absorption spectrum curves of the M target components, such that the wavelength range includes a wavelength band in which the target components have a high absorptivity; the high absorptivity specifically means that the absorptivity of the target components to light is not less than 1% of the total absorptivity of all components in the sample;

[0008] In the wavelength range, the illumination light of the endoscope is subjected to spectral broad-band modulation, and the number of modulations is N, where N>1;

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

[0010] The calibrated endoscope is used for in vivo biological tissue imaging, and one image or several images are collected under each illumination modulation state and averaged;

[0011] Based on the deep learning algorithm, the images collected under N illumination modulation states are calculated to obtain the content distribution of M target components in biological tissues.

[0012] Furthermore, the wide-band spectral modulation is specifically as follows: after the modulated illumination light is normalized according to the maximum intensity within the band, the wavelength range in which the spectral intensity is greater than 50% exceeds 50 nanometers.

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

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

[0015] A series of preset values ​​are configured for the contents of M target components in an operable range, and the scattering spectrum and the total absorption spectrum of non-target components are set according to the type of biological tissue; Monte Carlo modeling is performed on the photon propagation in the biological tissue to obtain the scattering reflection spectrum curve S (λ, α1, α2…α M ), where λ represents the wavelength, α1, α2…α M Indicates the content of M target components;

[0016] The calibrated full-field illumination spectrum curve I i (λ), detection response spectrum curve D j (λ) and the scattered reflection spectrum curve S(λ, α1, α2…α M ) is multiplied and integrated in the wavelength dimension to obtain the detection intensity value corresponding to the content of M components under the preset value , construct the relationship between the content of M components and the intensity value R ij When a grayscale camera is used, the intensity value R ij The number of is N, when using a color camera, the intensity value R ij The number of is 3N;

[0017] Constructing a deep learning neural network, where the number of network input values ​​is N when a grayscale camera is used; the number of network input values ​​is 3N when a color camera is used; the number of network output values ​​is M, corresponding to the contents of M components; and training the deep learning neural network based on the data set;

[0018] The intensity value corresponding to the same pixel in the images collected under N illumination modulation states is extracted and input into the trained deep learning neural network to obtain M output values, each of which corresponds to the content of M components at the pixel; this process is repeated for all pixels in each image to obtain a content distribution image of the M components.

[0019] The present invention also provides a spectral modulation endoscopy device for rapidly displaying body composition distribution, comprising: an electrically controlled wide-spectrum adjustable lighting 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 lighting 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;

[0021] 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;

[0022] 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.

[0023] Furthermore, 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 to adjust the spectrum of the output light of the wide-spectrum light source.

[0024] Furthermore, the electrically controlled wide-spectrum tunable 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 perform spectrum adjustment on the output light of the wide-spectrum light source.

[0025] Furthermore, after the spectrum of the output light of the electrically controlled wide-spectrum tunable lighting device is normalized according to the maximum intensity, the wavelength range in which the spectrum intensity is greater than 50% exceeds 50 nanometers.

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

[0027] Furthermore, the synchronization 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.

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

[0029] (1) The present invention does not require modification of the existing endoscope and has good compatibility with existing endoscope devices. An electrically controlled wide-spectrum adjustable lighting device and an illumination coupling device can be designed as a module connected to the existing endoscope body, and a synchronous control device can be designed to be connected to the image acquisition device of the existing endoscope to achieve illumination modulation and synchronous acquisition. At the same time, a commercial computer can be equipped with a tissue component content distribution calculation device. This makes it convenient for clinical staff to use.

[0030] (2) Compared with the method of using spatial dimension or wavelength dimension time-series scanning scheme to collect uncompressed hyperspectral information and then perform component analysis, the present invention realizes the compression encoding of spectral information by N-times illumination spectrum wide-band modulation, thereby reducing the number and time of image acquisition, and is more suitable for dynamic scenes.

[0031] (3) The present invention can quantitatively output content distribution images of multiple main components of biological tissues, and the spatial resolution is comparable to that of classic white light endoscopic imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of an example of an illumination modulation hard mirror;

[0033] Figure 2 Figure 1 is the structure diagram of the filter;

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

[0035] Figure 4 This is the result diagram of illumination channel spectrum calibration;

[0036] Figure 5 This is the result diagram of the detection channel spectral response calibration;

[0037] Figure 6 The scattering reflectance spectrum of mouse liver calculated for Monte Carlo modeling at two groups of blood oxygen to blood volume ratios;

[0038] Figure 7 The experimental results are shown in Figure 1, where (a) is a schematic diagram of the blood volume ratio output when observing the liver of a living mouse, and (b) is a diagram of the blood oxygen saturation results output when observing the liver of a living 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 DESCRIPTION

[0040] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0041] The terms used in this invention are for the purpose of describing specific embodiments only and are not intended to limit the invention. The singular forms "a," "the," and "the" used in this invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or 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 merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0043] The present invention will be described in detail below with reference to the accompanying drawings. Unless there is any conflict, the features of the following embodiments and implementations may be combined with each other.

[0044] An embodiment of the present invention provides a spectral modulation endoscopy method for rapidly displaying body composition distribution, comprising the following steps:

[0045] (1) The target components are selected as oxygenated hemoglobin and deoxygenated hemoglobin, both of which have high absorption rates in the visible light band. Therefore, the illumination coding band is set to 420 nm to 750 nm.

[0046] (2) The illumination light of the endoscope is spectrally modulated between 420 nm and 750 nm, with a modulation number of 3. Titanium oxide and silicon oxide thin film materials are used to realize filters with different transmittance functions between 420 nm and 750 nm. By designing the thickness of titanium oxide and silicon oxide films, the illumination light modulated by the three filters is made so that the correlation coefficient between the two filters is less than 0.5.

[0047] (3) Calibration of the illumination spectrum curve: Use a spectrometer to calibrate the illumination spectrum curve I of the endoscope under three illumination modulation states. i (λ) is calibrated, where λ represents the wavelength, ranging from 420 nm to 750 nm; the subscript i represents the i-th illumination modulation state, ranging from 1 to 3. The detection channel is calibrated by installing a black and white / color camera on the endoscope, and a bandpass filter from 420 nm to 750 nm is installed in front of the camera target. The endoscope is used to image a single-wavelength standard sample to obtain the response rate of the detection channel at that wavelength. The wavelength of the standard sample is scanned within the range of 420 nm to 750 nm to obtain the camera's response spectrum curve D j (λ), λ represents the wavelength, and its value range is 420 nm to 750 nm; when the camera is a grayscale camera, j is 1; when the endoscope camera is a color camera, the three color channels are calibrated separately, and the value of j ranges from 1 to 3.

[0048] (4) The calibrated endoscope is used for biological tissue imaging, and one image is collected under each illumination modulation state, for a total of three images.

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

[0050] The specific implementation of developing the deep learning algorithm in step (5) is discussed as follows:

[0051] (5.1) The oxygenated and deoxygenated hemoglobin contents in biological tissues are set to a series of preset values ​​within a reasonable range. The scattering spectrum and total absorption spectrum of non-target components are set based on the type of biological tissue. Monte Carlo modeling of photon propagation in biological tissues is performed within the range of 420 nm to 750 nm. The scattering reflectance spectrum curve S(λ, α1, α2) is established within the range of 420 nm to 750 nm, where λ represents the wavelength and α1 and α2 represent the oxygenated and deoxygenated hemoglobin contents, respectively.

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

[0053] (5.3) Construct a deep learning neural network. When using a grayscale camera, the network inputs three values; when using a color camera, the network inputs nine values. The network outputs two values, corresponding to the oxygenated and deoxygenated hemoglobin contents. Train the network based on the dataset established in (5.2).

[0054] The method for calculating the distribution of the target component content in the biological tissue by calculating the collected images in step (5) is to extract all the intensity values ​​(3 or 9) corresponding to the same pixel point in the three grayscale or color images collected in step (4) and input them into the neural network trained in (5.3) to obtain two output values ​​corresponding to the oxygenated and deoxygenated hemoglobin content at the pixel point. The above calculation method is applied to all pixels in the collected images to obtain the oxygenated and deoxygenated hemoglobin content.

[0055] like Figure 1As shown, an embodiment of the present invention provides a spectral modulation endoscopy device for rapidly displaying the distribution of body components, which comprises: an electrically controlled wide-spectrum adjustable lighting device, an illumination 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 electrically controlled wide-spectrum adjustable lighting device comprises a white light LED 1, a collimating lens 2, a fast electrically controlled filter wheel 3 and three filters 4, wherein the white light LED 1 is connected to the collimating lens 2, which is connected to the fast electrically controlled filter wheel 3; the filters 4 are placed on the fast electrically controlled filter wheel 3; the power of the white light LED 1 is greater than 90W, and the filters 4 are composed of five layers of titanium oxide and silicon oxide films, such as Figure 2 As shown. The transmittance curve of the three filters 4 is as follows Figure 3 As shown in Figure (a), the pairwise correlation coefficient matrix is ​​as follows Figure 3 As shown in Figure (b), all correlation coefficients are less than 0.5.

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

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

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

[0060] When the three filters 4 modulate the white light LED 1 respectively, the spectrum curve at the output end of the illumination channel of the endoscope body 7 is as follows: Figure 4 The total detection response spectrum curve corresponding to the superposition of the detection light path and the color camera 8 is shown as follows. Figure 5 shown.

[0061] Monte Carlo simulation was used to simulate a series of scattered reflection spectra under blood oxygen and blood volume ratios, such as Figure 6As shown. Let α1 and α2 represent the content of oxygenated and deoxygenated hemoglobin respectively (in mol / L), then blood oxygen saturation = α1 / (α1+α2), blood volume ratio = (α1+α2)*MW / C, where MW is the molar mass of hemoglobin, in g / mol; C is the mass of hemoglobin per unit volume of pure blood, with an average of 150g / L. For each set of scattered reflectance spectra, compare it with the spectral curve I at the output end of the illumination channel 7 of the endoscope body. i (λ)(i=1,2,3), detection response spectrum curve D j (λ) (j=1, 2, 3) is multiplied and integrated along the wavelength dimension to obtain nine values. A deep learning neural network is constructed to map these nine values ​​to blood oxygen and blood volume ratio values. This developed deep learning neural network is integrated into the tissue component distribution calculation device within computer 11.

[0062] The control signal from the synchronization control device in computer 11 is transmitted via synchronization signal transmission cable 10 to the electronically controlled filter wheel 3 and color camera 8, causing the three filters 4 to sequentially modulate the output light of the white light LED 1. When each filter 4 is in the modulation position, the color camera 8 simultaneously captures an image, for a total of three color images. These three color images are then transmitted via image transmission cable 9 to the tissue component content distribution calculation device in computer 11 for processing. The calculated blood oxygen to blood volume ratio distribution map is displayed on display 12.

[0063] Application Examples

[0064] Will Figure 1 A spectral modulation endoscopy device for rapidly displaying body composition distribution is shown and used to observe the liver of a living mouse. Figure 7 Figures (a) and (b) show the blood volume ratio and blood oxygen saturation distribution of mouse liver, respectively.

[0065] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only.

[0066] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A spectral modulation endoscopy method for rapidly displaying body composition distribution, characterized in that: include: Determining a wavelength range for spectral modulation based on absorption spectrum curves of the M target components, such that the wavelength range includes a wavelength band in which the target components have a high absorptivity; the high absorptivity specifically means that the absorptivity of the target components to light is not less than 1% of the total absorptivity of all components in the sample; Within the wavelength range, the illumination light of the endoscope is subjected to spectral broad-band modulation, wherein after normalization of the modulated illumination light according to the maximum intensity within the wavelength range, the wavelength range in which the spectral intensity is greater than 50% exceeds 50 nanometers, and the number of modulations is N, where N>1; Calibrate the full-field illumination spectrum curve of the endoscope under N illumination modulation states; calibrate the detection response spectrum curve of the endoscope, when the endoscope camera is a grayscale camera, perform a single calibration, when the endoscope camera is a color camera, calibrate the three color channels separately; The calibrated endoscope is used for in vivo biological tissue imaging, and one image or several images are collected under each illumination modulation state and averaged; Calculating images acquired under N illumination modulation states based on a deep learning algorithm to obtain the content distribution of M target components in biological tissues, including: configuring a series of preset values ​​for the content of the M target components within an operable range, and setting their scattering spectra and total absorption spectra of non-target components based on the type of biological tissue; performing Monte Carlo modeling on photon propagation in the biological tissue to obtain a scattering reflection spectrum curve within the wavelength range; The calibrated full-field illumination spectrum curve, detection response spectrum curve, and scattered reflection spectrum curve are multiplied and integrated in the wavelength dimension to obtain the detection intensity values ​​corresponding to the preset content of M components. A data set of the corresponding relationship between the content of M components and the intensity values ​​is constructed. When a grayscale camera is used, the number of intensity values ​​is N, and when a color camera is used, the number of intensity values ​​is 3N. Constructing a deep learning neural network, where the number of network input values ​​is N when a grayscale camera is used; the number of network input values ​​is 3N when a color camera is used; the number of network output values ​​is M, corresponding to the contents of M components; and training the deep learning neural network based on the data set; The intensity value corresponding to the same pixel in the images collected under N illumination modulation states is extracted and input into the trained deep learning neural network to obtain M output values, each of which corresponds to the content of M components at the pixel; this process is repeated for all pixels in each image to obtain a content distribution image of the M components.

2. The method according to claim 1, characterized in that The wide-band spectral modulation is specifically as follows: after the modulated illumination light is normalized according to the maximum intensity within the band, the wavelength range where the spectral intensity is greater than 50% exceeds 50 nanometers, so that the mutual correlation coefficient between the modulated illumination lights is less than 0.

5.

3. The method according to claim 1, characterized in that The wavelength calibration range of the illumination spectrum curve and the detection response spectrum curve covers the band where the product of the two in the wavelength dimension and normalization is greater than 1%.

4. A spectral modulation endoscopy device for rapidly displaying body composition distribution, used to implement the method according to any one of claims 1 to 3, characterized in that: include: An electrically controlled wide-spectrum adjustable lighting device, a lighting coupling device, an endoscope body, an image acquisition device, a synchronization 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.

5. The device according to claim 4, 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.

6. The device according to claim 4, 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.

7. The device according to claim 4, 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.

8. The device according to claim 4, characterized in that The image acquisition device is a grayscale camera or a color camera.

9. The device according to claim 4, 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