A tissue imaging method and system

Abstract

The present invention relates to the field of fluorescence analysis technology, and in particular to a tissue imaging method and system, comprising: using a fluorescence excitation unit to irradiate a single excitation light to a target tissue to form a light spot on the target tissue; the target tissue includes tumor tissue and non-tumor tissue; a signal collection unit collects a fluorescence signal in the 1100nm~1700nm band filtered by a filter group and sends it to a computer processing unit; the fluorescence signal is the autofluorescence generated by the target tissue after being irradiated with the light spot; the computer processing unit generates a first fluorescence image based on the received fluorescence signal, and obtains a second fluorescence image indicating a tumor tissue area and a non-tumor tissue area based on the signal intensity of the fluorescence signal at each pixel position on the first fluorescence image. Its beneficial effect is that the signal is not easily interfered by other factors, and the generated image has a high signal-to-noise ratio, and has strong specificity and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of fluorescence analysis technology, and in particular to a tissue imaging method and system. Background Art

[0002] Malignant tumors (cancer) have become one of the main factors affecting human health and life expectancy, and their clinical treatment currently relies heavily on surgical resection. However, due to the lack of real-time, highly accurate and specific intraoperative imaging technology, surgeons still face huge challenges in accurately identifying the complete tumor boundary during surgery. Studies have shown that the different optical properties of malignant tumors and normal tissues, such as elastic scattering, stimulated Raman scattering, light reflection and tissue autofluorescence, can be used to identify malignant tumor tissues. Among them, tissue autofluorescence imaging technology can achieve real-time imaging of tumors by identifying the differences in endogenous fluorescent substances in diseased tissues and normal tissues with the help of endoscopes or wide-field imaging systems. However, the existing tissue autofluorescence imaging technology system has low specificity for the identification of malignant tumors, and its signal is also easily interfered by endogenous pigments such as hemoglobin.

[0003] Therefore, there is an urgent need for a tissue image processing technology that can clearly display non-tumor tissue and tumor tissue images with a high signal-to-noise ratio and good specificity. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a tissue imaging method and system, which solves the technical problems that the fluorescence signal in tissue cell autofluorescence has low specificity and is easily interfered by other endogenous pigments, as well as the technical problems that the signal-to-noise ratio is low and is easily interfered by background noise.

[0006] (II) Technical solution

[0007] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0008] In a first aspect, an embodiment of the present invention provides a tissue imaging method, comprising:

[0009] S01. The fluorescence excitation unit uniformly irradiates the target tissue with excitation light to form a light spot on the target tissue, wherein the light spot covers the target tissue; the target tissue includes tumor tissue and non-tumor tissue; the excitation light is a single excitation light with any wavelength of 740nm~780nm or 800~810nm;

[0010] S02, the signal collection unit collects the fluorescence signal in the 1100nm~1700nm band after being filtered by the filter group and sends it to the computer processing unit; the fluorescence signal is the autofluorescence generated by the target tissue after being irradiated by the light spot;

[0011] S03. The computer processing unit generates a first fluorescent image according to the received fluorescent signal, and obtains a second fluorescent image indicating the tumor tissue area and the non-tumor tissue area based on the signal intensity of the fluorescent signal at each pixel position on the first fluorescent image; in the second fluorescent image, the ratio of the signal intensity of the fluorescent signal at any pixel position in the non-tumor tissue area to the signal intensity of the fluorescent signal at any pixel position in the tumor tissue area is greater than 1.

[0012] Optionally, the power density of the excitation light is in the range of 5 mW / cm 2 ~25mW / cm 2 ;

[0013] The size range of the light spot is 100cm 2 ~300cm 2 .

[0014] Optionally, the S01 further includes:

[0015] The exposure time of the excitation light is adjusted to a range of 200 ms to 500 ms.

[0016] Optionally, the filter set includes a 900nm long pass filter, a 950nm long pass filter, a 1000nm long pass filter and a 1100nm long pass filter;

[0017] In the second fluorescent image, a ratio of a signal intensity of a fluorescent signal at any pixel position in the non-tumor tissue region to a signal intensity of a fluorescent signal at any pixel position in the tumor tissue region is greater than 2.

[0018] Optionally, the wavelength of the excitation light is 760nm, and the power density range is 5mW / cm 2 ~15mW / cm 2 ;

[0019] The size range of the light spot is 200cm 2 ~300cm 2 ;

[0020] In the second fluorescent image, the ratio of the signal intensity of the fluorescent signal at any pixel position in the non-tumor tissue area to the signal intensity of the fluorescent signal at any pixel position in the tumor tissue area is 4-10.

[0021] Optionally, the S03 includes:

[0022] S03-1, the computer processing unit generates a first fluorescent image according to the received fluorescent signal;

[0023] S03-2, the computer processing unit compares the signal intensity of the fluorescence signal at each pixel position in the first fluorescence image with the first threshold value, and obtains a process image indicating the tumor tissue area, the non-tumor tissue area and the background area based on the comparison result;

[0024] S03-3. The computer processing unit performs signal enhancement on the tumor tissue area and the non-tumor tissue area in the process image, and performs signal elimination on the background area, to obtain a second fluorescent image in which the ratio of the signal intensity of the fluorescent signal at any pixel point position in the non-tumor tissue area to the signal intensity of the fluorescent signal at any pixel point position in the tumor tissue area is 6 to 10.

[0025] Optionally, the S03-2 includes:

[0026] The first fluorescent image is gray-scaled to obtain a gray-scale matrix of the first fluorescent image; and a process image indicating a tumor tissue area, a non-tumor tissue area and a background area is obtained according to the gray-scale matrix and a first threshold; the gray-scale matrix is ​​a matrix composed of gray-scale values ​​obtained after gray-scale processing of each pixel position in the first fluorescent image.

[0027] Optionally, the first threshold is obtained according to the grayscale matrix and formula 1;

[0028] The formula 1 is:

[0029] ;

[0030] Among them, I1~I n is the gray value of all pixel positions in the gray matrix after the gray values ​​are arranged from small to large, I1 is the gray value of the pixel position with the minimum gray value in the gray matrix, and I n is the gray value of the pixel with the maximum gray value in the gray matrix, I i I is the larger gray value of the two adjacent gray values ​​with the largest difference. i-1 It is the smaller gray value between the two adjacent gray values ​​with the largest difference.

[0031] Optionally, the non-tumor tissue includes normal tissue and / or non-malignant diseased tissue.

[0032] In a second aspect, an embodiment of the present invention provides a tissue imaging system, comprising:

[0033] A fluorescence excitation unit, used for emitting excitation light to the target tissue to form a light spot on the target tissue;

[0034] A filter set, used for filtering the fluorescence signal, wherein the fluorescence signal is the autofluorescence generated by lipofuscin in the target tissue after being irradiated by the light spot;

[0035] The signal collection unit is used to collect the filtered fluorescence signal and send it to the computer processing unit;

[0036] A computer processing unit, configured to obtain a second fluorescent image indicating a tumor tissue region and a non-tumor tissue region according to the received fluorescent signal;

[0037] Wherein, the angle between the optical path of the signal collection unit receiving the fluorescence signal and the optical path of the single excitation light generated by the fluorescence excitation unit is between 0 and 90 degrees, and the computer processing unit is in communication connection with the signal collection unit;

[0038] When the fluorescence excitation unit emits excitation light to the target tissue, the system performs the above-mentioned tissue imaging method.

[0039] (III) Beneficial effects

[0040] The beneficial effects of the present invention are as follows: a tissue imaging method of the present invention sets the excitation light with a wavelength range of 740nm-780nm or 800nm-810nm, and sets the power density of the excitation light to 5mW / cm 2 ~25mW / cm 2 , to stimulate the target tissue to produce autofluorescence and collect the autofluorescence in the second near-infrared zone. Compared with the existing technology, its signal is not easily interfered by other endogenous pigments, while reducing the interference of background signals. It has a higher signal-to-noise ratio, and the generated image has obvious differences in signals between non-tumor tissue and tumor tissue, with obvious boundaries, so it has strong specificity and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of a tissue imaging method according to Example 1 of the present invention;

[0042] Figure 2 From left to right are the bright field image of liver tissue No. 1 according to Example 3 of the present invention, the fluorescence image of liver tissue No. 1 in the visible light region after being excited by excitation light with a wavelength of 475 nm, the fluorescence image of liver tissue No. 1 in the visible light region after being excited by excitation light with a wavelength of 630 nm, the fluorescence image of liver tissue No. 1 in the visible light region after being excited by excitation light with a wavelength of 760 nm, and the pathological H&E section image corresponding to liver tissue No. 1;

[0043] Figure 3From left to right are the bright field image of liver tissue No. 2 according to Example 3 of the present invention, the fluorescence image of liver tissue No. 2 in the visible light region after being excited by excitation light with a wavelength of 475 nm, the fluorescence image of liver tissue No. 2 in the visible light region after being excited by excitation light with a wavelength of 630 nm, the fluorescence image of liver tissue No. 2 in the visible light region after being excited by excitation light with a wavelength of 760 nm, and the pathological H&E section image corresponding to liver tissue No. 2;

[0044] Figure 4 From left to right are the bright field image of liver tissue No. 3 according to Example 3 of the present invention, the fluorescence image of liver tissue No. 3 in the visible light region after being excited by excitation light with a wavelength of 475 nm, the fluorescence image of liver tissue No. 3 in the visible light region after being excited by excitation light with a wavelength of 630 nm, the fluorescence image of liver tissue No. 3 in the visible light region after being excited by excitation light with a wavelength of 760 nm, and the pathological H&E section image corresponding to liver tissue No. 3;

[0045] Figure 5 The autofluorescence quantitative images of tumor tissues and adjacent normal tissues in 9 human liver tissues according to Example 3 of the present invention in the visible light region;

[0046] Figure 6 The quantitative image of autofluorescence of tumor tissue and adjacent normal tissue in the near infrared region 1 in 9 cases of human liver tissue according to Example 3 of the present invention;

[0047] Figure 7 The quantitative image of autofluorescence of tumor tissue and adjacent normal tissue in the near-infrared region 2 in 9 cases of human liver tissue according to Example 3 of the present invention;

[0048] Figure 8 is a graph of the ratio of the autofluorescence intensities of normal tissue and tumor tissue in the visible light region, near infrared region 1 and near infrared region 2 of liver tissue specimens corresponding to 9 patients according to Example 3 of the present invention;

[0049] Fig. 9 is a curve diagram showing the change in the ratio of the autofluorescence intensity of normal tissue and tumor tissue in the near-infrared region 2 under excitation light of different wavelengths of liver tissue according to Example 4 of the present invention;

[0050] Fig.10 From left to right are the bright field image of a liver tissue according to Example 4 of the present invention and the near-infrared two-zone autofluorescence images under 700nm, 760nm, 850nm and 900nm excitation light;

[0051] Fig.11 From left to right are the bright field image of a liver tissue according to Example 4 of the present invention and the near-infrared two-zone autofluorescence images under 700nm, 760nm, 850nm and 900nm excitation light;

[0052] Fig.12 is a curve diagram showing the change in the ratio of the autofluorescence intensity of normal tissue and tumor tissue in the near-infrared region 2 of the liver tissue under different optical filters according to Example 4 of the present invention;

[0053] Fig.13 From left to right are the bright field image of a liver tissue according to Example 4 of the present invention and the autofluorescence image obtained by collecting 850-1700nm fluorescence signal, 1100-1700nm fluorescence signal, and 1300-1700nm fluorescence signal respectively under 760nm excitation light excitation;

[0054] Fig.14 From left to right are the bright field image of a liver tissue according to Example 4 of the present invention and the autofluorescence image obtained by collecting 850-1700nm fluorescence signal, 1100-1700nm fluorescence signal, and 1300-1700nm fluorescence signal respectively under 760nm excitation light excitation;

[0055] Fig.15 is a curve diagram showing the change in the ratio of the autofluorescence intensity of normal tissue and tumor tissue in the near-infrared region 2 of human liver tissue under different excitation light power densities according to Example 4 of the present invention;

[0056] Fig.16 is a curve diagram showing a change in signal-to-noise ratio of human liver tissue under different excitation light power densities according to Example 4 of the present invention;

[0057] Fig.17 is a curve diagram showing the change of the autofluorescence intensity ratio of normal tissue and tumor tissue of human liver tissue in near-infrared region 2 versus exposure time according to Example 4 of the present invention;

[0058] Fig.18 is a curve diagram showing changes in the signal-to-noise ratio of normal tissue and tumor tissue of human liver tissue in the second near-infrared region versus exposure time according to Example 4 of the present invention;

[0059] Fig.19 The near-infrared two-zone autofluorescence intensity distribution diagram of non-cirrhotic normal liver tissue and well-differentiated hepatocellular carcinoma tissue, moderately differentiated hepatocellular carcinoma tissue, and poorly differentiated hepatocellular carcinoma tissue in 12 specimens according to Example 5 of the present invention;

[0060] Fig. 20 From left to right are respectively a bright field view of a typical liver tissue specimen resected from a liver cirrhosis patient according to Example 5 of the present invention, a near-infrared zone II autofluorescence view, and a pathological H&E section view of the liver tissue of the liver cirrhosis patient;

[0061] Fig.21The near-infrared two-zone autofluorescence intensity distribution diagram of cirrhotic liver tissue and well-differentiated hepatocellular carcinoma tissue, moderately differentiated hepatocellular carcinoma tissue, and poorly differentiated hepatocellular carcinoma tissue in 18 specimens according to Example 5 of the present invention;

[0062] Fig. 22 From left to right, they are respectively microscopic fluorescence images of a normal liver tissue slice in the visible light region, a normal liver tissue slice in the near infrared region 1, and a normal liver tissue slice in the near infrared region 2 according to Example 6 of the present invention;

[0063] Fig.23 From left to right, they are respectively microscopic fluorescence images of a tumor tissue slice in the visible light region, a tumor tissue slice in the near infrared region 1, and a tumor tissue slice in the near infrared region 2 according to Example 6 of the present invention;

[0064] Fig.24 From left to right are respectively a bright field image of a patient's liver tissue during a live operation according to Example 7 of the present invention and a view of a patient's liver tissue during a live operation under near infrared region II;

[0065] Fig.25 From left to right are respectively a bright field image of the ex vivo liver tissue according to Example 7 of the present invention and a view of the ex vivo liver tissue under the second near-infrared region;

[0066] Fig.26 for Fig.24 and Fig.25 Figure 2 shows the ratio of the autofluorescence intensity of normal tissue to tumor tissue.

[0067] Fig. 27 for Fig.24 and Fig.25 H&E sections of liver tissue pathology of a Chinese patient. DETAILED DESCRIPTION

[0068] In order to better explain the present invention and facilitate understanding, the relevant technical terms are explained below.

[0069] NCR: ratio of fluorescence intensity of adjacent normal tissue to autofluorescence intensity; TNR: ratio of fluorescence intensity of tumor tissue to normal tissue;

[0070] p: represents p-value in statistics, that is, the probability of observing a sample statistic value or more extreme under the condition that the null hypothesis is true; J: is Youden index, which is generally used to evaluate the performance of binary classifiers. Its formula is J=true positive rate+true negative rate-1;

[0071] ROC: operating characteristic, which can be plotted by sensitivity and specificity using Graphpad software or the program that comes with Origin software;

[0072] AUC: Area under the curve, usually used to measure prediction accuracy;

[0073] Visible light region: The spectral region with wavelengths between approximately 400nm and 700nm that can be perceived by ordinary people's glasses; near-infrared region 1 refers to the spectral region with wavelengths between approximately 700nm and 900nm; near-infrared region 2 refers to the spectral region with wavelengths between approximately 1000nm and 1700nm.

[0074] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0075] Example 1

[0076] This embodiment provides a tissue image processing method based on analyzing the tailing signal of the autofluorescence of endogenous lipofuscin in the near-infrared second band, such as Figure 1 As shown, including:

[0077] S01. The fluorescence excitation unit evenly irradiates the target tissue with a single excitation light of 760 nm. The single excitation light is an excitation light of a specific wavelength, forming a 200 cm 2 ~300cm 2 The light spot completely covers the target tissue; the target tissue includes tumor tissue and non-tumor tissue, and the power density range of a single excitation light is 5mW / cm 2 ~25mW / cm 2 , preferably 5mW / cm 2 ~15mW / cm 2 . Adjust the exposure time of single excitation light to 200ms.

[0078] S02. The signal collection unit collects the fluorescence signal in the wavelength range of 1100nm~1700nm after being filtered by the filter group and sends it to the computer processing unit; the fluorescence signal is the autofluorescence generated by the lipofuscin in the target tissue after being irradiated by the light spot formed by a single laser.

[0079] S03-1, the computer processing unit generates a first fluorescent image according to the received fluorescent signal;

[0080] S03-2. Grayscale the first fluorescent image to obtain a grayscale matrix of the first fluorescent image; and obtain a process image indicating the tumor tissue area, non-tumor tissue area and background area based on the grayscale matrix and a first threshold; the grayscale matrix is ​​a matrix composed of grayscale values ​​obtained after grayscale processing of each pixel position in the first fluorescent image.

[0081] The first threshold is obtained according to the grayscale matrix and formula 1;

[0082] The formula 1 is:

[0083] ;

[0084] Among them, I1~I n is the gray value of all pixel positions in the gray matrix after the gray values ​​are arranged from small to large, I1 is the gray value of the pixel position with the minimum gray value in the gray matrix, and I n is the gray value of the pixel with the maximum gray value in the gray matrix, I i I is the larger gray value of the two adjacent gray values ​​with the largest difference. i-1 It is the smaller gray value between the two adjacent gray values ​​with the largest difference.

[0085] S03-3, the computer processing unit performs signal enhancement on the tumor tissue area and the non-tumor tissue area in the process image, and performs signal elimination on the background area, so as to obtain a ratio of the signal intensity of the fluorescence signal at any pixel position of the non-tumor tissue area to the signal intensity of the fluorescence signal at any pixel position of the tumor tissue area that is greater than 1; preferably, the ratio of the signal intensity of the fluorescence signal at any pixel position of the non-tumor tissue area to the signal intensity of the fluorescence signal at any pixel position of the tumor tissue area is greater than 2; preferably, the ratio of the signal intensity of the fluorescence signal at any pixel position of the non-tumor tissue area to the signal intensity of the fluorescence signal at any pixel position of the tumor tissue area is 4-10; preferably, the ratio of the signal intensity of the fluorescence signal at any pixel position of the non-tumor tissue area to the signal intensity of the fluorescence signal at any pixel position of the tumor tissue area is 6-10.

[0086] The embodiment of the present invention provides a tissue image processing method based on the near-infrared second zone autofluorescence signal to reduce the interference of endogenous pigments such as blood and bile on the tissue autofluorescence signal, thereby improving the signal-to-noise ratio, sensitivity and specificity of tissue imaging.

[0087] Example 2

[0088] This embodiment provides a tissue image processing system, which is based on analyzing the tailing signal of the autofluorescence of endogenous lipofuscin in the near-infrared band, and includes:

[0089] A fluorescence excitation unit is used to emit a single excitation light of any wavelength in the range of 740nm~780nm or 800nm~810nm to form a light spot on the target tissue, and the light spot completely covers the target tissue; the light beam generated by the unit is irradiated on the biological tissue to excite the endogenous lipofuscin of the tissue to produce autofluorescence;

[0090] The fluorescence excitation unit comprises: an excitation light source, a coupler, an optical fiber and a beam expansion module; wherein the excitation light source is used to generate a single excitation light of any wavelength in the range of 740nm~780nm or 800nm~810nm, preferably 760nm; the coupler is used to guide the single excitation light emitted by the excitation light source into the optical fiber; the optical fiber is used to focus the single excitation light onto the target tissue; the beam expansion module forms a focused light spot on the target tissue with the single excitation light focused by the optical fiber; the excitation light source is one of a krypton ion laser, a fixed laser, a semiconductor laser, a gas laser, an optical fiber laser and a dye laser; the working power density of the excitation light source is 5mW / cm 2 ~25mW / cm 2 , preferably 5 mW / cm 2 ~15mW / cm 2 ; The beam expansion module is a plano-concave lens that transmits more than 90% of near-infrared light; the angle between the light beam path generated by the fluorescence excitation unit and the path of the fluorescence emitted by endogenous lipofuscin entering the signal collection unit is between 0 and 90°, preferably, the angle is between 0 and 45°.

[0091] A filter set is used to filter the fluorescence signal, the wavelength range of the fluorescence signal passing through the filter set is the wavelength of the near-infrared second band, and the fluorescence signal is the autofluorescence generated by the target tissue after being irradiated with a single excitation light; the filter set can pass fluorescence in the band range of 850nm~1700nm, preferably long-pass filters of 1100nm, 1000nm, 950nm, and 900nm.

[0092] A signal collection unit, the signal collection unit comprising: an imaging objective lens, a signal detection subunit and a signal transmission unit; the imaging objective lens is used to collect the fluorescent signal filtered by the filter group; the signal detection subunit is used to convert the fluorescent signal collected by the imaging objective lens into an electrical signal; the signal transmission unit is used to send the electrical signal to the computer processing unit.

[0093] The signal detection subunit includes an InGaAs detector, and the effective wavelength range of the detection is 850nm~1700nm.

[0094] The computer processing unit amplifies the received electrical signal and converts it into a digital signal; preprocesses the digital signal and reconstructs the preprocessed digital signal into a first fluorescent image; inputs the first fluorescent image into a pre-set network model, the network model is a U-type network model, the U-type network model adopts a U-Net network architecture, and introduces a gated mechanism GFF module to output a probability matrix of each pixel belonging to the tissue and the background; inputs the first fluorescent image into the pre-set network model, and when each probability value of a pixel position is output, the loss function of the tissue is calculated by formula 2, and the formula 2 is:

[0095] ;

[0096] Among them, y j is the true value of the jth pixel in the target tissue with a total number of N pixels, is the estimated value of the jth pixel in the target tissue with a total number of pixels N, and c is e -3 , w1 and w2 are both pre-set weights, and w1+w2=1, D(x) is the distance between the true value of the pixel and the probability value of the pixel, and P(x) is the probability value output after the pixel prediction value is processed by the pre-set softmax function.

[0097] The computer processing unit adjusts the parameters in the U-shaped network model in real time according to the loss function.

[0098] Whether a single pixel is a tissue is determined by formula three, and the formula three is:

[0099] ;

[0100] Among them, Z is the result of judging whether a single pixel is tissue, 1 is marked as tissue, 0 is marked as background, and z is the probability value of whether a single pixel is tissue in the probability matrix.

[0101] The tissue is segmented from the background based on the tissue type at each pixel.

[0102] The segmented first fluorescent image is grayed out by formula 4 to generate a gray matrix for each pixel; the formula 4 is:

[0103] ;

[0104] Among them, H x represents the original grayscale value at the x pixel position, H is the grayscale value at the x pixel position after grayscale conversion, and H max is the gray value of the 98% position of each pixel position in the first fluorescent image after gray sorting, H minIt is the gray value of the 2% position of each pixel position in the first fluorescent image after gray sorting.

[0105] The tissue type is determined according to Formula 5 and the grayscale value of each pixel position in the grayscale matrix in the first fluorescent image. The Formula 5 is:

[0106] ;

[0107] Among them, H is the grayscale value of the x pixel position after grayscale conversion, A is the abnormal tissue type, B is the normal tissue type, and L1 is the tissue type judgment threshold.

[0108] Calculating the tissue type determination threshold L1 according to the grayscale value of each pixel position in the grayscale matrix of the first fluorescent image includes:

[0109] The computer processing unit sorts the grayscale values ​​of each pixel position in the grayscale matrix of the first fluorescent image from small to large, and represents them as I1~I n , I1 is the gray value of the pixel with the minimum gray value in the gray matrix, I n It is the gray value of the pixel with the maximum gray value in the gray matrix.

[0110] The computer processing unit calculates the tissue type determination threshold L1 according to a preset formula 1, wherein the formula 1 is:

[0111] ;

[0112] Among them, L max I is the difference between the adjacent maximum grayscale values ​​of each pixel position in the grayscale matrix after the grayscale values ​​are sorted from small to large. i I is the larger gray value of the two adjacent gray values ​​with the largest difference. i-1 It is the smaller gray value between the two adjacent gray values ​​with the largest difference.

[0113] The computer processing unit adds a label to each pixel in the first fluorescent image according to the grayscale matrix, the probability matrix and the tissue type of each pixel position to generate a process image. The label includes whether the pixel position belongs to tissue, if it is tissue, the tissue type of the pixel position and the grayscale value of the pixel position.

[0114] According to the label of each pixel in the process image, the process image is divided into a tumor tissue area, a non-tumor tissue area and a background area, and signal enhancement processing is performed on the tumor tissue area and the non-tumor tissue area according to the grayscale value of each pixel, and signal elimination processing is performed on the background area to generate a second fluorescent image with a dividing line for display.

[0115] The present embodiment provides an infrared tissue autofluorescence analysis system, which uses a single excitation light in the range of 740nm~780nm or 800nm~810nm to evenly irradiate the in vitro or in vivo exposed liver tissue, with a laser power density of 5mW / cm 2 ~25mW / cm 2 , stimulate the endogenous lipofuscin of the tissue to produce autofluorescence, and collect fluorescence in the 1100nm~1700nm band, and finally generate a fluorescent image with a clear boundary between cancer tissue and normal tissue. The generated image has bright normal tissue signals and weak tumor tissue signals. This difference in normal tissue and tumor tissue signals can outline the tumor boundary and provide a high signal-to-noise ratio. In addition, because lipofuscin is specifically reduced in tumor tissue and its signal is not easily interfered by other factors, the system has high specificity and accuracy when used to detect tumors.

[0116] Example 3

[0117] This embodiment provides a tissue imaging system, including:

[0118] A fluorescence excitation unit, used for emitting a single excitation light with a wavelength in the range of 740nm to 780nm or 800nm ​​to 810nm, to form a light spot on the target tissue, wherein the light spot completely covers the target tissue;

[0119] A filter set, used for filtering the fluorescence signal, wherein the wavelength range of the fluorescence signal passing through the filter set is the wavelength of the near-infrared second band, and the fluorescence signal is the autofluorescence generated by the target tissue after being irradiated by a single excitation light;

[0120] A signal collection unit, used for collecting the fluorescent signal filtered by the filter set in real time, and converting the collected fluorescent signal into an electrical signal;

[0121] The computer processing unit is used to generate a first fluorescent image according to the received electrical signal, and to process the first fluorescent image in real time to generate a fluorescent image with a demarcation line for display.

[0122] Near-infrared zone II autofluorescence imaging was performed on 9 liver tissues removed from patients without liver cirrhosis. Imaging parameters: excitation wavelength 760nm, filter set using 850nm, 1000nm, 1100nm long-pass filter combination, exposure time 200ms.

[0123] Among them, the signal collection unit is communicatively connected to the computer processing unit through the signal transmission unit, and the computer processing unit includes: a signal display unit for displaying processing results and a signal processing unit for signal processing; the computer processing unit is communicatively connected to the background database.

[0124] At the same time, a conventional visible light / near infrared region 1 imaging system was used, and the detector was a silicon charge-coupled detector, to perform autofluorescence imaging of the tissue in the visible light region and near infrared region 1. Among them, the imaging parameters in the visible light region were set as follows: the excitation light used LED light filtered by a 475 ± 20 nm bandpass filter, and the emission filter used a 540 ± 20 nm bandpass filter.

[0125] The imaging parameters in the near-infrared region 1 were set as follows: the excitation light was LED light filtered by a 630 ± 20 nm bandpass filter, and the emission filter used an 800 nm longpass filter.

[0126] The images were processed by Image J software and pseudo-colored, and the autofluorescence of the regions of interest (ROI) in the images of cancer tissue and adjacent liver tissue was quantified. The tissues were fixed with paraformaldehyde, then dehydrated, embedded in paraffin blocks, and sectioned in paraffin. Finally, the sections were stained with hematoxylin-eosin stain.

[0127] Three representative hepatocellular carcinoma tissues were selected, i.e., hepatocellular carcinoma tissues from different individuals, and were respectively recorded as hepatocellular carcinoma tissue No. 1, hepatocellular carcinoma tissue No. 2, and hepatocellular carcinoma tissue No. 3. Figure 2 , Figure 3 and Figure 4 As shown in the figure, in the visible light region, the autofluorescence of hepatocellular carcinoma tissue has large individual differences. Some tissues show stronger autofluorescence than adjacent liver tissue, while some tissues show weaker autofluorescence than adjacent liver tissue, and the fluorescence inside the same cancer tissue is not uniform. In the near-infrared zone 1 and zone 2, the autofluorescence of highly, moderately, and poorly differentiated hepatocellular carcinoma tissues is lower than that of adjacent normal liver tissue, and the fluorescence inside the tissue is relatively uniform. Among them, in the near-infrared zone 2, the difference in autofluorescence between normal tissue and cancer tissue is the greatest, the boundary between the two is the clearest, and it coincides with the boundary obtained by H&E sections.

[0128] Figure 5 , Figure 6 and Figure 7 The quantitative results of autofluorescence in the visible light region, near infrared region 1, and near infrared region 2 of 9 cases of hepatocellular carcinoma tissues and adjacent normal liver tissues are shown. Among them, the average autofluorescence intensity of cancer tissue in the visible light region is about 1.61 times that of normal liver tissue. Since there is a large inter-individual difference in the autofluorescence intensity of cancer tissue in the visible light region, statistical analysis shows that there is no significant difference in the autofluorescence intensity of cancer tissue and normal tissue in the visible light region. In near infrared regions 1 and 2, the average autofluorescence intensity of adjacent normal liver tissue is higher than that of cancer tissue, and the intensity difference reaches 2.19 times (p=0.0039) and 8.98 times (p=0.0039), respectively.

[0129] By analyzing the autofluorescence intensity of paired specimens, the ratio of the autofluorescence intensity of normal tissue and tumor tissue in the corresponding specimen of each patient, i.e., the contrast, was calculated. Figure 8 As shown in the figure, the average contrast provided by near-infrared zone II autofluorescence imaging is 9.24 ± 1.15 (mean ± sem), which is significantly higher than the values ​​provided by visible light (0.90 ± 0.21, p < 0.0001) and near-infrared zone I (2.31 ± 0.37, p < 0.0001) autofluorescence imaging. The contrast of near-infrared zone II autofluorescence imaging of patient specimens is about 7.25 times and 2.47 times higher than that of near-infrared zone I (tumor / normal tissue fluorescence intensity ratio TNR = 1.12) and zone II (TNR = 2.66) fluorescence imaging based on ICG, respectively. In addition, under near-infrared zone II autofluorescence imaging, the NCR of each specimen exceeded the contrast threshold widely accepted by clinical surgeons for easy judgment, which is generally 2.0. In contrast, under visible light autofluorescence imaging, the NCR value of each specimen was lower than 2.0; and under near-infrared autofluorescence imaging, the NCR of nearly half of the specimens was lower than 2.0.

[0130] This embodiment uses lipofuscin as an endogenous marker of tumors, thereby obtaining a highly specific imaging technology for malignant tumors, especially liver cancer. Lipofuscin is a cellular metabolic waste product, usually a mixture of lipids, cross-linked proteins, a small amount of polysaccharides and heavy metals. After a large number of experiments and studies, the inventors of the present application creatively discovered that since lipofuscin is a substance that is difficult to continue to degrade after degradation by lysosomes of cells, it will continue to be enriched in normal cells in the late stage of division, such as hepatocytes, cardiomyocytes, nerve cells, etc., but can be gradually diluted by rapidly dividing cancer cells. Utilizing this characteristic of lipofuscin, the present invention selects lipofuscin as a highly specific marker for malignant tumors, especially liver cancer.

[0131] The inventors of the present application have found through experimental research that by utilizing the wide excitation spectrum of lipofuscin and its residual emission in the near-infrared band, especially the residual emission in the second near-infrared region, it is possible to avoid the interference of most endogenous fluorescent substances in biological tissues, such as NADH, FAD, vitamin A, elastin and collagen, and the interference of endogenous pigments, such as bile pigments and hemoglobin, to obtain a clearer image. Specifically, long-wavelength monochromatic excitation light is used to excite biological tissues, and the excitation light wavelength range is 740~780nm or 800~810nm, and the preferred wavelength is 760 nm. Under the excitation wavelength, most endogenous fluorescent substances cannot be excited to fluoresce. In addition, the absorption of bile pigments and hemoglobin in this wavelength range is significantly weaker than that in the visible light region. Therefore, collecting the near-infrared autofluorescence signal of lipofuscin generated under the excitation wavelength can greatly amplify the difference between normal tissue and tumor tissue, and generate a clear image with bright normal tissue signals and weak tumor tissue signals, that is, dark imaging of tumors. This difference in normal tissue and tumor tissue signals can be used to outline the tumor boundary and provide a high signal-to-noise ratio. In addition, since lipofuscin is specifically reduced in tumor tissues and its signal is not easily interfered by other factors, the method has high specificity and accuracy when used to detect tumors.

[0132] Example 4

[0133] This embodiment provides an infrared tissue autofluorescence analysis system. On the basis of Embodiment 3, the monochromatic laser used in the fluorescence excitation unit is replaced with an ultrafast laser system oscillator with a continuous wavelength. In the range of 660nm-980nm, the excitation wavelength is switched every 5nm, and the light power density is controlled at 5mW / cm by adjusting the size of the excitation spot. 2 Then, five fresh hepatocellular carcinoma tissue specimens from patients were placed on a black plate for near-infrared zone II autofluorescence imaging, with an exposure time of 200 ms. Image J software was used to quantify the autofluorescence of the ROIs in the cancerous tissue and adjacent liver tissue on the image, and the NCR value of each specimen at different excitation wavelengths was calculated.

[0134] Subsequently, 7 freshly resected hepatocellular carcinoma tissue specimens were obtained and imaged using a near-infrared second-zone autofluorescence imaging system. A 760 nm laser was selected as the light source, and the power density and exposure time were set constant. The filters were switched to 850 nm, 900 nm, 950 nm, 1000 nm, 1100 nm, 1200 nm, and 1300 nm long-pass filters, respectively, and the tissues were imaged. Image J software was used to quantify the autofluorescence of the ROI in the cancerous tissue and adjacent liver tissue on the image, and the NCR value of each specimen at different excitation wavelengths was calculated.

[0135] like Fig. 9 The figure shows the curve of the change of the average normal / tumor tissue near-infrared zone 2 autofluorescence intensity ratio of the specimen under different wavelengths of excitation light. The results show that under 760 nm excitation light, the average normal / tumor tissue near-infrared zone 2 autofluorescence intensity ratio of the specimen is the largest, which is 6.63 ± 0.21. Moreover, when the excitation light wavelength exceeds 800 nm, the normal / tumor tissue near-infrared zone 2 autofluorescence intensity ratio gradually decreases. When the excitation light wavelength exceeds 855 nm, the normal / tumor tissue near-infrared zone 2 autofluorescence intensity ratio is less than 2, and the ability of the technology to distinguish tumors is close to disappearing.

[0136] Fig.10 and Fig.11 The two images are representative of two hepatocellular carcinoma tissues, that is, near-infrared zone II autofluorescence images of hepatocellular carcinoma tissues from two different individuals under different excitation light. When the excitation light is 760nm, the contrast between normal liver tissue and cancer tissue in the image is the largest, and the boundary between the two is the clearest. When the excitation light wavelength is switched to 700nm, 850nm or 900nm, the boundary between normal tissue and cancer tissue in the image gradually blurs to the point of being difficult to distinguish.

[0137] Fig.12 The figure shows the curve of the change of the average normal / tumor tissue near-infrared zone 2 autofluorescence intensity ratio of the specimen under different emission filters. The results show that when the 1100 nm long-pass filter is used to collect the autofluorescence signal in the 1100 nm~1700 nm band, the normal / tumor tissue near-infrared zone 2 autofluorescence intensity ratio of the specimen reaches the maximum, and then decreases as the filter wavelength increases. Fig.13 and Fig.14 The two are representative hepatocellular carcinoma tissues, that is, near-infrared second-zone autofluorescence images generated by hepatocellular carcinoma tissues from two different individuals under 760nm excitation light. When the mobile phone uses the 1100nm~1700nm band signal, the contrast between normal liver cells and cancer tissue in the image is the largest, and the two are the clearest. When collecting the 850nm~1700nm band signal, the signal contrast between liver tissue and cancer tissue decreases, and the boundary of cancer tissue in the image becomes blurred. When collecting the 1300nm~1700nm band signal, the autofluorescence signals of liver tissue and cancer tissue are almost gone, and the boundary between the two cannot be distinguished in the image.

[0138] Seven fresh human tumor tissue specimens were placed on a black plate and imaged in an infrared tissue autofluorescence analysis system provided in this embodiment. The imaging parameters were as follows: the excitation wavelength was 760 nm; the power density was 5 mW / cm 2 、10mW / cm 2 、15mW / cm 2 and 25 mW / cm2 The exposure time was set to 50 ms, 100 ms, 200 ms, 300 ms, 500 ms and 1000 ms respectively. The obtained autofluorescence images were processed using Image J software to calculate the ratio of normal / tumor tissue autofluorescence intensity, and the intensity ratio of normal tissue autofluorescence to the autofluorescence at the black plate was calculated as the signal-to-noise ratio.

[0139] Fig.15 The results show that the average normal / tumor tissue near-infrared second zone autofluorescence intensity ratio of the specimen increases with the increase of excitation light power density, and when the power density increases to 15 mW / cm 2 The signal-to-noise ratio of the specimen autofluorescence image also increases with the increase of the excitation light power density. Fig.16 As shown, when the power density increases to 20mW / cm 2 When the power density is too high, the ratio gradually tends to be constant. This is mainly due to the limited signal detected by the camera, and the signal generated by the excessive power density is saturated. Considering 20 mW / cm 2 With 15 mW / cm 2 At the power density of 20 mW / cm, the ratio of near-infrared autofluorescence intensity of normal and tumor tissues is close, while at 20 mW / cm 2 It is easy to generate higher Joule heat under light. Therefore, 15 mW / cm 2 For the optimal excitation light power density.

[0140] Fig.17 and Fig.18 The following are the curves of the ratio of near-infrared zone II autofluorescence intensity of normal / tumor tissue and the signal-to-noise ratio of the specimen with exposure time. The results show that with the increase of exposure time, the ratio of near-infrared zone II autofluorescence intensity of normal / tumor tissue of the specimen gradually decreases. However, the signal-to-noise ratio of the specimen's autofluorescence image shows a trend of first increasing and then decreasing. When the exposure time is in the range of 200ms to 500ms, the signal-to-noise ratio of the image reaches the highest. Finally, 200 ms was selected as the optimal exposure time for tissue near-infrared zone II autofluorescence imaging technology.

[0141] Example 5

[0142] Experiments were performed on tissue specimens of different hepatocellular carcinoma types.

[0143] Twelve specimens of different types of hepatocellular carcinoma, i.e., 1 case of well-differentiated hepatocellular carcinoma, 9 cases of moderately differentiated hepatocellular carcinoma, and 2 cases of poorly differentiated hepatocellular carcinoma, were obtained from 12 patients without liver cirrhosis during surgery and imaged on a black plate in an infrared tissue autofluorescence analysis system provided in this embodiment. The imaging parameters were unified as follows: excitation light wavelength 760nm, power density 15 mW / cm 2 , exposure time 200 ms, emission filter 850 nm, 1000 nm and 1100 nm long pass filter combination. The obtained autofluorescence images were processed using Image J software to calculate the autofluorescence intensity of normal liver tissue and tumor tissue.

[0144] Fig.19 The distribution diagram of near-infrared zone II autofluorescence intensity of normal tissue and well-differentiated, moderately differentiated, and poorly differentiated hepatocellular carcinoma tissues in 12 specimens. The results showed that the near-infrared zone II autofluorescence intensity of well-differentiated, moderately differentiated, and poorly differentiated hepatocellular carcinoma tissues was similar, all relatively weak, and significantly lower than the near-infrared zone II autofluorescence intensity of normal liver tissue adjacent to the cancer (p <0.0001). This significant difference in autofluorescence intensity between tissues can help clinicians find different types of hepatocellular carcinoma lesions in normal liver tissues. It can be seen that this scheme has a good recognition effect on different types of liver cancer cell tissues, such as well-differentiated, moderately differentiated, and poorly differentiated hepatocellular carcinoma tissues.

[0145] Eighteen specimens of different types of hepatocellular carcinoma, i.e., one case of well-differentiated hepatocellular carcinoma, nine cases of moderately differentiated hepatocellular carcinoma, and two cases of poorly differentiated hepatocellular carcinoma, were obtained from 18 patients with liver cirrhosis during surgery and imaged on a black plate in an infrared tissue autofluorescence analysis system provided in this embodiment. The imaging parameters were unified as follows: excitation light wavelength 760nm, power density 15 mW / cm 2 , exposure time 200 ms, emission filter 850 nm, 1000 nm and 1100 nm long pass filter combination. The obtained autofluorescence images were processed using Image J software to calculate the autofluorescence intensity of normal liver tissue and tumor tissue.

[0146] Fig. 20The bright field, near-infrared zone II autofluorescence, and H&E section views of a typical hepatocellular carcinoma specimen resected from a patient with liver cirrhosis. The results showed that, similar to non-cirrhotic liver tissue, the entire cirrhotic liver tissue exhibited uniform and strong near-infrared zone II autofluorescence. The hepatocellular carcinoma tissue showed significantly lower autofluorescence than the adjacent liver tissue. This difference can also clearly outline the boundary of the cancerous tissue, and the boundary is consistent with the results obtained from the H&E sections. Statistical results show that the near-infrared zone II autofluorescence intensity of the cirrhotic liver tissue of all specimens is significantly higher than that of the corresponding well-differentiated, moderately differentiated, and poorly differentiated hepatocellular carcinoma tissues, such as Fig.21 As shown. It can be seen that this solution can also distinguish cirrhotic tissue from liver cancer cell tissues of different differentiation types, such as well-differentiated, moderately differentiated, and poorly differentiated hepatocellular carcinoma tissues. In actual application scenarios, it can also be used for tumor imaging and resection of patients with cirrhosis.

[0147] Similar to the above process, this embodiment can also be used to identify colorectal cancer liver metastasis, intrahepatic bile duct cancer, focal nodular hyperplasia, liver abscess and inflammatory pseudotumor. In typical colorectal cancer liver metastasis, the whole adjacent liver tissue shows uniform and strong autofluorescence, but the autofluorescence in metastatic cancer tissue is so weak that it can be ignored. In typical intrahepatic bile duct cancer, bile duct cancer tissue also shows autofluorescence that is much weaker than that of adjacent normal liver tissue.

[0148] Clinically, some non-malignant lesions, such as focal nodular hyperplasia, liver abscess, and inflammatory pseudotumor, are usually difficult to distinguish from liver cancer through preoperative imaging, biopsy, or intraoperative clinical experience. Under near-infrared zone II autofluorescence imaging technology, focal nodular hyperplasia tissue and inflammatory pseudotumor tissue show near-infrared zone II autofluorescence similar to that of surrounding normal liver tissue. It can be seen that this scheme can also distinguish non-malignant lesion tissues, such as focal nodular hyperplasia, liver abscess, and inflammatory pseudotumor, from liver cancer tissue. In actual clinical application scenarios, it can help doctors make more accurate judgments.

[0149] Example 6

[0150] Paraffin sections of liver cancer tissue specimens were taken, deparaffinized with xylene, and then sealed with coverslips. Under a 40x objective, an upright confocal fluorescence microscope was used to perform microscopic fluorescence imaging of normal liver tissue and cancer tissue. In the same field of view, the excitation wavelengths used for imaging were 475 nm, 630 nm, and 760 nm, respectively. The filters were switched to obtain the tissue's visible light region, near-infrared region 1, and near-infrared region 2 autofluorescence microscopic images. The obtained images were processed using Image J software and the pseudo colors were adjusted.

[0151] Fig. 22 and Fig.23The following are microscopic fluorescence images of normal liver tissue and cancer tissue sections under different imaging channels. The results show that in the visible light region, the autofluorescence of normal liver tissue is diffusely distributed throughout the liver parenchyma, while the autofluorescence in the hepatic sinusoids is extremely weak. This fluorescence distribution pattern is consistent with the reported distribution of endogenous fluorescent substances such as NAD(P)H, flavins and vitamin A. In addition, cancer tissue also has strong autofluorescence in the visible light region, and it can be observed that the autofluorescence is widely distributed in liver cancer cells. Under this channel, the granular autofluorescence of lipofuscin in liver tissue is difficult to detect due to the interference of strong autofluorescence such as flavins. In the near-infrared region 1, normal liver tissue and cancer tissue also have diffusely distributed autofluorescence, but there is a large amount of granular autofluorescence of lipofuscin in normal liver tissue, while there is no such granular autofluorescence in cancer tissue. However, in the near-infrared region 2, there are only a large number of autofluorescent particles in normal liver tissue, and there is no obvious autofluorescence in cancer tissue. The fluorescence signals of other endogenous fluorescent substances cannot be detected in both tissues. This suggests that the autofluorescence signal of lipofuscin can avoid the interference of other endogenous fluorescence signals in the near-infrared region II, and that its content is high in normal liver tissue and low in cancer tissue, ultimately leading to high-contrast imaging of liver cancer tissue.

[0152] Example 7

[0153] Application of near-infrared zone II autofluorescence imaging technology in patient live resection. Combined with preoperative MRI / CT, ​​blood routine, and medical history, a volunteer diagnosed with hepatocellular carcinoma was included in the study. The patient underwent a preliminary localization of the lesion through MRI / CT imaging before surgery, and underwent a laparotomy on the day of surgery. The liver surface was tested for cancerous tissue using the near-infrared zone II autofluorescence imaging technology developed by the present invention. Based on the experience of clinicians and the results of the near-infrared zone II autofluorescence imaging technology, the cancerous tissue was resected. The near-infrared zone II autofluorescence imaging technology was then used to image the entire resected tumor. The near-infrared zone II autofluorescence imaging parameters are: excitation light wavelength 760 nm, power density 15 mW / cm 2 , emission filters 850 nm, 1000 nm and 1100 nm long-pass filters were combined. At the same time, a color camera was used to collect bright field information. The obtained autofluorescence images were processed using Image J software to calculate the autofluorescence intensity of normal liver tissue and tumor tissue, and the ratio between the two was calculated.

[0154] Similar to the results obtained on in vitro tissues, near-infrared zone II autofluorescence imaging technology can also clearly outline the boundaries of hepatocellular carcinoma lesions of about 3 cm in size on living human livers, such as Fig.24 As shown in the figure, the entire normal liver shows uniform and strong near-infrared zone II autofluorescence except for the cancerous lesions, while the autofluorescence of the cancerous area is negligible. Fig.24 The results of near-infrared autofluorescence imaging of resected liver cancer tissue are consistent with those of in vivo imaging of liver cancer. Fig.25 By calculating the ROIs of normal liver tissue and cancer tissue, the autofluorescence intensity ratio of normal liver tissue to tumor tissue in in vivo imaging of liver cancer was 12.17, and the autofluorescence intensity ratio of normal liver tissue to tumor tissue in ex vivo imaging was 10.88, as shown in Fig.26 This further demonstrates that near-infrared zone II autofluorescence imaging technology can produce comparable results on in vitro tissues or living humans. The final pathological section results verified that the tissue detected by the imaging technology was hepatocellular carcinoma, as shown in Figure 2. Fig. 27 shown.

Claims

1. A tissue imaging method, characterized in that: The method is applied to the detection of human liver malignant tumor tissue, comprising: S01. The fluorescence excitation unit uniformly irradiates the target tissue with excitation light to form a light spot on the target tissue, and the light spot covers the target tissue; the target tissue includes tumor tissue and non-tumor tissue; the excitation light is a single excitation light with any wavelength between 740nm and 780nm; the power density of the excitation light is in the range of 15mW / cm 2 ~20mW / cm 2 ; The size of the light spot ranges from 100cm 2 ~300cm 2 ; Adjust the exposure time of the excitation light to a range of 200ms~500ms; S02, the signal collection unit collects the fluorescence signal in the 1100nm~1300nm band after being filtered by the filter group and sends it to the computer processing unit; the fluorescence signal is the autofluorescence generated by the target tissue after being irradiated by the light spot; S03-1, the computer processing unit generates a first fluorescent image according to the received fluorescent signal; S03-2, inputting the first fluorescent image into a preset U-shaped network model, and outputting a probability matrix of each pixel in the first fluorescent image belonging to a tissue; Segmenting the tissue and the background in the first fluorescent image according to the probability matrix; grayscale the first fluorescent image to obtain a grayscale matrix corresponding to the first fluorescent image; According to the grayscale value of each pixel position in the grayscale matrix and the preset formula 5, the tissue type of each pixel position in the segmented first fluorescent image is determined to obtain a process image corresponding to the first fluorescent image; the formula 5 is: ； H is the grayscale value of the x pixel position after graying, A is the abnormal tissue type, B is the normal tissue type, and L1 is the threshold for determining the tissue type; The tissue type determination threshold L1 is obtained by a preset formula 1, which is: ； Among them, I1~I n is the gray value of all pixel positions in the gray matrix after the gray values ​​are arranged from small to large, I1 is the gray value of the pixel position with the minimum gray value in the gray matrix, and I n is the gray value of the pixel with the maximum gray value in the gray matrix, I i I is the larger gray value of the two adjacent gray values ​​with the largest difference. i-1 is the smaller gray value among the two adjacent gray values ​​with the largest difference; S03-3, the computer processing unit performs signal enhancement on the tumor tissue area and the non-tumor tissue area in the process image, and eliminates the signal on the background area to obtain a second fluorescent image; The ratio of the signal intensity of the fluorescence signal at any pixel position in the non-tumor tissue region to the signal intensity of the fluorescence signal at any pixel position in the tumor tissue region in the second fluorescence image is greater than 2; The tumor tissue region and the non-tumor tissue region in the second fluorescent image have an obvious boundary.

2. The tissue imaging method according to claim 1, characterized in that: The filter set includes a 900nm long pass filter, a 950nm long pass filter, a 1000nm long pass filter and a 1100nm long pass filter.

3. The tissue imaging method according to claim 1, characterized in that: The wavelength of the excitation light is 760nm; The size range of the light spot is 200cm 2 ~300cm 2 ; In the second fluorescent image, the ratio of the signal intensity of the fluorescent signal at any pixel position in the non-tumor tissue area to the signal intensity of the fluorescent signal at any pixel position in the tumor tissue area is 4-10.

4. The tissue imaging method according to claim 1, characterized in that: The S03 includes: S03-1, the computer processing unit generates a first fluorescent image according to the received fluorescent signal; S03-2, the computer processing unit compares the signal intensity of the fluorescence signal at each pixel position in the first fluorescence image with the first threshold value, and obtains a process image indicating the tumor tissue area, the non-tumor tissue area and the background area based on the comparison result; S03-3. The computer processing unit performs signal enhancement on the tumor tissue area and the non-tumor tissue area in the process image, and performs signal elimination on the background area, to obtain a second fluorescent image in which the ratio of the signal intensity of the fluorescent signal at any pixel point position in the non-tumor tissue area to the signal intensity of the fluorescent signal at any pixel point position in the tumor tissue area is 6 to 10.

5. The tissue imaging method according to claim 4, characterized in that: The S03-2 includes: The first fluorescent image is gray-scaled to obtain a gray-scale matrix of the first fluorescent image; and a process image indicating a tumor tissue area, a non-tumor tissue area and a background area is obtained according to the gray-scale matrix and a first threshold; the gray-scale matrix is ​​a matrix composed of gray-scale values ​​obtained after gray-scale processing of each pixel position in the first fluorescent image.

6. The tissue imaging method according to any one of claims 1 to 5, characterized in that: The non-tumor tissue includes normal tissue and / or non-malignant diseased tissue.

7. A tissue imaging system, characterized in that: include: A fluorescence excitation unit, used for emitting excitation light to the target tissue to form a light spot on the target tissue; A filter set, used for filtering the fluorescence signal, wherein the fluorescence signal is the autofluorescence generated by lipofuscin in the target tissue after being irradiated by the light spot; The signal collection unit is used to collect the filtered fluorescence signal and send it to the computer processing unit; A computer processing unit, configured to obtain a second fluorescent image indicating a tumor tissue region and a non-tumor tissue region according to the received fluorescent signal; Wherein, the angle between the optical path of the signal collection unit receiving the fluorescence signal and the optical path of the single excitation light generated by the fluorescence excitation unit is between 0 and 90 degrees, and the computer processing unit is in communication connection with the signal collection unit; When the fluorescence excitation unit emits excitation light to the target tissue, the system performs the tissue imaging method according to any one of claims 1 to 6.

Images