A measurement system for the area and intensity curves of intraoperative fluorescence images of colorectal cancer

Abstract

The present invention belongs to the technical field of image processing, and particularly relates to a measurement system for the area and intensity curve of intraoperative fluorescence images of colorectal cancer, aiming to solve the problem that the prior art cannot accurately judge the assessment of anastomotic blood supply. The present invention includes: acquiring a fluorescence image to be measured for fluorescence intensity and relative area, and preprocessing the fluorescence image to obtain a preprocessed image; segmenting the preprocessed image to obtain an intestinal segment region image and a fluorescence region image, and calculating the relative area S of the fluorescence region R ; calculating the intensity maximum point and the relative intensity maximum point of the fluorescence region image; calculating the signal-to-background ratio and the average signal-to-background ratio; generating a fluorescence intensity curve; and establishing a multi-layer perceptron neural network model in combination with the fluorescence intensity curve to achieve the purpose of objectively evaluating the anastomotic blood supply. The present invention can realize the quantification of the fluorescence intensity curve, thereby increasing the accuracy and precision of the analysis results, and thus can achieve a more accurate assessment of the anastomotic blood supply.

Description

Background Art

[0002] In laparoscopic colorectal cancer surgery, indocyanine green (ICG) near-infrared light imaging technology can enhance the visualization of tumor lesions, improve the detection rate of lymph nodes, and reduce the incidence of anastomotic leakage.

[0003] ICG imaging has been widely used in the evaluation of anastomotic blood supply, lymph node tracing, and intraoperative tumor localization in colorectal cancer surgery. The application of ICG near-infrared light imaging technology in the evaluation of intestinal segment blood supply can reduce the incidence of anastomotic leakage. Currently, the intensity score of ICG near-infrared light imaging of the anastomosis is mainly used to evaluate the blood supply. If the score is ≥3 points, it indicates that the local blood supply of the anastomosis is relatively sufficient, and the occurrence of anastomotic leakage caused by blood supply disorders can be more effectively avoided. However, this scoring method is a subjective evaluation method (for example, slightly stronger fluorescence imaging compared to other regions) lacking quantitative description, mainly relying on the rich surgical experience and practice of surgeons.

[0004] Therefore, there is a need in the art for a stable and convenient technical means for measuring the fluorescence intensity, area of intraoperative fluorescence images in colorectal cancer, and quantifying the fluorescence intensity curve, so as to achieve more accurate evaluation of anastomotic blood supply.

[0005] Based on this, the present invention proposes a measurement system for the area and intensity curve of intraoperative fluorescence images in colorectal cancer. Summary of the Invention

[0006] To solve the above problems in the prior art, that is, the method for evaluating anastomotic blood supply in the prior art is a subjective evaluation method, lacking quantitative description, mainly relying on the rich surgical experience and practice of surgeons, and unable to accurately judge the problem of anastomotic blood supply evaluation, the present invention provides a measurement system for the area and intensity curve of intraoperative fluorescence images in colorectal cancer.

[0007] One aspect of the present invention proposes a measurement system for the fluorescence area, intensity, and its change curve of intraoperative fluorescence images in colorectal cancer, the system includes:

[0008] A fluorescence image preprocessing module, configured to obtain a fluorescence image to be measured for fluorescence intensity and relative area, and preprocess the fluorescence image to obtain a preprocessed image; the fluorescence image is a fluorescence image during colorectal cancer surgery; the preprocessing includes normalization processing;

[0009] Segment the intestinal segment area and the fluorescence area in the preprocessed image to obtain an intestinal segment area image and a fluorescence area image;

[0010] A fluorescence area image area measurement module, configured to calculate the area S of the intestinal segment area image Cand the area S of the fluorescence region image I , and use the quotient of the S I and the S C corresponding area as the relative area S of the fluorescence region R ;

[0011] A fluorescence intensity curve quantization module configured to generate a fluorescence intensity curve based on the pixel values of each pixel point in the fluorescence region image;

[0012] A fluorescence intensity curve classification module configured to establish a blood perfusion evaluation neural network model based on the fluorescence intensity curve and in combination with the labels of the regions corresponding to the fluorescence intensity curve, and perform classification prediction on the fluorescence intensity curve.

[0013] In some preferred embodiments, the intraoperative fluorescence image is indocyanine green near-infrared light imaging.

[0014] In some preferred embodiments, the system further includes a fluorescence region image intensity measurement module configured to calculate the maximum fluorescence region intensity point (x1, y1) based on the pixel values of each pixel point in the fluorescence region image, and the method is:

[0015] (x1, y1) = arg max{f I (x, y)};

[0016] wherein the f I (x, y) represents the pixel value of each pixel point (x, y) in the fluorescence region image.

[0017] In some preferred embodiments, the fluorescence region image intensity measurement module is further configured to calculate the relative intensity maximum point (x′1, y′1) based on the pixel values of each pixel point in the fluorescence region image, and the method is:

[0018]

[0019] wherein the f C (x, y) is the pixel value of each pixel point (x, y) in the intestinal segment region image, and N is the total number of pixel points in the intestinal segment region.

[0020] In some preferred embodiments, calculate the signal-to-background ratio SBR and the average signal-to-background ratio aSBR according to the pixel points in the fluorescence region image and the intestinal segment region image, and the method is:

[0021]

[0022]

[0023] Wherein, M is the total number of pixel points of the fluorescence region image.

[0024] In some preferred embodiments, the method for generating the fluorescence intensity curve is as follows:

[0025] Obtain the intraoperative fluorescence image during the operation time, and divide the intraoperative fluorescence image into a plurality of the fluorescence images according to each frame;

[0026] Obtain a plurality of the fluorescence region images from the plurality of the fluorescence images;

[0027] Calculate the average value of the plurality of the fluorescence region images, and the method is as follows:

[0028]

[0029] Wherein, the is the pixel value of each pixel point (x, y) of the j-th fluorescence region image, and N j represents the total number of pixel points of the j-th fluorescence region image;

[0030] Generate coordinate points according to the plurality of the average values and the time corresponding to each frame of the fluorescence image, and connect the plurality of coordinate points in sequence to obtain the fluorescence intensity curve.

[0031] In some preferred embodiments, according to the fluorescence intensity curve, directly read the parameters for judging the blood supply condition, and the parameters include:

[0032] The maximum fluorescence intensity F max 、Half of the maximum fluorescence intensity F 1 / 2max 、The time T0 from the ICG injection to the appearance of the first fluorescence signal, the time T max from the ICG injection to the maximum fluorescence signal, and the time T 1 / 2max from the ICG injection to half of the maximum fluorescence signal, and the time ttp to reach the fluorescence intensity peak.

[0033] In some preferred embodiments, calculate the slope slope and the time ratio TR according to the fluorescence intensity curve, and the calculation method is as follows:

[0034] slope = F max / ttp.

[0035] In some preferred embodiments, the method for calculating the time ratio TR is as follows:

[0036] TR = T 1 / 2max / ttp.

[0037] In some preferred embodiments, the labels of the region corresponding to the fluorescence intensity curve are divided into good blood supply and bad blood supply.

[0038] In some preferred embodiments, the blood perfusion evaluation neural network model is a multi-layer perceptron with m input neurons and n outputs, where m and n are positive integers.

[0039] Advantages of the present invention:

[0040] By segmenting the fluorescence image region and the intestinal segment image region in the fluorescence image, and then measuring the area and fluorescence intensity of the fluorescence region, the quantification of the fluorescence intensity curve can be achieved. According to the fluorescence intensity curve, a multi-layer perceptron blood perfusion evaluation neural network model is established, so as to realize more accurate anastomotic blood perfusion evaluation and increase the accuracy and precision of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent:

[0042] Figure 1 is a schematic structural diagram of a measurement system for the area and intensity curve of intraoperative fluorescence images of colorectal cancer;

[0043] Figure 2 is a schematic flow diagram of a measurement system for the area and intensity curve of intraoperative fluorescence images of colorectal cancer;

[0044] Figure 3 is a schematic diagram of the intestinal segment region image and the fluorescence region image;

[0045] Figure 4 is a schematic diagram of the fluorescence intensity curve;

[0046] Figure 5 is a schematic flow diagram of a method for measuring the area and intensity curve of intraoperative fluorescence images of colorectal cancer

[0047] Figure 6 is a schematic structural diagram of a computer system of a server for implementing the system and device embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. Additionally, it should be noted that for the sake of description, only the parts related to the relevant invention are shown in the drawings.

[0049] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0050] As Figures 1-4As shown in the figure, in the first embodiment of the present invention, a measurement system for the area and intensity curve of intraoperative fluorescence imaging of colorectal cancer is provided. This system includes:

[0051] A fluorescence image preprocessing module, which is configured to obtain a fluorescence image to be measured for fluorescence intensity and relative area, and preprocess the fluorescence image to obtain a preprocessed image; the fluorescence image is a fluorescence image during colorectal cancer surgery; the preprocessing includes normalization processing;

[0052] Segment the intestinal segment area and the fluorescence area in the preprocessed image to obtain an intestinal segment area image and a fluorescence area image;

[0053] A fluorescence area image area measurement module, which is configured to calculate the area S of the intestinal segment area image C and the area S of the fluorescence area image I , and use the area corresponding to the quotient of the S I and the S C as the relative area S of the fluorescence area R ;

[0054] A fluorescence intensity curve quantification module, which is configured to generate a fluorescence intensity curve based on the pixel values of each pixel point in the fluorescence area image;

[0055] A fluorescence intensity curve classification module, which is configured to establish a blood perfusion evaluation neural network model based on the fluorescence intensity curve and combine the labels of the area corresponding to the fluorescence intensity curve, and perform classification prediction on the fluorescence intensity curve.

[0056] Among them, the preprocessing method is: resample the fluorescence image to 256×256×3, and perform Z-score normalization on the fluorescence image to make the mean of the image pixel values 0 and the variance 1.

[0057]

[0058] I represents the original intraoperative fluorescence image, and μ and σ represent the mean and variance of I.

[0059] Among them, the above preprocessed image is sent into a segmentation algorithm for segmentation, and finally there are two outputs, which respectively correspond to the intestinal segment area image and the fluorescence area image. As Figure 3 shown, where the outer contour line is the intestinal segment area image and the inner contour line is the fluorescence area image.

[0060] Preferably, the fluorescence image is indocyanine green near-infrared light imaging.

[0061] Preferably, the system further includes a fluorescence region image intensity measurement module, which is configured to calculate the intensity maximum point (x1, y1) of the fluorescence region image based on the pixel values of each pixel point in the fluorescence region image. The method is as follows:

[0062] (x1, y1) = arg max{fI(x, y)};

[0063] wherein the f I (x, y) represents the pixel value of each pixel point (x, y) in the fluorescence region image.

[0064] Preferably, the fluorescence region image intensity measurement module is further configured to calculate the relative intensity maximum point (x′1, y′1) based on the pixel values of each pixel point in the fluorescence region image. The method is as follows:

[0065]

[0066] wherein the f C (x, y) is the pixel value of each pixel point (x, y) in the intestinal segment region image, and N is the total number of pixel points in the intestinal segment region.

[0067] Preferably, the method for calculating the signal-to-background ratio SBR and the average signal-to-background ratio aSBR based on the pixel points in the fluorescence region image and the intestinal segment region image is as follows:

[0068]

[0069]

[0070] wherein M is the total number of pixel points in the fluorescence region image.

[0071] The main function of calculating the signal-to-background ratio SBR and the average signal-to-background ratio aSBR is to evaluate the signal of the fluorescence region image, and it can reflect the ratio of the signal of the fluorescence region image to the average gray value of the background.

[0072] Preferably, the method for generating a fluorescence intensity curve is as follows:

[0073] Obtain intraoperative fluorescence images during the operation time, and divide the intraoperative fluorescence images into multiple fluorescence images according to each frame;

[0074] Obtain multiple fluorescence region images from the multiple fluorescence images;

[0075] Calculate the average value of the multiple fluorescence region images. The method is as follows:

[0076] wherein the is the pixel value of each pixel point (x, y) of the j-th fluorescence region image, N j represents the total number of pixel points of the j-th fluorescence region image;

[0077] Coordinate points are generated according to the multiple averages and the time corresponding to each frame of fluorescence image, and the multiple coordinate points are connected in sequence to obtain a fluorescence intensity curve.

[0078] Preferably, refer to Figure 4 , according to the fluorescence intensity curve, directly read the parameters for judging the blood circulation condition, and the parameters include:

[0079] Maximum fluorescence intensity F max , half of the maximum fluorescence intensity F 1 / 2max , the time T0 from ICG injection to the appearance of the first fluorescence signal, the time T from ICG injection to the maximum fluorescence signal max , the time T from ICG injection to half of the maximum fluorescence signal 1 / 2max , the time ttp to reach the peak fluorescence intensity.

[0080] Preferably, calculate the slope slope and the time ratio TR according to the fluorescence intensity curve, and the calculation method is:

[0081] slope = F max / ttp.

[0082] Preferably, the method for calculating the time ratio TR is:

[0083] TR = T 1 / 2max / ttp

[0084] Preferably, the labels of the region corresponding to the fluorescence intensity curve are divided into good blood circulation and bad blood circulation.

[0085] Preferably, the blood circulation evaluation neural network model is a multi-layer perceptron with m input neurons and n outputs, and the m and the n are positive integers;

[0086] In this embodiment, the neural network model is a multi-layer perceptron with m = 1500 input neurons and n = 3 outputs.

[0087] Among them, the relative area S R of the fluorescence region calculated by the present invention, the signal-to-background ratio SBR, the average signal-to-background ratio aSBR, the fluorescence intensity curve, the parameters read and calculated from the fluorescence intensity curve, and the result of classifying and predicting the fluorescence intensity curve are used to provide a reference for doctors to judge the blood circulation condition of the user.

[0088] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related explanations of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein.

[0089] It should be noted that the measurement system for the area and intensity curve of the intraoperative fluorescence image of colorectal cancer provided in the above embodiments is only illustrated by dividing the above functional modules. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only used to distinguish each module or step, and are not regarded as an improper limitation of the present invention.

[0090] See Figure 5 , the second embodiment of the present invention provides a method for measuring the area and intensity curve of the intraoperative fluorescence image of colorectal cancer, and the method includes:

[0091] Obtain a fluorescence image to be measured for fluorescence intensity and relative area, and preprocess the fluorescence image to obtain a preprocessed image; the fluorescence image is a fluorescence image during colorectal cancer surgery; the preprocessing includes normalization processing;

[0092] Segment the intestinal segment area and the fluorescence area in the preprocessed image to obtain an intestinal segment area image and a fluorescence area image;

[0093] Calculate the area S of the intestinal segment area image C and the area S of the fluorescence area image I , and use the area corresponding to the quotient of the S I and the S C as the relative area S of the fluorescence area R ;

[0094] Generate a fluorescence intensity curve based on the pixel values of each pixel point in the fluorescence area image;

[0095] Based on the fluorescence intensity curve, combined with the labels of the area corresponding to the fluorescence intensity curve, establish a blood perfusion evaluation neural network model to classify and predict the fluorescence intensity curve.

[0096] As Figure 6 shown, the third embodiment of the present invention provides an electronic device, including:

[0097] At least one processor; and a memory communicatively connected to at least one of the processors;

[0098] Among them, the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above method for measuring the area and intensity curve of the fluorescence image during colorectal cancer surgery.

[0099] As Figure 6 shown, the fourth embodiment of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above method for measuring the area and intensity curve of the fluorescence image during colorectal cancer surgery.

[0100] Those skilled in the art should be able to realize that the modules and systems of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and systems can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field. To clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0101] Next, refer to Figure 6 , which shows a schematic structural diagram of a computer system of a server for implementing the system and device embodiments of the present application. Figure 6 The server shown is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present application.

[0102] As Figure 6 shown, the computer system includes a central processing unit (CPU, Central Processing Unit) 601, which can execute various appropriate actions and processes according to the program stored in the read-only memory (ROM, Read Only Memory) 602 or the program loaded from the storage section 608 into the random access memory (RAM, Random Access Memory) 603. In the RAM 603, various programs and data required for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. The input / output (I / O, Input / Output) interface 605 is also connected to the bus 604.

[0103] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

[0104] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, the computer program including program code for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 609 and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-described functions defined in the system of the present application are performed. It should be noted that the computer-readable medium in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0105] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0107] The terms "first", "second", etc. are used to distinguish similar objects and not to describe or represent a specific order or sequence.

[0108] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or device / apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent in these process, method, article, or device / apparatus.

[0109] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A measurement system for the area and intensity curves of fluorescence images during colorectal cancer surgery, characterized in that, The system includes: A fluorescence image preprocessing module configured to obtain a fluorescence image to be measured for fluorescence intensity and relative area, and preprocess the fluorescence image to obtain a preprocessed image; the fluorescence image is a fluorescence image during colorectal cancer surgery; the preprocessing includes normalization processing; Segment the intestinal segment area and the fluorescence area in the preprocessed image to obtain an intestinal segment area image and a fluorescence area image; A fluorescence region image area measurement module, configured to calculate the area S of the intestinal segment region image C and the area S of the fluorescence region image I , and take the area corresponding to the quotient of the said S I and the said S C as the relative area S of the fluorescence region R ; A fluorescence intensity curve quantification module configured to generate a fluorescence intensity curve based on the pixel values of each pixel point in the fluorescence area image; To generate the fluorescence intensity curve, the method is: Obtain intraoperative fluorescence images during the operation time, and divide the intraoperative fluorescence images into a plurality of the fluorescence images according to each frame; Obtain a plurality of the fluorescence area images according to the plurality of the fluorescence images; Calculate the average value of the plurality of the fluorescence area images, the method is: Among them, the is the pixel value of each pixel point (x, y) of the j-th fluorescence region image, and N j represents the total number of pixel points of the j-th fluorescence region image; Generate coordinate points according to the plurality of the average values and the time corresponding to each frame of fluorescence image, and sequentially connect the plurality of coordinate points to obtain a fluorescence intensity curve; A fluorescence intensity curve classification module configured to establish a blood perfusion evaluation neural network model based on the fluorescence intensity curve and in combination with the labels of the areas corresponding to the fluorescence intensity curve, and perform classification prediction on the fluorescence intensity curve.

2. The measurement system for the area and intensity curves of the fluorescence image during colorectal cancer surgery according to claim 1, characterized in that The fluorescence image is indocyanine green near-infrared light imaging.

3. The measurement system for the area and intensity curve of the fluorescence image during colorectal cancer surgery according to claim 1, wherein The system further includes a fluorescence area image intensity measurement module configured to calculate the intensity maximum point (x1, y1) of the fluorescence area image based on the pixel values of each pixel point in the fluorescence area image, the method is: (x1,y1) = argmax{f I (x,y)}; Among them, the f I (x, y) represents the pixel value of each pixel point (x, y) in the fluorescence region image.

4. The measurement system for the area and intensity curves of the fluorescence image during colorectal cancer surgery according to claim 3, characterized in that, The fluorescence area image intensity measurement module is further configured to calculate the relative intensity maximum point (x1′, y1′) based on the pixel values of each pixel point in the fluorescence area image, the method is: wherein, the f C (x, y) is the pixel value of each pixel point (x, y) in the intestinal segment area image, and the N is the total number of pixel points in the intestinal segment area.

5. A measurement system for the area and intensity curves of intraoperative fluorescence images of colorectal cancer according to claim 4, characterized in that, Calculate the signal-to-background ratio SBR and the average signal-to-background ratio aSBR according to the pixel points in the fluorescence area image and the intestinal segment area image, the method is: Wherein, M is the total number of pixel points of the fluorescence area image.

6. The measurement system for the area and intensity curve of the fluorescence image during colorectal cancer surgery according to claim 1, wherein, Directly read the parameters for judging the blood perfusion condition according to the fluorescence intensity curve, and the parameters include: Maximum fluorescence intensity F max 、Half of the maximum fluorescence intensity F 1 / 2max 、Time T0 from ICG injection to the appearance of the first fluorescence signal, time T from ICG injection to the maximum fluorescence signal max ,Time T from ICG injection to half of the maximum fluorescence signal 1 / 2max ,Time ttp to reach the peak fluorescence intensity.

7. A measurement system for the area and intensity curves of intraoperative fluorescence images of colorectal cancer according to claim 6, characterized in that Calculate the slope slope and the time ratio TR according to the fluorescence intensity curve, and the calculation method is: TR = T 1 / 2max / ttp。 8. The measurement system for the area and intensity curves of the intraoperative fluorescence image of colorectal cancer according to claim 1, characterized in that The labels of the areas corresponding to the fluorescence intensity curve are divided into good blood perfusion and bad blood perfusion.

9. The measurement system for the area and intensity curves of the fluorescence image during colorectal cancer surgery according to claim 1, wherein, The blood perfusion evaluation neural network model is a multi-layer perceptron with m input neurons and n outputs, and m and n are positive integers.

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