Microcirculation perfusion boundary area calculation method, device, terminal and storage medium

Abstract

The present invention is applicable to the field of image processing technology, and provides a method, device, terminal and storage medium for calculating the microcirculation perfusion boundary area. The method comprises: acquiring a microcirculation image; segmenting the microcirculation image, determining the outer edge of the perfusion boundary area in the microcirculation image, and obtaining an outer edge mask image; extracting the blood vessel area containing the perfusion boundary area in the microcirculation image using the outer edge mask image, and obtaining a target area image; segmenting the target area image, determining the inner edge of the perfusion boundary area in the target area image, and obtaining an inner edge mask image; and calculating the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image. The present invention can accurately calculate the size of the perfusion boundary area in the microcirculation image and reduce the calculation complexity, thereby providing strong support for evaluating the health of microcirculation and the state of endothelial glycocalyx.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method, device, terminal and storage medium for calculating a microcirculation perfusion boundary area. Background Art

[0002] Perfused Boundary Region (PBR) is a commonly used parameter in microcirculation research. This parameter refers to the area between red blood cells and vascular endothelium in microvessels (usually capillaries), that is, the area between the inner wall of the blood vessel and the flowing blood cells. This area can reflect the thickness and integrity of the endothelial glycocalyx. When the endothelial glycocalyx is intact, the PBR is small because the glycocalyx layer prevents red blood cells from approaching the endothelial surface. When the glycocalyx is damaged, the PBR increases because red blood cells can get closer to the endothelial surface.

[0003] The endothelial glycocalyx is a layer of polysaccharide-protein complex covering the surface of endothelial cells. It plays an important role in regulating vascular permeability, protecting endothelial cells and preventing blood cell adhesion. When the endothelial glycocalyx is damaged or thinned, the PBR value will increase, reflecting the increased exposure of endothelial cells to the blood flow, which may be related to a variety of pathological conditions, such as sepsis, trauma, acute respiratory distress syndrome, diabetes, etc. Therefore, the measurement and calculation of PBR is of great significance in evaluating microcirculatory health and endothelial glycocalyx status.

[0004] However, the inventors found in the process of implementing the present invention that the current calculation of PBR size mainly remains in theory, and there are few PBR calculation methods at the application level. In addition, the current PBR calculation methods either need to calculate the distance between each red blood cell and the cell wall, which has high calculation complexity and is difficult to implement, or use the change in the width of the red blood cell column in the microvessel for calculation, ignoring the impact of changes in the thickness of the blood vessel itself, and the accuracy is not high. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, terminal and storage medium for calculating the boundary area of ​​microcirculation perfusion, so as to solve the problems of complex calculation and insufficient accuracy of the boundary area of ​​microcirculation perfusion at present.

[0006] In a first aspect, an embodiment of the present invention provides a method for calculating a microcirculation perfusion boundary region, comprising:

[0007] Acquire microcirculatory images;

[0008] Segmenting the microcirculation image, determining the outer edge of the perfusion boundary area in the microcirculation image, and obtaining an outer edge mask image;

[0009] Extracting the blood vessel region including the perfusion boundary region in the microcirculation image using the outer edge mask image to obtain a target region image;

[0010] Segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining an inner edge mask image;

[0011] The size of the perfusion boundary area in the microcirculation image is calculated based on the outer edge mask image and the inner edge mask image.

[0012] In a possible implementation, segmenting the microcirculation image, determining the outer edge of the perfusion boundary area in the microcirculation image, and obtaining the outer edge mask image includes:

[0013] Determine a preset area around each pixel in the microcirculation image as a neighborhood of the pixel;

[0014] Calculate the local segmentation threshold corresponding to the neighborhood of each pixel according to the gray value of each pixel in the neighborhood;

[0015] Comparing the gray value of each pixel in the microcirculation image with the local segmentation threshold of the neighborhood where the pixel is located;

[0016] The outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain an outer edge mask image.

[0017] In a possible implementation, determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result to obtain the outer edge mask image includes:

[0018] determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result, and obtaining a first outer edge mask image;

[0019] Comparing the grayscale value of each pixel in the microcirculation image with a preset global segmentation threshold, determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result, and obtaining a second outer edge mask image;

[0020] A bitwise AND operation is performed on the first outer edge mask image and the second outer edge mask image to obtain an outer edge mask image.

[0021] In a possible implementation, the outer edge mask image is used to extract the blood vessel region including the perfusion boundary region in the microcirculation image to obtain a target region image, including:

[0022] The outer edge mask image and the microcirculation image are superimposed, and the superimposed image is determined as the target area image.

[0023] In a possible implementation, segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining the inner edge mask image includes:

[0024] Segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining an initial inner edge mask image;

[0025] Performing hole filling processing on the initial inner edge mask image to obtain a modified inner edge mask image;

[0026] An OR operation is performed on the initial inner edge mask image and the modified inner edge mask image to obtain an inner edge mask image.

[0027] In a possible implementation manner, calculating the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image includes:

[0028] Performing skeleton extraction on the outer edge mask image, and calculating the distance from each pixel point on the skeleton of the outer edge mask image to the corresponding pixel point on the outer edge of the perfusion boundary area, recorded as a first distance;

[0029] Performing skeleton extraction on the inner edge mask image, and calculating the distance from each pixel point on the skeleton of the inner edge mask image to the corresponding pixel point of the inner edge of the perfusion boundary area, recorded as a second distance;

[0030] The difference between each first distance and the second distance at the corresponding position is calculated, and the size of the perfusion boundary area in the microcirculation image is determined according to the difference.

[0031] In a possible implementation manner, determining the size of the perfusion boundary area in the microcirculation image according to the difference includes:

[0032] Segmenting the blood vessels in the microcirculation image according to the diameters of the blood vessels in the microcirculation image;

[0033] For each blood vessel segment, the size of the perfusion boundary area of ​​the blood vessel segment in the microcirculation image is determined according to the average value of the difference values ​​corresponding to each position in the blood vessel segment.

[0034] In a second aspect, an embodiment of the present invention provides a microcirculation perfusion boundary area calculation device, comprising:

[0035] An acquisition module, used for acquiring microcirculation images;

[0036] A first processing module is used to segment the microcirculation image, determine the outer edge of the perfusion boundary area in the microcirculation image, and obtain an outer edge mask image;

[0037] A second processing module is used to extract the blood vessel region including the perfusion boundary region in the microcirculation image by using the outer edge mask image to obtain a target region image;

[0038] A third processing module is used to segment the target area image, determine the inner edge of the perfusion boundary area in the target area image, and obtain an inner edge mask image;

[0039] A calculation module is used to calculate the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image.

[0040] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0042] The embodiment of the present invention provides a microcirculation perfusion boundary area method, device, terminal and storage medium. The method obtains a microcirculation image, and first segments the microcirculation image to determine the outer edge of the perfusion boundary area in the microcirculation image, and obtains an outer edge mask image; then the blood vessel area containing the perfusion boundary area in the microcirculation image is extracted using the outer edge mask image to obtain a target area image; then the target area image is segmented twice to determine the inner edge of the perfusion boundary area in the target area image, and obtain an inner edge mask image; thereby, the size of the perfusion boundary area in the microcirculation image can be accurately calculated based on the outer edge mask image and the inner edge mask image, which not only reduces the computational complexity, but also accurately measures the perfusion boundary area between red blood cells and vascular endothelium in microvessels through the outer edge mask image and the inner edge mask image, thereby providing strong support for evaluating microcirculation health and endothelial glycocalyx status. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1is a flow chart of the implementation of the microcirculation perfusion boundary area calculation method provided by an embodiment of the present invention;

[0045] Figure 2 is an example diagram of a microcirculation image provided by an embodiment of the present invention;

[0046] Figure 3 is an example diagram of an outer edge mask image provided by an embodiment of the present invention;

[0047] Figure 4 is an example diagram of a target area image provided by an embodiment of the present invention;

[0048] Figure 5 is an example diagram of an initial inner edge mask image provided by an embodiment of the present invention;

[0049] Figure 6 is an example diagram of a corrected inner edge mask image provided by an embodiment of the present invention;

[0050] Figure 7 is an example diagram of an inner edge mask image provided by an embodiment of the present invention;

[0051] Figure 8 is a superimposed image of an outer edge mask image and an inner edge mask image provided by an embodiment of the present invention;

[0052] Fig. 9 is a schematic diagram of performing skeleton extraction on an outer edge mask image and calculating a first distance according to an embodiment of the present invention;

[0053] Fig.10 is a schematic diagram of performing skeleton extraction on an inner edge mask image and calculating a second distance provided by an embodiment of the present invention;

[0054] Fig.11 is a schematic structural diagram of a microcirculation perfusion boundary area calculation device provided by an embodiment of the present invention;

[0055] Fig.12 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0058] See also Figure 1 , which shows a flowchart of the implementation of the microcirculation perfusion boundary area calculation method provided by an embodiment of the present invention, and is described in detail as follows:

[0059] In step 101, a microcirculation image is acquired.

[0060] For example, the microcirculation image may be a sublingual microcirculation image or a nail fold microcirculation image, etc., which is not limited in this embodiment. Figure 2 An example of an acquired microcirculatory image.

[0061] Optionally, after acquiring the microcirculation image, the microcirculation image may be preprocessed to reduce noise and enhance contrast, thereby helping to improve the effect of subsequent image segmentation.

[0062] Exemplarily, Gaussian blur processing can be used to denoise the microcirculation image to smooth the image and reduce the impact of noise on subsequent image segmentation results. This embodiment does not limit the specific image denoising processing method.

[0063] Exemplarily, adaptive histogram equalization processing may be used to perform contrast enhancement on the microcirculation image to enhance the local contrast of the image. This embodiment does not limit the specific contrast enhancement processing method.

[0064] In step 102, the microcirculation image is segmented to determine the outer edge of the perfusion boundary area in the microcirculation image, thereby obtaining an outer edge mask image.

[0065] like Figure 2 As shown in the figure, in the microcirculation image, most of the areas such as area A are background areas, the darker area B is the blood flow area or the red blood cell area where blood flows in the blood vessel, and the virtual shadow adjacent to area B (that is, area C) is the area between the red blood cells and the vascular endothelium, that is, the perfusion boundary area. In order to calculate the size of the perfusion boundary area in the microcirculation image, combined with the grayscale values ​​of area A, area B, and area C in the microcirculation image, consider extracting the outermost boundary of the perfusion boundary area in the microcirculation image through image segmentation, that is, the outer edge of the perfusion boundary area.

[0066] Exemplarily, the outer edge of the perfusion boundary area in the microcirculation image can be determined by image segmentation methods such as threshold segmentation, region growing, watershed algorithm, clustering, etc., to obtain an outer edge mask image.

[0067] Optionally, in order to more accurately determine the outer edge of the perfusion boundary area in the microcirculation image, the microcirculation image is segmented, the outer edge of the perfusion boundary area in the microcirculation image is determined, and the process of obtaining the outer edge mask image may include:

[0068] A preset area around each pixel in the microcirculation image is determined as the neighborhood of the pixel.

[0069] According to the gray value of each pixel in the neighborhood, the local segmentation threshold corresponding to the neighborhood of each pixel is calculated.

[0070] The gray value of each pixel in the microcirculation image is compared with the local segmentation threshold of the neighborhood where the pixel is located.

[0071] The outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain an outer edge mask image.

[0072] Combination Figure 2 As shown, in this embodiment, considering that the grayscale values ​​of the perfusion boundary area and the background area in the microcirculation image are not much different, in order to improve the accuracy of image segmentation, based on a local thresholding method such as an adaptive thresholding method, a preset area around each pixel in the microcirculation image is determined as the neighborhood of the pixel, and the local segmentation threshold corresponding to the neighborhood of each pixel is calculated according to the grayscale value of each pixel in the neighborhood, and the grayscale value of each pixel in the microcirculation image is compared with the local segmentation threshold of the neighborhood where the pixel is located, and the outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain an outer edge mask image.

[0073] For example, Figure 3 As shown in FIG, because the color of the background area in the microcirculation image is the lightest, the corresponding grayscale value is also the largest. Therefore, after comparing the grayscale value of each pixel in the microcirculation image with the local segmentation threshold of the neighborhood where the pixel is located, if the grayscale value of the pixel is less than the local segmentation threshold of the neighborhood where the pixel is located, the grayscale value of the pixel can be assigned to the first preset value representing the outer edge of the perfusion boundary area in the microcirculation image, such as the grayscale value 255 corresponding to white. If the grayscale value of the pixel is greater than or equal to the local segmentation threshold of the neighborhood where the pixel is located, the grayscale value of the pixel can be assigned to the second preset value representing the background in the microcirculation image, such as the grayscale value 0 corresponding to black, thereby obtaining Figure 3 The outer edge mask image is shown.

[0074] Among them, by calculating the local segmentation threshold in a small area of ​​the microcirculation image through the local thresholding method, a better binarization effect can be achieved under uneven illumination. This embodiment uses an adaptive thresholding method, which calculates a local segmentation threshold for each pixel in the microcirculation image. The local segmentation threshold can be the weighted average of the grayscale values ​​in the pixel neighborhood minus a constant, so that a good binarization effect can still be achieved in the area with low local contrast in the image.

[0075] Optionally, in order to further improve the accuracy of the outer edge mask image, determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result to obtain the outer edge mask image may include:

[0076] The outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain a first outer edge mask image.

[0077] The gray value of each pixel in the microcirculation image is compared with a preset global segmentation threshold, and the outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain a second outer edge mask image.

[0078] A bitwise AND operation is performed on the first outer edge mask image and the second outer edge mask image to obtain an outer edge mask image.

[0079] In this embodiment, the gray value of each pixel in the microcirculation image is compared with the preset global segmentation threshold, and the outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain a second outer edge mask image, that is, a single threshold (that is, the preset global segmentation threshold) is calculated for the entire microcirculation image through a global thresholding method to divide the microcirculation image into a foreground and a background. Exemplarily, the Otsu's method can be used to automatically select the optimal threshold as the preset global segmentation threshold by maximizing the inter-class variance.

[0080] This embodiment performs a bitwise AND operation on the first edge mask image and the second edge mask image to obtain an edge mask image, that is, combines the local thresholding method with the global thresholding method, comprehensively utilizes the advantages of global thresholding and local thresholding, and effectively retains the common parts of the two thresholding results through the bitwise AND operation, reduces the influence of noise, and enhances the connectivity of the region of interest, so as to better obtain the edge mask image.

[0081] In step 103, the outer edge mask image is used to extract the blood vessel region including the perfusion boundary region in the microcirculation image to obtain a target region image.

[0082] Exemplarily, extracting the blood vessel region including the perfusion boundary region in the microcirculation image using the outer edge mask image to obtain the target region image may include:

[0083] The outer edge mask image and the microcirculation image are superimposed, and the superimposed image is determined as the target area image.

[0084] In this embodiment, considering that the ultimate goal is to determine the size of the perfusion boundary area, and the perfusion boundary area is contained in the blood vessel area of ​​the microcirculation image, after obtaining the outer edge mask image, the blood vessel area that needs to be paid attention to (that is, the target area) can be limited to the outer edge mask area by superimposing the outer edge mask image and the microcirculation image, so as to facilitate the subsequent determination of the size of the perfusion boundary area through further processing of the blood vessel area. Figure 4 is an example of an image of the target area.

[0085] In step 104, the target region image is segmented, and the inner edge of the perfusion boundary region in the target region image is determined to obtain an inner edge mask image.

[0086] Combination Figure 4 As shown, in the target area image, except for the background area, the darker area is the blood flow area. If the outline of the blood flow area can be obtained, the innermost boundary of the perfusion boundary area can be determined, that is, the inner edge of the perfusion boundary area. Combined with the outer edge of the perfusion boundary area, the size of the perfusion boundary area can be determined.

[0087] Exemplarily, in order to obtain the contour of the blood flow area, the above-mentioned image segmentation methods such as threshold segmentation, region growing, watershed algorithm, clustering, etc. can be used to segment the target area image, determine the inner edge of the perfusion boundary area in the target area image, and obtain the inner edge mask image.

[0088] Optionally, in order to improve the accuracy of the inner edge mask image, segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining the inner edge mask image may include:

[0089] The target region image is segmented, the inner edge of the perfusion boundary region in the target region image is determined, and an initial inner edge mask image is obtained.

[0090] The initial inner edge mask image is subjected to hole filling processing to obtain a modified inner edge mask image.

[0091] An OR operation is performed on the initial inner edge mask image and the modified inner edge mask image to obtain an inner edge mask image.

[0092] like Figure 5 As shown in FIG, the initial inner edge mask image obtained by directly segmenting the target area image through the image segmentation method has many holes. In order to avoid the influence of the holes in the inner edge mask image on the subsequent perfusion boundary area calculation, the initial inner edge mask image can be filled with holes. Figure 6As shown in FIG. 1 , the effect of the hole filling process on the initial inner edge mask image is not ideal. Some small holes will be disconnected due to the hole filling process, resulting in many breakpoints in the modified inner edge mask image, which is also not conducive to the calculation of the subsequent perfusion boundary area. Therefore, this embodiment performs an OR operation on the initial inner edge mask image and the modified inner edge mask image, and superimposes the initial inner edge mask image and the modified inner edge mask image to obtain a better hole filling process effect, as shown in FIG. Figure 7 A more accurate inner edge mask image is shown.

[0093] In step 105, the size of the perfusion boundary area in the microcirculation image is calculated based on the outer edge mask image and the inner edge mask image.

[0094] like Figure 8 As shown, the outer mask image and the inner mask image are superimposed, and the area between the outer mask image and the inner mask image is the perfusion boundary area. After obtaining the outer mask image and the inner mask image, it is equivalent to obtaining the outer diameter and inner diameter of the blood vessel in the microcirculation image. According to the difference between the outer diameter and the inner diameter of the blood vessel, the size of the perfusion boundary area in the microcirculation image can be determined.

[0095] Optionally, calculating the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image may include:

[0096] The skeleton of the outer edge mask image is extracted, and the distance from each pixel point on the skeleton of the outer edge mask image to the corresponding pixel point of the outer edge of the perfusion boundary area is calculated, which is recorded as the first distance.

[0097] The skeleton of the inner edge mask image is extracted, and the distance from each pixel point on the skeleton of the inner edge mask image to the corresponding pixel point of the inner edge of the perfusion boundary area is calculated, which is recorded as the second distance.

[0098] The difference between each first distance and the second distance at the corresponding position is calculated, and the size of the perfusion boundary area in the microcirculation image is determined according to the difference.

[0099] In this embodiment, after the perfusion boundary area is segmented, Fig. 9 and Fig.10 As shown in FIG. 1 , the outer mask image and the inner mask image are skeletonized respectively, and the distance from each point on the skeleton to the boundary area is calculated, that is, the first distance from each pixel point on the skeleton of the outer mask image to the corresponding pixel point on the outer edge of the perfusion boundary area, and the second distance from each pixel point on the skeleton of the inner mask image to the corresponding pixel point on the inner edge of the perfusion boundary area, and the pixel-level PBR size can be obtained. On this basis, the pixel-level PBR values ​​are averaged to obtain the average pixel PBR value of the entire microcirculation image.

[0100] In order to more intuitively measure the size of PBR in microcirculation images, after obtaining the PBR value at the pixel level and the average pixel PBR, the pixel unit can be converted into micron unit according to the scale.

[0101] Optionally, determining the size of the perfusion boundary area in the microcirculation image according to the difference may include:

[0102] The blood vessels in the microcirculation image are segmented according to their diameters.

[0103] For each blood vessel segment, the size of the perfusion boundary area of ​​the blood vessel segment in the microcirculation image is determined according to the average value of the difference values ​​corresponding to each position in the blood vessel segment.

[0104] Combination Figure 2 As shown in the figure, the microcirculation image may contain multiple blood vessels, and the diameter of each blood vessel varies. For blood vessels with larger diameters, their perfusion boundary areas are relatively thick, while for blood vessels with smaller diameters, their perfusion boundary areas are relatively thin. Therefore, calculating the average PBR value of all blood vessels in the microcirculation image may not represent the size of the perfusion boundary area well.

[0105] Therefore, in this embodiment, when determining the size of the perfusion boundary area in the microcirculation image, the blood vessels in the microcirculation image are first classified according to their diameters, for example, into large blood vessels, medium blood vessels, small blood vessels, etc. Then, the corresponding average PBR is calculated for each category of blood vessels.

[0106] The embodiment of the present invention obtains a microcirculation image, and first segments the microcirculation image, determines the outer edge of the perfusion boundary area in the microcirculation image, and obtains an outer edge mask image; then uses the outer edge mask image to extract the blood vessel area containing the perfusion boundary area in the microcirculation image to obtain a target area image; then performs a second segmentation on the target area image to determine the inner edge of the perfusion boundary area in the target area image to obtain an inner edge mask image; thereby, the size of the perfusion boundary area in the microcirculation image can be accurately calculated based on the outer edge mask image and the inner edge mask image, which not only reduces the computational complexity, but also accurately measures the perfusion boundary area between the red blood cells and the vascular endothelium in the microvessels through the outer edge mask image and the inner edge mask image, thereby providing strong support for evaluating the health of microcirculation and the state of endothelial glycocalyx.

[0107] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0108] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0109] Fig.11 The schematic diagram of the structure of the microcirculation perfusion boundary area calculation device provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0110] like Fig.11 As shown, the microcirculation perfusion boundary area calculation device includes: an acquisition module 111, a first processing module 112, a second processing module 113, a third processing module 114 and a calculation module 115.

[0111] The acquisition module 111 is used to acquire microcirculation images.

[0112] The first processing module 112 is used to segment the microcirculation image, determine the outer edge of the perfusion boundary area in the microcirculation image, and obtain an outer edge mask image.

[0113] The second processing module 113 is used to extract the blood vessel region including the perfusion boundary region in the microcirculation image by using the outer edge mask image to obtain a target region image.

[0114] The third processing module 114 is used to segment the target region image, determine the inner edge of the perfusion boundary region in the target region image, and obtain an inner edge mask image.

[0115] The calculation module 115 is used to calculate the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image.

[0116] The embodiment of the present invention obtains a microcirculation image, and first segments the microcirculation image, determines the outer edge of the perfusion boundary area in the microcirculation image, and obtains an outer edge mask image; then uses the outer edge mask image to extract the blood vessel area containing the perfusion boundary area in the microcirculation image to obtain a target area image; then performs a second segmentation on the target area image to determine the inner edge of the perfusion boundary area in the target area image to obtain an inner edge mask image; thereby, the size of the perfusion boundary area in the microcirculation image can be accurately calculated based on the outer edge mask image and the inner edge mask image, which not only reduces the computational complexity, but also accurately measures the perfusion boundary area between the red blood cells and the vascular endothelium in the microvessels through the outer edge mask image and the inner edge mask image, thereby providing strong support for evaluating the health of microcirculation and the state of endothelial glycocalyx.

[0117] In a possible implementation, the first processing module 112 can be used to determine a preset area around each pixel in the microcirculation image as a neighborhood of the pixel; calculate a local segmentation threshold corresponding to the neighborhood of each pixel according to the grayscale value of each pixel in the neighborhood; compare the grayscale value of each pixel in the microcirculation image with the local segmentation threshold of the neighborhood where the pixel is located; determine the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result, and obtain an outer edge mask image.

[0118] In a possible implementation, the first processing module 112 can be used to determine the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result to obtain a first outer edge mask image; compare the grayscale value of each pixel in the microcirculation image with a preset global segmentation threshold, and determine the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result to obtain a second outer edge mask image; perform a bitwise AND operation on the first outer edge mask image and the second outer edge mask image to obtain an outer edge mask image.

[0119] In a possible implementation, the second processing module 113 may be used to superimpose the outer edge mask image and the microcirculation image, and determine the superimposed image as the target area image.

[0120] In one possible implementation, the third processing module 114 can be used to segment the target area image, determine the inner edge of the perfusion boundary area in the target area image, and obtain an initial inner edge mask image; perform hole filling processing on the initial inner edge mask image to obtain a revised inner edge mask image; perform an OR operation on the initial inner edge mask image and the revised inner edge mask image to obtain an inner edge mask image.

[0121] In a possible implementation, the calculation module 115 can be used to perform skeleton extraction on the outer edge mask image, and calculate the distance from each pixel point on the skeleton of the outer edge mask image to the corresponding pixel point on the outer edge of the perfusion boundary area, recorded as the first distance; perform skeleton extraction on the inner edge mask image, and calculate the distance from each pixel point on the skeleton of the inner edge mask image to the corresponding pixel point on the inner edge of the perfusion boundary area, recorded as the second distance; calculate the difference between each first distance and the second distance of the corresponding position, and determine the size of the perfusion boundary area in the microcirculation image according to the difference.

[0122] In a possible implementation, the calculation module 115 can be used to segment the blood vessels in the microcirculation image according to the diameter of the blood vessels in the microcirculation image; for each blood vessel segment, the size of the perfusion boundary area of ​​the blood vessel segment in the microcirculation image is determined according to the average value of the difference values ​​corresponding to each position in the blood vessel segment.

[0123] Fig.12 is a schematic diagram of a terminal provided by an embodiment of the present invention. Fig.12As shown, the terminal 12 of this embodiment includes: a processor 120, a memory 121, and a computer program 122 stored in the memory 121 and executable on the processor 120. When the processor 120 executes the computer program 122, the steps in the above-mentioned microcirculation perfusion boundary area calculation method embodiments are implemented, for example Figure 1 Alternatively, when the processor 120 executes the computer program 122, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Fig.11 The functions of each module are shown.

[0124] Exemplarily, the computer program 122 may be divided into one or more modules / units, one or more modules / units are stored in the memory 121, and are executed by the processor 120 to implement the present invention. One or more modules / units may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 122 in the terminal 12. For example, the computer program 122 may be divided into Fig.11 The modules shown.

[0125] The terminal 12 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 12 may include, but is not limited to, a processor 120 and a memory 121. Those skilled in the art will appreciate that Fig.12 It is only an example of the terminal 12 and does not constitute a limitation on the terminal 12. The terminal 12 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0126] The processor 120 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0127] The memory 121 may be an internal storage unit of the terminal 12, such as a hard disk or memory of the terminal 12. The memory 121 may also be an external storage device of the terminal 12, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal 12. Further, the memory 121 may also include both an internal storage unit of the terminal 12 and an external storage device. The memory 121 is used to store computer programs and other programs and data required by the terminal. The memory 121 may also be used to temporarily store data that has been output or is to be output.

[0128] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0129] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0131] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned microcirculation perfusion boundary area calculation method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for calculating microcirculation perfusion boundary area, characterized in that: include: Acquire microcirculatory images; Segmenting the microcirculation image, determining the outer edge of the perfusion boundary area in the microcirculation image, and obtaining an outer edge mask image; Extracting the blood vessel region including the perfusion boundary region in the microcirculation image using the outer edge mask image to obtain a target region image; Segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining an inner edge mask image; Calculating the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image; Segmenting the target area image, determining the inner edge of the perfusion boundary area in the target area image, and obtaining an inner edge mask image, including: Segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining an initial inner edge mask image; Performing hole filling processing on the initial inner edge mask image to obtain a modified inner edge mask image; An OR operation is performed on the initial inner edge mask image and the modified inner edge mask image to obtain an inner edge mask image.

2. The microcirculation perfusion boundary area calculation method according to claim 1, characterized in that: Segmenting the microcirculation image, determining the outer edge of the perfusion boundary area in the microcirculation image, and obtaining an outer edge mask image, including: Determine a preset area around each pixel in the microcirculation image as a neighborhood of the pixel; Calculate the local segmentation threshold corresponding to the neighborhood of each pixel according to the gray value of each pixel in the neighborhood; Comparing the gray value of each pixel in the microcirculation image with the local segmentation threshold of the neighborhood where the pixel is located; The outer edge of the perfusion boundary area in the microcirculation image is determined according to the comparison result to obtain an outer edge mask image.

3. The microcirculation perfusion boundary area calculation method according to claim 2, characterized in that: Determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result to obtain the outer edge mask image includes: determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result, and obtaining a first outer edge mask image; Comparing the grayscale value of each pixel in the microcirculation image with a preset global segmentation threshold, determining the outer edge of the perfusion boundary area in the microcirculation image according to the comparison result, and obtaining a second outer edge mask image; A bitwise AND operation is performed on the first outer edge mask image and the second outer edge mask image to obtain an outer edge mask image.

4. The microcirculation perfusion boundary area calculation method according to claim 1, characterized in that: The blood vessel region including the perfusion boundary region in the microcirculation image is extracted by using the outer edge mask image to obtain a target region image, including: The outer edge mask image and the microcirculation image are superimposed, and the superimposed image is determined as the target area image.

5. The microcirculation perfusion boundary area calculation method according to claim 1, characterized in that: Calculating the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image includes: Performing skeleton extraction on the outer edge mask image, and calculating the distance from each pixel point on the skeleton of the outer edge mask image to the corresponding pixel point on the outer edge of the perfusion boundary area, recorded as a first distance; Performing skeleton extraction on the inner edge mask image, and calculating the distance from each pixel point on the skeleton of the inner edge mask image to the corresponding pixel point of the inner edge of the perfusion boundary area, recorded as a second distance; The difference between each first distance and the second distance at the corresponding position is calculated, and the size of the perfusion boundary area in the microcirculation image is determined according to the difference.

6. The microcirculation perfusion boundary area calculation method according to claim 5, characterized in that: Determining the size of the perfusion boundary area in the microcirculation image according to the difference includes: Segmenting the blood vessels in the microcirculation image according to the diameters of the blood vessels in the microcirculation image; For each blood vessel segment, the size of the perfusion boundary area of ​​the blood vessel segment in the microcirculation image is determined according to the average value of the difference values ​​corresponding to each position in the blood vessel segment.

7. A microcirculation perfusion boundary area calculation device, characterized in that: include: An acquisition module, used for acquiring microcirculation images; A first processing module is used to segment the microcirculation image, determine the outer edge of the perfusion boundary area in the microcirculation image, and obtain an outer edge mask image; A second processing module is used to extract the blood vessel region including the perfusion boundary region in the microcirculation image by using the outer edge mask image to obtain a target region image; A third processing module is used to segment the target area image, determine the inner edge of the perfusion boundary area in the target area image, and obtain an inner edge mask image; A calculation module, configured to calculate the size of the perfusion boundary area in the microcirculation image based on the outer edge mask image and the inner edge mask image; The third processing module is specifically used for: Segmenting the target region image, determining the inner edge of the perfusion boundary region in the target region image, and obtaining an initial inner edge mask image; Performing hole filling processing on the initial inner edge mask image to obtain a modified inner edge mask image; An OR operation is performed on the initial inner edge mask image and the modified inner edge mask image to obtain an inner edge mask image.

8. A terminal, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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