Method, device and storage medium for dividing large intestinal fluid

Abstract

The present application discloses a method, device and storage medium for segmenting large intestinal effusion. The method includes: extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image and using it as the initial segmentation area; based on the initial segmentation area, using the active contour model to perform contour extraction to obtain the intestinal segmentation result; calculating the minimum enclosing circle of the intestinal segmentation result in the cross-sectional area; using the Gaussian mixture model to fit the probability distribution of the large intestine area and the probability distribution of the large intestine effusion area within the minimum enclosing circle; and inputting the fitting result into the graph cut algorithm model for effusion segmentation to segment the large intestine effusion area from the large intestine area. Using the solution of the present application, the large intestine effusion area can be accurately extracted, and subsequently by combining the large intestine effusion area with the large intestine area, the complete large intestine area can be segmented.

Description

Technical Field

[0001] The present application generally relates to the field of image processing technology. More specifically, the present application relates to a method, device, and computer-readable storage medium for segmenting large intestinal fluid. Background Art

[0002] Virtual endoscopy, with its significant advantages such as non-invasiveness and repeatability, has been widely used in many clinical trials and various medical diagnostics, such as intestinal examinations. Virtual endoscopy first obtains tomographic data of the human body using medical scanning equipment such as computed tomography (CT) and magnetic resonance imaging (MRI). Then, using image processing technology, it reconstructs a three-dimensional image, creating a virtual human tissue.

[0003] In actual filming scenarios, the large intestine may not be fully inflated or may accumulate excessive fluid when capturing 3D images. Fluid accumulation is the incomplete removal of water from the colon wall. This can result in an incomplete image of the large intestine in the reconstructed 3D image, impacting subsequent analysis (such as conformal unfolding or 3D navigation). Currently, methods such as region growing, watershed, and level set segmentation have been used to segment the colon. However, these methods cannot automatically identify the features of the large intestine region and cannot segment the complete large intestine region, so they are only applicable to ideal images. Alternatively, deep learning methods can be used to segment the large intestine, but because the intestinal region is constantly moving, it can even vary between two different inspections, unlike other organs, which have fixed shapes. Therefore, deep learning methods have difficulty learning the fixed pattern of this constantly changing large intestine region, and similarly have difficulty segmenting the complete large intestine region.

[0004] In view of this, there is an urgent need to provide a solution for segmenting large intestinal effusion. By directly extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image as the initial segmentation point, and combining the prior knowledge of the large intestine shape to calculate the minimum enclosing circle, the probability distribution of the large intestine region and the probability distribution of the large intestine effusion region can be more accurately fitted, thereby excluding irrelevant areas and similar effusion areas. The fitting results are then input into the graph cut algorithm model for effusion segmentation. Through global optimization, the global optimal solution is converged to accurately extract the large intestinal effusion area. Subsequently, the large intestinal effusion area is combined with the large intestine area to segment the complete large intestine area. Summary of the Invention

[0005] In order to at least solve one or more of the technical problems mentioned above, the present application proposes solutions for segmenting large intestinal fluid accumulation in multiple aspects.

[0006] In a first aspect, the present application provides a method for segmenting large intestinal effusion, comprising: extracting a target skeleton with the largest skeleton volume from a three-dimensional intestinal image and using it as an initial segmentation area; based on the initial segmentation area, using an active contour model to perform contour extraction to obtain an intestinal segmentation result; calculating the minimum enclosing circle of the intestinal segmentation result in a cross-sectional area; using a Gaussian mixture model to fit the probability distribution of the large intestine area and the probability distribution of the large intestine effusion area within the minimum enclosing circle; and inputting the fitting result into a graph cut algorithm model for effusion segmentation to segment the large intestine effusion area from the large intestine area.

[0007] In one embodiment, before extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image, the method further includes: setting a grayscale threshold; and roughly segmenting the three-dimensional intestinal image based on the grayscale threshold.

[0008] In another embodiment, extracting a target skeleton with the largest skeleton volume from a three-dimensional intestinal image includes: extracting a two-dimensional surface of the large intestine region from the three-dimensional intestinal image; calculating the shortest Euclidean distance from a voxel point in the three-dimensional intestinal image to the two-dimensional surface; and extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image according to the shortest Euclidean distance.

[0009] In another embodiment, extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image based on the shortest Euclidean distance includes: comparing the shortest Euclidean distance with a preset distance threshold; and extracting voxel points whose shortest Euclidean distance is greater than the preset distance threshold from the three-dimensional intestinal image to obtain the target skeleton with the largest skeleton volume.

[0010] In another embodiment, contour extraction is performed using an active contour model based on the initial segmentation area to obtain the intestinal segmentation result, which includes: constructing an energy function under the active contour model based on the initial segmentation area; and minimizing the energy function to perform contour extraction to obtain the intestinal segmentation result.

[0011] In yet another embodiment, fitting the probability distribution of the large intestine region and the probability distribution of the large intestinal effusion region within the minimum enclosing circle using a Gaussian mixture model includes fitting the probability distribution of the large intestine region and the probability distribution of the large intestinal effusion region using the following formula:

[0012]

[0013] in, represents the fitting result, represents the grayscale value within the minimum enclosing circle, represents the probability distribution of the large intestinal fluid accumulation area, represents the probability distribution of the large intestine region, and represents the mean, and represents the variance, and represents the weight of the corresponding probability distribution.

[0014] In another embodiment, inputting the fitting result into the graph cut algorithm model for fluid segmentation to segment the large intestinal fluid accumulation area from the large intestinal area includes: inputting the fitting result into the graph cut algorithm model for optimization; and taking the area formed by the point where the median value of the optimization result is 1 as the large intestinal fluid accumulation area to segment the large intestinal fluid accumulation area from the large intestinal area.

[0015] In yet another embodiment, the fitting result is input into the graph cut algorithm model for optimization using the following formula:

[0016]

[0017] in, f represents the grayscale value within the minimum enclosing circle, represents the fitting result, Y Indicates the value 0 or 1 in the optimization, subscript i represents a voxel point, subscript j Represents voxel points i 's neighborhood points.

[0018] In a second aspect, the present application provides a device for segmenting large intestinal effusion, comprising: a processor; and a memory, in which program instructions for segmenting large intestinal effusion are stored. When the program instructions are executed by the processor, the device implements one or more embodiments of the aforementioned first aspect.

[0019] In a third aspect, the present application provides a computer-readable storage medium having stored thereon computer-readable instructions for segmenting large intestinal effusion, wherein when the computer-readable instructions are executed by one or more processors, one or more embodiments of the aforementioned first aspect are implemented.

[0020] Through the scheme for segmenting large intestinal effusion provided above, the embodiment of the present application extracts the target skeleton with the largest skeleton volume from the three-dimensional intestinal image and uses it as the initial segmentation area. Then, the points within the initial segmentation area are used as the initial points, and the active contour model is used to perform contour extraction to obtain the intestinal segmentation result. Furthermore, based on the prior knowledge of the shape of the large intestine (that is, the large intestine is circular in each cross-section), the minimum enclosing circle is calculated, and the Gaussian mixture model is used to fit the probability distribution of the large intestine area and the probability distribution of the large intestine effusion area within the minimum enclosing circle. In this way, irrelevant areas and similar effusion areas can be excluded. Then, the fitting results are input into the graph cut algorithm model for effusion segmentation, so as to converge to the global optimal solution through global optimization to accurately extract the large intestine effusion area. Subsequently, the large intestine effusion area is combined with the large intestine area to segment out the complete large intestine area. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0022] Figure 1 is an exemplary flow chart illustrating a method for segmenting large intestinal fluid accumulation according to an embodiment of the present application;

[0023] Figure 2 is an exemplary schematic diagram showing a roughly segmented three-dimensional intestinal image according to an embodiment of the present application;

[0024] Figure 3 is an exemplary schematic diagram showing a skeleton including a large intestine and a small intestine according to an embodiment of the present application;

[0025] Figure 4 is an exemplary schematic diagram showing a target skeleton with the largest skeleton volume according to an embodiment of the present application;

[0026] Figure 5 is an exemplary schematic diagram showing a large intestine region obtained by segmentation according to an embodiment of the present application;

[0027] Figure 6 is an exemplary schematic diagram showing a cross-sectional area of a large intestine region according to an embodiment of the present application;

[0028] Figure 7 is an exemplary schematic diagram showing a complete large intestine region according to an embodiment of the present application;

[0029] Figure 8 is a flowchart illustrating an exemplary process of obtaining a complete large intestine region according to an embodiment of the present application;

[0030] Figure 9 2 is an exemplary structural block diagram showing an apparatus for segmenting large intestinal fluid accumulation according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0032] It should be understood that the terms "include" and "comprising" used in the description and claims of this application indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this specification and claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" as used in this specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0034] As used in this specification and claims, the term “if” can be interpreted as “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [described condition or event] is detected” can be interpreted as meaning “upon determination” or “in response to determining” or “upon detection of [described condition or event]” or “in response to detecting [described condition or event],” depending on the context.

[0035] The specific implementation of the present application will be described in detail below with reference to the accompanying drawings.

[0036] Figure 1 FIG. 1 is an exemplary flow chart showing a method 100 for segmenting large intestinal effusion according to an embodiment of the present application. Figure 1As shown in FIG, in step S101, a target skeleton with the largest skeleton volume is extracted from the three-dimensional intestinal image and used as the initial segmentation region. In one implementation scenario, the three-dimensional intestinal image can be acquired by an acquisition device such as CT or MRI and obtained through image reconstruction. Before extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image, the three-dimensional intestinal image can be roughly segmented to filter out interfering information such as air and fat in the image.

[0037] In one embodiment, a grayscale threshold can be set to roughly segment the three-dimensional intestinal image based on the grayscale threshold. According to the Hounsfield scale, the HU value (also known as the CT value, or grayscale value) of air is -1000, and the HU value of fat is -120. Therefore, in some embodiments, the aforementioned grayscale threshold can be set to -1000 to -120. Based on this grayscale threshold, the three-dimensional intestinal image can be roughly segmented, and after the rough segmentation, it also includes, for example, the lungs, small intestine, large intestine, and interference noise areas. Furthermore, by performing subsequent segmentation operations on the results of the rough segmentation, the large intestine area can be extracted.

[0038] It can be understood that the biggest difference between the intestinal area and other areas is that the intestinal area is a long and narrow area, so in theory the intestinal area can be judged according to the length of the central axis of the intestine. However, the large intestine and the small intestine cannot be distinguished based on the length of the central axis alone, and because the large intestine is thicker than the small intestine, the small intestine can be further excluded based on the thickness. For example, in some embodiments, the large intestine and small intestine can be obtained based on the length of the central axis, and then the large intestine can be obtained based on the thickness. In an embodiment of the present application, the large intestine area is segmented by multiplying the length by the thickness, that is, by the volume of the skeleton. Specifically, the target skeleton with the largest skeleton volume is extracted from the three-dimensional intestinal image and used as the initial segmentation area.

[0039] In one embodiment, a two-dimensional surface of the large intestine region can be first extracted from a three-dimensional intestinal image. The shortest Euclidean distance from a voxel point in the three-dimensional intestinal image to the two-dimensional surface is then calculated. This is used to extract a target skeleton with the largest skeleton volume from the three-dimensional intestinal image based on the shortest Euclidean distance. In an implementation scenario, for a voxel point in a three-dimensional intestinal image, if its neighborhood (e.g., a six-neighborhood neighborhood) contains both the large intestine and other tissues or organs (e.g., the small intestine, lungs, etc.), then that voxel point is considered a two-dimensional surface point of the large intestine region. By performing this evaluation on all voxel points, the two-dimensional surface of the large intestine region can be extracted.

[0040] Based on the extracted two-dimensional surface of the large intestine area, for the remaining voxel points in the three-dimensional intestinal image (i.e., the voxel points other than the two-dimensional surface points), the shortest Euclidean distance of the voxel point to the two-dimensional surface is calculated. In some embodiments, for example, a binary image Euclidean distance transformation acceleration algorithm can be used to implement the aforementioned calculation of the shortest Euclidean distance of the voxel point to the two-dimensional surface. It can be understood that the binary image Euclidean distance transformation is a process of representing the distance between spatial points (target points and background points), and finally converting the binary image into a grayscale image. Specifically, it can be calculated by the formula Implement the Euclidean distance transform of binary images, where Represents voxel points To a 2D surface point The shortest Euclidean distance, (*) represents the Euclidean distance function, represents the remaining voxel point sets (i.e., target sets) in the three-dimensional intestinal image, Represents a two-dimensional surface (i.e., the background set). Based on the calculated shortest Euclidean distance, the target skeleton with the largest skeleton volume can be extracted from the three-dimensional intestinal image.

[0041] In one embodiment, the shortest Euclidean distance can be compared with a preset distance threshold to extract voxel points whose shortest Euclidean distance is greater than the preset distance threshold from the three-dimensional intestinal image to obtain the target skeleton with the largest skeleton volume. It should be understood that inside the large intestine, the closer to the central axis, the greater the distance. Therefore, a preset distance threshold can be used so that when all points greater than the preset distance threshold are extracted, the small intestine area can be filtered out. As can be seen from the foregoing, the small intestine is thinner than the large intestine, so the area in the small intestine that is greater than the preset distance threshold is far less than that in the large intestine. Preferably, the aforementioned preset distance threshold can be, for example, 10. In combination with the anatomical characteristics of the large intestine and the small intestine, by setting the preset distance threshold to 10, the small intestine can be filtered out while avoiding exceeding the thickness of the large intestine, causing the large intestine area to disappear, thereby being able to extract the target skeleton with the largest volume.

[0042] Based on the target skeleton with the largest skeleton volume obtained above, it is used as the initial segmentation region, and at step S102, based on the initial segmentation region, the active contour model is used to perform contour extraction to obtain the intestinal segmentation result. In one embodiment, an energy function can be constructed under the active contour model based on the initial segmentation region, and then the energy function can be minimized to perform contour extraction to obtain the intestinal segmentation result. Specifically, by taking the initial segmentation region as the initial value and optimizing the initial segmentation region to minimize the energy function constructed under the active contour model, the intestinal segmentation result is obtained. In the embodiment of the present application, the intestinal segmentation result is the large intestine region.

[0043] In some embodiments, the above-mentioned active contour model can be, for example, an edge-based active contour model (such as a snake algorithm model), or a region-based active contour model (such as a Chan-Vese algorithm model). Among them, the edge-based active contour model is to make the curve evolve to a specific edge of the image, that is, to find the target curve. The region-based active contour model is to evolve a specific region with the target curve as the boundary, that is, to find the specific region. Taking the region-based active contour model as an example, an energy function is constructed by, for example, a Chan-Vese algorithm model. Preferably, the embodiment of the present application also uses, for example, a level set method to optimize the initial segmentation area, so as to minimize the energy function for contour extraction, thereby obtaining the large intestine area.

[0044] Specifically, the Chan-Vese algorithm model constructs an energy function using the grayscale values of the 3D intestinal image as energy to evolve the curve to the target area. In the embodiment of the present application, the initial segmented area is used as a level set function (a function of the surface), and this level set function replaces the curve evolution in the Chan-Vese algorithm model. For example, in one implementation scenario, the energy function can be constructed using the following formula:

[0045] (1)

[0046] in, Represents the initial segmentation area (that is, the gray value of the level set function), Represents the grayscale value of the original three-dimensional intestinal image, represents the energy of the area within the curve, represents the energy outside the curve, and Represent the pixel mean values in the area inside the curve and outside the curve respectively, and Represent the weights of the area inside the curve and the area outside the curve respectively. and denote the regularization terms for length and surface area respectively, represents a step function, controlling the area within the curve ( ) and the area outside the curve ( ), represents the Diktonic function.

[0047] In some embodiments, the initial segmented regions can be optimized by, for example, variational methods and gradient descent flow. , to minimize the above energy function, and when optimized to When >0, the intestinal segmentation result is obtained, that is, the large intestine area is obtained.

[0048] Then, in step S103, the minimum enclosing circle of the intestinal segmentation result in the cross-sectional area is calculated. It can be understood that the grayscale distribution of the large intestine fluid accumulation area is significantly different from that of the large intestine area. Therefore, based on the prior knowledge of the shape of the large intestine, the large intestine is circular in each cross-section. Based on this, the embodiment of the present application calculates the minimum enclosing circle of the large intestine area in each cross-sectional area, and the minimum enclosing circle can contain the fluid accumulation. In some embodiments, for example, the minimum circle covering algorithm can be used to calculate the minimum enclosing circle of the large intestine area in each cross-sectional area. The minimum circle covering algorithm can find the minimum circle covering n points within linear time complexity.

[0049] Furthermore, at step S104, a Gaussian mixture model is used to fit the probability distribution of the large intestine region and the probability distribution of the large intestine fluid accumulation region within the minimum enclosing circle. Based on the prior knowledge of the large intestine, only the large intestine distribution and the fluid accumulation distribution exist within the minimum enclosing circle. Therefore, the embodiment of the present application uses two Gaussian distributions in the Gaussian mixture model, one of which fits the large intestine distribution and the other fits the fluid accumulation distribution. For example, in an exemplary scenario, the probability distribution of the large intestine region and the probability distribution of the large intestine fluid accumulation region can be fitted by the following formula:

[0050] (2)

[0051] in, represents the fitting result, Represents the grayscale value within the minimum enclosing circle, represents the probability distribution of the large intestine fluid accumulation area, represents the probability distribution of the large intestine region, and represents the mean, and represents the variance, and represents the weight of the corresponding probability distribution.

[0052] Based on the fitting results of the above fitting, at step S105, the fitting results are input into a graph cut algorithm model for fluid segmentation, thereby segmenting the large intestinal fluid accumulation region from the large intestine region. In one embodiment, the fitting results are input into a graph cut algorithm model for optimization, and the region formed by the points where the median value of the optimization result is 1 is used as the large intestinal fluid accumulation region, thereby segmenting the large intestinal fluid accumulation region from the large intestine region.

[0053] In some embodiments, the distribution corresponding to the larger mean value in the above fitting process is used as the probability distribution of the large intestinal effusion area, and then the probability distribution of the large intestinal effusion area after fitting is used as the initial value of the graph cut algorithm model to optimize it through the graph cut algorithm model. In one implementation scenario, the fitting result can be input into the graph cut algorithm model for optimization using the following formula:

[0054] (3)

[0055] in, f Represents the grayscale value within the minimum enclosing circle, represents the fitting result (i.e. the probability distribution of large intestinal fluid accumulation area), Y Indicates the value 0 or 1 in the optimization, subscript i represents a voxel point, subscript j Represents voxel points i Furthermore, the point where the median value of the optimization result is 1 (that is, ) is used as the large intestine fluid accumulation region. In some embodiments, a complete large intestine region can be obtained by combining the large intestine fluid accumulation region with the large intestine region.

[0056] In combination with the above description, it can be seen that the embodiment of the present application extracts the target skeleton with the largest skeleton volume from the three-dimensional intestinal image and uses it as the initial segmentation area. Then, the points within the initial segmentation area are used as the initial points, and the active contour model is used to perform contour extraction to obtain the intestinal segmentation result. Furthermore, the minimum enclosing circle is calculated based on the prior knowledge of the shape of the large intestine (that is, the large intestine is a circle in each cross-section), and the Gaussian mixture model is used to fit the probability distribution of the large intestine area and the probability distribution of the large intestine fluid accumulation area within the minimum enclosing circle. In this way, irrelevant areas and similar fluid accumulation areas can be excluded. Then, the fitting results are input into the graph cut algorithm model for fluid accumulation segmentation, so as to converge to the global optimal solution through global optimization to accurately extract the large intestine fluid accumulation area. Subsequently, the large intestine fluid accumulation area is combined with the large intestine area to segment out the complete large intestine area.

[0057] Figure 2 is an exemplary schematic diagram showing a roughly segmented three-dimensional intestinal image according to an embodiment of the present application. In some embodiments, the original collected three-dimensional intestinal image can be roughly segmented by setting a grayscale threshold to obtain the following: Figure 2 The roughly segmented 3D intestinal image is shown in . As an example, the grayscale threshold can be set to -1000 to -120. As can be seen from the figure, the roughly segmented image also includes, for example, lung 201, small intestine 202, large intestine 203, and interference noise areas.

[0058] Figure 3 This is an exemplary schematic diagram showing a skeleton including large intestine and small intestine according to an embodiment of the present application. As can be seen from the above, the biggest difference between the intestinal region and other regions is that the intestinal region is a narrow and long region. Therefore, for example, the lung or noise region can be filtered out by the length of the central axis to obtain the following: Figure 3The skeleton shown in includes the large intestine and the small intestine. In addition, since the large intestine is thicker than the small intestine, the small intestine can be further filtered out to obtain the large intestine. In an embodiment of the present application, the large intestine region is segmented by the volume of the skeleton. In an implementation scenario, the two-dimensional surface of the large intestine region can be first extracted from the three-dimensional intestinal image, and then the voxel points whose shortest Euclidean distance from the remaining voxel points in the three-dimensional intestinal image to the two-dimensional surface are extracted to be greater than a preset threshold, so as to obtain the target skeleton with the largest skeleton volume.

[0059] Figure 4 : is an exemplary schematic diagram showing a target skeleton with the largest skeleton volume according to an embodiment of the present application. Figure 4 The target skeleton is shown in . As can be seen from the figure, the small intestine skeleton is filtered out of the target skeleton, and the large intestine skeleton remains. In an implementation scenario, by using the target skeleton as the initial segmentation area, an energy function is constructed under the active contour model, and contour extraction is performed by minimizing the energy function to obtain the intestinal segmentation result, that is, the large intestine area. Preferably, the energy function can be constructed by, for example, the Chan-Vese algorithm model, and combined with, for example, the level set method to optimize the initial segmentation area, so as to minimize the energy function for contour extraction, thereby obtaining the large intestine area (for example Figure 5 For more details on the construction of energy function and its optimization, please refer to the above Figure 1 The content described will not be repeated in this application.

[0060] Figure 5 FIG. 1 is an exemplary schematic diagram showing a large intestine region obtained by segmentation according to an embodiment of the present application. Figure 5 Shown in the figure is the large intestine region obtained by segmentation using the Chan-Vese algorithm combined with the level set method. As can be seen from the figure, the large intestine region where there is fluid accumulation is missing, such as the area indicated by the arrow A in the figure. Therefore, the embodiment of the present application further extracts the fluid accumulation region, and then combines the fluid accumulation region with the large intestine region to obtain the complete large intestine region. In an implementation scenario, the minimum enclosing circle of the large intestine region in each cross-sectional area is first calculated, and then the probability distribution of the large intestine region and the probability distribution of the large intestine fluid accumulation region are fitted within the minimum enclosing circle to extract the probability distribution of the large intestine fluid accumulation region.

[0061] Figure 6 FIG is an exemplary schematic diagram showing a cross-sectional area of a large intestine region according to an embodiment of the present application. Figure 6 Figure (a) shows the original image of the large intestine in the cross-section area. Figure 6 Figure (b) shows the large intestine regions S1, S2 and S3 in the original image of the cross-sectional area. As mentioned above, the minimum enclosing circle of the large intestine region in each cross-sectional area is first calculated, for example Figure 6As shown in c1, c2 and c3 in Figure (c). In some embodiments, the minimum circle covering algorithm, for example, can be used to calculate the minimum enclosing circle of the large intestine region in each cross-sectional area. Then, based on the above formula (2), the Gaussian mixture model is used to fit the probability distribution of the large intestine region and the probability distribution of the large intestine fluid accumulation region within the minimum enclosing circle, and the distribution corresponding to the larger mean value in the fitting process is used as the probability distribution of the large intestine fluid accumulation region. Further, using the probability distribution of the large intestine fluid accumulation region as the initial value, the above formula (3) is used to perform fluid accumulation segmentation through the graph cut algorithm to obtain the fluid accumulation region, for example Figure 6 The partial fluid accumulation is shown in region E of FIG (d). Similarly, by obtaining the fluid accumulation region in each cross-sectional area of the large intestine region in the embodiment of the present application, all fluid accumulation regions can be obtained. Subsequently, the fluid accumulation region and the large intestine region can be combined to obtain the complete large intestine region, for example Figure 7 shown.

[0062] Figure 7 FIG. 1 is an exemplary schematic diagram showing a complete large intestine region according to an embodiment of the present application. Figure 7 The figure shows a complete large intestine region obtained by combining the fluid accumulation region with the large intestine region. The arrows in the figure indicate the fluid accumulation region. In an implementation scenario, by performing analyses such as conformal unfolding or 3D navigation on this complete large intestine region, intestinal diseases, such as polyp detection, can be detected.

[0063] Figure 8 FIG. 1 is an exemplary flowchart illustrating obtaining a complete large intestine region according to an embodiment of the present application. Figure 8 As shown in , at step S801, a three-dimensional intestinal image is acquired. In an implementation scenario, the three-dimensional intestinal image can be acquired by acquisition equipment such as CT, MRI, etc. At step S802, a grayscale threshold is set for rough segmentation. In some embodiments, the aforementioned grayscale threshold can be set to -1000 to -120. After rough segmentation, it also includes, for example, lungs, small intestine, large intestine, and interference noise areas. Then, at step S803, the target skeleton with the largest skeleton volume is extracted and used as the initial segmentation area. Specifically, the voxel points whose shortest Euclidean distance is greater than a preset distance threshold can be extracted from the three-dimensional intestinal image to obtain the target skeleton with the largest skeleton volume, and use it as the initial segmentation area.

[0064] Based on the initial segmented area, at step S804, the active contour model is used to perform contour extraction to obtain the intestinal segmentation result. In some embodiments, the intestinal segmentation result, that is, the large intestine area, can be extracted by, for example, the Chan-Vese algorithm combined with the level set method. Then, at step S805, the minimum enclosing circle of the intestinal segmentation result in the cross-sectional area is calculated, and at step S806, the probability distribution of the large intestine fluid accumulation area is fitted. By using the probability distribution of the large intestine fluid accumulation area as the initial value, at step S807, it is input into the graph cut algorithm model for fluid accumulation segmentation to obtain the large intestine fluid accumulation area. Further, at step S808, the segmented large intestine fluid accumulation area is combined with the large intestine area to obtain a complete large intestine area.

[0065] Figure 9 is a block diagram illustrating an exemplary structure of a device 900 for segmenting large intestinal fluid according to an embodiment of the present application. It is understood that the device 900 may include the apparatus of the embodiment of the present application, and the device implementing the solution of the present application may be a single device (e.g., a computing device) or a multifunctional device including various peripheral devices.

[0066] like Figure 9 As shown in , the device of the present application may also include a central processing unit ("CPU") 911, which can be a general-purpose CPU, a dedicated CPU, or other execution unit for information processing and program execution. Furthermore, the device 900 may also include a mass storage device 912 and a read-only memory ("ROM") 913. The mass storage device 912 may be configured to store various types of data, including various data related to three-dimensional intestinal images, initial segmentation regions, intestinal segmentation results, fluid accumulation regions, algorithm data, intermediate results, and various programs required by the device 900. The ROM 913 may be configured to store data and instructions required for the power-on self-test of the device 900, initialization of various functional modules in the system, drivers for the system's basic input / output, and booting the operating system.

[0067] Optionally, device 900 may also include other hardware platforms or components, such as the illustrated tensor processing unit ("TPU") 914, graphics processing unit ("GPU") 915, field programmable gate array ("FPGA") 916, and machine learning unit ("MLU") 917. It will be appreciated that while various hardware platforms or components are shown in device 900, these are merely exemplary and non-limiting, and those skilled in the art may add or remove corresponding hardware as needed. For example, device 900 may include only a CPU, associated storage devices, and interface devices to implement the method for segmenting large intestinal effusions of the present application.

[0068] In some embodiments, to facilitate data transmission and interaction with external networks, the device 900 of the present application further includes a communication interface 918, which allows the device 900 to connect to a local area network / wireless local area network ("LAN / WLAN") 905 via the communication interface 918, and further connect to a local server 906 or the Internet 907 via the LAN / WLAN. Alternatively or additionally, the device 900 of the present application may also directly connect to the Internet or a cellular network via the communication interface 918 using wireless communication technology, such as third generation ("3G"), fourth generation ("4G"), or fifth generation ("5G") wireless communication technology. In some application scenarios, the device 900 of the present application may also access a server 908 and a database 909 on an external network as needed to obtain various known algorithms, data, and modules, and may remotely store various data, such as various data or instructions used to present, for example, three-dimensional intestinal images, initial segmented regions, intestinal segmentation results, and fluid accumulation areas.

[0069] Peripheral devices of device 900 may include a display device 902, an input device 903, and a data transmission interface 904. In one embodiment, display device 902 may include, for example, one or more speakers and / or one or more visual displays, configured to provide voice prompts and / or display images and videos of the method for segmenting large intestinal fluid accumulation described herein. Input device 903 may include, for example, a keyboard, a mouse, a microphone, a gesture capture camera, or other input buttons or controls configured to receive audio data input and / or user commands. Data transmission interface 904 may include, for example, a serial interface, a parallel interface, a Universal Serial Bus (USB), a Small Computer System Interface (SCSI), Serial ATA, FireWire, PCI Express, or a High-Definition Multimedia Interface (HDMI), configured to transmit and interact with other devices or systems. According to the solution of the present application, data transmission interface 904 may receive three-dimensional intestinal images acquired by a CT device and transmit the three-dimensional intestinal images or various other types of data or results to device 900.

[0070] The CPU 911, mass storage 912, ROM 913, TPU 914, GPU 915, FPGA 916, MLU 917, and communication interface 918 of the device 900 of the present application can be interconnected via a bus 919 and can interact with peripheral devices via the bus. In one embodiment, the CPU 911 can control other hardware components in the device 900 and its peripheral devices via the bus 919.

[0071] Combination of the above Figure 9The present invention describes a device for separating large intestinal fluid that can be used to implement the present invention. It should be understood that the device structure or architecture herein is merely exemplary, and the implementation and implementation of the present invention are not limited thereto, but may be modified without departing from the spirit of the present invention.

[0072] According to the above description in combination with the accompanying drawings, those skilled in the art can also understand that the embodiments of the present application can also be implemented by software programs. Therefore, the present application also provides a computer-readable storage medium, which stores computer-readable instructions for segmenting large intestinal effusion. When the computer-readable instructions are executed by one or more processors, they can be used to implement the present application in combination with the accompanying drawings. Figure 1 The described method for segmenting large bowel fluid collections.

[0073] It should be noted that although the operations of the present method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in that particular order, or that all of the operations shown must be performed to achieve the desired results. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be broken down into multiple steps.

[0074] It should be understood that when the terms "first," "second," "third," and "fourth," etc., are used in the claims, specification, and drawings of this application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the specification and claims of this application indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0075] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this specification and claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" as used in this specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0076] Although the implementation methods of this application are as described above, the contents are only examples adopted to facilitate understanding of this application and are not intended to limit the scope and application scenarios of this application. Any technician in the technical field described in this application can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in this application, but the scope of patent protection of this application shall still be based on the scope defined by the attached claims.

[0077] In addition, the collection and acquisition of various data in this application complies with relevant laws and regulations and is authorized by the data provider. Any organization or individual that needs to obtain external data must obtain authorization in accordance with the law and ensure data security. They must not illegally collect, use, process, or transmit unauthorized or unprotected data, nor illegally buy, sell, provide, or disclose unauthorized or unprotected data.

Claims

1. A method for dividing large intestinal fluid, characterized in that: include: Extract the target skeleton with the largest skeleton volume from the 3D intestinal image and use it as the initial segmentation region; Based on the initial segmented area, an active contour model is used to perform contour extraction to obtain an intestinal segmentation result; Calculating the minimum enclosing circle of the intestinal segmentation result in the cross-sectional area; Use a Gaussian mixture model to fit the probability distribution of the large intestine area and the probability distribution of the large intestine fluid accumulation area within the minimum enclosing circle; as well as The fitting results are input into the graph cut algorithm model for fluid segmentation, so as to segment the large intestine fluid accumulation area from the large intestine area; Based on the initial segmented region, the active contour model is used to perform contour extraction to obtain the intestinal segmentation result, which includes: Based on the initial segmented region, constructing an energy function under the active contour model; and Minimizing the energy function to perform contour extraction to obtain the intestinal segmentation result; The method of fitting the probability distribution of the large intestine region and the probability distribution of the large intestinal effusion region within the minimum enclosing circle using the Gaussian mixture model includes fitting the probability distribution of the large intestine region and the probability distribution of the large intestinal effusion region using the following formula: in, represents the fitting result, represents the grayscale value within the minimum enclosing circle, represents the probability distribution of the large intestinal fluid accumulation area, represents the probability distribution of the large intestine region, and represents the mean, and represents the variance, and represents the weight of the corresponding probability distribution; The fitting results are input into the graph cut algorithm model for fluid segmentation, so as to segment the large intestinal fluid area from the large intestine area, including: Inputting the fitting results into a graph cut algorithm model for optimization; and The area formed by the points where the median value of the optimization result is 1 is used as the large intestinal fluid accumulation area, so as to segment the large intestinal fluid accumulation area from the large intestine area.

2. The method according to claim 1, wherein Before extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image, the following steps are also included: Setting grayscale thresholds; and The three-dimensional intestinal image is roughly segmented based on the grayscale threshold.

3. The method according to claim 1 or 2, characterized in that The target skeletons with the largest skeleton volume extracted from the 3D intestinal image include: extracting a two-dimensional surface of the large intestine region from the three-dimensional intestinal image; Calculating the shortest Euclidean distance from a voxel point in the three-dimensional intestinal image to the two-dimensional surface; and The target skeleton with the largest skeleton volume is extracted from the three-dimensional intestinal image according to the shortest Euclidean distance.

4. The method according to claim 3, wherein Extracting the target skeleton with the largest skeleton volume from the three-dimensional intestinal image according to the shortest Euclidean distance includes: Comparing the shortest Euclidean distance with a preset distance threshold; and Voxel points whose shortest Euclidean distance is greater than the preset distance threshold are extracted from the three-dimensional intestinal image to obtain the target skeleton with the largest skeleton volume.

5. The method according to claim 1, wherein The fitting results are input into the graph cut algorithm model for optimization using the following formula: in, f represents the grayscale value within the minimum enclosing circle, represents the fitting result, Y Indicates the value 0 or 1 in the optimization, subscript i represents a voxel point, subscript j Represents voxel points i Neighborhood points.

6. A device for dividing large intestinal fluid, characterized in that: include: processor; as well as A memory storing program instructions for segmenting large intestinal effusion, wherein when the program instructions are executed by the processor, the device for segmenting large intestinal effusion implements the method according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that Computer-readable instructions for segmenting large intestinal fluid are stored thereon, and when the computer-readable instructions are executed by one or more processors, the method according to any one of claims 1 to 5 is implemented.

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