Method for acquiring blood vessel information, electronic device, and storage medium

Abstract

The application provides a blood vessel information acquisition method, an electronic device and a storage medium. The method comprises the following steps: acquiring a blood vessel mask image; acquiring position coordinates of a starting point and an ending point according to the blood vessel mask image; determining a target path between the starting point and the ending point by using a preset algorithm according to the position coordinates of the starting point and the ending point, and taking the target path as a blood vessel center line; acquiring a point set of a cross section corresponding to each pixel point on the blood vessel center line according to the blood vessel mask image; and acquiring cross section feature information of the blood vessel according to the point set of the cross section corresponding to each pixel point on the blood vessel center line. The application can quickly and automatically calculate the blood vessel center line, thereby reducing the cumbersome operation of human-computer interaction and improving the calculation efficiency. In addition, after the blood vessel center line is acquired, the application also calculates the feature attributes of the cross section corresponding to the blood vessel center line, so that more comprehensive blood vessel information can be provided for doctors.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method for acquiring vascular information, an electronic device, and a storage medium. Background Technology

[0002] Vascular diseases, especially cardiovascular diseases, have become one of the major threats to human life. During surgery, doctors use vascular imaging technology to assist in the diagnosis of various vascular diseases, such as calcification, aortic dissection, and aneurysm. Based on the centerline and cross-section of the blood vessel, corresponding curved or straightened images of the vessel can be reconstructed, and a series of post-processed vascular images can also be generated. This is of great significance for doctors' diagnosis and subsequent treatment plans (such as selecting the appropriate stent type for aortic dissection).

[0003] Although many techniques exist for extracting the centerline of blood vessels, it remains a very challenging task. For example, one existing technique discloses a method for extracting the centerline of blood vessels by selecting at least one candidate centerline from multiple candidate centerlines as the centerline of the blood vessel region, and obtaining the centerline by comparing the vertices on the candidate centerline with the reference blood vessel size. It is evident that the existing method for extracting the centerline of blood vessels requires a template, is slow, and cannot obtain cross-sectional feature information of the blood vessel. Summary of the Invention

[0004] The purpose of this invention is to provide a method, electronic device, and storage medium for acquiring vascular information, which can not only improve the calculation efficiency of the vascular centerline, but also acquire the cross-sectional feature information of the vascular vessel.

[0005] To achieve the above objectives, the present invention provides a method for acquiring vascular information, comprising:

[0006] Acquire vascular mask images;

[0007] Based on the vascular mask image, obtain the position coordinates of the starting point and the ending point;

[0008] Based on the position coordinates of the starting point and the ending point, a preset algorithm is used to determine the target path between the starting point and the ending point, and the target path is used as the blood vessel centerline;

[0009] Based on the blood vessel mask image, obtain the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel;

[0010] Based on the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel, the cross-sectional feature information of the blood vessel is obtained.

[0011] Optionally, the method further includes:

[0012] Based on the vascular mask image, obtain the position coordinates of the midpoint;

[0013] The step of determining the target path between the starting point and the ending point using a preset algorithm based on the position coordinates of the starting point and the ending point includes:

[0014] Based on the position coordinates of the starting point, the intermediate point, and the ending point, a preset algorithm is used to determine a first target path between the starting point and the intermediate point, and a second target path between the intermediate point and the ending point.

[0015] Connect the first target path and the second target path to determine the target path between the starting point and the ending point.

[0016] Optionally, the step of using a preset algorithm to determine the first target path between the starting point and the intermediate point includes:

[0017] Using the starting point as the starting node and the intermediate point as the target node, the A* algorithm is used to determine the first target path between the starting point and the intermediate point;

[0018] The step of using a preset algorithm to determine the second target path between the intermediate point and the termination point includes:

[0019] Using the intermediate point as the starting node and the ending point as the target node, the A* algorithm is used to determine the second target path between the intermediate point and the ending point.

[0020] Optionally, the cost function used in the A* algorithm is as follows:

[0021] F(P) = w1*G(P) + w2*H(P)

[0022]

[0023]

[0024] In the formula, F(P) is the total cost, G(P) is the actual cost from the starting node to node P, and H(P) is the estimated cost from node P to the ending node D. Let be the Euclidean distance from node P to the terminal node D. Let w1 be the Euclidean distance from node P to its parent node, w2 be the first weight coefficient, and w1 be the second weight coefficient.

[0025] Optionally, the first weighting coefficient w1 is a dynamic coefficient related to the node P.

[0026] Optionally, the method further includes:

[0027] For each non-zero pixel in the blood vessel mask image, its surrounding neighboring pixels are traversed from near to far until the zero pixel closest to the non-zero pixel is found. The distance between the non-zero pixel and the zero pixel is calculated and set as the pixel value of the non-zero pixel to obtain the blood vessel distance transformation image.

[0028] The formula for calculating the first weighting coefficient w1 is as follows:

[0029] w1=β*e ΔP

[0030] Wherein, β is the first adjustment factor, and 0 < β < 1, and ΔP is the absolute value of the difference between the maximum pixel value in the blood vessel distance transformation image and the pixel value of node P in the blood vessel distance transformation image.

[0031] Optionally, the second weighting coefficient w2 is a dynamic coefficient associated with the node P.

[0032] Optionally, the formula for calculating the second weighting coefficient w2 is as follows:

[0033]

[0034] Where σ is the second adjustment factor, and 0 < σ < 1, |Z P -Z D | is the Z-coordinate of the node P. P The Z coordinate of the termination node D D The absolute value of the difference between them.

[0035] Optionally, obtaining the point set of the cross-section corresponding to each pixel on the center line of the blood vessel based on the blood vessel mask image includes:

[0036] Obtain the position coordinates of each non-zero pixel on the blood vessel mask image;

[0037] For each pixel on the center line of the blood vessel:

[0038] The pixel and its neighboring pixels on the center line of the blood vessel are combined to form a first vector, and the pixel and each non-zero pixel are combined to form a second vector.

[0039] Calculate the angle between the first vector and the second vector based on the position coordinates of the pixel, the neighboring pixels, and the non-zero pixels;

[0040] The set of points consisting of non-zero pixels whose included angle is within a preset range is taken as the set of points of the cross section corresponding to the pixel.

[0041] The step of obtaining cross-sectional feature information of the blood vessel based on the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel includes:

[0042] For each pixel on the center line of the blood vessel, the pixel and its corresponding cross-section point set are fitted to obtain the corresponding cross-section.

[0043] Based on the cross-section corresponding to each pixel point on the center line of the blood vessel, the cross-sectional feature information of the blood vessel is obtained.

[0044] Optionally, obtaining the cross-sectional feature information of the blood vessel based on the cross-section corresponding to each pixel point on the center line of the blood vessel includes:

[0045] Map the cross section corresponding to each pixel point on the center line of the blood vessel to the plane Z=0 to obtain the corresponding mapping plane;

[0046] Based on the position coordinates of each pixel on the mapping plane, the feature information of the mapping plane is obtained;

[0047] Based on the feature information of the mapping plane, the cross-sectional feature information of the blood vessel is obtained.

[0048] Optionally, the method further includes:

[0049] The first correction is performed on each pixel on the center line of the unbranched blood vessel region to obtain the corresponding first correction point;

[0050] A second correction is performed on each pixel on the center line of the bifurcated blood vessel region to obtain the corresponding second correction point;

[0051] The corrected vascular centerline is obtained based on the first correction point and the second correction point.

[0052] Optionally, the method further includes:

[0053] For each non-zero pixel in the blood vessel mask image, its surrounding neighboring pixels are traversed from near to far until the zero pixel closest to the non-zero pixel is found. The distance between the non-zero pixel and the zero pixel is calculated and set as the pixel value of the non-zero pixel to obtain the blood vessel distance transformation image.

[0054] The first correction is performed on each pixel on the center line of the unbranched blood vessel region to obtain the corresponding first correction point, including:

[0055] For each pixel on the center line of the unbranched vessel region:

[0056] The first cross-sectional image corresponding to the pixel point on the blood vessel distance transformation image is mapped onto the plane Z=0 to obtain the corresponding first mapped image;

[0057] The pixel with the largest pixel value in the first mapped image is taken as the corresponding first mapping point;

[0058] Based on the first mapping point, obtain the corresponding first correction point.

[0059] Optionally, the second correction of each pixel on the center line of the bifurcated vessel region to obtain the corresponding second correction point includes:

[0060] For each pixel on the center line of the bifurcated blood vessel region:

[0061] The second cross-sectional image corresponding to the pixel point on the blood vessel distance transformation image is mapped onto the plane Z=0 to obtain the corresponding second mapped image;

[0062] Based on the pixel values ​​of each pixel in the second mapped image, two peak points are obtained;

[0063] The peak point closest to the pixel is taken as the corresponding second mapping point;

[0064] Based on the second mapping point, obtain the corresponding second correction point.

[0065] Optionally, the method further includes:

[0066] For each pixel on the center line of the bifurcated blood vessel region:

[0067] Using the second mapping point corresponding to the pixel as the seed point, the watershed algorithm is used to segment the corresponding second mapping image;

[0068] Based on the feature information of the connected domain where the second mapping point is located, the corresponding blood vessel cross-sectional feature information is corrected.

[0069] To address the aforementioned technical problems, the present invention also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for acquiring vascular information described above is implemented.

[0070] To address the aforementioned technical problems, the present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the method for acquiring vascular information described above.

[0071] Compared with existing technologies, the vascular information acquisition method, electronic device, and storage medium provided by this invention have the following advantages: This invention first acquires a vascular mask image; then, based on the vascular mask image, it obtains the position coordinates of the starting and ending points; then, based on the position coordinates of the starting and ending points, it uses a preset algorithm to determine the target path between the starting and ending points, and uses this target path as the vascular centerline; then, based on the vascular mask image, it obtains the point set of the cross-section corresponding to each pixel on the vascular centerline; finally, based on the point set of the cross-section corresponding to each pixel on the vascular centerline, it obtains the cross-sectional feature information of the vascular vessel. Therefore, this invention can quickly and automatically calculate the vascular centerline, not only reducing the cumbersome operation of human-computer interaction but also improving computational efficiency. Furthermore, after acquiring the vascular centerline, this invention also calculates the feature attributes of the cross-section corresponding to the vascular centerline, thereby providing doctors with more comprehensive vascular information and better assisting them in improving diagnostic accuracy. Attached Figure Description

[0072] Figure 1 This is a flowchart illustrating a method for acquiring vascular information according to an embodiment of the present invention.

[0073] Figure 2 This is a flowchart illustrating the process of determining the target path between the starting node and the ending node in one embodiment of the present invention.

[0074] Figure 3 This is a schematic diagram of the process for obtaining the point set of a cross section in one embodiment of the present invention;

[0075] Figure 4 This is a schematic diagram of the process for obtaining cross-sectional feature information of blood vessels according to one embodiment of the present invention.

[0076] Figure 5 This is a schematic diagram of a segment of a blood vessel in a specific example;

[0077] Figure 6 This is a schematic diagram of a blood vessel cross-section in a specific example;

[0078] Figure 7 This is a schematic diagram illustrating the process of correcting the center line of a blood vessel in an unbranched blood vessel region according to one embodiment of the present invention.

[0079] Figure 8This is a schematic diagram illustrating the process of correcting the center line of a bifurcated blood vessel region according to one embodiment of the present invention.

[0080] Figure 9 This is a schematic diagram illustrating the acquisition of the second mapping point in one embodiment of the present invention;

[0081] Figure 10 This is a schematic diagram of a connected component obtained using the watershed algorithm in one embodiment of the present invention;

[0082] Figure 11 This is a block diagram of an electronic device according to one embodiment of the present invention.

[0083] The accompanying figure is labeled as follows:

[0084] Outline -1; Pixels -10; Peaks -21, 22;

[0085] Processor-31; Communication interface-32; Memory-33; Communication bus-34. Detailed Implementation

[0086] The following is in conjunction with the appendix Figures 1 to 11 The present invention will further describe in detail the method for acquiring vascular information, the electronic device, and the storage medium proposed in this invention, along with specific embodiments. The advantages and features of the present invention will become clearer from the following description. It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions, used only to facilitate and clearly illustrate the embodiments of the present invention. Please refer to the accompanying drawings to make the objectives, features, and advantages of the present invention more apparent and understandable. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are only used to complement the content disclosed in the specification, for those skilled in the art to understand and read, and are not intended to limit the implementation conditions of the present invention. Any modifications to the structure, changes in proportions, or adjustments to the size, provided that the effects and objectives achieved by the present invention are the same or similar, should still fall within the scope of the technical content disclosed in the present invention.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0088] The core idea of ​​this invention is to provide a method, electronic device, and storage medium for acquiring vascular information, which can not only improve the calculation efficiency of vascular centerline but also acquire cross-sectional feature information of the vascular system. It should be noted that, as those skilled in the art will understand, the position coordinates of a pixel referred to herein are its coordinates in the image coordinate system.

[0089] Additionally, it should be noted that the electronic device in the embodiments of the present invention can be a personal computer, a mobile terminal, etc., and the mobile terminal can be a mobile phone, tablet computer, or other hardware device with various operating systems. Furthermore, it should be noted that although this article uses the acquisition of vascular information of the aorta as an example for illustration, as those skilled in the art will understand, the present invention can also be used to acquire vascular information of other blood vessels, such as neurovascular vessels, radial arteries, etc.

[0090] To achieve the above-mentioned goals, this invention provides a method for acquiring vascular information. Please refer to [the relevant documentation]. Figure 1 The diagram illustrates a flowchart of a method for acquiring vascular information according to an embodiment of the present invention. Figure 1 As shown, the method for obtaining vascular information includes the following steps:

[0091] Step S100: Obtain the blood vessel mask image.

[0092] The vascular mask image (i.e., a binary image in which the pixel value of the vascular region is 1 and the pixel value of the non-vascular region is 0) can be obtained by segmenting the acquired medical image, such as CTA or MRA, using existing image segmentation methods, such as thresholding, region growing, neural network segmentation, or other image segmentation methods. The size of the vascular mask image can be set according to specific circumstances, and this invention does not limit it. For example, the size of the vascular mask image can be 512×512×347 pixels.

[0093] Step S200: Obtain the position coordinates of the starting point and the ending point based on the blood vessel mask image.

[0094] Specifically, based on actual needs, the center position of the cross-section at the beginning of the blood vessel region on the blood vessel mask image where the centerline needs to be calculated can be taken as the starting point, and the center position of the cross-section at the end of the blood vessel region can be taken as the ending point. Based on the positions of the starting point and the ending point on the blood vessel mask image, the position coordinates (coordinates in the image coordinate system) of the starting point and the ending point can be obtained. It should be noted that, as those skilled in the art will understand, the starting point and the ending point can be selected manually or by a computer according to a pre-set algorithm; this invention does not impose any limitations on this.

[0095] To further improve the accuracy of the calculation, before performing step S200, the method further includes:

[0096] The vascular mask image is smoothed to remove noise.

[0097] Specifically, a three-dimensional Gaussian filter can be used to smooth the blood vessel mask image. By smoothing the blood vessel mask image, imperfections on the surface of the blood vessels can be effectively removed.

[0098] Correspondingly, step S200 is: obtaining the position coordinates of the start node and the end node based on the smoothed blood vessel mask image.

[0099] Step S300: Based on the position coordinates of the starting point and the ending point, a preset algorithm is used to determine the target path between the starting point and the ending point, and the target path is used as the blood vessel centerline.

[0100] Since the vascular centerline is a line connecting the center points of each cross-section of the vascular region, and the starting node is located at the center of the cross-section at the beginning of the vascular region, and the ending node is located at the center of the cross-section at the end of the vascular region, the target path (including the starting node and the ending node) between the starting point and the ending point is the vascular centerline of the vascular region.

[0101] To further improve the accuracy of the extracted vascular centerline, the method for obtaining vascular information provided by this invention further includes:

[0102] Based on the vascular mask image, obtain the position coordinates of the midpoint.

[0103] Correspondingly, step S300, which involves determining the target path between the starting point and the ending point using a preset algorithm based on the position coordinates of the starting point and the ending point, and using the target path as the blood vessel centerline, includes:

[0104] Based on the position coordinates of the starting point, the intermediate point, and the ending point, a preset algorithm is used to determine a first target path between the starting point and the intermediate point, and a second target path between the intermediate point and the ending point.

[0105] Connect the first target path and the second target path to determine the target path between the starting point and the ending point.

[0106] Specifically, the midpoint is a necessary point along which the center line of the blood vessel to be extracted is obtained. Since the midpoint is a necessary point along which the center line of the blood vessel to be extracted is obtained, the accuracy of the obtained center line of the blood vessel can be further improved.

[0107] Furthermore, the step of using a preset algorithm to determine the first target path between the starting point and the intermediate point includes: using the starting point as the starting node and the intermediate point as the target node, and employing the A* algorithm to determine the first target path between the starting point and the intermediate point.

[0108] The step of using a preset algorithm to determine the second target path between the intermediate point and the termination point includes: using the intermediate point as the starting node and the termination point as the target node, and using the A* algorithm to determine the second target path between the intermediate point and the termination point.

[0109] It should be noted that although this invention uses the A* algorithm as an example, as those skilled in the art will understand, other existing path algorithms, such as breadth-first search, Dijkstra's algorithm, and best-first search, can also be used to determine the target path between the starting node and the ending node. This invention does not limit this.

[0110] Further, please refer to Figure 2 This diagram illustrates a flowchart of determining the target path between the starting node and the target node according to an embodiment of the present invention. Figure 2 As shown, the target path between the starting node and the target node can be determined using the following steps:

[0111] Step A: Create an open list to store nodes to be detected and a closed list to store detected nodes, and put the starting point into the open list;

[0112] Step B: Determine whether the open list is an empty set. If yes, end the calculation; otherwise, proceed to step C.

[0113] Step C: Sort the cost function F values ​​of each node in the open list, select the node with the smallest cost function F value as the current node, and move the current node from the open list to the closed list, wherein:

[0114] F(P) = w1*G(P) + w2*H(P);

[0115]

[0116]

[0117] In the formula, F(P) is the cost function, G(P) is the actual cost from the starting node to node P, and H(P) is the estimated cost from node P to the ending node D. Let be the Euclidean distance from node P to the terminal node D. Let w1 be the Euclidean distance from node P to its parent node, w2 be the first weight coefficient, and w1 be the second weight coefficient.

[0118] Step D: Determine whether the current node is a termination node. If yes, proceed to step E; otherwise, proceed to step F1.

[0119] Step E: Starting from the termination node, trace back the parent node step by step until the starting node is reached. Connect all the traced nodes sequentially from the starting node to form the target path.

[0120] Step F1: Based on the blood vessel mask image, determine all neighboring nodes in the surrounding area of ​​the current node, and select one of the neighboring nodes as the current neighboring node;

[0121] Step F2: Determine whether the current neighboring node is in the closed list. If yes, proceed to step F3; otherwise, proceed to step F4.

[0122] Step F3: Skip the current neighbor node, and take the next neighbor node as the current neighbor node, then return to execute step F2;

[0123] Step F4: Determine whether the current neighboring node is in the open list. If yes, proceed to step F5; otherwise, proceed to step F6.

[0124] Step F5: Calculate the G value of the current neighbor node relative to the current node. If the newly calculated G value is less than the existing G value of the current neighbor node, update the existing G value of the current neighbor node to the newly calculated G value, update the parent node of the current neighbor node to the current node, and execute step F7.

[0125] Step F6: Add the neighboring node to the open list, set the current node as the parent node of the neighboring node, and execute step F7;

[0126] Step F7: Determine whether the current neighbor node is the last neighbor node. If yes, return to step B; otherwise, proceed to step F8.

[0127] Step F8: Select the next neighboring node as the current neighboring node and return to step F2.

[0128] Specifically, when only the starting node exists in the open list, the starting node is removed from the open list. Each node in the open list has its own stored total cost (F value), actual cost (G value), and estimated cost (H value), which can be respectively referred to as the node's stored total cost (F value), stored actual cost (G value), and stored estimated cost (H value). The starting node's stored actual cost is 0. As the node's parent node information is updated, the node's stored total cost (F value) and stored actual cost (G value) change accordingly. The neighboring nodes adjacent to the starting node initially all have the starting node as their parent node.

[0129] in:

[0130]

[0131]

[0132] When obtaining the neighboring nodes of the current node, the non-zero pixels in the 26 neighborhoods of the current node on the blood vessel mask image (preferably the smoothed blood vessel mask image) are selected as the neighboring nodes of the current node.

[0133] Therefore, by taking the starting point as the starting node and the intermediate point as the target node, and using... Figure 2 The process shown can obtain the target path (i.e., the first target path) between the starting point and the intermediate point; by using the intermediate point as the starting node and the ending point as the target node, and employing... Figure 2 The process shown allows us to obtain the target path (i.e., the second target path) between the intermediate point and the termination point. It should be noted that, as those skilled in the art will understand, in step E, after traversing all neighboring nodes of the current node, we return to step B. Thus, by repeatedly executing steps B to E until the final selected current node is the termination node, we move from the termination node towards its parent node, and from the parent node towards its own parent node, and so on, until we reach the starting node. The path formed by these nodes is the target path, i.e., the vascular centerline.

[0134] Furthermore, the first weighting coefficient is a dynamic coefficient related to the node P. Therefore, by setting the first weighting coefficient to a dynamic coefficient related to the node P, the present invention can achieve dynamic adjustment of the cost function, thereby facilitating efficient finding of the target path.

[0135] Specifically, the method further includes:

[0136] For each non-zero pixel in the blood vessel mask image, its surrounding neighboring pixels are traversed from near to far until the zero pixel closest to the non-zero pixel is found. The distance between the non-zero pixel and the zero pixel is calculated and set as the pixel value of the non-zero pixel to obtain the blood vessel distance transformation image.

[0137] The formula for calculating the first weighting coefficient w1 is as follows:

[0138] w1=β*e ΔP

[0139] Wherein, β is the first adjustment factor, and 0 < β < 1, and ΔP is the absolute value of the difference between the maximum pixel value in the blood vessel distance transformation image and the pixel value of node P in the blood vessel distance transformation image.

[0140] Since the center point of each cross-section of a blood vessel is furthest from the vessel wall, the pixel value of the pixel at the center point of each cross-section of the blood vessel region is the largest in the blood vessel distance transformation image. If the pixel value of node P in the blood vessel distance transformation image is larger, the value of w1 is smaller. That is, the first weight coefficient corresponding to node P which is closer to the actual center line of the blood vessel is smaller. This setting can ensure that the finally obtained path node walks along the center of the blood vessel as much as possible, that is, ensure that the obtained target path is closer to the actual center line of the blood vessel, thereby ensuring the accuracy of the obtained blood vessel center line.

[0141] Furthermore, the second weighting coefficient w2 is also a dynamic coefficient related to the node P. Therefore, by setting the second weighting coefficient as a dynamic coefficient related to the node P, dynamic adjustment of the cost function can be further achieved, which is more conducive to efficiently finding the target path.

[0142] Specifically, the formula for calculating the second weighting coefficient w2 is as follows:

[0143]

[0144] Where σ is the second adjustment factor, and 0 < σ < 1, |Z P -Z D | is the Z-coordinate of the node P. P The Z coordinate of the termination node D D The absolute value of the difference between them.

[0145] Since the slice layers of the starting node and the ending node are furthest apart by default (i.e., the absolute value of the difference between the Z coordinates of the starting and ending nodes is the largest), for example, the aorta runs from top to bottom along the head and neck of the human body. Therefore, by setting the second weighting coefficient W2 to be dynamically related to the slice layer (i.e., the Z coordinate), where the larger the absolute value of the difference between the Z coordinates of node P and the ending node D, the larger W2 becomes, it is more conducive to efficiently finding the optimal path. It should be noted that, as those skilled in the art will understand, if the acquired vascular mask image is a neurovascular mask image, then the parameter W2 can be directly set to 1.

[0146] Step S400: Based on the blood vessel mask image, obtain the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel.

[0147] Therefore, by obtaining the point set of the cross-section corresponding to each pixel on the center line of the blood vessel, a theoretical basis can be provided for subsequently obtaining the cross-sectional feature information of the blood vessel.

[0148] For details, please refer to Figure 3The diagram illustrates a process for obtaining a point set of a cross-section according to an embodiment of the present invention. Figure 3 As shown, step S400, obtaining the point set of the cross-section corresponding to each pixel on the center line of the blood vessel based on the blood vessel mask image, specifically includes:

[0149] Obtain the position coordinates of each non-zero pixel on the blood vessel mask image;

[0150] For each pixel on the center line of the blood vessel:

[0151] The pixel and its neighboring pixels on the center line of the blood vessel are combined to form a first vector, and the pixel and each non-zero pixel are combined to form a second vector.

[0152] Calculate the angle between the first vector and the second vector based on the position coordinates of the pixel, the neighboring pixels, and the non-zero pixels;

[0153] The set of points consisting of non-zero pixels whose included angle is within a preset range is taken as the set of points on the cross section where the pixel is located.

[0154] Specifically, the position coordinates of all non-zero pixels (pixels with a pixel value of 1) can be stored in a valid set. Then, the first pixel on the blood vessel centerline (i.e., the starting pixel on the blood vessel centerline) is taken as the current point, and the current point and the next pixel (i.e., the second pixel on the blood vessel centerline) are combined to form a first vector. And to form a second vector by combining the current point (i.e., the starting pixel) with the first non-zero pixel A1 in the valid set. By calculating the first vector With the second vector The included angle θ 11 And determine the included angle θ 11 Whether it is within a preset range (e.g., 90°±1°), if the judgment result is the included angle θ 11 If the pixel is within a preset range, it indicates that the non-zero pixel is a point on the cross-section corresponding to the current point. Therefore, point A1 is saved to the set of points used to store the cross-section of the current point. Then, the current point and the next non-zero pixel A2 in the valid set are combined to form a second vector. And determine the first vector With the second vector The included angle θ 12If the pixel A2 is within a preset range, and the result is yes, then the pixel A2 is saved to a set of points representing the cross-section of the current pixel. This process is repeated for each non-zero pixel in the valid set until all non-zero pixels in the valid set have been traversed to obtain the point set of the cross-section corresponding to the first pixel. After obtaining the point set of the cross-section corresponding to the first pixel, the second pixel is used as the current point, and the above process is repeated to obtain the point set of the cross-section corresponding to the second pixel. This continues until the current point is the last pixel. For the last pixel, a first vector can be formed by the last pixel and its adjacent previous pixel to obtain the point set of the cross-section corresponding to the last pixel.

[0155] It should be noted that, as those skilled in the art will understand, the coordinates of the current point are assumed to be (X... j ,Y j Z j The coordinates of the neighboring pixels are (X... k ,Y k Z k The coordinates of the non-zero pixel point Ai are (X... Ai ,Y Ai Z Ai If ), then the first vector Second vector The angle between the first vector and the second vector is:

[0156]

[0157] Furthermore, it should be noted that in some other embodiments, when obtaining the first vector, if the current point is neither the first pixel on the center line of the blood vessel nor the last pixel on the center line of the blood vessel, the current point can be combined with the previous or next pixel on the center line of the blood vessel to form the first vector. This invention does not impose any limitations on this.

[0158] Step S500: Obtain the cross-sectional feature information of the blood vessel based on the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel.

[0159] Therefore, by using the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel, the present invention can obtain the cross-sectional feature information of the blood vessel (such as perimeter and / or area and / or contour coordinates and / or major and minor axis coordinates, etc.), thereby providing doctors with more comprehensive vascular information and better assisting doctors in improving the accuracy of diagnosis. In addition, the obtained cross-sectional feature information of the blood vessel can also be used to assess the degree of vascular stenosis and guide the selection of stent models, etc.

[0160] Further, the step of obtaining the cross-sectional feature information of the blood vessel based on the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel includes:

[0161] For each pixel on the center line of the blood vessel, the pixel and its corresponding cross-section point set are fitted to obtain the corresponding cross-section.

[0162] Based on the cross-section corresponding to each pixel point on the center line of the blood vessel, the cross-sectional feature information of the blood vessel is obtained.

[0163] Since the obtained cross-sectional point set data is discrete, the feature information of the blood vessel cross-section cannot be directly calculated. Therefore, by fitting each pixel on the blood vessel centerline to its corresponding cross-sectional point set, the cross-section corresponding to each pixel on the blood vessel centerline can be obtained. Then, based on the cross-section corresponding to each pixel, the cross-sectional feature information of the blood vessel can be obtained. Specifically, by fitting the first pixel on the blood vessel centerline to its corresponding cross-sectional point set, for example, using least squares fitting, the cross-section corresponding to the first pixel can be obtained. Based on the cross-section corresponding to the first pixel, the cross-sectional feature information of the blood vessel at the position corresponding to the first pixel can be obtained. Similarly, by fitting the second pixel on the blood vessel centerline to its corresponding cross-sectional point set, for example, using least squares fitting, the cross-section corresponding to the second pixel can be obtained. Based on the cross-section corresponding to the second pixel, the cross-sectional feature information of the blood vessel at the position corresponding to the second pixel can be obtained. And so on, the cross-sectional feature information of the blood vessel at each position can be obtained.

[0164] Furthermore, please refer to Figure 4 The diagram illustrates a flowchart of an embodiment of the present invention for obtaining cross-sectional feature information of blood vessels. Figure 4 As shown, obtaining the cross-sectional feature information of the blood vessel based on the cross-section corresponding to each pixel point on the center line of the blood vessel includes:

[0165] Map the cross section corresponding to each pixel point on the center line of the blood vessel to the plane Z=0 to obtain the corresponding mapping plane;

[0166] Based on the position coordinates of each pixel on the mapping plane, the feature information of the mapping plane is obtained;

[0167] Based on the feature information of the mapping plane, the cross-sectional feature information of the blood vessel is obtained.

[0168] Since the cross-section is a plane perpendicular to the centerline of the blood vessel, it may have arbitrary angles, making it impossible to directly calculate the cross-sectional attributes (i.e., feature information). Therefore, a transformation is required. Thus, this invention first maps the cross-sections corresponding to each pixel on the centerline of the blood vessel to a plane with Z=0 (i.e., a plane with Z coordinate 0, also known as the XOY plane with the image coordinate system as the reference), and then calculates the feature information, which greatly reduces the computational load. Specifically, taking one cross-section as an example, the cross-section can be moved to the plane with Z=0 through rotation and translation operations to obtain the corresponding mapping plane (the Z coordinate of each pixel on the mapping plane is 0). The mapping relationship between the mapping plane and the cross-section can be represented by a rotation and translation matrix, with different cross-sections corresponding to different rotation and translation matrices. Based on the position coordinates of each pixel on the mapping plane, the perimeter, area, contour coordinates, major and minor axis coordinates, and other feature information of the mapping plane can be obtained. Since the perimeter and area are fixed attributes and do not change with rotation and translation, the perimeter and area of ​​the mapping plane are the same as the perimeter and area of ​​the cross-section. By inversely transforming the contour coordinates and major and minor axis coordinates of the mapping plane onto the cross-section using the rotation and translation matrix, the contour coordinates and major and minor axis coordinates of the cross-section can be obtained. Based on the perimeter and / or area and / or contour coordinates and / or major and minor axis coordinates of the cross-section, the cross-sectional feature information of the blood vessel can be obtained. By repeating the above method, the feature information of the cross-sections corresponding to other pixels on the center line of the blood vessel can be obtained, thereby obtaining the feature information of the blood vessel at each cross-section. It should be noted that, as those skilled in the art will understand, the obtained vascular cross-sectional feature information can be any one or more of the following: perimeter, area, contour coordinates, and major and minor axis coordinates. The specific selection can be made according to the actual situation, and the present invention does not impose any limitations on this.

[0169] Please refer to Figure 5 and Figure 6 ,in Figure 5 A schematic diagram of a segment of a blood vessel in a specific example is given; Figure 6 A schematic diagram of a blood vessel cross-section is given in a specific example, such as... Figure 5 and Figure 6 As shown, the coordinates of the major axis a of the blood vessel cross-section are represented by the coordinates of point A on the contour 1 of the blood vessel cross-section, which is farthest from the center point O of the blood vessel cross-section. The coordinates of the minor axis b of the blood vessel cross-section are represented by the coordinates of point B on the contour 1 of the blood vessel cross-section, which is closest to the center point O of the blood vessel cross-section. It should be noted that, as those skilled in the art will understand, the perimeter of contour 1 is the perimeter of the blood vessel cross-section, and the area defined by contour 1 is the area of ​​the blood vessel cross-section.

[0170] As a preferred embodiment, the method for obtaining vascular information provided by the present invention further includes:

[0171] The centerline of the blood vessel is corrected to obtain the corrected centerline of the blood vessel.

[0172] Although the present invention dynamically adjusts the values ​​of the first weighting coefficient W1 and the second weighting coefficient W2 when determining the target path, there is still a possibility that some pixels on the calculated target path (i.e., the blood vessel centerline) may not be in the middle of the blood vessel, but instead travel along the blood vessel wall. Therefore, the present invention corrects the blood vessel centerline to ensure that all points on the final obtained blood vessel centerline travel along the center of the blood vessel, thus ensuring the accuracy of the final obtained blood vessel centerline.

[0173] Specifically, the step of correcting the vessel centerline to obtain the corrected vessel centerline includes:

[0174] The first correction is performed on each pixel on the center line of the unbranched blood vessel region to obtain the corresponding first correction point;

[0175] A second correction is performed on each point segment on the central line of the bifurcation vessel region to obtain the corresponding second correction point;

[0176] The corrected vascular centerline is obtained based on the first correction point and the second correction point.

[0177] Therefore, the present invention uses a first correction method to correct each pixel on the center line of the blood vessel in the unbranched blood vessel region, and a second correction method to correct each pixel on the center line of the blood vessel in the branched blood vessel region. That is, different correction methods are used for the center lines of the blood vessels in the unbranched blood vessel region and the branched blood vessel region, which can further ensure the accuracy of the final obtained center line of the blood vessel.

[0178] Further, please refer to Figure 7 The diagram illustrates a process for correcting the centerline of a blood vessel in an unbranched vessel region according to an embodiment of the present invention. Figure 7 As shown, the first correction is performed on each pixel on the center line of the unbranched blood vessel region to obtain the corresponding first correction point, including:

[0179] For each pixel on the center line of the unbranched vessel region:

[0180] The first cross-sectional image corresponding to the pixel point on the blood vessel distance transformation image is mapped onto the plane Z=0 to obtain the corresponding first mapped image;

[0181] The pixel with the largest pixel value in the first mapped image is taken as the corresponding first mapping point;

[0182] Based on the first mapping point and the mapping relationship between the first cross-sectional image and the first mapping image, the corresponding first correction point is obtained.

[0183] Regarding how to obtain the blood vessel distance transformation image, please refer to the relevant description above, which will not be repeated here. Taking a pixel on the center line of a blood vessel in an unbranched blood vessel region as an example, the first cross-sectional image corresponding to the pixel on the blood vessel distance transformation image can be obtained based on the point set of the cross-section corresponding to the pixel (refer to the relevant description above). Then, the first cross-sectional image is mapped to the Z=0 plane (i.e., the plane with Z coordinate 0, i.e., the XOY plane with the image coordinate system as the reference) through a rotation and translation matrix to obtain the corresponding first mapped image. Then, each pixel in the first mapped image is traversed to find the pixel with the largest pixel value (i.e., the point farthest from the edge of the blood vessel, i.e., the center point), and the pixel with the largest pixel value is taken as the first mapped point corresponding to the pixel. Finally, the first mapped point is mapped to the first cross-sectional image through the inverse matrix of the rotation and translation matrix to obtain the first correction point corresponding to the pixel. Similarly, other pixels on the center line of the blood vessel in the unbranched blood vessel region can be corrected to obtain the corresponding first correction points. By connecting the first correction points in sequence, the corrected center line of the blood vessel in the unbranched blood vessel region can be obtained.

[0184] Please continue to refer to this. Figure 8 The diagram illustrates a process for correcting the centerline of a bifurcated blood vessel region according to an embodiment of the present invention. Figure 8 As shown, the second correction of each pixel on the center line of the bifurcated blood vessel region to obtain the corresponding second correction point includes:

[0185] For each pixel on the center line of the bifurcated blood vessel region:

[0186] The second cross-sectional image corresponding to the pixel point on the blood vessel distance transformation image is mapped onto the plane Z=0 to obtain the corresponding second mapped image;

[0187] Based on the pixel values ​​of each pixel in the second mapped image, two peak points are obtained (these two peak points may be the two points with the largest pixel values ​​in the second mapped image, or they may be the point with the largest pixel value and the second largest pixel value in the second mapped image).

[0188] The peak point closest to the pixel is taken as the corresponding second mapping point;

[0189] Based on the second mapping point and the mapping relationship between the second cross-sectional image and the second mapped image, the corresponding second correction point is obtained.

[0190] Specifically, taking a pixel on the center line of the bifurcation vessel region as an example, the second cross-sectional image corresponding to the pixel on the vessel distance transformation image can be obtained based on the point set of the cross-section corresponding to the pixel (refer to the relevant description above); then, the second cross-sectional image is mapped to the Z=0 plane (i.e., the plane with Z coordinate 0, i.e., the XOY plane with the image coordinate system as the reference system) through a rotation and translation matrix to obtain the corresponding second mapped image; then, each pixel in the second mapped image is traversed to find two pixel peak points (these two peak points correspond to the centers of the two branch vessels respectively), and these two images... The peak points may be the two points with the largest pixel values ​​in the second mapped image (i.e., these two peak points have the same pixel value and are the largest), or they may be the largest and second largest pixel value points in the second mapped image (i.e., one peak point is the largest pixel value point and the other is the second largest pixel value point). The distances between these two peak points and the pixel are calculated, and the peak point closest to the pixel is taken as the second mapped point of that pixel. Finally, the second mapped point is mapped onto the second cross-sectional image using the inverse of the rotation and translation matrix to obtain the second correction point corresponding to the pixel. Similarly, other pixels on the vessel centerline of the bifurcation vessel region can be corrected to obtain corresponding second correction points. Connecting the second correction points sequentially yields the corrected vessel centerline of the bifurcation vessel region. Please refer to [reference needed]. Figure 9 The diagram illustrates the acquisition of the second mapping point according to an embodiment of the present invention. Figure 9 As shown, in the second mapped image corresponding to pixel 10, two peak points 21 and 22 were found. Peak point 21 is closer to pixel 10, so peak point 21 is taken as the second correction point corresponding to pixel 10.

[0191] Because the vessel centerline determined by the target path is parallel to the theoretical vessel centerline within the length of a microvessel, meaning the vessel centerline determined by the target path is parallel to the corrected vessel centerline, the cross-sectional feature information of the vessel obtained from the cross-sectional point set corresponding to each pixel on the uncorrected vessel centerline in the unbranched vessel region does not need to be corrected. However, for the bifurcation vessel region, the cross-sectional feature information of the vessel needs to be corrected.

[0192] Furthermore, the cross-sectional feature information of the blood vessel corresponding to each pixel on the center line of the bifurcated blood vessel region can be corrected using the following process:

[0193] For each pixel on the center line of the bifurcated blood vessel region, the second mapping point corresponding to the pixel is used as a seed point, and the watershed algorithm is used to segment the corresponding second mapping image; and the feature information of the corresponding blood vessel cross section is corrected according to the feature information of the connected domain where the second mapping point is located.

[0194] Specifically, in the second mapped image, the second mapped point can be used as a seed point. Then, based on the seed point, surrounding pixels are traversed. When a pixel with a value of 1 is encountered or no pixel is found, the process stops, thus separating the two branch vessel regions. The connected component where the second mapped point is located is the branch vessel region of interest. For more information on the watershed algorithm, please refer to existing technologies; it will not be elaborated upon here. Please refer to [reference needed]. Figure 10 The diagram illustrates a connected component obtained using the watershed algorithm in one embodiment of the present invention. Figure 10 As shown, by employing the watershed algorithm, the regions containing peak point 21 (i.e., the second mapping point) and peak point 22 can be completely separated. Therefore, based on the feature information corresponding to the connected component where the second mapping point is located, the feature information of the blood vessel cross-section at the corresponding location can be corrected. Specifically, based on the position coordinates of each pixel in the connected component where the second mapping point is located, the perimeter, area, contour coordinates, and major and minor axis coordinates of the connected component can be obtained. The perimeter and area of ​​this connected component are the perimeter and area of ​​the bifurcation blood vessel at the corresponding cross-section. By using the inverse matrix of the rotation and translation matrix between the second cross-sectional image and the second mapping image, the contour coordinates and major and minor axis coordinates can be mapped onto the second cross-sectional image, thereby obtaining the contour coordinates and major and minor axis coordinates of the bifurcation blood vessel at the corresponding cross-section.

[0195] Based on the same inventive concept, the present invention also provides an electronic device, please refer to... Figure 11 The diagram illustrates a block structure of an electronic device according to an embodiment of the present invention. Figure 11 As shown, the electronic device includes a processor 31 and a memory 33. The memory 33 stores a computer program. When the computer program is executed by the processor 31, it implements the method for acquiring vascular information described above.

[0196] like Figure 11As shown, the electronic device also includes a communication interface 32 and a communication bus 34, wherein the processor 31, the communication interface 32, and the memory 33 communicate with each other via the communication bus 34. The communication bus 34 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 34 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not indicate that there is only one bus or one type of bus. The communication interface 32 is used for communication between the aforementioned electronic device and other devices.

[0197] The processor 31 referred to in this invention can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 31 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines.

[0198] The memory 33 can be used to store the computer program. The processor 31 implements various functions of the electronic device by running or executing the computer program stored in the memory 33 and calling the data stored in the memory 33.

[0199] The memory 33 may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0200] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can implement the method for acquiring vascular information described above.

[0201] The readable storage medium of embodiments of the present invention can be any combination of one or more computer-readable media. The readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer hard disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device.

[0202] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0203] In summary, compared with existing technologies, the vascular information acquisition method, electronic device, and storage medium provided by this invention have the following advantages: This invention first acquires a vascular mask image; then, based on the vascular mask image, it obtains the position coordinates of the starting and ending points; then, based on the position coordinates of the starting and ending points, it uses a preset algorithm to determine the target path between the starting and ending points, and uses this target path as the vascular centerline; then, based on the vascular mask image, it obtains the point set of the cross-section corresponding to each pixel on the vascular centerline; finally, based on the point set of the cross-section corresponding to each pixel on the vascular centerline, it obtains the cross-sectional feature information of the vascular vessel. Therefore, this invention can quickly and automatically calculate the vascular centerline, not only reducing the cumbersome operation of human-computer interaction but also improving computational efficiency. Furthermore, after acquiring the vascular centerline, this invention also calculates the feature attributes of the cross-section corresponding to the vascular centerline, thereby providing doctors with more comprehensive vascular information and better assisting them in improving diagnostic accuracy.

[0204] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0205] It should be noted that the apparatus and methods disclosed in the embodiments herein can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments herein. In this regard, each block in a flowchart or block diagram may represent a module, program, or part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system to perform the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0206] In addition, the functional modules in the various embodiments of this article can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0207] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure are within the protection scope of the present invention. Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A method for acquiring vascular information, characterized in that, include: Acquire vascular mask images; Based on the vascular mask image, obtain the position coordinates of the starting point and the ending point; Based on the position coordinates of the starting point and the ending point, a preset algorithm is used to determine the target path between the starting point and the ending point, and the target path is used as the blood vessel centerline; Based on the blood vessel mask image, obtain the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel; Based on the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel, the cross-sectional feature information of the blood vessel is obtained. The step of obtaining the point set of the cross-section corresponding to each pixel on the center line of the blood vessel based on the blood vessel mask image includes: Obtain the position coordinates of each non-zero pixel on the blood vessel mask image; For each pixel on the center line of the blood vessel: The pixel and its neighboring pixels on the center line of the blood vessel are combined to form a first vector, and the pixel and each non-zero pixel are combined to form a second vector. Calculate the angle between the first vector and the second vector based on the position coordinates of the pixel, the neighboring pixels, and the non-zero pixels; The set of points consisting of non-zero pixels whose included angle is within a preset range is taken as the set of points of the cross section corresponding to the pixel. The step of obtaining cross-sectional feature information of the blood vessel based on the point set of the cross-section corresponding to each pixel point on the center line of the blood vessel includes: For each pixel on the center line of the blood vessel, the pixel and its corresponding cross-section point set are fitted to obtain the corresponding cross-section. Based on the cross-section corresponding to each pixel point on the center line of the blood vessel, the cross-sectional feature information of the blood vessel is obtained.

2. The method for acquiring vascular information according to claim 1, characterized in that, The method further includes: Based on the vascular mask image, obtain the position coordinates of the midpoint; The step of determining the target path between the starting point and the ending point using a preset algorithm based on the position coordinates of the starting point and the ending point includes: Based on the position coordinates of the starting point, the intermediate point, and the ending point, a preset algorithm is used to determine a first target path between the starting point and the intermediate point, and a second target path between the intermediate point and the ending point. Connect the first target path and the second target path to determine the target path between the starting point and the ending point.

3. The method for acquiring vascular information according to claim 2, characterized in that, The step of using a preset algorithm to determine the first target path between the starting point and the intermediate point includes: Using the starting point as the starting node and the intermediate point as the target node, the A* algorithm is used to determine the first target path between the starting point and the intermediate point; The step of using a preset algorithm to determine the second target path between the intermediate point and the termination point includes: Using the intermediate point as the starting node and the ending point as the target node, the A* algorithm is used to determine the second target path between the intermediate point and the ending point.

4. The method for acquiring vascular information according to claim 3, characterized in that, The cost function used in the A* algorithm is as follows: In the formula, F(P) is the total cost, G(P) is the actual cost from the starting node to node P, and H(P) is the estimated cost from node P to the ending node D. Let be the Euclidean distance from node P to the terminal node D. Let w1 be the Euclidean distance from node P to its parent node, w2 be the first weight coefficient, and w1 be the second weight coefficient.

5. The method for acquiring vascular information according to claim 4, characterized in that, The first weighting coefficient w1 is a dynamic coefficient related to the node P.

6. The method for acquiring vascular information according to claim 4, characterized in that, The method further includes: For each non-zero pixel in the blood vessel mask image, its surrounding neighboring pixels are traversed from near to far until the zero pixel closest to the non-zero pixel is found. The distance between the non-zero pixel and the zero pixel is calculated and set as the pixel value of the non-zero pixel to obtain the blood vessel distance transformation image. The formula for calculating the first weighting coefficient w1 is as follows: in, It is the first regulating factor, and , It is the absolute value of the difference between the maximum pixel value in the blood vessel distance transformation image and the pixel value of node P in the blood vessel distance transformation image.

7. The method for acquiring vascular information according to claim 6, characterized in that, The second weighting coefficient w2 is a dynamic coefficient related to the node P.

8. The method for acquiring vascular information according to claim 7, characterized in that, The formula for calculating the second weighting coefficient w2 is as follows: in, It is the second regulating factor, and , Let Z be the Z coordinate of node P. P The Z coordinate of the termination node D D The absolute value of the difference between them.

9. The method for acquiring vascular information according to claim 1, characterized in that, The step of obtaining cross-sectional feature information of the blood vessel based on the cross-section corresponding to each pixel point on the center line of the blood vessel includes: Map the cross section corresponding to each pixel point on the center line of the blood vessel onto the plane Z=0 to obtain the corresponding mapping plane; Based on the position coordinates of each pixel on the mapping plane, the feature information of the mapping plane is obtained; Based on the feature information of the mapping plane, the cross-sectional feature information of the blood vessel is obtained.

10. The method for acquiring vascular information according to claim 9, characterized in that, The method further includes: The first correction is performed on each pixel on the center line of the unbranched blood vessel region to obtain the corresponding first correction point; A second correction is performed on each pixel on the center line of the bifurcated blood vessel region to obtain the corresponding second correction point; The corrected vascular centerline is obtained based on the first correction point and the second correction point.

11. The method for acquiring vascular information according to claim 10, characterized in that, The method further includes: For each non-zero pixel in the blood vessel mask image, its surrounding neighboring pixels are traversed from near to far until the zero pixel closest to the non-zero pixel is found. The distance between the non-zero pixel and the zero pixel is calculated and set as the pixel value of the non-zero pixel to obtain the blood vessel distance transformation image. The first correction is performed on each pixel on the center line of the unbranched blood vessel region to obtain the corresponding first correction point, including: For each pixel on the center line of the unbranched vessel region: The first cross-sectional image corresponding to the pixel point on the blood vessel distance transformation image is mapped onto the Z=0 plane to obtain the corresponding first mapped image; The pixel with the largest pixel value in the first mapped image is taken as the corresponding first mapping point; Based on the first mapping point, obtain the corresponding first correction point.

12. The method for acquiring vascular information according to claim 11, characterized in that, The second correction is performed on each pixel on the center line of the bifurcated blood vessel region to obtain the corresponding second correction point, including: For each pixel on the center line of the bifurcated blood vessel region: The second cross-sectional image corresponding to the pixel point on the blood vessel distance transformation image is mapped onto the Z=0 plane to obtain the corresponding second mapped image; Based on the pixel values ​​of each pixel in the second mapped image, two peak points are obtained; The peak point closest to the pixel is taken as the corresponding second mapping point; Based on the second mapping point, obtain the corresponding second correction point.

13. The method for acquiring vascular information according to claim 12, characterized in that, The method further includes: For each pixel on the center line of the bifurcated blood vessel region: Using the second mapping point corresponding to the pixel as the seed point, the watershed algorithm is used to segment the corresponding second mapping image; Based on the feature information of the connected domain where the second mapping point is located, the corresponding blood vessel cross-sectional feature information is corrected.

14. An electronic device, characterized in that, The device includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for acquiring vascular information according to any one of claims 1 to 13.

15. A readable storage medium, characterized in that, The readable storage medium stores a computer program, which, when executed by a processor, implements the method for acquiring vascular information according to any one of claims 1 to 13.

Images