Blood vessel image segmentation method and device, electronic equipment and storage medium

Abstract

A blood vessel image segmentation method and device, electronic equipment and storage medium are disclosed. The method comprises: acquiring an initial blood vessel image, determining a coronary skeleton image of the initial blood vessel image; determining each skeleton point on the skeleton line in the coronary skeleton image, and determining at least one partition skeleton point in each skeleton point based on the degree of each skeleton point; determining the partition skeleton line segment corresponding to each partition skeleton point, and performing vein identification on each partition skeleton line segment to determine the vein branch line segment in the coronary skeleton image; and determining the coronary segmentation image of the initial blood vessel image based on the initial blood vessel image and the vein branch line segment. The technical solution disclosed in the present application solves the problem of low accuracy of coronary segmentation of blood vessels in the prior art, and improves the accuracy of coronary segmentation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image processing, and in particular to a blood vessel image segmentation method and device, an electronic device, and a storage medium. BACKGROUND

[0002] With the improvement of imaging speed and scanning accuracy of CT (Computed Tomography, i.e. electronic computed tomography) equipment, CT medical images have been widely used in heart examination and plaque diagnosis. Heart coronary artery segmentation based on CT medical images is widely used, which can extract the outline of the coronary artery and plaque in the lumen, facilitate the observation of stenosis, calcification and plaque by doctors, and provide a basis for early prevention and diagnosis of cardiovascular diseases by doctors.

[0003] At present, in the process of heart coronary artery segmentation, due to the similar image features (close distance or even adhesion, close CT value, etc.) of veins and coronary arteries, veins are easily misidentified, and the accuracy of heart coronary artery segmentation is low. SUMMARY

[0004] The present application provides a blood vessel image segmentation method, device, electronic device and storage medium, which removes the vein branches in the coronary artery rough segmentation result, to solve the problem of low accuracy of coronary artery segmentation of blood vessels in the prior art, and to improve the accuracy of coronary artery segmentation.

[0005] In a first aspect, an embodiment of the present application provides a blood vessel image segmentation method, which comprises:

[0006] obtaining an initial blood vessel image, and determining a coronary artery skeleton image of the initial blood vessel image;

[0007] determining each skeleton point on a skeleton line in the coronary artery skeleton image, and determining at least one partition skeleton point in each skeleton point based on the degree of each skeleton point;

[0008] determining a partition skeleton line segment corresponding to each partition skeleton point, and identifying veins for each partition skeleton line segment to determine a vein branch line segment in the coronary artery skeleton image;

[0009] determining a coronary artery segmentation image of the initial blood vessel image based on the initial blood vessel image and the vein branch line segment.

[0010] Optionally, the vein identification for each partition skeleton line segment to determine the vein branch line segment in the coronary artery skeleton image comprises:

[0011] determining a line segment serial number of each partition skeleton line segment based on a skeleton point serial number of each partition skeleton point;

[0012] For any partition skeleton segment, determine the previous partition skeleton segment of the current partition skeleton segment, and determine the vein branch segment identification result of the current partition skeleton segment based on the previous partition skeleton segment.

[0013] Optionally, determining the vein branch segment identification result of the current partition skeleton segment based on the previous partition skeleton segment includes:

[0014] The mean pixel value, curvature, and angle between the current partition skeleton line segment and the previous partition skeleton line segment are obtained respectively.

[0015] The vein branch line segment identification result of the current partition skeleton line segment is determined based on the average pixel value of the line segment, the curvature of the line segment, the included angle of the line segment, and the corresponding weights.

[0016] Optionally, the partition skeleton points include skeleton endpoints and skeleton branch points;

[0017] Determining at least one partition skeleton point among the skeleton points based on the degree of each skeleton point includes:

[0018] For any skeleton point, determine each neighboring point of the current skeleton point, and determine the degree of the current skeleton point based on the skeleton points contained in the neighboring points;

[0019] Based on the degree of each skeleton point, determine the skeleton endpoints and multiple candidate branch points in each skeleton point, and filter the candidate branch points based on the skeleton endpoints to obtain the skeleton branch points.

[0020] Optionally, the step of filtering each candidate branch point based on the skeleton endpoints to obtain skeleton branch points includes:

[0021] Determine the root endpoint and at least one terminal endpoint among the skeleton endpoints, and determine the endpoint number of each terminal endpoint respectively;

[0022] Determine the skeleton connection path between the root endpoint and the last branch endpoints of any two adjacent indices, and filter each candidate branch point based on the skeleton connection path to obtain the skeleton branch point among each candidate branch point.

[0023] Optionally, determining the partition skeleton line segment corresponding to each partition skeleton point based on the coronary artery skeleton image includes:

[0024] Based on the skeleton points of each partition, the skeleton lines in the coronary artery skeleton image are segmented to obtain at least one partition skeleton line segment.

[0025] Optionally, determining the coronary artery segmentation image of the initial vascular image based on the initial vascular image and the venous branches includes:

[0026] The initial vascular image is subjected to coarse coronary artery segmentation to obtain a coarse coronary artery segmentation image of the initial vascular image;

[0027] Obtain the branch expansion parameters of the vein branch, and perform expansion processing on the vein branch based on the branch expansion parameters to obtain the vein expansion image corresponding to the vein branch;

[0028] Based on the vein extension image, the coarse segmentation image of the coronary artery is processed to remove veins, thereby obtaining the coronary artery segmentation image of the initial blood vessel image.

[0029] Secondly, embodiments of the present invention also provide a blood vessel image segmentation apparatus, characterized in that it comprises:

[0030] A coronary artery skeleton image determination module is used to acquire an initial vascular image and determine the coronary artery skeleton image of the initial vascular image;

[0031] The partition skeleton point determination module is used to determine each skeleton point on the skeleton line in the coronary skeleton image, and to determine at least one partition skeleton point among the skeleton points based on the degree of each skeleton point.

[0032] The vein branch segment determination module is used to determine the partition skeleton segment corresponding to each partition skeleton point, and to perform vein identification on each partition skeleton segment to determine the vein branch segment in the coronary artery skeleton image.

[0033] The coronary artery segmentation image determination module is used to determine the coronary artery segmentation image of the initial blood vessel image based on the initial blood vessel image and the venous branch line segments.

[0034] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0035] At least one processor; and

[0036] A memory communicatively connected to the at least one processor; wherein,

[0037] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the blood vessel image segmentation method according to any embodiment of the present invention.

[0038] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions that are used to cause a processor to execute the blood vessel image segmentation method of any embodiment of the present invention.

[0039] This invention provides a method for segmenting blood vessel images. The method involves acquiring an initial blood vessel image and determining its coronary artery skeleton image; identifying skeleton points along the skeleton lines in the coronary artery skeleton image, and determining at least one partition skeleton point among these points based on the degree of each skeleton point; determining the partition skeleton line segments corresponding to each partition skeleton point, and performing vein identification on these partition skeleton line segments to determine the venous branch lines in the coronary artery skeleton image; and determining the coronary artery segmentation image of the initial blood vessel image based on the initial blood vessel image and the venous branch lines. This technical solution processes the blood vessel image to be segmented to obtain branch points and endpoints within the blood vessel; identifies each branch using these branch points and endpoints to obtain venous branches; and then optimizes the coronary artery segmentation results based on the identified venous branches to obtain the final coronary artery segmentation result, thereby improving the accuracy of coronary artery segmentation.

[0040] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a blood vessel image segmentation method provided according to an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of a coarse segmentation image of a coronary artery provided according to an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of a coronary artery skeleton image provided according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of a skeleton point provided according to an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of another coarse segmentation image of the coronary artery provided according to an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of another skeleton point provided according to an embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram of another skeleton point provided according to an embodiment of the present invention;

[0049] Figure 8 This is a schematic diagram of a partitioned skeleton line segment provided according to an embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram of a vein branch segment provided according to an embodiment of the present invention;

[0051] Figure 10 This is a schematic diagram of a vein expansion image provided according to an embodiment of the present invention;

[0052] Figure 11 This is a schematic diagram of a coronary artery segmentation image provided according to an embodiment of the present invention;

[0053] Figure 12 This is a flowchart of another blood vessel image segmentation method provided according to an embodiment of the present invention;

[0054] Figure 13 This is a schematic diagram of the included angle between segmented skeleton lines according to an embodiment of the present invention;

[0055] Figure 14 This is a schematic diagram of the structure of a blood vessel image segmentation device according to an embodiment of the present invention;

[0056] Figure 15 This is a schematic diagram of the structure of an electronic device that implements the blood vessel image segmentation method of this invention. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0058] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0059] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0060] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0061] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0062] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0063] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0064] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0065] Figure 1 The present invention provides a flowchart of a blood vessel image segmentation method, which can be applied to the case of coronary artery segmentation.

[0066] In existing technologies, segmentation of vascular images often results in inaccurate coronary artery segmentation due to the inclusion of intersecting venous branches. To address this issue, this embodiment provides an image segmentation method. This method processes the vascular image to obtain branch points and endpoints within the vessels. Each branch is then identified using these points and endpoints to identify venous branches. Finally, the identified venous branches are used to optimize the coronary artery segmentation results, leading to a final segmentation outcome that improves accuracy.

[0067] This method can be executed by a blood vessel image segmentation device, which can be implemented in hardware and / or software and can be configured in a smart terminal or cloud server. For example... Figure 1 As shown, the method includes:

[0068] S110. Obtain the initial vascular image and determine the coronary artery skeleton image of the initial vascular image.

[0069] In this embodiment of the invention, the initial vascular image can be understood as a vascular image obtained by scanning the blood vessels surrounding the heart. Specifically, the initial vascular image includes images of veins that collect blood flowing back into the heart and images of coronary arteries that distribute blood from the heart to the rest of the body.

[0070] Specifically, the method for obtaining initial vascular images can be to read image data from a local database or a cloud server database to obtain the original initial vascular image, or it can be to obtain the initial vascular image by scanning the heart area of ​​the scanned object using a medical scanning device. This embodiment does not limit the acquisition method. Optionally, the medical scanning device can be, but is not limited to, a CT device, a PET (Positron Emission Computed Tomography)-CT device, and an MRI (Magnetic Resonance Imaging) device. The corresponding initial vascular image obtained can include, but is not limited to, at least one of CT image data, PET image data, and MRI image data.

[0071] In this embodiment, the coronary artery skeleton image can be understood as an image containing coronary artery skeleton information obtained by first performing coarse segmentation of the initial blood vessel image and then refining the results of the coarse segmentation.

[0072] Based on the initial vascular image, the technical solution of this embodiment can acquire a pre-trained skeleton segmentation model, input the initial vascular image into the skeleton segmentation model, and obtain a coronary artery skeleton image output by the skeleton segmentation model after segmenting and refining the initial vascular image. Optionally, this embodiment can also use existing segmentation techniques to perform coarse coronary artery segmentation on the initial vascular image to obtain a coarse coronary artery segmentation image, and then use a refining method (such as an optimal path algorithm) to refine the coarse coronary artery segmentation image to obtain the coronary artery skeleton image.

[0073] See examples Figure 2 , Figure 2 The image shows the coarsely segmented coronary artery image after coarse segmentation of the initial vessel image. The coarsely segmented coronary artery image is then refined to obtain the following result: Figure 3 The image shown is of the coronary artery skeleton.

[0074] Of course, the coronary artery skeleton image of the initial vascular image can also be obtained through other existing methods in this embodiment, and there is no limitation on the method of acquisition.

[0075] S120. Determine each skeleton point on the skeleton line in the coronary artery skeleton image, and determine at least one partition skeleton point among the skeleton points based on the degree of each skeleton point.

[0076] In this embodiment of the invention, the skeleton line can be considered as being composed of an infinite number of skeleton points. See also the exemplary embodiments. Figure 3 , Figure 3 The skeleton line in an undirected graph includes multiple skeleton points. Examples include skeleton branch points where the skeleton line branches off and skeleton endpoints at skeleton endpoints. For any node in an undirected graph, the number of other nodes associated with that node is called the node's degree. Based on this, in this embodiment, the degree of a skeleton point can be understood as the number of points that are associated with the current skeleton point and other skeleton points on its own skeleton line.

[0077] Based on the determination of each skeleton point on the skeleton line, the degree of each skeleton point is determined. Furthermore, the skeleton points of each partition are determined based on the degree of the skeleton points.

[0078] In this context, the partition skeleton points can be understood as points located at specific positions within the skeleton line. Based on these points, the skeleton line is divided into multiple skeleton line segments, which are then used to identify vein branches.

[0079] Optionally, the method for determining at least one partition skeleton point among the skeleton points based on the degree of each skeleton point in this embodiment may include: for any skeleton point, determining each neighboring point of the current skeleton point, and determining the degree of the current skeleton point based on the skeleton points contained in the neighboring points; determining the skeleton endpoints and multiple candidate branch points in each skeleton point based on the degree of each skeleton point, and filtering each candidate branch point based on the skeleton endpoints to obtain skeleton branch points.

[0080] It should be noted that in practical applications, some adhesion inevitably occurs at the branching points of blood vessels, resulting in multiple branching points with more than 3 neighboring points. In this embodiment, such branching points are defined as candidate branching points. If multiple candidate branching points appear at a branching point, the candidate branching points need to be screened to obtain the final skeleton branching point, and other candidate branching points are optimized into skeleton center points to avoid bulging in the segmentation results.

[0081] Specifically, candidate branch points can be filtered based on the skeleton endpoints in the skeleton points and the skeleton connection paths between the endpoints to obtain skeleton branch points.

[0082] Specifically, a pre-set degree threshold of 1 is set for skeleton endpoints, and a degree threshold greater than 3 is set for candidate branch points. Further, based on the degree of the current skeleton point and the degree thresholds for each type of skeleton point, the type of the current skeleton point is determined, thereby identifying the skeleton endpoints and candidate branch points within each skeleton point. See the example below. Figure 4 , Figure 4 The multiple circular points at the bifurcation point represent multiple candidate branch points, and the square points represent skeleton endpoints. Of course, the diagram also includes other types of skeleton points, such as skeleton center points. It should be noted that the degree threshold corresponding to the skeleton endpoint is set to 1 because: if the degree of a skeleton point is 1, it means that the current skeleton point has one neighboring point that overlaps with other skeleton points. That is, the current skeleton point has one adjacent point in the skeleton line. Based on this, it can be determined that the current skeleton point is located at an endpoint in the skeleton line, thus identifying the current skeleton point as a skeleton endpoint.

[0083] In this embodiment, the root endpoint can be understood as the branch endpoint of a coronary artery branch originating from the aorta. Specifically, the root endpoint includes a left root endpoint and a right root endpoint. In practical applications, the aorta branches into two coronary artery branches on the left and right sides, respectively, in the left and right sinus regions. Therefore, the point where the left sinus region connects to the left coronary artery branch is the left root endpoint, and the point where the right sinus region connects to the right coronary artery branch is the right root endpoint. The terminal endpoint can be understood as the endpoint of the final branch of each coronary artery branch.

[0084] See examples Figure 5 , Figure 5 The thickest vessel in the middle is the aorta, which branches off from the main artery. The aorta branches into two branches: the left coronary artery and the right coronary artery. The point where the left branch connects to the aorta is the left endpoint of the root, and the point where the right branch connects to the aorta is the right endpoint of the root. (Continue reading...) Figure 5 The terminal endpoints of the branches of the coronary arteries are called terminal branch endpoints.

[0085] Optionally, the method for determining the root endpoint and at least one terminal branch endpoint among the skeleton endpoints in this embodiment may include: determining the aorta location in the coarse segmentation image of the coronary artery, and determining the root endpoint and at least one terminal branch endpoint among the skeleton endpoints based on the aorta location and the location of each skeleton endpoint.

[0086] Specifically, a pre-trained endpoint classification model can be obtained. Based on the endpoint positions of each skeletal endpoint and the arterial position of the aorta, the root endpoint and terminal branch endpoint among the skeletal endpoints can be determined using the aforementioned endpoint classification model. Optionally, the technical solution of this embodiment can also be to determine whether each skeletal endpoint is connected to the left or right sinus in the aorta. If so, it is identified as the left or right root endpoint; otherwise, it is identified as the terminal branch endpoint. This embodiment can also use other methods to identify endpoints, and the comparison is not limited.

[0087] See also the examples. Figure 4 , Figure 4 This is a schematic diagram of the left coronary artery branches. The square dot at the top right of the diagram is the root endpoint, and the other square dots of the coronary artery branches in the diagram are the terminal endpoints.

[0088] Specifically, determine the sequence number of each terminal point; see examples for further details. Figure 4 , Figure 4 The algorithm includes four terminal endpoints. Each terminal endpoint is encoded sequentially to obtain its encoding number. Further, taking terminal endpoints 3 and 4 as examples, which are adjacent terminal endpoints, the skeleton connection paths between the root left endpoint and terminal endpoint 3, and between the root left endpoint and terminal endpoint 4, are determined respectively. Based on these determined skeleton connection paths, the skeleton branch points among the candidate branch points at the branch locations are then determined.

[0089] Optionally, the method for determining the skeleton branch point among the candidate branch points at the branch in the path can be: determining each candidate branch point that is repeatedly traversed in each skeleton connected path, and determining the skeleton distance between each candidate branch point and the root endpoint; and determining the skeleton branch point among the candidate branch points based on the comparison results of each skeleton distance.

[0090] Specifically, for the skeleton connected paths corresponding to the two terminal endpoints mentioned above, candidate branch points at the branch points in the paths are determined. Further, candidate branch points that are repeatedly traversed on the two skeleton connected paths are determined, and the skeleton distance from each candidate branch point to the root endpoint is determined. Optionally, if multiple candidate branch points are repeatedly traversed, the candidate branch point corresponding to the smallest skeleton distance is determined as the skeleton branch point. See the example below. Figure 6 , Figure 6 The candidate branch points pointed to by the middle arrow are the selected skeleton branch points. Optionally, based on the above method, all candidate branch points contained in each skeleton point are filtered to obtain skeleton branch points, and other candidate branch points are optimized into skeleton center points.

[0091] Optionally, based on the above embodiments, the method for determining skeleton branch points in this embodiment further includes: obtaining a degree threshold corresponding to the skeleton branch point, and determining whether the current skeleton point is a skeleton branch point based on the degree threshold and the degree of the current skeleton point. Optionally, the degree of the current skeleton point is compared with the degree threshold. If the degree of the current skeleton point is within the range of the degree threshold, the current skeleton point is determined to be a skeleton branch point on the skeleton line; otherwise, the current skeleton point is determined to be another type of skeleton point in the skeleton line.

[0092] For example, if the degree threshold of 3 is obtained for the skeleton branch point, and the degree of the current skeleton point is determined to be 3, then the current skeleton point can be determined to be a skeleton branch point based on the degree threshold; conversely, if the degree of the current skeleton point is determined to be 2, 4, or other values, then the current skeleton point can be determined to be another type of skeleton point based on the degree threshold. See also Figure 7 In the diagram, circular dots represent skeleton branch points, while rectangular and square dots represent other types of skeleton points, such as skeleton endpoints and skeleton center points. It's worth noting that the degree threshold for skeleton branch points is set to 3 because: if a skeleton point has a degree of 3, it means that the current skeleton point has three neighboring points that overlap with other skeleton points. That is, the current skeleton point has three adjacent points within the skeleton line. Based on this, it can be determined that the current skeleton point is located at a branch point within the skeleton line, thus identifying it as a skeleton branch point.

[0093] S130. Determine the partition skeleton line segments corresponding to each partition skeleton point, and perform vein identification on each partition skeleton line segment to determine the vein branch line segments in the coronary artery skeleton image.

[0094] In this embodiment of the invention, a partition skeleton segment can be understood as a skeleton segment composed of any two partition skeleton points and all skeleton points between the two partition skeleton points.

[0095] Optionally, the method for determining the partition skeleton line segment in this embodiment may include: segmenting the skeleton line in the coronary artery skeleton image based on each partition skeleton point to obtain at least one partition skeleton line segment.

[0096] Specifically, the skeleton points of each partition are determined, i.e., the endpoints and branch points in the skeleton lines. Further, based on these endpoints and branch points, the skeleton lines in the coarsely segmented coronary artery image are segmented to obtain multiple partition skeleton line segments. For example, based on the root endpoints of the left and right coronary artery trees, the branch points are sequentially numbered from near to far using either pixel (physical) distance or a depth-first search algorithm (DFS, the classic algorithm), resulting in... Figure 8The branch point numbers shown are then used to generate partition skeleton segments for root point-branch point, branch point-branch point, and branch point-endpoint relationships based on the above implementation method, using either a breadth-first search algorithm (BFS, the classic algorithm) or a depth-first search algorithm (DFS, the classic algorithm). For example, the partition skeleton segments include skeleton segments between the root endpoint and branch points, skeleton segments between branch points, and skeleton segments between a branch point and its final endpoint.

[0097] Furthermore, based on the vein identification of the determined skeleton segments of each region, vein branch segments are obtained.

[0098] Optionally, a pre-trained vein branch recognition algorithm can be obtained, and the obtained partition skeleton segments can be input into the vein branch recognition algorithm to obtain the vein branch segment recognition result output by the model. Alternatively, the vein branch recognition result can be based on the segment characteristics of each skeleton segment contained in the partition skeleton segment, which is not limited in this embodiment of the invention.

[0099] For example, based on the identification of each skeleton branch point and skeleton endpoint in the skeleton line, partitioned skeleton line segments are determined between any two adjacent skeleton branch points and skeleton endpoints. A schematic diagram of the partitioned skeleton line segments is shown below. Figure 8 As shown. Furthermore, regarding... Figure 8 The vein branches are identified by performing vein branch identification on each branch skeleton segment in the diagram, resulting in vein branch segments. An example vein branch segment is shown below. Figure 9 As shown.

[0100] S140. Determine the coronary artery segmentation image of the initial vascular image based on the initial vascular image and venous branch segments.

[0101] In this embodiment of the invention, based on the determination of venous branch segments, vein removal is performed on the coarse segmentation image of the initial vascular image of the coronary artery based on the venous branch segments to obtain the coronary artery segmentation result.

[0102] Optionally, the method for determining the coronary artery segmentation image of the initial vascular image in this embodiment may include: performing coarse coronary artery segmentation on the initial vascular image to obtain a coarse coronary artery segmentation image of the initial vascular image; obtaining branch extension parameters of the vein branches, and performing extension processing on the vein branches based on the branch extension parameters to obtain a vein extension image corresponding to the vein branches; and performing vein removal processing on the coarse coronary artery segmentation image based on the vein extension image to obtain the coronary artery segmentation image of the initial vascular image.

[0103] In this embodiment, the branch expansion parameter can be understood as the expansion diameter parameter that the venous branch segment needs to be expanded. The branch expansion parameter may include the endpoint expansion parameter and the center expansion parameter of the venous branch segment.

[0104] It should be noted that, in this embodiment, the vein extension image obtained after extending the vein branch segments based on the extension parameters must completely cover the vein branches in the coarse coarse segmentation image of the coronary artery. Only under these conditions can the vein branches in the coarse coarse segmentation image of the coronary artery be completely removed based on the vein extension image. Therefore, it is necessary to determine the branch extension parameters for extending the vein branch segments based on the coarse segmentation results in the coarse coarse segmentation image of the coronary artery.

[0105] Specifically, a coarse segmentation image of the coronary arteries can be obtained by performing coarse segmentation of the initial vascular image using existing segmentation techniques, or by performing segmentation processing on the initial vascular image using traditional image processing algorithms. This embodiment does not limit the segmentation method.

[0106] Furthermore, based on the determination of each venous branch segment, the venous branch segments are expanded to obtain an expanded venous image. Optionally, the expansion processing method in this embodiment may include: for any endpoint of any venous branch segment, the endpoint expansion parameter is determined based on the segment endpoint parameters of the venous branch segment and the vascular parameters of other vessels connected to the venous branch in the coarse segmentation image of the coronary artery. Further, based on the determination of the endpoint expansion parameters of the two endpoints of the venous branch segment, the expansion parameters of the midpoint of the venous branch segment are fitted using the two endpoint expansion parameters to determine at least one center point expansion parameter of the venous branch segment. Then, each endpoint expansion parameter and each center point expansion parameter are used as expansion targets to expand the venous branch segment, obtaining an expanded venous image of the venous branch segment. Optionally, based on the above implementation method, all venous branch segments are traversed to obtain the expanded venous image.

[0107] Furthermore, based on the determined venous extension image and coarse coarse segmentation image of the ...

[0108] For example, for Figure 9 The venous branch segments shown are expanded to obtain the following: Figure 10 The image shown depicts an expanded vein, and based on this expanded vein image, [the following is a list of parameters / methods]. Figure 2 The coarse segmentation image of the coronary artery shown is processed by removing vein branches to obtain the following result: Figure 11 The image shown is a segmented image of the coronary arteries.

[0109] This invention provides a method for segmenting blood vessel images. The method involves acquiring an initial blood vessel image and determining its coronary artery skeleton image; identifying skeleton points along the skeleton lines in the coronary artery skeleton image, and determining at least one partition skeleton point among these points based on the degree of each skeleton point; determining the partition skeleton line segments corresponding to each partition skeleton point, and performing vein identification on these partition skeleton line segments to determine the venous branch lines in the coronary artery skeleton image; and determining the coronary artery segmentation image of the initial blood vessel image based on the initial blood vessel image and the venous branch lines. This technical solution processes the blood vessel image to be segmented to obtain branch points and endpoints within the blood vessel; identifies each branch using these branch points and endpoints to obtain venous branches; and then optimizes the coronary artery segmentation results based on the identified venous branches to obtain the final coronary artery segmentation result, thereby improving the accuracy of coronary artery segmentation.

[0110] Figure 12 This is a flowchart of another blood vessel image segmentation method provided by an embodiment of the present invention. Based on the above embodiments, this embodiment optionally includes vein identification of the skeleton segments in each region to determine the vein branch segments in the coronary artery skeleton image, including:

[0111] The segment number of the skeleton line segment in each partition is determined based on the skeleton point number of each partition skeleton point;

[0112] For any partition skeleton segment, determine the preceding partition skeleton segment, and based on the preceding partition skeleton segment, determine the vein branch segment identification result of the current partition skeleton segment. For example... Figure 12 As shown, the method includes:

[0113] S210. Obtain the initial vascular image and determine the coronary artery skeleton image of the initial vascular image.

[0114] S220. Determine each skeleton point on the skeleton line in the coronary artery skeleton image, and determine at least one partition skeleton point among the skeleton points based on the degree of each skeleton point.

[0115] S230. Determine the partition skeleton line segment corresponding to each partition skeleton point.

[0116] S240. Determine the segment number of the skeleton line segment of each partition based on the skeleton point number of each partition skeleton point.

[0117] In this embodiment of the invention, each partition skeleton point is traversed to determine the sequence number of each partition skeleton point, and based on the sequence number order of each partition skeleton point, the partition skeleton line segments are encoded to obtain the line segment sequence number of each partition skeleton line segment.

[0118] S250. For any partition skeleton segment, determine the previous partition skeleton segment of the current partition skeleton segment, and determine the vein branch segment identification result of the current partition skeleton segment based on the previous partition skeleton segment.

[0119] In practical applications, since coronary artery branches grow downwards in a tree-like pattern, meaning the branches always grow downwards and never upwards. In other words, this can also be understood as the angle between two adjacent skeletal segments in the skeleton line of this embodiment being greater than a preset degree, such as greater than 90 degrees; otherwise, if the first branch is determined to be a coronary artery branch, the second branch would most likely be considered a venous branch.

[0120] See examples Figure 13 , Figure 13 Using the branch points with smaller order as vertices (p), calculate the angle between the direction vector u of the first segment of the skeletal framework and the direction vector v of the second segment. Optionally, referring to the two branches on the left side of the figure, if the angle between direction vector u and direction vector v is greater than 90 degrees, it indicates that the direction vectors of the first and second segments of the skeletal framework are opposite. Therefore, the second segment is likely to be considered a different vascular branch from the first segment. In other words, if the first segment is determined to be a coronary artery branch, the second segment is a venous branch. Conversely, referring to the branches on the right side of the figure, if the angle between direction vector u and direction vector v is less than 90 degrees, it indicates that the direction vectors of the first and second segments are the same. Therefore, the second segment is likely to be considered the same branch as the first segment, meaning that if the first segment is determined to be a coronary artery branch, the second segment is also a coronary artery branch.

[0121] Based on the above, the identification of vein branches in the partition skeleton line segment requires the joint identification of two adjacent line segments.

[0122] Optionally, the method for identifying vein branches based on two adjacent partition skeleton segments in this embodiment may include: obtaining the average pixel value, curvature, and angle between the current partition skeleton segment and the previous partition skeleton segment; and determining the vein branch segment identification result of the current partition skeleton segment based on the average pixel value, curvature, angle, and corresponding weights.

[0123] It should be noted that the angle between line segments can characterize the branching growth direction of the two skeletal segment lines. The curvature of the line segment can represent the degree of bending of the skeletal segment. The average pixel value of the line segment can characterize the image value of the coronary artery branch corresponding to the skeletal segment.

[0124] In practical applications, compared to veins, coronary arteries have different vascular characteristics, including greater tortuosity. During CT angiography, to better visualize the coronary arteries, the contrast agent concentration in the coronary arteries of the circulatory system is designed to be higher, resulting in brighter images of the coronary arteries during CT imaging. Furthermore, the branching growth direction of coronary arteries is tree-like and downward. The average pixel value, curvature, and angle between the current segment and the previous segment of the partition skeleton line segment can characterize these vascular characteristics. Therefore, by determining the average pixel value, curvature, and angle of the segment, venous branches in the branch skeleton line segment can be identified.

[0125] Specifically, in this embodiment, the method for determining the average pixel value of a partition skeleton line segment may include: for any partition skeleton line segment, determining the blood vessel segment corresponding to the current partition skeleton line segment based on the position of the current partition skeleton line segment in the initial blood vessel, and calculating the pixel value of each pixel point corresponding to the current blood vessel segment, thereby obtaining the average pixel value corresponding to the current partition skeleton line segment.

[0126] Specifically, in this embodiment, the method for determining the curvature of the partition skeleton line segment can be to obtain a preset curvature calculation expression, substitute the line segment parameters of the partition skeleton line segment into the curvature calculation expression, and obtain the line segment curvature of the partition skeleton line segment.

[0127] Specifically, in this embodiment, the method for determining the included angle between two adjacent partition skeleton line segments can be based on an angle recognition model. For example, the positions of the two partition skeleton line segments are input into the model to obtain the included angle output by the model.

[0128] Of course, the above methods for determining the average pixel value, curvature, and included angle of line segments are merely illustrative descriptions of the technical solution in this embodiment and are not intended to limit the technical solution in this embodiment. This embodiment can also calculate the above parameters based on other existing methods, and there are no limitations on this.

[0129] Furthermore, based on determining the mean pixel value, curvature, and angle of the line segment, the method for determining whether the corresponding partition skeleton line segment is a vein branch line segment can be as follows: Based on the parameter thresholds corresponding to each parameter, determine the recognition probability of each parameter, then determine the final vein branch recognition probability, and determine whether the partition skeleton line segment is a vein branch line segment based on the recognition probability threshold. Optionally, recognition can also be performed based on a pre-trained vein branch recognition model to obtain the vein branch line segment recognition result. Of course, recognition can also be performed based on other recognition methods; this embodiment does not limit this.

[0130] S260. Determine the coronary artery segmentation image of the initial vascular image based on the initial vascular image and venous branch segments.

[0131] The technical solution of this invention, based on determining the skeleton line segments of each partition in the skeleton line, performs vein branch identification based on the preset line segment parameters of the partition skeleton line segments to obtain vein branch identification results, thereby obtaining accurate coronary artery segmentation results, so as to further improve the accuracy of coronary artery segmentation.

[0132] Figure 14 This is a schematic diagram of a blood vessel image segmentation device provided in an embodiment of the present invention. Figure 14 As shown, the device includes: a coronary artery skeleton image determination module 310, a partition skeleton point determination module 320, a venous branch line segment determination module 330, and a coronary artery segmentation image determination module 340;

[0133] The coronary artery skeleton image determination module 310 is used to acquire an initial vascular image and determine the coronary artery skeleton image of the initial vascular image;

[0134] The partition skeleton point determination module 320 is used to determine each skeleton point on the skeleton line in the coronary skeleton image, and to determine at least one partition skeleton point among the skeleton points based on the degree of each skeleton point.

[0135] The vein branch segment determination module 330 is used to determine the partition skeleton segment corresponding to each partition skeleton point, and to perform vein identification on each partition skeleton segment to determine the vein branch segment in the coronary artery skeleton image.

[0136] The coronary artery segmentation image determination module 340 is used to determine the coronary artery segmentation image of the initial blood vessel image based on the initial blood vessel image and the venous branch segments.

[0137] Based on the above implementation method, optionally, the vein branch segment determination module 330 includes:

[0138] The segment number determination unit is used to determine the segment number of each partition skeleton line segment based on the skeleton point number of each partition skeleton point.

[0139] The vein branch segment identification unit is used to determine the previous segment of the partition skeleton line segment for any partition skeleton line segment, and to determine the vein branch segment identification result of the current partition skeleton line segment based on the previous segment of the partition skeleton line segment.

[0140] Based on the above embodiments, optionally, the vein branch segment recognition unit includes:

[0141] The information acquisition unit is used to acquire the average pixel value of the current partition skeleton line segment, the curvature of the line segment, and the angle between the current partition skeleton line segment and the previous partition skeleton line segment, respectively.

[0142] The vein branch segment recognition subunit is used to determine the vein branch segment recognition result of the current partition skeleton segment based on the average pixel value of the segment, the curvature of the segment, the included angle of the segment, and the corresponding weights.

[0143] Based on the above implementation method, the partition skeleton point includes skeleton endpoints and skeleton branch points;

[0144] Optionally, the partition skeleton point determination module 320 includes:

[0145] The skeleton point degree determination unit is used to determine each neighboring point of the current skeleton point for any skeleton point, and determine the degree of the current skeleton point based on the skeleton points contained in the neighboring points.

[0146] The skeleton branch point determination unit is used to determine the skeleton endpoints and multiple candidate branch points in each skeleton point based on the degree of each skeleton point, and to perform branch point filtering on each candidate branch point based on the skeleton endpoints to obtain the skeleton branch points.

[0147] Based on the above embodiments, optionally, the skeleton branch point determination unit includes:

[0148] An endpoint number determination unit is used to determine the root endpoint and at least one end endpoint among the skeleton endpoints, and to determine the endpoint number of each end endpoint respectively.

[0149] The skeleton branch point determination unit is used to determine the skeleton connection path between the root endpoint and the last branch endpoints of any two adjacent numbers, and to filter each candidate branch point based on the skeleton connection path to obtain the skeleton branch point among each candidate branch point.

[0150] Based on the above implementation method, optionally, the vein branch segment determination module 330 includes:

[0151] The partition skeleton line segment determination unit is used to perform segmentation processing on the skeleton line in the coronary skeleton image based on each partition skeleton point to obtain at least one partition skeleton line segment.

[0152] Based on the above embodiments, optionally, the coronary artery segmentation image determination module 340 includes:

[0153] The coarse segmentation image determination unit is used to perform coarse segmentation of the initial blood vessel image to obtain a coarse segmentation image of the coarse blood vessel image of the initial blood vessel image.

[0154] A vein expansion image determination unit is used to obtain the branch expansion parameters of the vein branch, and perform expansion processing on the vein branch based on the branch expansion parameters to obtain the vein expansion image corresponding to the vein branch;

[0155] The coronary artery segmentation image is used to determine individual vessels, which are then used to perform vein removal processing on the coarse coronary artery segmentation image based on the vein extension image, to obtain the coronary artery segmentation image of the initial vascular image.

[0156] The blood vessel image segmentation device provided in the embodiments of the present invention can execute the blood vessel image segmentation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0157] Figure 15 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0158] like Figure 15 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0159] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0160] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as blood vessel image segmentation methods.

[0161] In some embodiments, the blood vessel image segmentation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the blood vessel image segmentation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the blood vessel image segmentation method by any other suitable means (e.g., by means of firmware).

[0162] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0163] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0164] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer 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.

[0165] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0166] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0167] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0168] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0169] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for segmenting blood vessel images, characterized in that, include: Acquire an initial vascular image and determine the coronary artery skeleton image of the initial vascular image; Determine each skeleton point on the skeleton line in the coronary artery skeleton image, and determine at least one partition skeleton point among the skeleton points based on the degree of each skeleton point. Determine the partition skeleton line segments corresponding to each partition skeleton point, and perform vein identification on each partition skeleton line segment to determine the vein branch line segments in the coronary artery skeleton image; The coronary artery segmentation image of the initial vascular image is determined based on the initial vascular image and the vein branch segments; The step of identifying veins in each of the partition skeleton segments to determine the vein branch segments in the coronary artery skeleton image includes: The segment number of each partition skeleton line segment is determined based on the skeleton point number of each partition skeleton point. For any partition skeleton segment, determine the previous partition skeleton segment of the current partition skeleton segment, and determine the vein branch segment identification result of the current partition skeleton segment based on the previous partition skeleton segment; The determination of the vein branch segment identification result of the current partition skeleton segment based on the previous partition skeleton segment includes: The mean pixel value, curvature, and angle between the current partition skeleton line segment and the previous partition skeleton line segment are obtained respectively. The vein branch line segment identification result of the current partition skeleton line segment is determined based on the average pixel value of the line segment, the curvature of the line segment, the included angle of the line segment, and the corresponding weights.

2. The method according to claim 1, characterized in that, The partition skeleton points include skeleton endpoints and skeleton branch points; Determining at least one partition skeleton point among the skeleton points based on the degree of each skeleton point includes: For any skeleton point, determine each neighboring point of the current skeleton point, and determine the degree of the current skeleton point based on the skeleton points contained in the neighboring points; Based on the degree of each skeleton point, determine the skeleton endpoints and multiple candidate branch points in each skeleton point, and filter the candidate branch points based on the skeleton endpoints to obtain the skeleton branch points.

3. The method according to claim 2, characterized in that, The step of filtering candidate branch points based on the skeleton endpoints to obtain skeleton branch points includes: Determine the root endpoint and at least one terminal endpoint among the skeleton endpoints, and determine the endpoint number of each terminal endpoint respectively; Determine the skeleton connection path between the root endpoint and the last branch endpoints of any two adjacent indices, and filter each candidate branch point based on the skeleton connection path to obtain the skeleton branch point among each candidate branch point.

4. The method according to claim 1, characterized in that, Determining the partition skeleton line segment corresponding to each partition skeleton point includes: Based on the skeleton points of each partition, the skeleton lines in the coronary artery skeleton image are segmented to obtain at least one partition skeleton line segment.

5. The method according to claim 1, characterized in that, The step of determining the coronary artery segmentation image of the initial vascular image based on the initial vascular image and the venous branches includes: The initial vascular image is subjected to coarse coronary artery segmentation to obtain a coarse coronary artery segmentation image of the initial vascular image; Obtain the branch expansion parameters of the vein branch, and perform expansion processing on the vein branch based on the branch expansion parameters to obtain the vein expansion image corresponding to the vein branch; Based on the vein extension image, the coarse segmentation image of the coronary artery is processed to remove veins, thereby obtaining the coronary artery segmentation image of the initial blood vessel image.

6. A blood vessel image segmentation device, characterized in that, include: A coronary artery skeleton image determination module is used to acquire an initial vascular image and determine the coronary artery skeleton image of the initial vascular image; The partition skeleton point determination module is used to determine each skeleton point on the skeleton line in the coronary skeleton image, and to determine at least one partition skeleton point among the skeleton points based on the degree of each skeleton point. The vein branch segment determination module is used to determine the partition skeleton segment corresponding to each partition skeleton point, and to perform vein identification on each partition skeleton segment to determine the vein branch segment in the coronary artery skeleton image. A coronary artery segmentation image determination module is used to determine a coronary artery segmentation image of the initial blood vessel image based on the initial blood vessel image and the venous branch line segments; The vein branch segment determination module includes: a segment number determination unit and a vein branch segment identification unit; The line segment number determination unit is used to determine the line segment number of each partition skeleton line segment based on the skeleton point number of each partition skeleton point. The vein branch segment identification unit is used to determine the previous segment of the partition skeleton segment for any partition skeleton segment, and to determine the vein branch segment identification result of the current partition skeleton segment based on the previous segment of the partition skeleton segment. The vein branch segment identification unit includes: an information acquisition unit and a vein branch segment identification subunit; The information acquisition unit is used to acquire the average pixel value of the current partition skeleton line segment, the curvature of the line segment, and the angle between the current partition skeleton line segment and the previous partition skeleton line segment, respectively. The vein branch segment recognition subunit is used to determine the vein branch segment recognition result of the current partition skeleton segment based on the average pixel value of the segment, the curvature of the segment, the included angle of the segment, and the corresponding weights.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the blood vessel image segmentation method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the blood vessel image segmentation method according to any one of claims 1-5.

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