CN116721148A

CN116721148A - Center line generation method and device, electronic equipment and storage medium

Info

Publication number: CN116721148A
Application number: CN202310748496.XA
Authority: CN
Inventors: 刘俊; 张欢; 王少康; 陈宽
Original assignee: Infervision Medical Technology Co Ltd
Current assignee: Infervision Medical Technology Co Ltd
Priority date: 2023-06-21
Filing date: 2023-06-21
Publication date: 2023-09-08

Abstract

The invention discloses a center line generation method, a center line generation device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a tube image, and extracting a skeleton image of the tube image; for pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree; a centerline of the tubular image is determined based on the skeletal tree. The invention effectively adapts to the situation that the central line cannot be accurately obtained when the problems of tortuosity or adhesion and the like are faced, and improves the accuracy of the obtained central line.

Description

Center line generation method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of medical image processing technologies, and in particular, to a method and apparatus for generating a center line, an electronic device, and a storage medium.

Background

Tube centerline is a commonly used technique in medical imaging that can extract a tube centerline from a three-dimensional medical image, such as a neck vessel in the head and neck, a coronary artery in the coronary artery, a ureter in the urinary tract, etc. The center line extraction technology has great clinical treatment significance in medical imaging, and can help doctors to know the shape, position, length, curvature and other information of the tubular objects more accurately, so that the diagnosis and treatment effects of diseases are improved.

However, the conventional center line extraction method cannot accurately acquire the center line in the face of problems such as meandering or sticking.

Disclosure of Invention

The invention provides a center line generating method, a center line generating device, electronic equipment and a storage medium, so as to solve the problems.

According to an aspect of the present invention, there is provided a center line generating method including:

acquiring a tube image, and extracting a skeleton image of the tube image;

for pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree;

a centerline of the tubular image is determined based on the skeletal tree.

According to another aspect of the present invention, there is provided a center line generating apparatus including:

the framework image extraction module is used for acquiring a tubular object image and extracting a framework image of the tubular object image;

the skeleton tree module is used for carrying out sorting assignment on the pixel points in the skeleton image according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree;

and the central line determining module is used for determining the central line of the tubular object image based on the skeleton tree.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the centerline generation method of any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the centerline generation method according to any of the embodiments of the present invention when executed.

According to the technical scheme, through obtaining the tubular object image, a skeleton image of the tubular object image is extracted; for pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree; a centerline of the tubular image is determined based on the skeletal tree. The method effectively adapts to the situation that the central line cannot be accurately obtained when the problems of tortuosity, adhesion and the like are faced, and improves the accuracy of the obtained central line.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for generating a center line according to an embodiment of the present invention;

FIG. 2 is a segmented image of an intracranial vessel image and a portion of a region to be treated provided in an embodiment of the present invention;

FIG. 3 is a left anterior arterial skeleton image provided by an embodiment of the present invention;

FIG. 4 is a flowchart of a method for generating a center line according to an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a center line generating device according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Fig. 1 is a flowchart of a method for generating a center line according to an embodiment of the present invention, where the method may be performed by a center line generating device, which may be implemented in hardware and/or software, and the center line generating device may be configured in an electronic apparatus according to an embodiment of the present invention, where the method is applicable to a case of extracting a center line from a three-dimensional medical image of a tubular object such as a neck blood vessel, a coronary artery, or a urinary tract. As shown in fig. 1, the method includes:

s110, acquiring a tube image, and extracting a skeleton image of the tube image.

The tube image refers to a three-dimensional medical image including a tube, and specifically, the tube image includes, but is not limited to, a three-dimensional medical image of a tube such as a cervical vessel, a coronary artery, and a urinary tract, for example, an electronic computed tomography (Computed Tomography, CT) image of a tube such as a cervical vessel, a coronary artery, and a urinary tract, a magnetic resonance imaging (Magnetic Resonance Imaging, MRI), and the like, which are not limited herein.

In the embodiment, a tubular object image is obtained, skeletonized, and a skeleton image of the tubular object image is extracted; specifically, in the actual processing process, as the structure of the tubular object of the cervical blood vessel, the coronary artery and the urinary tract in the tubular object image is complex, the tubular object image needs to be segmented according to the segmented image of the tubular object image, so as to obtain segmented images of a plurality of areas to be processed in the tubular object image; and then respectively carrying out skeletonizing treatment on the segmented images of the areas to be treated to obtain skeleton images of the areas to be treated.

Taking intracranial blood vessels as an example, fig. 2 is an intracranial blood vessel image and a segmented image of a part of a region to be processed provided in the embodiment of the present invention, and as shown in fig. 2, the intracranial blood vessel image, the intracranial left anterior artery image, the intracranial left middle artery image and the intracranial left posterior artery image are sequentially from left to right. Taking the left anterior artery image as an example, skeletonizing the left anterior artery image, extracting a skeleton image of the left anterior artery, referring to fig. 3, fig. 3 is a left anterior artery skeleton image provided by the embodiment of the present invention.

S120, for the pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree.

The starting point refers to a starting point of generating a center line of the tubular object image, and the starting point is selected by a person skilled in the art according to requirements, and is not limited herein. In this embodiment, the pixel points in the skeleton image may be sorted and assigned according to the accumulated arc length distance between the pixel points and the starting point, so as to obtain a skeleton tree.

On the basis of the foregoing embodiment, optionally, performing sorting assignment according to the cumulative arc length distance between the pixel point and the starting point to obtain a skeleton tree, including: assigning the starting point to an initial value; the starting point is taken as a root node to spread around, and the accumulated arc length distance between the pixel point and the starting point is sequenced to obtain a sequencing result; sequentially carrying out accumulated assignment on pixel points corresponding to the accumulated arc length distances based on the sorting result to obtain a skeleton tree; wherein the values assigned to the pixels having the same cumulative arc length distance are the same, and the larger the cumulative arc length distance is, the larger the value assigned to the pixel is.

The initial value may be any value, and the initial value is only set by those skilled in the art according to experience and requirements as a value for assigning, and exemplary initial values include, but are not limited to, 0, 1, …, 100, etc., and are not limited thereto. In the embodiment, a starting point is assigned as an initial value, the starting point is used as a root node to spread all around, and sorting is performed according to the accumulated arc length distance between the starting point and the pixel point, so that a sorting result is obtained; performing accumulated assignment on the pixel points according to the sorting result to obtain a skeleton tree; that is, the initial value of the starting point is used for carrying out accumulated assignment according to the sorting result of the accumulated arc length distance between the pixel point and the starting point, the smaller the accumulated arc length distance is, the smaller the value given to the pixel point is, and the values given to the pixel points with the same accumulated arc length distance are the same. Taking an initial value of a starting point as 0 as an example, assigning a value of 1 to a pixel point with the smallest accumulated arc length distance from the starting point, and so on, assigning values to all the pixel points in the skeleton diagram; it can be understood that, for the adjacent pixel, if the value assigned to the pixel a is 5 and the value assigned to the pixel B is 6, the pixel a is the parent node of the pixel B in the skeleton tree.

It will be appreciated that the above process of obtaining the skeleton tree is similar to the process that water flows continuously spread from the starting point to the periphery, and the skeleton tree can be named as a waters tree in an image.

S130, determining the center line of the tubular object image based on the skeleton tree.

Specifically, a shortest path between a starting point and an ending point in the skeleton image is determined based on the skeleton tree, and a center line of the tubular image is determined based on the shortest path.

The end point is the end point of the central line, and optionally, the end point can be set by a person skilled in the art according to experience and requirements, and a target area can also be obtained, wherein the point with the largest value in the skeleton tree is set as the end point in the target area; it can be understood that the target area is an interested area, the target area is an area including a starting point, and the target area is determined by a person skilled in the art according to requirements; the specific manner of determining the endpoint is selected by those skilled in the art according to actual needs and is not limited herein. In this embodiment, a shortest path between a starting point and an ending point in a skeleton image is determined according to a skeleton tree, and the determined shortest path is a center line from the starting point to the ending point in a tube image.

In some embodiments, optionally, the TEASAR function may be used to extract an optimal path between the start and end points of the vessel skeleton, with the optimal path being taken as the centerline from the start point to the end point in the vessel image.

On the basis of the foregoing embodiment, optionally, the determining, based on the skeleton tree, a shortest path between a start point and an end point in the skeleton image includes: taking a starting point as a root node, performing depth-first traversal on the skeleton tree until the skeleton tree is traversed to the end point; and backtracking the traversal path between the starting point and the end point to obtain the shortest path between the starting point and the end point in the skeleton image.

In this embodiment, the method for determining the shortest path between the starting point and the end point in the skeleton image is as follows: and performing depth-first traversal on the nodes in the skeleton tree by taking the starting point as a root node until the nodes reach the end point, and then backtracking a traversal path between the starting point and the end point to obtain a shortest path between the starting point and the end point, wherein the shortest path is the central line between the starting point and the end point. According to the embodiment, the center line between the starting point and the end point is obtained by traversing the nodes in the skeleton tree, only the relation between the nodes is considered, and reasonable connection can be realized for the case of malformation or fracture of the tubular object, so that the accuracy of the obtained center line is improved.

According to the technical scheme, a skeleton image of a tubular object image is extracted by acquiring the tubular object image; for pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree; a centerline of the tubular image is determined based on the skeletal tree. The method effectively adapts to the situation that the central line cannot be accurately obtained when the problems of tortuosity, adhesion and the like are faced, and improves the accuracy of the obtained central line.

Fig. 4 is a flowchart of a method for generating a center line according to an embodiment of the present invention, where the method is based on the above embodiment, and performs smoothing processing on the center line based on an optimization algorithm to obtain a target center line of the tubular image. Wherein, the explanation of the same or corresponding terms as the above embodiments is not repeated herein. As shown in fig. 4, the method includes:

s410, acquiring a tube image, and extracting a skeleton image of the tube image.

S420, for the pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree.

S430, determining the center line of the tubular object image based on the skeleton tree.

S440, carrying out smoothing treatment on the central line based on an optimization algorithm to obtain a target central line of the tubular object image.

The target center line is the center line of the smoothed tubular object image. In the embodiment, after the center line of the tubular object image is determined based on the skeleton tree, smoothing is performed on the obtained center line of the tubular object image based on an optimization algorithm by constructing a secondary optimization problem to obtain a target center line; the optimization algorithm includes, but is not limited to, gradient descent method, newton method, quasi-newton method, etc., and is not limited herein. It can be understood that in practical application, the target center line can be obtained by solving by using an optimizer corresponding to the optimization algorithm.

On the basis of the above embodiment, optionally, the loss function of the optimization algorithm includes a smoothness loss, a length loss, a deviation loss from the original point, and a centering loss with respect to the tubular.

In this embodiment, the key of constructing the secondary optimization problem is to construct a loss function corresponding to the optimization algorithm, and the loss function of the optimization algorithm is as follows:

cost＝alpha·cost _smooth +beta·cost _length +gamma·cost _deviation +

delta·cost _centerness

the smoothness loss function is as follows:

the length loss function is as follows:

the deviation loss function with respect to the original point is as follows:

the centrality loss function with respect to the tubing is as follows:

wherein the values of alpha, beta, gamma, delta represent the loss of smoothness, length, offset from the original point, and centering relative to the tubular, respectively, and are illustratively set to 1.0, 0.4, 0.2, and 1.0, respectively, it being understood that the purpose of setting the loss ratio of each loss is to balance each loss, and to promote the optimization.

Wherein, cost represents the loss function of the optimization algorithm _smooth Representation smoothingLoss of degree, cost _length Representing length loss, cost _deviation Representing the loss of deviation from the original point, cost _centerness Represents the loss of centering with respect to the tube, n represents the number of pixels in the target centerline, P _k Represents the coordinate point, P after the optimization of the kth pixel point _k-ref Representing the original coordinate point before optimization, dist of the kth pixel point _pk And a distance parameter representing the distance between the coordinate point after the k-th pixel optimization and the outline data of the tubular object, wherein the distance parameter is obtained through Euclidean distance conversion algorithm (Euclidean Distance Transform, EDT).

According to the embodiment, the loss function of the optimization algorithm is constructed through constructing the secondary optimization problem, so that the optimization algorithm can carry out smoothing treatment on the central line from multiple dimensions, and the smoothness of the central line and the accuracy of the central line are improved.

On the basis of the above embodiment, optionally, the method further includes: and carrying out straightening operation on the target center line based on curvature smoothing operation and frame smoothing operation to obtain a straightening image corresponding to the target center line.

In this embodiment, after the target center line is obtained, arc length parameterization is performed on the target center line to obtain the curvature of the target center line, and curvature smoothing operation is performed on the curvature to obtain a smoothed curvature. Wherein the smoothed curvature corresponds to a coordinate system perpendicular to each other at each point on the target centerline, thereby constructing an initial minimum rotation frame (Rotation Minimization Frames, RMF). Smoothing operations are performed on normals and negative normals in the initial RMF to obtain a smoothed RMF. And (3) carrying out a straightening operation on the center line of the target based on the smoothed RMF to obtain a straightening (lumen) image. It will be appreciated that the pixel value differences between adjacent pixels are not large due to the smaller rotation of the smoothed RMF, thus making the lumen image smoother. According to the method, the smooth straightening image is obtained by straightening the target center line, so that accurate analysis and diagnosis of diseases can be conveniently carried out through the straightening image.

Fig. 5 is a schematic structural diagram of a center line generating device according to an embodiment of the present invention. As shown in fig. 5, the apparatus includes:

a skeleton image extraction module 510, configured to obtain a tube image, and extract a skeleton image of the tube image;

the skeleton tree module 520 is configured to perform sorting assignment on the pixel points in the skeleton image according to the accumulated arc length distance between the pixel points and the starting point, so as to obtain a skeleton tree;

a centerline determination module 530 for determining a centerline of the tubular image based on the skeletal tree.

Based on the above embodiment, optionally, the skeleton tree module 520 is specifically configured to assign the starting point to an initial value; the starting point is taken as a root node to spread around, and the accumulated arc length distance between the pixel point and the starting point is sequenced to obtain a sequencing result; sequentially carrying out accumulated assignment on pixel points corresponding to the accumulated arc length distances based on the sorting result to obtain a skeleton tree; wherein the values assigned to the pixels having the same cumulative arc length distance are the same, and the larger the cumulative arc length distance is, the larger the value assigned to the pixel is.

Based on the above embodiment, optionally, the center line determining module 530 is specifically configured to determine, based on the skeleton tree, a shortest path between a start point and an end point in the skeleton image, and determine, based on the shortest path, a center line of the tubular image.

On the basis of the above embodiment, optionally, the center line determining module 530 includes a shortest path determining unit, configured to perform depth-first traversal on the skeleton tree with a starting point as a root node until the traversal reaches the end point; and backtracking the traversal path between the starting point and the end point to obtain the shortest path between the starting point and the end point in the skeleton image.

Based on the above embodiment, optionally, the apparatus further includes a centerline smoothing module, configured to smooth the centerline based on an optimization algorithm to obtain a target centerline of the tubular image.

Based on the above embodiment, optionally, the apparatus further includes a straightened image obtaining module, configured to perform a straightening operation on the target center line based on the curvature smoothing operation and the frame smoothing operation, to obtain a straightened image corresponding to the target center line.

The center line generating device provided by the embodiment of the invention can execute the center line generating method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, 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 meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having 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 specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the centerline generation method.

In some embodiments, the centerline generation method may be implemented as a computer program tangibly embodied on 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 installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the centerline generation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the centerline generation method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

The computer program for implementing the centerline generation method 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, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

The embodiment of the invention also provides a computer readable storage medium, the computer readable storage medium stores computer instructions for causing a processor to execute a center line generating method, the method comprising:

acquiring a tubular object image, and extracting a skeleton image of the tubular object image; for pixel points in the skeleton image, sorting and assigning values according to the accumulated arc length distance between the pixel points and the starting point to obtain a skeleton tree; a centerline of the tubular image is determined based on the skeletal tree.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A center line generation method, comprising:

acquiring a tube image, and extracting a skeleton image of the tube image;

a centerline of the tubular image is determined based on the skeletal tree.

2. The method of claim 1, wherein the performing the sorting assignment according to the cumulative arc length distance between the pixel point and the starting point to obtain the skeleton tree includes:

assigning the starting point to an initial value;

the starting point is taken as a root node to spread around, and the accumulated arc length distance between the pixel point and the starting point is sequenced to obtain a sequencing result;

sequentially carrying out accumulated assignment on pixel points corresponding to the accumulated arc length distances based on the sorting result to obtain a skeleton tree; wherein the values assigned to the pixels having the same cumulative arc length distance are the same, and the larger the cumulative arc length distance is, the larger the value assigned to the pixel is.

3. The method of claim 2, wherein the determining a centerline of the tubular image based on the skeletal tree comprises:

determining a shortest path between a starting point and an ending point in the skeleton image based on the skeleton tree, and determining a center line of the tubular image based on the shortest path.

4. A method according to claim 3, wherein said determining a shortest path between a start point and an end point in the skeleton image based on the skeleton tree comprises:

taking a starting point as a root node, performing depth-first traversal on the skeleton tree until the skeleton tree is traversed to the end point;

and backtracking the traversal path between the starting point and the end point to obtain the shortest path between the starting point and the end point in the skeleton image.

5. The method according to claim 1, wherein the method further comprises:

and carrying out smoothing treatment on the central line based on an optimization algorithm to obtain a target central line of the tubular object image.

6. The method of claim 5, wherein the loss function of the optimization algorithm comprises a smoothness loss, a length loss, a deviation loss from an original point, and a centering loss with respect to the tubular.

7. The method of claim 5, wherein the method further comprises:

and carrying out straightening operation on the target center line based on curvature smoothing operation and frame smoothing operation to obtain a straightening image corresponding to the target center line.

8. A center line generating apparatus, comprising:

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the centerline generation method of any of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the centreline generation method of any of claims 1-7 when executed.