An image processing method, apparatus, electronic device, and storage medium

Abstract

The application discloses an image processing method and device, electronic equipment and storage medium. The method comprises the following steps: acquiring a plurality of coronary images in a cardiac cycle, and extracting displacement field data of an aortic vessel in the coronary images, wherein the displacement field data comprises displacement field data of each blood vessel position point in the aortic vessel in the cardiac cycle; determining spatial movement parameters of each blood vessel position point based on the displacement field data; and determining a target position point set in the aortic vessel based on the spatial movement parameters of each blood vessel position point. According to the scheme, the spatial movement parameters of each blood vessel position point are determined by extracting the displacement field data of the aortic vessel in the plurality of coronary images in the cardiac cycle, and then the target position point set in the aortic vessel is determined, so that the spatial movement of the blood vessel wall in the cardiac cycle can be more accurately determined, the accuracy of image processing is improved, and the reference value of the image processing result is improved.

Description

Technical Field

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

[0002] With the continuous development of image processing technology, it has been widely applied in medicine. Through image processing technology, the observation of lesions inside the body can be more direct and clearer.

[0003] Current medical imaging technology primarily relies on operators observing two-dimensional or three-dimensional images and making judgments based on the operators' experience.

[0004] Taking valvular heart disease as an example, after acquiring vascular images using computed tomography (CT) technology, the operator uses these images to locate the surgical procedure. Because cardiac movement causes blood vessels to shift, the surgical procedure location is highly dependent on the operator's experience, which is detrimental to accurate positioning. Summary of the Invention

[0005] This invention provides an image processing method, apparatus, electronic device, and storage medium to analyze the movement of blood vessels through image processing, thereby providing selectable blood vessel locations.

[0006] According to one aspect of the present invention, an image processing method is provided, comprising:

[0007] Multiple coronary artery images were acquired during the cardiac cycle, and displacement field data of the aorta in the coronary artery images were extracted. The displacement field data included displacement field data of each vessel location in the aorta during the cardiac cycle.

[0008] The spatial movement parameters of each blood vessel location point are determined based on displacement field data.

[0009] The target location set in the aorta is determined based on the spatial movement parameters of each blood vessel location.

[0010] Optionally, the spatial movement parameters include one or more of the average displacement, spatial gradient of displacement, and displacement oscillation index.

[0011] Optionally, for each blood vessel location point:

[0012] The average displacement is the mean of the absolute values ​​of multiple displacement data points of the blood vessel location point during the cardiac cycle;

[0013] The spatial gradient of displacement is the rate of change of displacement of the blood vessel location point during the cardiac cycle.

[0014] The displacement oscillation index is the ratio of the displacement change of a blood vessel location point during the cardiac cycle to the total movement path during the cardiac cycle.

[0015] Optionally, the displacement data includes three-dimensional spatial displacement data and radial displacement data;

[0016] Accordingly, the spatial movement parameters include one or more of the first average displacement, the first spatial gradient of displacement, and the first oscillation index determined based on three-dimensional spatial displacement data, and one or more of the second average displacement, the second spatial gradient of displacement, and the second oscillation index determined based on radial displacement data.

[0017] Optionally, the set of target locations in the aorta can be determined based on the spatial movement parameters of each vessel location, including:

[0018] For any blood vessel location, one or more of the average displacement, spatial gradient of displacement, and displacement oscillation index of the blood vessel location are compared with the corresponding thresholds to obtain the comparison results;

[0019] Based on the comparison results, blood vessel locations that meet the conditions for motion stability are selected to form a target location set.

[0020] Optionally, displacement field data of the aorta in the coronary images can be extracted, including:

[0021] Multiple coronary artery images are processed using a pre-set displacement field prediction model to obtain displacement field data; or...

[0022] Image segmentation is performed on multiple coronary artery images, and the aortic vessel contour in each coronary artery image is determined. The coordinate information of the vessel location points in the aortic vessel contour of each coronary artery image is then used to generate displacement field data.

[0023] Optionally, the method also includes:

[0024] Any coronary artery image is labeled based on the target location point set to obtain a labeled image, which is then displayed.

[0025] According to another aspect of the present invention, an image processing apparatus is provided, comprising:

[0026] The displacement field data determination module is used to acquire multiple coronary artery images within the cardiac cycle and extract displacement field data of the aortic vessels in the coronary artery images. The displacement field data includes displacement field data of each vessel location point in the aortic vessels within the cardiac cycle.

[0027] The spatial movement parameter determination module is used to determine the spatial movement parameters of each blood vessel location point based on displacement field data.

[0028] The target location point set determination module is used to determine the target location point set in the aorta based on the spatial movement parameters of each blood vessel location point.

[0029] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0030] At least one processor; and

[0031] A memory that is communicatively connected to at least one processor; wherein,

[0032] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the image processing method of any embodiment of the present invention.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the image processing method of any embodiment of the present invention.

[0034] The technical solution of this invention determines the spatial movement parameters of each vessel location point by extracting displacement field data of the aortic vessels from multiple coronary artery images within the cardiac cycle, thereby determining the target location point set in the aortic vessels. This solves the problem of not being able to accurately determine the motion of the aortic vessel wall, and can more accurately determine the spatial movement of the vessel wall during the cardiac cycle, which helps to improve the accuracy of image processing and thus improve the reference value of the image processing results.

[0035] 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

[0036] 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.

[0037] Figure 1 This is a flowchart of an image processing method provided in Embodiment 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of an image processing device provided in Embodiment 2 of the present invention;

[0039] Figure 3This is a schematic diagram of the structure of an electronic device that implements the image processing method of the present invention. Detailed Implementation

[0040] 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.

[0041] 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 orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] Example 1

[0043] Figure 1 This is a flowchart of an image processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to determining the motion of the aortic vessel wall. The method can be executed by an image processing device, which can be implemented in hardware and / or software. This image processing device can be configured in electronic devices such as computers and image processing equipment. Figure 1 As shown, the method includes:

[0044] S110. Acquire multiple coronary artery images during the cardiac cycle and extract displacement field data of the aorta in the coronary artery images.

[0045] The displacement field data includes the displacement data of each blood vessel location in the aorta during the cardiac cycle.

[0046] The cardiac cycle can be understood as a complete heartbeat cycle, or the period between two adjacent R waves on an electrocardiogram (ECG). Displacement field data can be understood as the spatial distribution of displacement vectors within a three-dimensional space, such as the spatial distribution of displacement vectors in the aorta. Coronary artery images can be acquired using techniques such as CT angiography and magnetic resonance imaging (MRI), which are not limited here. The number of coronary artery images acquired can be determined based on the time interval set according to the cardiac cycle. For example, if the time interval for acquiring images at adjacent moments is set to 10% of the cardiac cycle, then coronary artery images of the complete cardiac cycle can be represented as 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% of the cardiac cycle. Therefore, acquiring images at the above-set time intervals can yield 10 coronary artery images. It can be understood that the number of coronary artery images acquired within a cardiac cycle can be determined according to image acquisition requirements, and correspondingly, the acquisition time interval for coronary artery images can be further adjusted based on the determined number of acquisitions. Displacement field data of the aorta in coronary artery images can be extracted using pre-set displacement field prediction models, image segmentation models, or image registration-segmentation models; no specific method is used here.

[0047] The extracted displacement field data mainly includes the displacement data of various vessel locations within the aorta during the cardiac cycle. Specifically, the vessel locations are contour points on the cross-sectional profile of the aorta; correspondingly, the displacement field data can include the displacement data of multiple contour points on the cross-sectional profile of the aorta.

[0048] Optionally, the displacement field data of the aorta in coronary artery images is extracted, including: performing prediction processing on multiple coronary artery images based on a pre-set displacement field prediction model to obtain displacement field data. Specifically, the pre-set displacement field prediction model is invoked, multiple coronary artery images are input into the displacement field prediction model, and the displacement field data of the aorta in each coronary artery image is output. This displacement field prediction model can be a machine learning model, such as a neural network model, trained through multiple coronary artery image sample groups, and is a model with displacement field data extraction capabilities. The coronary artery image sample groups include multiple coronary artery images of the sample object within the cardiac cycle, and the corresponding displacement field label data for each coronary artery image. It is understood that before inputting multiple coronary artery images into the displacement field prediction model, preprocessing of the multiple coronary artery images is also included, including but not limited to image enhancement, image scaling, and cropping.

[0049] Optionally, displacement field data of the aorta in coronary artery images is extracted, including: extracting the aortic vessel contour from multiple coronary artery images, determining the coordinate information of the vessel location points in the aortic vessel contour of each coronary artery image, and generating displacement field data. The aortic vessel contour extraction can be achieved by segmenting multiple coronary artery images separately using image segmentation techniques, and obtaining the aortic vessel contour based on the segmentation results. Alternatively, the aortic vessel contour can be extracted using an image registration-segmentation model. This model is a machine learning model that combines registration and segmentation functions, capable of registering and segmenting multiple input coronary artery images separately to obtain the aortic vessel contour in multiple coronary artery images. Cross-sections of the aorta in each coronary artery image are obtained, point sampling is performed on the cross-sections to obtain contour points on the cross-sections, and these points are labeled. The position coordinate information of each contour point is determined, and displacement field data is generated based on the position coordinate information of the same contour point in multiple coronary artery images.

[0050] For example, the segmentation result of the aorta in the coronary artery image at any time is obtained, and the segmentation is refined to obtain the corresponding centerline. The centerline is then resampled at equal intervals to a fixed number M, such as 1024 center points. For each center point, the cross-section of the current center point is obtained based on the curve straightening algorithm, and the coordinates of the contour points are obtained according to the segmentation result. The contour points are then resampled at equal intervals to a fixed number N, such as 512 contour points. Thus, there are 1024*512 contour points in the coronary artery image. The contour points are the location points of the blood vessels. The displacement field data of the aorta in the coronary artery image is determined based on the coordinate information of the location points.

[0051] In this embodiment, by acquiring multiple coronary artery images within a complete cardiac cycle and processing these images, the displacement field data of the aorta in the coronary artery images is determined. This provides a data foundation for subsequently determining the spatial displacement of the aorta and helps to more accurately determine important location information in the images.

[0052] S120. Based on the displacement field data, determine the spatial movement parameters of each blood vessel location point.

[0053] Specifically, displacement field data can be used to obtain the spatial displacement data of each blood vessel location point, and further determine the spatial movement parameters of each blood vessel location point. The spatial movement parameters can characterize the movement of the blood vessel location point during the cardiac cycle. The larger the spatial movement parameter, the stronger the movement of the blood vessel location point. Correspondingly, the smaller the spatial movement parameter, the more stable the blood vessel location point is during the cardiac cycle.

[0054] Optionally, the spatial movement parameters include one or more of the following: average displacement, spatial gradient of displacement, and displacement oscillation index. Accordingly, for each vascular location: the average displacement is the mean of the absolute values ​​of multiple displacement data of the vascular location during the cardiac cycle; the spatial gradient of displacement is the rate of change of displacement of the vascular location during the cardiac cycle; and the displacement oscillation index is the ratio of the amount of displacement change of the vascular location during the cardiac cycle to the total movement path during the cardiac cycle.

[0055] Specifically, common displacements are spatial displacements, defined as displacements in a spatial direction, denoted by D, and are three-dimensional data. Radial displacements, on the other hand, refer to the displacement along the radial direction of the vessel wall contour, i.e., the distance from a contour point to the center point of the cross-section, denoted by R, and are one-dimensional data. The average displacement is the mean of the absolute values ​​of multiple displacement data points corresponding to each vessel location within one cardiac cycle T, calculated using the following formula:

[0056]

[0057]

[0058] Here, the prefix A stands for average, AR represents the mean absolute value of radial displacement, and AD represents the mean absolute value of spatial displacement.

[0059] The spatial gradient of displacement is the rate of change of displacement at each vascular location point within a cardiac cycle T, and is calculated using the following formula:

[0060]

[0061]

[0062]

[0063]

[0064] In this context, the suffix G is an abbreviation for gradient, RG represents the rate of change of radial displacement, ARG represents the mean of the rate of change of radial displacement, DG represents the rate of change of spatial displacement, and ADG represents the mean of the rate of change of spatial displacement.

[0065] The displacement oscillation index is the displacement of each blood vessel location over time within a cardiac cycle T.

[0066] The ratio of the change to the total movement path is calculated using the following formula:

[0067]

[0068]

[0069] In this context, the prefix T stands for time, TR represents the radial displacement oscillation index, and TD represents the spatial displacement oscillation index.

[0070] Optionally, the displacement data includes three-dimensional spatial displacement data and radial displacement data; correspondingly, the spatial movement parameters include one or more of the first average displacement, the first spatial gradient of displacement and the first oscillation index determined based on the three-dimensional spatial displacement data, and one or more of the second average displacement, the second spatial gradient of displacement and the second oscillation index determined based on the radial displacement data.

[0071] Among these, the spatial movement parameters determined in three-dimensional space can be referred to as the first average displacement, the first spatial gradient of displacement, and the first displacement oscillation index, respectively. Depending on the actual spatial displacement of the blood vessel location point, the spatial movement parameters may include one or more of the first average displacement, the first spatial gradient of displacement, and the first displacement oscillation index. For the radial displacement data determined based on the actual radial displacement of the blood vessel location point, it may include one or more of the second average displacement, the second spatial gradient of displacement, and the second displacement oscillation index.

[0072] Specifically, to better determine the spatial displacement of blood vessel locations, it is necessary to acquire and analyze the three-dimensional spatial displacement data and radial displacement data corresponding to the blood vessel locations, and comprehensively determine the displacement data of the blood vessel locations. The spatial displacement parameters and radial displacement parameters can be calculated using the formulas mentioned above.

[0073] The average displacement refers to the average absolute value of the displacement path of any blood vessel location point within one cardiac cycle. It reflects the overall movement trajectory of the blood vessel location point; the longer the movement trajectory, the closer the blood vessel location point is to the surrounding area.

[0074] In this embodiment, the spatial movement parameters of the blood vessel location points are determined based on the displacement field data of the aortic blood vessel and formulas such as average displacement, displacement spatial gradient, and displacement oscillation index. This provides a data foundation for subsequent dynamic analysis of the blood vessel location points and helps to better determine the target location point set.

[0075] S130. Determine the set of target locations in the aorta based on the spatial movement parameters of each blood vessel location.

[0076] Specifically, the target location set can be understood as a set of location points where the spatial displacement exhibits a certain pattern, or it can be a set of location points whose spatial displacement parameters satisfy a pre-set threshold. For example, a set of location points with a displacement oscillation index greater than 0.3 is called a target location point. The thresholds for different spatial displacement parameters are different and can be set based on prior experience or displacement field data of various blood vessels.

[0077] It should be noted that the average displacement refers to the absolute average of the displacement path of the blood vessel location point within one cardiac cycle. It reflects the overall movement trajectory of the blood vessel location point. The longer the movement trajectory, the greater the stress that may be acting on the blood vessel location point. The spatial gradient of displacement calculates the rate of change of the blood vessel location point in the spatial dimension. The rate of change of spatial change reflects the difference between the blood vessel location point and other surrounding blood vessel location points. The larger the spatial gradient, the more significant the difference between the blood vessel location point and surrounding blood vessel location points, suggesting that it is subjected to greater local stress, resulting in uneven stress at the point. The displacement oscillation index calculates the ratio between the amount of displacement change and the total movement path of the blood vessel location point within one cardiac cycle. For example, the value range is 0-0.5. An oscillation index of 0 indicates that the direction of movement of the blood vessel location point has not changed. An oscillation index of 0.5 indicates that the blood vessel location point returns to the starting point after one cardiac cycle. The larger the oscillation index, the more complex the stress that the blood vessel location point is subjected to during movement, causing a change in the direction of movement.

[0078] Optionally, the target location point set in the aorta is determined based on the spatial movement parameters of each blood vessel location point, including: for any blood vessel location point, comparing one or more of the average displacement, spatial gradient of displacement, and displacement oscillation index of the blood vessel location point with the corresponding threshold to obtain the comparison result; and selecting blood vessel location points that meet the motion stability conditions according to the comparison result to form the target location point set.

[0079] Specifically, by determining the spatial movement parameters of each blood vessel location, the blood vessel location points corresponding to the parameters that satisfy the motion stability conditions are identified and used as target location points. The set of all target location points that satisfy the motion stability conditions is called the target location point set. The preset conditions can be set according to actual needs. Thresholds can be set for one or more of the average displacement, spatial gradient of displacement, and displacement oscillation index of the blood vessel location points. For example, the motion stability conditions can be set as follows: the average displacement of the blood vessel location points is preset to 0.01, the spatial gradient of displacement is 0.02, and the threshold of displacement oscillation index is 0.1. If the average displacement, spatial gradient of displacement, and displacement oscillation index of a certain blood vessel location point are all less than or equal to the set thresholds, then this blood vessel location point can be determined as a target location point. Other location points are screened according to this method until all location points are screened. Finally, the target location point set is formed by all the target location points.

[0080] Optionally, the method further includes: labeling any coronary artery image based on the target location point set to obtain a labeled image, and displaying the labeled image.

[0081] Specifically, based on the target location point set determined by the above method, any coronary artery image is selected for marking. The marking method can be to set the color of the target location point to be different from the colors of other location points, thereby distinguishing the target location point from other location points. Alternatively, labels can be added for marking. The marking method can be selected according to actual needs and is not limited here. The marked coronary artery image is then displayed through a visualization device, allowing users to observe the target location point set more intuitively and clearly.

[0082] The technical solution of this embodiment determines the spatial movement parameters of each vessel location point by extracting displacement field data of the aorta from multiple coronary artery images within the cardiac cycle, introducing radial movement parameters, and combining the spatial movement parameters to determine the target location point set in the aorta. This solves the problem of not being able to accurately determine the spatial displacement of the aortic vessel wall, and can more accurately determine the movement of the vessel wall during the cardiac cycle, which helps to improve the accuracy of image processing, more accurately determine the target location points on the vessel wall, and thus improve the reference value of the image processing results.

[0083] Example 2

[0084] Figure 2 This is a schematic diagram of the structure of an image processing device provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes:

[0085] The displacement field data determination module 210 is used to acquire multiple coronary artery images within the cardiac cycle and extract displacement field data of the aortic vessels in the coronary artery images. The displacement field data includes displacement field data of each vessel location point in the aortic vessels within the cardiac cycle.

[0086] The spatial movement parameter determination module 220 is used to determine the spatial movement parameters of each blood vessel location point based on the displacement field data.

[0087] The target location point set determination module 230 is used to determine the target location point set in the aortic blood vessel based on the spatial movement parameters of each blood vessel location point.

[0088] Optionally, the displacement field data determination module 210 is specifically used to extract displacement field data of the aorta in the coronary artery image, including:

[0089] Multiple coronary artery images are processed using a pre-set displacement field prediction model to obtain displacement field data; or...

[0090] Image segmentation is performed on multiple coronary artery images, and the aortic vessel contour in each coronary artery image is determined. The coordinate information of the vessel location points in the aortic vessel contour of each coronary artery image is then used to generate displacement field data.

[0091] Optionally, the displacement and movement parameter determination module 220 is specifically used for:

[0092] Spatial movement parameters include one or more of the average displacement, spatial gradient of displacement, and displacement oscillation index.

[0093] For each blood vessel location:

[0094] The average displacement is the mean of the absolute values ​​of multiple displacement data points of the blood vessel location point during the cardiac cycle;

[0095] The spatial gradient of displacement is the rate of change of displacement of the blood vessel location point during the cardiac cycle.

[0096] The displacement oscillation index is the ratio of the displacement change of a blood vessel location point during the cardiac cycle to the total movement path during the cardiac cycle.

[0097] Displacement data includes three-dimensional spatial displacement data and radial displacement data;

[0098] Accordingly, the spatial movement parameters include one or more of the first average displacement, the first spatial gradient of displacement, and the first oscillation index determined based on three-dimensional spatial displacement data, and one or more of the second average displacement, the second spatial gradient of displacement, and the second oscillation index determined based on radial displacement data.

[0099] The target location set in the aorta is determined based on the spatial movement parameters of each vessel location, including:

[0100] For any blood vessel location, one or more of the average displacement, spatial gradient of displacement, and displacement oscillation index of the blood vessel location are compared with the corresponding thresholds to obtain the comparison results;

[0101] Based on the comparison results, blood vessel locations that meet the conditions for motion stability are selected to form a target location set.

[0102] Optionally, the target location point set determination module 230, specifically used in the method, also includes:

[0103] Any coronary artery image is labeled based on the target location point set to obtain a labeled image, which is then displayed.

[0104] The image processing apparatus provided in the embodiments of the present invention can execute the image processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0105] Example 3

[0106] Figure 3This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. The electronic device 10 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as 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.

[0107] like Figure 3 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.

[0108] 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.

[0109] 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 image processing methods.

[0110] In some embodiments, the image processing 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 image processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the image processing method by any other suitable means (e.g., by means of firmware).

[0111] 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.

[0112] Computer programs for implementing the image processing methods of the present invention can be written in any combination of one or more programming languages. These computer programs can 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 implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0113] Example 4

[0114] Embodiment 4 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute an image processing method, the method comprising:

[0115] Multiple coronary artery images were acquired during the cardiac cycle, and displacement field data of the aorta in the coronary artery images were extracted. The displacement field data included displacement field data of each vessel location in the aorta during the cardiac cycle.

[0116] The spatial movement parameters of each blood vessel location point are determined based on displacement field data.

[0117] The target location set in the aorta is determined based on the spatial movement parameters of each blood vessel location.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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. An image processing method, characterized in that, include: Multiple coronary artery images are acquired during the cardiac cycle, and displacement field data of the aortic vessels in the coronary artery images are extracted, wherein the displacement field data includes displacement data of each vessel location point in the aortic vessels during the cardiac cycle; Based on the displacement field data, the spatial movement parameters of each of the blood vessel location points are determined respectively; The target location point set in the aortic blood vessel is determined based on the spatial movement parameters of each of the blood vessel location points; The spatial movement parameters include at least two of the following: average displacement, spatial gradient of displacement, and displacement oscillation index; or, spatial gradient of displacement, or displacement oscillation index; for each blood vessel location point: the average displacement is the mean of the absolute values ​​of multiple displacement data of the blood vessel location point within the cardiac cycle; the spatial gradient of displacement is the rate of change of displacement of the blood vessel location point within the cardiac cycle; the displacement oscillation index is the ratio of the amount of displacement change of the blood vessel location point within the cardiac cycle to the total movement path within the cardiac cycle.

2. The method according to claim 1, characterized in that, The displacement data includes three-dimensional spatial displacement data and radial displacement data; Accordingly, the spatial movement parameters respectively include at least two of the following: a first average displacement, a first spatial gradient of displacement, and a first oscillation index determined based on the three-dimensional spatial displacement data, or the first spatial gradient of displacement, or the first oscillation index of displacement, and at least two of the following: a second average displacement, a second spatial gradient of displacement, and a second oscillation index of displacement, or the second spatial gradient of displacement, or the second oscillation index of displacement, determined based on the radial displacement data.

3. The method according to claim 1, characterized in that, Determining the set of target locations in the aorta based on the spatial movement parameters of each of the blood vessel locations includes: For any of the aforementioned blood vessel locations, the spatial movement parameters of the blood vessel locations are compared with the corresponding thresholds to obtain the comparison results; Based on the comparison results, blood vessel location points that meet the motion stability conditions are selected to form a target location point set.

4. The method according to claim 1, characterized in that, The extraction of displacement field data of the aorta in the coronary artery image includes: The displacement field data is obtained by predicting the multiple coronary artery images based on a pre-set displacement field prediction model; or... Image segmentation is performed on the multiple coronary artery images respectively, and the aortic vessel contour in each of the coronary artery images is determined to generate the displacement field data by determining the coordinate information of the vessel position points in the aortic vessel contour of each of the coronary artery images.

5. The method according to claim 1, characterized in that, The method further includes: Based on the target location point set, any one of the coronary artery images is labeled to obtain a labeled image, and the labeled image is displayed.

6. An image processing apparatus, characterized in that, include: The displacement field data determination module is used to acquire multiple coronary artery images within the cardiac cycle and extract displacement field data of the aortic vessels in the coronary artery images, wherein the displacement field data includes displacement data of each vessel location point in the aortic vessels within the cardiac cycle. A spatial movement parameter determination module is used to determine the spatial movement parameters of each of the blood vessel location points based on the displacement field data. The target location point set determination module is used to determine the target location point set in the aortic blood vessel based on the spatial movement parameters of each of the blood vessel location points; The spatial movement parameters include at least two of the following: average displacement, spatial gradient of displacement, and displacement oscillation index; or, spatial gradient of displacement, or displacement oscillation index; for each blood vessel location point: the average displacement is the mean of the absolute values ​​of multiple displacement data of the blood vessel location point within the cardiac cycle; the spatial gradient of displacement is the rate of change of displacement of the blood vessel location point within the cardiac cycle; the displacement oscillation index is the ratio of the amount of displacement change of the blood vessel location point within the cardiac cycle to the total movement path within the cardiac cycle.

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 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 image processing 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 image processing method according to any one of claims 1-5.

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