Method and electronic device for generating 3-dimensional blood vessel profile data

Abstract

To provide a method for generating 3-dimensional blood vessel profile data by using a plurality of 2-dimensional images.SOLUTION: The method includes the steps of: acquiring a plurality of images including blood vessels; generating three-dimensional first blood vessel profile data by using a first set of images including a first image and a second image of the plurality of images; generating three-dimensional second blood vessel profile data by using a second set of images including a third image and a fourth image; identifying a first coordinate value indicating a common point based on the first set of images and the second set of images; identifying a second coordinate value in the first blood vessel profile data corresponding to the common point based on the first coordinate value; identifying a third coordinate value in the second blood vessel profile data corresponding to the common point based on the first coordinate value; and acquiring a third blood vessel profile data by merging the first blood vessel profile data and the second blood vessel profile data based on the second coordinate value and the third coordinate value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a method and electronic device for generating three-dimensional vascular profile data. [Background technology]

[0002] In the medical field, by analyzing medical images acquired using X-ray, CT (Computed Tomography), angiography, etc., lesions in a subject (e.g., a patient) can be diagnosed. For example, lesions in blood vessels can be diagnosed by analyzing angiographic images. However, lesions such as myocardial ischemia caused by stenosis of the coronary artery are difficult to accurately diagnose only by analyzing two-dimensional angiographic images. For example, even if the degree of stenosis of the coronary artery is judged to be severe in the image, it may not have much effect on the actual blood flow and may not induce myocardial ischemia, so it is difficult to determine ischemia occurring in the myocardium only by the degree of stenosis of the coronary artery analyzed from two-dimensional angiographic images.

[0003] On the other hand, blood flow feature values ​​such as FFR (Fractional Flow Reserve) can be used to quickly and accurately diagnose vascular lesions such as coronary artery stenosis. Also, 3D vascular profile data can be used to predict blood flow feature values. This has led to a demand for the development of technology that can generate 3D vascular profile data using multiple 2D images in order to quickly and accurately analyze and diagnose vascular lesions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Korean Patent Publication No. 10-2019-0125592 Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure provides a method and electronic device for generating three-dimensional vascular profile data to solve the above problem. [Means for solving the problem]

[0006] The present disclosure can be embodied in numerous ways, including as a method, an apparatus (system) and / or a computer program product.

[0007] According to one embodiment of the present disclosure, a method for generating three-dimensional vascular profile data performed by at least one processor includes the steps of acquiring a plurality of images including a blood vessel, generating three-dimensional first vascular profile data using a first set of images from the plurality of images, the first set including a first image and a second image, generating three-dimensional second vascular profile data using a second set of images from the plurality of images, the second set including a third image and a fourth image, identifying a first coordinate value indicating a common point based on at least one of the images from the first set and at least one of the images from the second set, identifying a second coordinate value in the first vascular profile data corresponding to the common point based on the identified first coordinate value, identifying a third coordinate value in the second vascular profile data corresponding to the common point based on the identified first coordinate value, and merging at least the first vascular profile data and the second vascular profile data based on at least the second coordinate value and the third coordinate value to acquire three-dimensional third vascular profile data.

[0008] According to one embodiment of the present disclosure, a method for generating three-dimensional vascular profile data performed by at least one processor includes the steps of acquiring a plurality of images including a blood vessel, generating three-dimensional first vascular profile data using a first image and a second image from the plurality of images, generating three-dimensional second vascular profile data using a second image and a third image from the plurality of images, identifying a first coordinate value corresponding to a first point of the blood vessel from the second vascular profile data, identifying a second coordinate value from the second image based on the first coordinate value, identifying a third coordinate value corresponding to the first point of the blood vessel from the first vascular profile data based on the second coordinate value, and merging at least the first vascular profile data and the second vascular profile data based on at least the third coordinate value to acquire three-dimensional third vascular profile data.

[0009] According to an embodiment of the present disclosure, a computer program for executing the above-described method for generating three-dimensional vascular profile data on a computer can be provided.

[0010] According to one embodiment of the present disclosure, an electronic device includes a memory and at least one processor coupled to the memory and configured to execute at least one computer-readable program contained in the memory, the at least one program including instructions for acquiring a plurality of images including a blood vessel, generating three-dimensional first vascular profile data using a first set of images including a first image and a second image of the plurality of images, generating three-dimensional second vascular profile data using a second set of images including a third image and a fourth image of the plurality of images, identifying a first coordinate value indicating a common point based on at least one of the images of the first set and at least one of the images of the second set, identifying a second coordinate value in the first vascular profile data corresponding to the common point based on the identified first coordinate value, identifying a third coordinate value in the second vascular profile data corresponding to the common point based on the identified first coordinate value, and merging at least the first vascular profile data and the second vascular profile data based on at least the second coordinate value and the third coordinate value to acquire three-dimensional third vascular profile data.

[0011] According to one embodiment of the present disclosure, an electronic device includes a memory and at least one processor coupled to the memory and configured to execute at least one computer-readable program contained in the memory, the at least one program including instructions for acquiring a plurality of images including a blood vessel, generating three-dimensional first vascular profile data using a first image and a second image of the plurality of images, generating three-dimensional second vascular profile data using a second image and a third image of the plurality of images, identifying a first coordinate value from the second vascular profile data corresponding to a first point of the blood vessel, identifying a second coordinate value from the second image based on the first coordinate value, identifying a third coordinate value from the first vascular profile data corresponding to the first point of the blood vessel based on the second coordinate value, and merging at least the first vascular profile data and the second vascular profile data based on at least the third coordinate value to acquire three-dimensional third vascular profile data. Effect of the Invention

[0012] According to some embodiments of the present disclosure, multiple two-dimensional images are used to generate three-dimensional vascular profile data, which can facilitate prediction of blood flow feature values, and the predicted blood flow feature values ​​can be used to support rapid and accurate analysis and diagnosis of vascular lesions.

[0013] The effects of the present disclosure are not limited to these, and other effects not mentioned should be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains (hereinafter referred to as a "person skilled in the art") from the description in the claims. [Brief description of the drawings]

[0014] BRIEF DESCRIPTION OF THE DRAWINGS Examples of the present disclosure will now be described, without limitation, with reference to the accompanying drawings, in which like reference numerals refer to like elements and in which: FIG. [Figure 1] FIG. 2 illustrates an example of an electronic device for generating three-dimensional vascular profile data according to one embodiment of the present disclosure. [Diagram 2]FIG. 1 is a diagram for explaining a configuration of an electronic device according to an embodiment of the present disclosure. [Diagram 3] FIG. 2 is a diagram for explaining the configuration of a processor of an electronic device according to an embodiment of the present disclosure. [Figure 4] 1A to 1C are diagrams for explaining a first method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Diagram 5] 1 is a schematic diagram of image information included in vascular profile data in a first method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. FIG. [Figure 6] 11A to 11C are diagrams for explaining a method for predicting blood flow feature values ​​in a first method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Figure 7] 11A to 11C are diagrams for explaining a second method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Figure 8] 11 is a schematic diagram of image information included in vascular profile data in a second method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. FIG. [Figure 9] FIG. 11 is a diagram for explaining a third method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Figure 10] 13 is a schematic diagram of image information included in vascular profile data in a third method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. FIG. [Figure 11] FIG. 11 is a diagram for explaining a fourth method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Figure 12] 13 is a schematic diagram of image information included in vascular profile data in a fourth method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. FIG. [Figure 13] FIG. 11 is a diagram for explaining a fifth method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Figure 14]1A to 1C are diagrams for explaining a method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. [Figure 15] 11A to 11D are diagrams for explaining a first method for generating three-dimensional vascular profile data according to another embodiment of the present disclosure. [Figure 16] FIG. 11 is a diagram for explaining a second method for generating three-dimensional vascular profile data according to another embodiment of the present disclosure. [Figure 17] 13 is a diagram for explaining a method for generating three-dimensional vascular profile data according to another embodiment of the present disclosure. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] <Summary of the Invention> According to one embodiment, the multiple images may include multiple angiographic images taken from different viewing angles.

[0016] According to one embodiment, identifying a first coordinate value indicating a common point may include outputting at least one of the images of the first set and at least one of the images of the second set, receiving user input for a common point in the outputted at least one of the images of the first set and at least one of the images of the second set, and identifying the first coordinate value based on the received user input.

[0017] According to one embodiment, identifying the first coordinate value indicating the common point may include inputting at least one of the first set of images and at least one of the second set of images into a Common Image Point (CIP) extraction model to automatically detect the common point, and identifying the first coordinate value based on the detected common point.

[0018] According to one embodiment, the method for generating three-dimensional vascular profile data may further include the steps of generating three-dimensional fourth vascular profile data using a third set of images including a fifth image and a sixth image from the plurality of images, identifying a fourth coordinate value indicating a common point based on at least one of the images of the third set, identifying a fifth coordinate value in the fourth vascular profile data corresponding to the common point based on the identified fourth coordinate value, and merging at least the third vascular profile data and the fourth vascular profile data based on at least the fifth coordinate value to obtain three-dimensional fifth vascular profile data.

[0019] According to one embodiment, the first point of the blood vessel may include a boundary point where a value indicating the degree to which a particular region of the blood vessel is identified from the third image is less than a predetermined value between the proximal and distal portions of the blood vessel.

[0020] According to one embodiment, the method for generating three-dimensional vascular profile data further includes a step of predicting a blood flow feature value of the blood vessel using third vascular profile data, wherein the blood flow feature value from a proximal portion of the blood vessel to a first point of the blood vessel corresponds to a first blood flow feature value predicted using the second vascular profile data, and from the first point of the blood vessel to a distal portion of the blood vessel corresponds to a second blood flow feature value predicted using the first vascular profile data with the first blood flow feature value corresponding to the first point of the blood vessel as an initial value.

[0021] According to one embodiment, the method for generating three-dimensional vascular profile data may further include the steps of generating three-dimensional fourth vascular profile data using a first image and a fourth image among the plurality of images, identifying a three-dimensional fourth coordinate value from the third vascular profile data corresponding to a second point of the blood vessel, identifying a two-dimensional fifth coordinate value from the first image based on the fourth coordinate value, identifying a three-dimensional sixth coordinate value from the fourth vascular profile data corresponding to the second point of the blood vessel based on the fifth coordinate value, and merging at least the third vascular profile data and the fourth vascular profile data based on at least the sixth coordinate value to obtain three-dimensional fifth vascular profile data.

[0022] According to one embodiment, the method for generating three-dimensional vascular profile data further includes the steps of generating three-dimensional sixth vascular profile data using a first image and a fifth image among the plurality of images, identifying a three-dimensional seventh coordinate value from the sixth vascular profile data corresponding to a third point of the blood vessel, identifying a two-dimensional eighth coordinate value from the first image based on the seventh coordinate value, and identifying a three-dimensional ninth coordinate value from the fourth vascular profile data corresponding to the third point of the blood vessel based on the eighth coordinate value, and the step of obtaining fifth vascular profile data may include the step of merging at least the third vascular profile data, the fourth vascular profile data and the sixth vascular profile data based on at least the sixth coordinate value and the ninth coordinate value to obtain the fifth vascular profile data.

[0023] According to one embodiment, the method for generating three-dimensional vascular profile data further includes the steps of generating three-dimensional fourth vascular profile data using a second image and a fourth image from the plurality of images, identifying a three-dimensional fourth coordinate value corresponding to a second point of the blood vessel from the fourth vascular profile data, identifying a two-dimensional fifth coordinate value from the second image based on the fourth coordinate value, and identifying a three-dimensional sixth coordinate value corresponding to the second point of the blood vessel from the first vascular profile data based on the fifth coordinate value, and the step of obtaining third vascular profile data may include the step of merging at least the first vascular profile data, the second vascular profile data and the fourth vascular profile data based on at least the third coordinate value and the sixth coordinate value to obtain the third vascular profile data.

[0024] According to one embodiment, the method for generating three-dimensional vascular profile data further includes the steps of generating three-dimensional fourth vascular profile data using a first image and a fourth image among the plurality of images, identifying a three-dimensional fourth coordinate value from the fourth vascular profile data corresponding to a second point of the blood vessel, identifying a two-dimensional fifth coordinate value from the first image based on the fourth coordinate value, and identifying a three-dimensional sixth coordinate value from the first vascular profile data corresponding to the second point of the blood vessel based on the fifth coordinate value, and the step of obtaining third vascular profile data may include the step of merging at least the first vascular profile data, the second vascular profile data and the fourth vascular profile data based on at least the third coordinate value and the sixth coordinate value to obtain the third vascular profile data.

[0025] <Detailed Description of the Invention> Hereinafter, specific contents for carrying out the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, detailed descriptions of known functions and configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

[0026] In the accompanying drawings, the same or corresponding components are given the same reference numerals. In addition, in the following description of the embodiments, duplicated descriptions of the same or corresponding components may be omitted. However, even if the description of a component is omitted, it should not be intended that such a component is not included in a certain embodiment.

[0027] The advantages and features of the disclosed embodiments and the methods of achieving them will become apparent from the following examples, taken in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, and may be embodied in various different forms. However, the embodiments are provided only to complete the disclosure and to allow those skilled in the art to accurately recognize the category of the invention.

[0028] The terms used in this disclosure are briefly explained, and the disclosed embodiments are specifically described. The terms used in this disclosure are selected as currently widely used general terms as much as possible while taking into consideration the functions in this disclosure, but these may change depending on the intentions or precedents of engineers engaged in related fields, the emergence of new technologies, etc. In addition, in certain cases, there may be terms arbitrarily selected by the applicant, but the meanings of these will be described in detail in the description of the invention. Therefore, the terms used in this disclosure should be defined based on the meanings of the terms and the overall content of this disclosure, rather than simply the names of the terms.

[0029] In this disclosure, unless otherwise clearly specified in the context, singular expressions include plural expressions, and plural expressions include singular expressions. Throughout the specification, when a part "comprises" a certain element, this does not exclude other elements, and means that other elements may also be included, unless otherwise specified to the contrary.

[0030] In addition, the term "module" or "unit" as used in the specification means a software or hardware component, and the "module" or "unit" performs a certain function. However, the term "module" or "unit" is not limited to software or hardware. The "module" or "unit" may be configured to be in an addressable storage medium or to execute one or more processors. Thus, by way of example, a "module" or "unit" may include at least one of a software component, an object-oriented software component, a class component, a task component, and a process, a function, an attribute, a procedure, a subroutine, a program code segment, a driver, firmware, microcode, a circuit, data, a database, a data structure, a table, an array, or a variable. The components and "modules" or "units" may be combined into fewer components and "modules" or "units" or further separated into additional components and "modules" or "units" in which the functions provided therein are provided.

[0031] According to an embodiment of the present disclosure, a "module" or a "unit" may be embodied with a processor and a memory. A "processor" should be broadly construed to include a general purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, and the like. In some environments, a "processor" may also refer to an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), and the like. A "processor" may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or any other such configuration. Additionally, a "memory" should be broadly construed to include any electronic component capable of storing electronic information. "Memory" may refer to various types of processor-readable media such as Random Access Memory (RAM), Read Only Memory (ROM), Non-Volatile Random Access Memory (NVRAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, magnetic or optical data storage devices, registers, etc. Memory is said to be in electronic communication with a processor when the processor can read / write information from the memory or write information to the memory. Memory that is integrated into a processor is in electronic communication with the processor.

[0032] Furthermore, terms such as first, second, A, B, (a), (b) and the like used in the following examples are used only to distinguish one component from another, and are not intended to limit the essence, order, or procedure of the component.

[0033] In addition, in the following examples, when a certain component is "coupled," "coupled," or "connected" to another component, it should be understood that although the components may be directly coupled or connected to each other, other components may also be "coupled," "coupled," or "connected" between each component.

[0034] Furthermore, the word "comprise" or "comprising" as used in the following examples does not exclude the presence or addition of one or more other components, steps, operations and / or elements to a referenced component, step, operation and / or element.

[0035] Various embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0036] FIG. 1 is a diagram illustrating an example of an electronic device for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to FIG. 1, an electronic device 100 can generate three-dimensional vascular profile data 120 based on a plurality of images 110 including blood vessels. Here, the plurality of images 110 including blood vessels can include images of all modalities, such as X-ray images, ultrasound images, chest radiography images, computed tomography (CT) images, positron emission tomography (PET) images, magnetic resonance imaging (MRI) images, ultrasound (US) images, functional magnetic resonance imaging (fMRI) images, digital pathology whole slide images (WSI), and digital breast tomosynthesis (DBT) images, as medical images. As an example, each of the plurality of images 110 including blood vessels can include a medical image in which the blood vessels of a target patient are photographed in a state in which a contrast agent is administered to the target patient. In addition, the vascular profile data 120 may include not only data representing the shape of the blood vessel, but also data regarding the shape of the blood vessel itself (e.g., a 3D reconstructed vascular model), or data corresponding to the shape of the blood vessel (e.g., FFR data corresponding to the 3D vascular model).

[0037] Although FIG. 1 does not show a storage system capable of communicating with the electronic device 100, the electronic device 100 may be configured to be connected to or capable of communicating with one or more storage systems. The storage system connected to or capable of communicating with the electronic device 100 may include a device or cloud system that stores and manages various data related to the operation of generating the three-dimensional vascular profile data 120 based on the plurality of images 110. In order to efficiently manage data, the storage system may store and manage various data using a database. Here, the various data may include any data related to the generation of the vascular profile data 120. For example, the various data may include, but are not limited to, a machine learning model, training data, the plurality of images 110, and the like, related to the generation of the vascular profile data 120.

[0038] The electronic device 100 can obtain a plurality of images 110 including blood vessels. Thereafter, the electronic device 100 can generate a plurality of three-dimensional vascular profile data using at least two images of the plurality of images 110. For example, the electronic device 100 can generate a first three-dimensional vascular profile data using a first image and a second image among the plurality of images 110, and can generate a second three-dimensional vascular profile data using a second image and a third image among the plurality of images 110. At this time, among the plurality of images 110, there may be an image that is commonly used when generating a plurality of vascular profile data, and in the following description, this may be referred to as a bridge image. For example, when the first image and the second image are used when generating the first vascular profile data, and the second image and the third image are used when generating the second vascular profile data, the second image may be a bridge image. Here, one or more bridge images may be used depending on a method for generating the three-dimensional vascular profile data.

[0039] As described above, when a plurality of vascular profile data (e.g., first vascular profile data and second vascular profile data) are generated using at least one bridge image, the electronic device 100 can merge the plurality of vascular profile data to obtain three-dimensional vascular profile data. For example, the electronic device 100 can merge the first vascular profile data and the second vascular profile data to obtain third vascular profile data. At this time, the electronic device 100 can merge the plurality of vascular profile data using the bridge image. For example, the electronic device 100 can identify a first coordinate value corresponding to a specific point of the blood vessel from any one of the plurality of vascular profile data. Here, the specific point of the blood vessel can include a boundary point at which it is determined whether or not the blood vessel can be identified. For example, the specific point of the blood vessel can include a boundary point at which a value indicating the degree to which a specific region of the blood vessel is identified from the image is less than a predetermined value between a proximal portion and a distal portion of the blood vessel. Then, the electronic device 100 can identify a second coordinate value from the bridge image based on the identified first coordinate value. For example, the electronic device 100 can identify a two-dimensional second coordinate value corresponding to the three-dimensional first coordinate value. Then, the electronic device 100 can identify a third coordinate value corresponding to a specific point of a blood vessel from another one of the plurality of vascular profile data based on the identified second coordinate value. For example, the electronic device 100 can use the second coordinate value in the commonly used bridge image to identify a third coordinate value in the other one of the vascular profile data that corresponds to the first coordinate value in any one of the vascular profile data. Then, the electronic device 100 can merge the plurality of vascular profile data based on the identified third coordinate value. For example, the electronic device 100 can merge the first vascular profile data and the second vascular profile data generated using the commonly used bridge image to obtain a three-dimensional third vascular profile data.

[0040] FIG. 2 is a diagram illustrating a configuration of an electronic device according to an embodiment of the present disclosure. Referring to FIG. 2, the electronic device 100 may include a memory 210, a processor 220, a communication module 230, and an input / output interface 240. However, the configuration of the electronic device 100 is not limited thereto. According to various embodiments, the electronic device 100 may omit at least one of the above-mentioned components and may further include at least one other component. As an example, the electronic device 100 may further include a display. In this case, the electronic device 100 may display at least one of medical images (e.g., the plurality of images 110 in FIG. 1) of blood vessels or blood vessel profile data 120 on the display.

[0041] The memory 210 may store various data for use by at least one other component (e.g., the processor 220) of the electronic device 100. The data may include, for example, input data or output data for software (or programs) and their associated instructions.

[0042] The memory 210 may include any non-transitory computer-readable recording medium. According to an embodiment, the memory 210 may include a permanent mass storage device such as a read only memory (ROM), a disk drive, a solid state drive (SSD), and a flash memory. As another example, a permanent mass storage device such as a ROM, an SSD, a flash memory, and a disk drive may be included in the electronic device 100 as a separate permanent storage device separate from the memory 210. The memory 210 may also store an operating system and at least one program code (e.g., instructions for generating vascular profile data that are installed and operated in the electronic device 100). In FIG. 2, the memory 210 is illustrated as a single memory, but this is for convenience of explanation, and the memory 210 may include multiple memories and / or buffer memories.

[0043] The software components, etc. can be loaded from a computer-readable recording medium separate from the memory 210. Such a separate computer-readable recording medium can include a recording medium directly connectable to the electronic device 100, and can include, for example, a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, and a memory card. As another example, the software components, etc. can be loaded into the memory 210 via the communication module 230, rather than a computer-readable recording medium. For example, at least one program can be loaded into the memory 210 based on a computer program (e.g., a program for transferring data such as medical images of blood vessels) that is installed by a file provided via the communication module 230 by a developer or a file distribution system that distributes an application installation file.

[0044] The processor 220 may execute software (or programs) to control at least one other component (e.g., a hardware or software component) of the electronic device 100 coupled to the processor 220, and may perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculation, the processor 220 may load instructions or data received from another component (e.g., the communications module 230) into volatile memory, process the instructions or data stored in the volatile memory, and store the resulting data in non-volatile memory.

[0045] The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. The instructions may be provided to the electronic device 100 or other external systems via the memory 210 or the communication module 230. For example, the processor 220 may generate three-dimensional vascular profile data. The processor 220 may then store the generated three-dimensional vascular profile data in the memory 210, display it on a display of the electronic device 100, or transmit it to an external electronic device via the communication module 230. Alternatively, the processor 220 may perform additional analytical operations, such as predicting blood flow characteristics using the three-dimensional vascular profile data. Although the processor 220 in FIG. 2 depicts a single processor, this is for convenience of explanation and the processor 220 may include multiple processors.

[0046] The communication module 230 can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 100 and an external electronic device, and the performance of communication through the established communication channel. For example, the communication module 230 can provide a configuration or function for the electronic device 100 and an external electronic device (e.g., a user terminal or a cloud server) to communicate with each other through a network. As an example, control signals, instructions, data, etc. provided under the control of the processor 220 of the electronic device 100 can be transferred to an external electronic device via the communication module of the external electronic device through the communication module 230 and the network. For example, the electronic device 100 can receive a medical image of a blood vessel of a subject from the external electronic device through the communication module 230.

[0047] The I / O interface 240 may be a means for interfacing with a device (not shown) for input or output that may be coupled to the electronic device 100 or may be included in the electronic device 100. For example, the I / O interface 240 may include at least one of a PCI express interface and an Ethernet (registered trademark) interface. In FIG. 2, the I / O interface 240 is illustrated as an element configured separately from the processor 220, but is not limited thereto, and the I / O interface 240 may be configured to be included in the processor 220.

[0048] According to an embodiment, the processor 220 may perform functions associated with generating three-dimensional vascular profile data. The processor 220 may execute at least one computer-readable program included in the memory 210 to perform functions associated with generating three-dimensional vascular profile data. Here, the at least one program may include instructions for acquiring a plurality of images including a blood vessel, generating three-dimensional first vascular profile data using a first image and a second image of the plurality of images, generating three-dimensional second vascular profile data using a second image and a third image of the plurality of images, identifying a first coordinate value corresponding to a first point of the blood vessel from the second vascular profile data, identifying a second coordinate value from the second image based on the first coordinate value, identifying a third coordinate value corresponding to the first point of the blood vessel from the first vascular profile data based on the second coordinate value, and merging at least the first vascular profile data and the second vascular profile data based on at least the third coordinate value to acquire three-dimensional third vascular profile data. In the following description, for convenience of explanation, the processor 220 executing at least one program to perform a function related to generating three-dimensional vascular profile data may be described as the processor 220 performing a function related to generating three-dimensional vascular profile data. For example, the at least one program including instructions related to generating three-dimensional vascular profile data may be described as the processor 220 performing a function related to generating three-dimensional vascular profile data.

[0049] FIG. 3 is a diagram for explaining a configuration of a processor of an electronic device according to an embodiment of the present disclosure. Referring to FIG. 3, the processor 220 can generate three-dimensional vascular profile data (e.g., vascular profile data 120 of FIG. 1) based on a plurality of images including blood vessels (e.g., a plurality of images 110 of FIG. 1). To this end, the processor 220 can include an image acquisition unit 310, a vascular profile data generation unit 320, a specific point identification unit 330, and a vascular profile data merging unit 340. However, the types of components included in the processor 220 are classified according to functions related to the generation of three-dimensional vascular profile data, and the types and numbers of the components are not limited thereto. In addition, at least one of the components included in the processor 200 can be embodied in the form of an instruction stored in a memory (e.g., the memory 210 of FIG. 2).

[0050] The image acquisition unit 310 may acquire a plurality of images including blood vessels (e.g., a plurality of images 110 in FIG. 1). According to an embodiment, the processor 220 may acquire medical images of blood vessels of a target patient in a state where a contrast agent is administered to the target patient. For example, the plurality of images may include a plurality of angiographic images captured from different viewing angles. Such medical images may be received from a storage system (e.g., a hospital system, an electronic duty record, a prescription delivery system, a medical image system, an examination information system, a local / cloud storage system, etc.) connected to or configured to be able to communicate with the electronic device, an internal memory, and / or a user terminal. The image acquisition unit 310 may provide at least one of the acquired plurality of images to the vascular profile data generation unit 320, the specific point identification unit 330, and / or the vascular profile data merging unit 340.

[0051] The vascular profile data generating unit 320 can generate a plurality of three-dimensional vascular profile data using at least two of the plurality of images acquired by the image acquiring unit 310. For example, the vascular profile data generating unit 320 can generate a first three-dimensional vascular profile data using a first image and a second image of the plurality of images. Additionally or alternatively, the vascular profile data generating unit 320 can generate a second three-dimensional vascular profile data using a second image and a third image of the plurality of images.

[0052] The specific point identification unit 330 may identify a specific point of a blood vessel included in an image. According to an embodiment, the specific point of the blood vessel may include a boundary point at which it is determined whether or not the blood vessel can be identified. For example, the specific point of the blood vessel may include a boundary point at which a value indicating the degree to which a specific region of the blood vessel is identified from an image is less than a predetermined value between a proximal portion and a distal portion of the blood vessel. According to another embodiment, the specific point of the blood vessel may indicate a common point in at least two images, for example, but is not limited to, a bifurcation.

[0053] The vascular profile data merging unit 340 can merge a plurality of vascular profile data to obtain three-dimensional vascular profile data. For example, the vascular profile data merging unit 340 can merge the first vascular profile data and the second vascular profile data to obtain the third vascular profile data. According to an embodiment, the vascular profile data merging unit 340 can merge a plurality of vascular profile data using a bridge image. Here, the bridge image can refer to an image commonly used when generating a plurality of vascular profile data used for merging. For example, when the first image and the second image are used when generating the first vascular profile data, and the second image and the third image are used when generating the second vascular profile data, the second image can be a bridge image. Here, one or more bridge images can be used depending on a method for generating the three-dimensional vascular profile data.

[0054] According to another embodiment, the vascular profile data merging unit 340 may generate three-dimensional vascular profile data by merging the first vascular profile data and the second vascular profile data using common points in at least one image used in generating the first vascular profile data and at least one image used in generating the second vascular profile data. A method of merging a plurality of three-dimensional vascular profile data using such common points to generate new three-dimensional vascular profile data will be described below with reference to Figures 15 to 17.

[0055] In the merging process of the blood vessel profile data, the blood vessel profile data merging unit 340 can identify a first coordinate value corresponding to a specific point of the blood vessel from any one of the blood vessel profile data of the plurality of blood vessel profile data. Here, the specific point of the blood vessel can be identified by the specific point identifying unit 330. Next, the blood vessel profile data merging unit 340 can identify a second coordinate value from the bridge image based on the identified first coordinate value. For example, the blood vessel profile data merging unit 340 can identify a two-dimensional second coordinate value corresponding to the three-dimensional first coordinate value. Then, the blood vessel profile data merging unit 340 can identify a third coordinate value corresponding to a specific point of the blood vessel from another one of the plurality of blood vessel profile data based on the identified second coordinate value. For example, the blood vessel profile data merging unit 340 can identify a third coordinate value in another one of the blood vessel profile data, which corresponds to the first coordinate value in any one of the blood vessel profile data, by using the second coordinate value in the commonly used bridge image. The vascular profile data merger 340 can then merge the multiple vascular profile data based on the identified third coordinate value.

[0056] According to an embodiment, the processor 220 can predict a blood flow characteristic value of a blood vessel using the merged vascular profile data (e.g., the third vascular profile data). The blood flow characteristic value can include, for example, FFR (Fractional Flow Reserve). The FFR can refer to a value obtained by evaluating a pressure difference between a distal part and a proximal part of a lesion site caused by a decrease in coronary pressure when a lesion is present in the blood vessel. According to an embodiment, the processor 220 can predict a blood flow characteristic value of a blood vessel through a machine learning model using the vascular profile data as an input. The machine learning model can include any model used to infer a ground truth for a given input. According to an embodiment, the machine learning model can include an artificial neural network model including an input layer, a plurality of hidden layers, and an output layer. Here, each layer can include one or more nodes. The machine learning model can also include weights associated with a plurality of nodes included in the machine learning model. Here, the weights can include any parameters associated with the machine learning model. The machine learning model of the present disclosure can be a model trained by various learning methods. For example, various learning methods are used in the present disclosure, such as supervised learning, semi-supervised learning, unsupervised learning (or autonomous learning), reinforcement learning, etc. In the present disclosure, a machine learning model can refer to an artificial neural network model, and an artificial neural network model can refer to a machine learning model.

[0057] According to one embodiment, the blood flow feature value of the blood vessel predicted using the blood vessel profile data is associated with a first blood flow feature value predicted using the second blood vessel profile data from a proximal portion of the blood vessel to a specific point of the blood vessel, and the blood flow feature value of the blood vessel predicted using the first blood vessel profile data from the specific point of the blood vessel to a distal portion of the blood vessel is associated with a second blood flow feature value predicted using the first blood vessel profile data with the first blood flow feature value corresponding to the specific point of the blood vessel as an initial value.

[0058] The method of acquiring vascular profile data using a plurality of images exceeding three may be performed in various ways depending on which of the plurality of images is used as a bridge image. Such methods may include, for example, a chain combining method, a one bridge combining method, a side combining method, or a combination of these. The method of acquiring vascular profile data using a plurality of images exceeding three will be described in detail with reference to FIGS. 7 to 13.

[0059] Fig. 4 is a diagram for explaining a first method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure, Fig. 5 is a diagram for illustrating image information included in vascular profile data in the first method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure, and Fig. 6 is a diagram for explaining a method for predicting a blood flow feature value in the first method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to Figs. 4 to 6, a processor (e.g., processor 220 in Figs. 2 and 3) of an electronic device (e.g., electronic device 100 in Figs. 1 and 2) can generate three-dimensional vascular profile data 432, 434, 422 based on a plurality of images 412, 414, 416 including blood vessels. Figs. 4 and 5 explain a method for acquiring vascular profile data using three images. 5, the plurality of image data 512, 514, 516 used in generating the vascular profile data 432, 434, 422 are associated with the plurality of images 412, 414, 416 and are diagrammed in the form of straight lines for ease of explanation. For example, the first image data 512 is associated with the first image 412, the second image data 514 is associated with the second image 414, and the third image data 516 is associated with the third image 416.

[0060] To explain the method of acquiring vascular profile data, the processor can generate three-dimensional first vascular profile data 432 using the first image 412 and the second image 414. For example, the processor can generate the first vascular profile data 432 using the first image data 512 and the second image data 514. The processor can also generate three-dimensional second vascular profile data 434 using the second image 414 and the third image 416. For example, the processor can generate the second vascular profile data 434 using the second image data 514 and the third image data 516. In this case, the second image 414 can be a bridge image commonly used when generating the first vascular profile data 432 and the second vascular profile data 434.

[0061] When the first vascular profile data 432 and the second vascular profile data 434 are generated using the bridge image, the processor can merge the first vascular profile data 432 and the second vascular profile data 434 to obtain the three-dimensional third vascular profile data 422. More specifically, the processor can identify a first coordinate value 434a corresponding to a first point of the blood vessel from the second vascular profile data 434. For example, the processor can identify a first coordinate value 434a corresponding to the first point of the blood vessel from the second vascular profile data 434 assuming that a proximal portion of the blood vessel is the origin of a three-dimensional coordinate system. The processor can also obtain a predicted blood flow feature value (f3) at the first coordinate value 434a corresponding to the first point of the blood vessel. Here, the first point of the blood vessel can include a boundary point at which it is determined whether or not the blood vessel can be identified. For example, the first point of the blood vessel may include a boundary point where a value indicating the degree to which a particular region of the blood vessel is identified from the second blood vessel profile data 434 or an image used in generating the second blood vessel profile data 434 (e.g., the second image 414 or the third image 416) is less than a predetermined value between the proximal and distal portions of the blood vessel.

[0062] The processor may then identify a second coordinate value 514a from the bridge image second image 414 based on the first coordinate value 434a. For example, the processor may identify a two-dimensional second coordinate value 514a that corresponds to the three-dimensional first coordinate value 434a. According to one embodiment, the processor may identify the two-dimensional second coordinate value 514a in the second image 414 from the three-dimensional first coordinate value 434a using a back projection method or the like.

[0063] The processor can then identify a third coordinate value 432a corresponding to the first point of the blood vessel from the first blood vessel profile data 432 based on the second coordinate value 514a. For example, the processor can use the second coordinate value 514a in a commonly used bridge image (e.g., the second image 414) to identify a third coordinate value 432a in the first blood vessel profile data 432 that corresponds to the first coordinate value 434a in the second blood vessel profile data 434. The processor can also inject (or substitute) a predicted blood flow feature value (f3) at the first coordinate value 434a corresponding to the first point of the blood vessel into the third coordinate value 432a in the first blood vessel profile data 432.

[0064] Thereafter, the processor can merge at least the first vascular profile data 432 and the second vascular profile data 434 based on at least the third coordinate value 432a to obtain three-dimensional third vascular profile data 422. In this manner, the first vascular profile data 432 and the second vascular profile data 434 generated based on the second image 414, which is a bridge image, can be merged to obtain three-dimensional third vascular profile data 422. At this time, the third vascular profile data 422 can be acquired from the proximal portion of the blood vessel to the first point of the blood vessel using the second vascular profile data 434, and from the first point of the blood vessel to the distal portion of the blood vessel using the first vascular profile data 432. In this process, the processor can recalculate the blood flow characteristic value of the blood vessel in the third vascular profile data 422. For example, since the predicted blood flow feature value (f3) at the first coordinate value 434a corresponding to the first point of the blood vessel is injected (or substituted) into the third coordinate value 432a in the first blood vessel profile data 432, the processor can recalculate the blood flow feature value with the injected (or substituted) value (f3) as an initial value. As a result, as shown in Fig. 6, the blood flow feature value of the blood vessel from the proximal part of the blood vessel to the first point of the blood vessel is associated with the first blood flow feature value group (f1 → f2 → f3) predicted using the second blood vessel profile data, and from the first point of the blood vessel to the distal part of the blood vessel is associated with the second blood flow feature value group (f3 → f11 → f12) predicted using the first blood vessel profile data with the first blood flow feature value (f3) corresponding to the first point of the blood vessel as an initial value.

[0065] Fig. 7 is a diagram for explaining a second method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure, and Fig. 8 is a diagram for illustrating image information included in vascular profile data in the second method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to Figs. 7 and 8, a processor (e.g., processor 220 in Figs. 2 and 3) of an electronic device (e.g., electronic device 100 in Figs. 1 and 2) can generate three-dimensional vascular profile data 732, 734, 736, 722, and 724 based on a plurality of images 712, 714, 716, and 718 including blood vessels. Figs. 7 and 8 explain a chain combination method among methods for acquiring vascular profile data using four images. 8, the plurality of image data 812, 814, 816, 818 used in generating the vascular profile data 732, 734, 736, 722, 724 are associated with the plurality of images 712, 714, 716, 718, and are diagrammed in the form of straight lines for ease of explanation. For example, the first image data 812 is associated with the first image 712, the second image data 814 is associated with the second image 714, the third image data 816 is associated with the third image 716, and the fourth image data 818 is associated with the fourth image 718.

[0066] To explain the method of acquiring vascular profile data (chaining method), the processor can generate three-dimensional first vascular profile data 732 using the first image 712 and the second image 714. For example, the processor can generate the first vascular profile data 732 using the first image data 812 and the second image data 814. The processor can also generate three-dimensional second vascular profile data 734 using the second image 714 and the third image 716. For example, the processor can generate the second vascular profile data 734 using the second image data 814 and the third image data 816. The processor can also generate three-dimensional fourth vascular profile data 736 using the first image 712 and the fourth image 718. For example, the processor can generate the fourth vascular profile data 736 using the first image data 812 and the fourth image data 818. In this case, the second image 714 may be a first bridge image commonly used when generating the first vascular profile data 732 and the second vascular profile data 734, and the first image 712 may be a second bridge image commonly used when generating the first vascular profile data 732 and the fourth vascular profile data 736.

[0067] After the first vascular profile data 732 and the second vascular profile data 734 are generated using the first bridge image, the processor can merge the first vascular profile data 732 and the second vascular profile data 734 to obtain the three-dimensional third vascular profile data 722. The process of merging the first vascular profile data 732 and the second vascular profile data 734 to obtain the three-dimensional third vascular profile data 722 is the same as or similar to the process of obtaining the vascular profile data described based on Figures 4 to 6, so a detailed description thereof will be omitted.

[0068] The processor can then identify a three-dimensional fourth coordinate value 722a corresponding to the second point of the blood vessel from the third blood vessel profile data 722. The processor can also obtain a predicted blood flow feature value at the fourth coordinate value 722a corresponding to the second point of the blood vessel. Here, the second point of the blood vessel is a point different from the first point of the blood vessel described based on FIG. 4 to FIG. 6, and can include a boundary point at which it is determined whether or not the blood vessel can be identified, similarly to the first point of the blood vessel. For example, the second point of the blood vessel can include a boundary point at which a value indicating the degree to which a specific region of the blood vessel can be identified from the third blood vessel profile data 722 or an image (e.g., the first image 712, the second image 714, or the third image 716) used when generating the third blood vessel profile data 722 is less than a predetermined value between the proximal and distal parts of the blood vessel.

[0069] The processor may then identify a two-dimensional fifth coordinate value 812a from the first image 712, which is the second bridge image, based on the fourth coordinate value 722a. For example, the processor may identify the two-dimensional fifth coordinate value 812a that corresponds to the three-dimensional fourth coordinate value 722a. According to one embodiment, the processor may identify the two-dimensional fifth coordinate value 812a in the first image 712 from the three-dimensional fourth coordinate value 722a, such as by using a back projection method.

[0070] The processor may then identify a three-dimensional sixth coordinate value 736a corresponding to the second point of the blood vessel from the fourth vascular profile data 736 based on the fifth coordinate value 812a. For example, the processor may use the fifth coordinate value 812a in the commonly used second bridge image (e.g., the first image 712) to identify a sixth coordinate value 736a in the fourth vascular profile data 736 that corresponds to the fourth coordinate value 722a in the third vascular profile data 722. The processor may also inject (or substitute) the predicted blood flow feature value at the fourth coordinate value 722a corresponding to the second point of the blood vessel into the sixth coordinate value 736a in the fourth vascular profile data 736.

[0071] Next, the processor can merge at least the third vascular profile data 722 and the fourth vascular profile data 736 based on at least the sixth coordinate value 736a to obtain three-dimensional fifth vascular profile data 724. In this manner, the first vascular profile data 732 and the second vascular profile data 734 generated based on the second image 714, which is the first bridge image, can be merged to obtain three-dimensional third vascular profile data 722, and the third vascular profile data 722 and the fourth vascular profile data 736 generated (or merged) based on the first image 712, which is the second bridge image, can be merged to obtain three-dimensional fifth vascular profile data 724. Since such a process is diagrammed as successively combining images, it can be referred to as a chain combining method. Also, at this time, the fifth vascular profile data 724 can be acquired from the proximal portion of the blood vessel to the second point of the blood vessel using the third vascular profile data 722, and from the second point of the blood vessel to the distal portion of the blood vessel using the fourth vascular profile data 736. In this process, the processor can recalculate the blood flow feature value of the blood vessel in the fifth vascular profile data 724. For example, since the predicted blood flow feature value at the fourth coordinate value 722a corresponding to the second point of the blood vessel is injected (or substituted) into the sixth coordinate value 736a in the fourth vascular profile data 736, the processor can recalculate the blood flow feature value by using the initial value as the injected (or substituted) value.

[0072] Fig. 9 is a diagram for explaining a third method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure, and Fig. 10 is a diagram for illustrating image information included in vascular profile data in the third method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to Figs. 9 and 10, a processor (e.g., processor 220 in Figs. 2 and 3) of an electronic device (e.g., electronic device 100 in Figs. 1 and 2) can generate three-dimensional vascular profile data 932, 934, 936, and 922 based on a plurality of images 912, 914, 916, and 918 including blood vessels. Figs. 9 and 10 explain a one-bridge joining method among methods for acquiring vascular profile data using four images. 10, the plurality of image data 1012, 1014, 1016, 1018 used in generating the vascular profile data 932, 934, 936, 922 are associated with the plurality of images 912, 914, 916, 918, and are diagrammed in the form of straight lines for ease of explanation. For example, the first image data 1012 is associated with the first image 912, the second image data 10104 is associated with the second image 914, the third image data 1016 is associated with the third image 916, and the fourth image data 1018 is associated with the fourth image 918.

[0073] To explain the method of acquiring vascular profile data (one-bridge joining method), the processor can generate three-dimensional first vascular profile data 932 using the first image 912 and the second image 914. For example, the processor can generate the first vascular profile data 932 using the first image data 1012 and the second image data 1014. The processor can also generate three-dimensional second vascular profile data 934 using the second image 914 and the third image 916. For example, the processor can generate the second vascular profile data 934 using the second image data 1014 and the third image data 1016. The processor can also generate three-dimensional fourth vascular profile data 936 using the second image 914 and the fourth image 918. For example, the processor can generate the fourth vascular profile data 936 using the second image data 1014 and the fourth image data 1018. In this case, the second image 914 may be a bridge image commonly used when generating the first vascular profile data 932, the second vascular profile data 934, and the fourth vascular profile data 936.

[0074] Once the first vascular profile data 932, the second vascular profile data 934, and the fourth vascular profile data 936 are generated using the bridge image, the processor can merge the first vascular profile data 932, the second vascular profile data 934, and the fourth vascular profile data 936 to obtain three-dimensional third vascular profile data 922. That is, the processor can merge three or more vascular profile data using one bridge image.

[0075] More specifically, the processor can identify a first coordinate value 934a corresponding to a first point of the blood vessel from the second blood vessel profile data 934. The processor can also obtain a predicted blood flow feature value at the first coordinate value 934a corresponding to the first point of the blood vessel. Similarly, the processor can identify a fourth coordinate value 936a corresponding to a second point of the blood vessel from the fourth blood vessel profile data 936. Here, the first point and the second point of the blood vessel can include a boundary point at which it is determined whether or not the blood vessel can be identified. For example, the first point of the blood vessel can include a boundary point at which a value indicating the degree to which a specific region of the blood vessel can be identified from the second blood vessel profile data 934 or an image (e.g., the second image 914 or the third image 916) used when generating the second blood vessel profile data 934 is less than a predetermined value between a proximal portion and a distal portion of the blood vessel. The second point of the blood vessel may also include a boundary point where a value indicating the degree to which a particular region of the blood vessel is identified from the fourth blood vessel profile data 936 or an image used in generating the fourth blood vessel profile data 936 (e.g., the second image 914 or the fourth image 918) is less than a predetermined value between the proximal and distal portions of the blood vessel.

[0076] The processor can then identify a second coordinate value 1014a from the second image 914, which is a bridge image, based on the first coordinate value 934a. For example, the processor can identify a second two-dimensional coordinate value 1014a that corresponds to the first three-dimensional coordinate value 934a. The processor can also identify a fifth coordinate value 1014b from the second image 914, which is a bridge image, based on the fourth coordinate value 936a. For example, the processor can identify a fifth two-dimensional coordinate value 1014b that corresponds to the fourth three-dimensional coordinate value 936a. According to one embodiment, the processor can identify the second two-dimensional coordinate value 1014a in the second image 914 from the first three-dimensional coordinate value 934a, and identify the fifth two-dimensional coordinate value 1014b in the second image 914 from the fourth three-dimensional coordinate value 936a, such as by using a back projection method.

[0077] The processor can then identify a third coordinate value 932a corresponding to the first point of the blood vessel from the first vascular profile data 932 based on the second coordinate value 1014a. For example, the processor can use the second coordinate value 1014a in a commonly used bridge image (e.g., the second image 914) to identify a third coordinate value 932a in the first vascular profile data 932 that corresponds to the first coordinate value 934a in the second vascular profile data 934. The processor can also identify a sixth coordinate value 932b corresponding to the second point of the blood vessel from the first vascular profile data 932 based on the fifth coordinate value 1014b. For example, the processor can use the fifth coordinate value 1014b in a commonly used bridge image (e.g., the second image 914) to identify a sixth coordinate value 932b in the first vascular profile data 932 that corresponds to the fourth coordinate value 936a in the fourth vascular profile data 936. The processor may also inject (or substitute) the predicted blood flow feature value at the first coordinate value 93a corresponding to the first point of the blood vessel into the third coordinate value 932a in the first vascular profile data 932, and inject (or substitute) the predicted blood flow feature value at the fourth coordinate value 936a corresponding to the second point of the blood vessel into the sixth coordinate value 932b in the first vascular profile data 932.

[0078] Next, the processor can merge at least the first vascular profile data 932, the second vascular profile data 934, and the fourth vascular profile data 936 based on at least the third coordinate value 932a and the sixth coordinate value 932b to obtain the third vascular profile data 922. In this manner, the first vascular profile data 932, the second vascular profile data 934, and the fourth vascular profile data 936 generated based on the second image 914, which is one bridge image, can be merged to obtain the three-dimensional third vascular profile data 922. At this time, the third vascular profile data 922 can be acquired from the proximal portion of the blood vessel to the second point of the blood vessel using the fourth vascular profile data 936, from the second point of the blood vessel to the first point of the blood vessel using the second vascular profile data 934, and from the first point of the blood vessel to the distal portion of the blood vessel using the first vascular profile data 932. In such a process, the processor can recalculate the blood flow feature value of the blood vessel in the third blood vessel profile data 922. For example, the predicted blood flow feature value at the first coordinate value 934a corresponding to the first point of the blood vessel is injected (or substituted) into the third coordinate value 932a in the first blood vessel profile data 932, and the predicted blood flow feature value at the fourth coordinate value 936a corresponding to the second point of the blood vessel is injected (or substituted) into the sixth coordinate value 932b in the first blood vessel profile data 932, so that the processor can recalculate the blood flow feature value by using the injected (or substituted) value as the initial value.

[0079] Fig. 11 is a diagram for explaining a fourth method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure, and Fig. 12 is a diagram for illustrating image information included in vascular profile data in the fourth method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to Figs. 11 and 12, a processor (e.g., processor 220 in Figs. 2 and 3) of an electronic device (e.g., electronic device 100 in Figs. 1 and 2) can generate three-dimensional vascular profile data 1132, 1134, 1136, 1122 based on a plurality of images 1112, 1114, 1116, 1118 including blood vessels. Figs. 11 and 12 explain a side combining method among methods for acquiring vascular profile data using four images. 12, the plurality of image data 1212, 1214, 1216, 1218 used in generating the vascular profile data 1132, 1134, 1136, 1122 are associated with the plurality of images 1112, 1114, 1116, 1118, and are diagrammed in the form of straight lines for ease of explanation. For example, the first image data 1212 is associated with the first image 1112, the second image data 1214 is associated with the second image 1114, the third image data 1216 is associated with the third image 1116, and the fourth image data 1218 is associated with the fourth image 1118.

[0080] To explain the method of acquiring vascular profile data (side combining method), the processor can generate three-dimensional first vascular profile data 1132 using the first image 1112 and the second image 1114. For example, the processor can generate the first vascular profile data 1132 using the first image data 1212 and the second image data 1214. The processor can also generate three-dimensional second vascular profile data 1134 using the second image 1114 and the third image 1116. For example, the processor can generate the second vascular profile data 1134 using the second image data 1214 and the third image data 1216. The processor can also generate three-dimensional fourth vascular profile data 1136 using the first image 1112 and the fourth image 1118. For example, the processor can generate the fourth vascular profile data 1136 using the first image data 1212 and the fourth image data 1218. In this case, the second image 1114 may be a first bridge image commonly used when generating the first vascular profile data 1132 and the second vascular profile data 1134, and the first image 1112 may be a second bridge image commonly used when generating the first vascular profile data 1132 and the fourth vascular profile data 1136.

[0081] Once the first vascular profile data 1132, the second vascular profile data 1134 and the fourth vascular profile data 1136 are generated using the bridge image, the processor can merge the first vascular profile data 1132, the second vascular profile data 1134 and the fourth vascular profile data 1136 to obtain three-dimensional third vascular profile data 1122.

[0082] More specifically, the processor can identify a first coordinate value 1134a corresponding to a first point of the blood vessel from the second blood vessel profile data 1134. The processor can also obtain a predicted blood flow feature value at the first coordinate value 1134a corresponding to the first point of the blood vessel. Similarly, the processor can identify a fourth coordinate value 1136a corresponding to a second point of the blood vessel from the fourth blood vessel profile data 1136. Here, the first point and the second point of the blood vessel can include a boundary point at which it is determined whether or not the blood vessel can be identified. For example, the first point of the blood vessel can include a boundary point at which a value indicating the degree to which a specific region of the blood vessel can be identified from the second blood vessel profile data 1134 or an image (e.g., the second image 1114 or the third image 1116) used when generating the second blood vessel profile data 1134 is less than a predetermined value between a proximal portion and a distal portion of the blood vessel. The second point of the blood vessel may also include a boundary point where a value indicating the degree to which a particular region of the blood vessel is identified from the fourth blood vessel profile data 1136 or an image used in generating the fourth blood vessel profile data 1136 (e.g., the first image 1112 or the fourth image 1118) is less than a predetermined value between the proximal and distal portions of the blood vessel.

[0083] The processor can then identify a second coordinate value 1214a from the second image 1114, which is a bridge image, based on the first coordinate value 1134a. For example, the processor can identify a second coordinate value 1214a in two dimensions that corresponds to the first coordinate value 1134a in three dimensions. The processor can also identify a fifth coordinate value 1212a from the first image 1112, which is a bridge image, based on the fourth coordinate value 1136a. For example, the processor can identify a fifth coordinate value 1212a in two dimensions that corresponds to the fourth coordinate value 1136a in three dimensions. According to one embodiment, the processor can identify the second coordinate value 1214a in the second image 1114 from the first coordinate value 1134a in three dimensions, and identify the fifth coordinate value 1212a in the first image 1112 from the fourth coordinate value 1136a in three dimensions, such as by using a back projection method.

[0084] The processor can then identify a third coordinate value 1132a corresponding to a first point of the blood vessel from the first vascular profile data 1132 based on the second coordinate value 1214a. For example, the processor can use the second coordinate value 1214a in a commonly used first bridge image (e.g., the second image 1114) to identify a third coordinate value 1132a in the first vascular profile data 1132 that corresponds to the first coordinate value 1134a in the second vascular profile data 1134. The processor can also identify a sixth coordinate value 1132b corresponding to a second point of the blood vessel from the first vascular profile data 1132 based on the fifth coordinate value 1212a. For example, the processor may use the fifth coordinate value 1212a in the commonly used second bridge image (e.g., the first image 1112) to identify a sixth coordinate value 1132b in the first vascular profile data 1132 that corresponds to a fourth coordinate value 1136a in the fourth vascular profile data 1136. The processor may also inject (or substitute) a predicted blood flow feature value at a first coordinate value 1134a corresponding to a first point of the blood vessel into a third coordinate value 1132a in the first vascular profile data 1132, and inject (or substitute) a predicted blood flow feature value at a fourth coordinate value 1136a corresponding to a second point of the blood vessel into a sixth coordinate value 1132b in the first vascular profile data 1132.

[0085] Next, the processor can merge at least the first vascular profile data 1132, the second vascular profile data 1134, and the fourth vascular profile data 1136 based on at least the third coordinate value 1132a and the sixth coordinate value 1132b to obtain the third vascular profile data 1122. In this manner, the first vascular profile data 1132, the second vascular profile data 1134, and the fourth vascular profile data 1136 are merged with the first vascular profile data 1132 generated based on the two bridge images, the first image 1112 and the second image 1114, as the center, and such a process can be referred to as a side combining method because it is diagrammed as combining both vascular profile data (e.g., the second vascular profile data 1134 and the fourth vascular profile data 1136) with the central vascular profile data (e.g., the first vascular profile data 1132). Also, at this time, the third vascular profile data 1122 can be obtained by using the fourth vascular profile data 1136 from the proximal portion of the blood vessel to the second point of the blood vessel, by using the second vascular profile data 1134 from the second point of the blood vessel to the first point of the blood vessel, and by using the first vascular profile data 1132 from the first point of the blood vessel to the distal portion of the blood vessel. In this process, the processor can recalculate the blood flow characteristic value of the blood vessel in the third vascular profile data 1122. For example, the predicted blood flow feature value at the first coordinate value 1134a corresponding to the first point of the blood vessel is injected (or substituted) into the third coordinate value 1132a in the first vascular profile data 1132, and the predicted blood flow feature value at the fourth coordinate value 1136a corresponding to the second point of the blood vessel is injected (or substituted) into the sixth coordinate value 1132b in the first vascular profile data 1132, so that the processor can recalculate the blood flow feature value using the initial value as the injected (or substituted) value.

[0086] FIG. 13 is a diagram for explaining a fifth method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to FIG. 13, a processor (e.g., processor 220 in FIG. 2 and FIG. 3) of an electronic device (e.g., electronic device 100 in FIG. 1 and FIG. 2) can generate three-dimensional vascular profile data 1332, 1334, 1336, 1338, 1322, 1324 based on a plurality of images 1312, 1314, 1316, 1318, 1320 including blood vessels. Although FIG. 13 explains a method for acquiring vascular profile data using five images, the number of images is not limited thereto. FIG. 13 explains a method for acquiring vascular profile data by a combination of a chain linking method and a one-bridge linking method.

[0087] Regarding a method for acquiring vascular profile data (a combination of the chain linking method and the one-bridge linking method), the processor can use the first image 1312 and the second image 1314 to generate three-dimensional first vascular profile data 1332. The processor can also use the second image 1314 and the third image 1316 to generate three-dimensional second vascular profile data 1334. The processor can also use the first image 1312 and the fourth image 1318 to generate three-dimensional fourth vascular profile data 1336. The processor can also use the first image 1312 and the fifth image 1320 to generate three-dimensional sixth vascular profile data 1338. In this case, the second image 1314 may be a first bridge image commonly used when generating the first vascular profile data 1332 and the second vascular profile data 1334, and the first image 1312 may be a second bridge image commonly used when generating the first vascular profile data 1332, the fourth vascular profile data 1336, and the sixth vascular profile data 1338.

[0088] Once the first vascular profile data 1332 and the second vascular profile data 1334 are generated using the first bridge image, the processor can merge the first vascular profile data 1332 and the second vascular profile data 1334 to obtain the three-dimensional third vascular profile data 1322. The process of merging the first vascular profile data 1332 and the second vascular profile data 1334 to obtain the three-dimensional third vascular profile data 1322 can be the same as or similar to the process of obtaining the vascular profile data described with reference to Figures 4 to 6.

[0089] The processor may then merge the third vascular profile data 1322, the fourth vascular profile data 1336, and the sixth vascular profile data 1338 generated (or merged) based on the second bridge image to obtain three-dimensional fifth vascular profile data 1324. The process of merging the third vascular profile data 1322, the fourth vascular profile data 1336, and the sixth vascular profile data 1338 to obtain the three-dimensional fifth vascular profile data 1324 may be the same as or similar to the process of obtaining the vascular profile data described with reference to Figures 9 and 10.

[0090] More specifically, the processor may identify a three-dimensional fourth coordinate value corresponding to the second point of the blood vessel from the third blood vessel profile data 1322. The processor may also identify a three-dimensional seventh coordinate value corresponding to the third point of the blood vessel from the sixth blood vessel profile data 1338. The processor may also obtain a predicted blood flow feature value at the fourth coordinate value corresponding to the second point of the blood vessel and a predicted blood flow feature value at the seventh coordinate value corresponding to the third point of the blood vessel.

[0091] The processor may then identify a second dimensional fifth coordinate value from the first image 1312, which is the second bridge image, based on the fourth coordinate value. For example, the processor may identify a second dimensional fifth coordinate value that corresponds to the third dimensional fourth coordinate value. The processor may also identify a second dimensional eighth coordinate value from the first image 1312, which is the second bridge image, based on the seventh coordinate value. For example, the processor may identify a second dimensional eighth coordinate value that corresponds to the third dimensional seventh coordinate value. According to one embodiment, the processor may identify a second dimensional fifth coordinate value and an eighth coordinate value in the first image 1312 from the third dimensional fourth coordinate value and the seventh coordinate value, respectively, using a back projection method or the like.

[0092] The processor can then identify a six three-dimensional coordinate value corresponding to the second point of the blood vessel from the fourth vascular profile data 1336 based on the fifth coordinate value. For example, the processor can use the fifth coordinate value in the commonly used second bridge image (e.g., the first image 1312) to identify a sixth coordinate value in the fourth vascular profile data 1336 that corresponds to the fourth coordinate value in the third vascular profile data 1322. The processor can also identify a ninth three-dimensional coordinate value corresponding to the third point of the blood vessel from the fourth vascular profile data 1336 based on the eighth coordinate value. For example, the processor can use the eighth coordinate value in the commonly used second bridge image (e.g., the first image 1312) to identify a ninth coordinate value in the fourth vascular profile data 1336 that corresponds to the seventh coordinate value in the sixth vascular profile data 1338. The processor may also inject (or substitute) a predicted blood flow feature value at a fourth coordinate value corresponding to a second point on the blood vessel into a sixth coordinate value in the fourth vascular profile data 1336, and inject (or substitute) a predicted blood flow feature value at a seventh coordinate value corresponding to a third point on the blood vessel into a ninth coordinate value in the fourth vascular profile data 1336.

[0093] Next, the processor may merge at least the third vascular profile data 1322, the fourth vascular profile data 1336, and the sixth vascular profile data 1338 based on at least the sixth coordinate value and the ninth coordinate value to obtain the fifth vascular profile data 1324. The processor may also recalculate the blood flow feature value of the blood vessel in the fifth vascular profile data 1324. For example, since the sixth coordinate value in the fourth vascular profile data 1336 is filled (or substituted) with the predicted blood flow feature value at the fourth coordinate value corresponding to the second point of the blood vessel, and the ninth coordinate value in the fourth vascular profile data 1336 is filled (or substituted) with the predicted blood flow feature value at the seventh coordinate value corresponding to the third point of the blood vessel, the processor may recalculate the blood flow feature value with the initial value as the filled (or substituted) value.

[0094] 14 is a diagram illustrating a method for generating three-dimensional vascular profile data according to an embodiment of the present disclosure. Referring to FIG. 14, a processor (e.g., processor 220 in FIGS. 2 and 3) of an electronic device (e.g., electronic device 100 in FIGS. 1 and 2) may acquire a plurality of images in step S1410. For example, the processor may acquire a plurality of images including blood vessels (e.g., a plurality of images 110 in FIG. 1). According to an embodiment, the plurality of images may include a plurality of angiographic images taken from different viewing angles.

[0095] In step S1420, the processor can generate three-dimensional first vascular profile data using the first image and the second image. For example, the processor can generate three-dimensional first vascular profile data using the first image and the second image of the plurality of images.

[0096] In step S1430, the processor can generate three-dimensional second vascular profile data using the second image and the third image. For example, the processor can generate three-dimensional second vascular profile data using the second image and the third image among the multiple images. In this case, the second image can be a bridge image commonly used when generating the first vascular profile data and the second vascular profile data.

[0097] In step S1440, the processor can identify a first coordinate value corresponding to a first point of the blood vessel from the second blood vessel profile data. For example, the processor can identify a first coordinate value corresponding to the first point of the blood vessel from the second blood vessel profile data, assuming that a proximal portion of the blood vessel is the origin of a three-dimensional coordinate system. The processor can also obtain a predicted blood flow feature value at the first coordinate value corresponding to the first point of the blood vessel. Here, the first point of the blood vessel can include a boundary point at which it is determined whether or not the blood vessel can be identified. For example, the first point of the blood vessel can include a boundary point at which a value indicating the degree to which a specific region of the blood vessel can be identified from the second blood vessel profile data or an image (e.g., the second image or the third image) used when generating the second blood vessel profile data is less than a predetermined value between the proximal portion and the distal portion of the blood vessel.

[0098] In step S1450, the processor may identify a second coordinate value from the second image based on the first coordinate value. That is, the processor may identify a second coordinate value corresponding to the first coordinate value from the second image, which is a bridge image. For example, the processor may identify a two-dimensional second coordinate value corresponding to the three-dimensional first coordinate value. According to one embodiment, the processor may identify the two-dimensional second coordinate value in the second image from the three-dimensional first coordinate value using a back projection method or the like.

[0099] In step S1460, the processor may identify a third coordinate value from the first vascular profile data corresponding to the first point of the blood vessel based on the second coordinate value. For example, the processor may use the second coordinate value in the second image, which is a bridge image, to identify a third coordinate value in the first vascular profile data that corresponds to the first coordinate value in the second vascular profile data. The processor may also inject (or substitute) a predicted blood flow feature value at the first coordinate value corresponding to the first point of the blood vessel into the third coordinate value in the first vascular profile data.

[0100] In step S1470, the processor can merge at least the first vascular profile data and the second vascular profile data based on at least the third coordinate value to obtain three-dimensional third vascular profile data. For example, the processor can merge the first vascular profile data and the second vascular profile data generated based on the second image, which is a bridge image, to obtain three-dimensional third vascular profile data. In this case, the third vascular profile data can be obtained from the proximal part of the blood vessel to the first point of the blood vessel using the second vascular profile data, and from the first point of the blood vessel to the distal part of the blood vessel using the first vascular profile data. In this process, the processor can recalculate the blood flow feature value of the blood vessel in the third vascular profile data. For example, since the predicted blood flow feature value at the first coordinate value corresponding to the first point of the blood vessel is injected (or substituted) into the third coordinate value in the first vascular profile data, the processor can recalculate the blood flow feature value with the initial value as the injected (or substituted) value. As a result, the blood flow characteristic value of the blood vessel from the proximal part of the blood vessel to the first point of the blood vessel is associated with the first blood flow characteristic value predicted using the second blood vessel profile data, and from the first point of the blood vessel to the distal part of the blood vessel, is associated with the second blood flow characteristic value predicted using the first blood vessel profile data with the first blood flow characteristic value corresponding to the first point of the blood vessel as an initial value.

[0101] The above flow chart and description are merely illustrative and may be implemented in different ways in some embodiments, for example, the order of steps may be changed, some steps may be repeated, some steps may be omitted, or some steps may be added.

[0102] FIG. 15 is a diagram for explaining a first method for generating three-dimensional vascular profile data according to another embodiment of the present disclosure. Referring to FIG. 15, a processor (e.g., processor 220 in FIG. 2 and FIG. 3) of an electronic device (e.g., electronic device 100 in FIG. 1 and FIG. 2) can generate three-dimensional vascular profile data 1520, 1522, 1524 based on a plurality of images 1512, 1514, 1516, 1518 including blood vessels. FIG. 15 illustrates that the three-dimensional vascular profile data is generated using a first image 1512 and a second image 1514 included in a first set of images, and a third image 1516 and a fourth image 1518 included in a second set of images, but is not limited thereto. For example, the first set of images can include one or more images, and the second set of images can include one or more images.

[0103] The electronic device can generate three-dimensional first vascular profile data 1520 using a first set of images including a first image 1512 and a second image 1514. The electronic device can also generate three-dimensional second vascular profile data 1522 using a second set of images including a third image 1516 and a fourth image 1518. In this case, the first image 1512 and the second image 1514 included in the first set of images can refer to angiographic images taken from different viewing angles. The third image 1516 and the fourth image 1518 included in the second set of images can refer to angiographic images taken from different viewing angles. Additionally or alternatively, the first image 1512 and the second image 1514 included in the first set of images and the third image 1516 and the fourth image 1518 included in the second set of images can each refer to angiographic images taken from different viewing angles.

[0104] The electronic device can merge at least the three-dimensional first vascular profile data 1520 and the three-dimensional second vascular profile data 1522 to generate three-dimensional third vascular profile data 1524. To this end, the electronic device can identify a first coordinate value indicating a common point based on at least one of the first set of images and at least one of the second set of images. Based on the identified first coordinate value, the electronic device can identify a second coordinate value in the first vascular profile data 1520 corresponding to the common point. Based on the identified first coordinate value, the electronic device can also identify a third coordinate value in the second vascular profile data 1522 corresponding to the common point. Here, the common point can refer to any point in a blood vessel that indicates the same region or point in at least two images, for example, but not limited to, a branch.

[0105] According to one embodiment, the electronic device can output at least one of the first set of images and at least one of the second set of images to identify a first coordinate value indicative of the common point. For example, a second image 1514 of the first set of images and a third image 1516 of the second set of images can be output or displayed by an output device (e.g., a display, etc.) of the electronic device. The electronic device can then receive user input from the second image 1514 and the third image 1516 to the common point by an input device (e.g., a touch screen, a mouse, a keyboard, etc.) of the electronic device. Based on such user input, the first coordinate value can be identified.

[0106] Additionally or alternatively, the electronic device may input at least one of the first set of images and at least one of the second set of images into a Common Image Point (CIP) extraction model to automatically detect common points, where the CIP extraction model may refer to any known algorithm and / or artificial neural network model for receiving at least two images and extracting common points in the two images. For example, the CIP extraction model may be, but is not limited to, an artificial neural network model trained to extract at least one branch in a blood vessel as a common point in at least two received images including the blood vessel.

[0107] The blood flow feature value in the third vascular profile data 1524 thus generated can be predicted and provided to a user. According to an embodiment, the common point can include a boundary point where a value indicating a degree to which a specific region of the blood vessel is identified from an image including the common point is less than a predetermined value between the proximal part and the distal part of the blood vessel. In this case, the blood flow feature value from the proximal part of the blood vessel to the common point of the blood vessel is associated with a first blood flow feature value predicted using the first blood flow profile data 1520 or the second blood flow profile data 1522. In addition, the blood flow feature value from the common point of the blood vessel to the distal part of the blood vessel is associated with a second blood flow predicted value predicted using the blood vessel profile data not used for the first blood flow feature value, with the first blood flow feature value corresponding to the common point of the blood vessel as an initial value.

[0108] Fig. 16 is a diagram for explaining a second method for generating three-dimensional vascular profile data according to another embodiment of the present disclosure. Referring to Fig. 16, a processor (e.g., processor 220 in Figs. 2 and 3) of an electronic device (e.g., electronic device 100 in Figs. 1 and 2) can generate three-dimensional vascular profile data 1622, 1624, 1626, 1636, and 1638 based on a plurality of images 1612, 1614, 1616, 1618, 1632, and 1634 including blood vessels. Here, the first to fourth images 1612, 1614, 1616, and 1618 in Fig. 16 correspond to the first to fourth images 1512, 1514, 1516, and 1518 in Fig. 15, respectively. Similarly, the first to third vascular profile data 1622, 1624, and 1626 in Fig. 16 correspond to the first to third vascular profile data 1520, 1522, and 1524 in Fig. 15, respectively. In Fig. 16, in order to explain the chain combination method based on a common point among the methods for generating vascular profile data using six images, the configurations that overlap with Fig. 15 are omitted, and the configurations that differ from Fig. 15 will be described below.

[0109] The electronic device can generate three-dimensional fourth vascular profile data 1636 using a third set of images including a fifth image 1632 and a sixth image 1634. Although FIG. 16 illustrates the use of the fifth image 1632 and the sixth image 1634 included in the third set of images to generate three-dimensional vascular profile data, the present invention is not limited thereto. For example, the third set of images can include one or more than two images. Also, the fifth image 1632 and the sixth image 1634 included in the third set of images can refer to angiographic images taken from different viewing angles.

[0110] The electronic device can then identify, based on at least one of the images in the third set, a fourth coordinate value that indicates the same common point used to generate the third vascular profile data 1626. For example, the electronic device can receive user input for the coordinate value corresponding to the common point from the sixth image 1634. Additionally or alternatively, the electronic device can input at least one of the images in the third set into the CIP extraction model described in FIG. 15 along with at least one of the images in the first set and at least one of the images in the second set to identify the common point.

[0111] Based on the fourth coordinate value thus identified, a fifth coordinate value in the fourth vascular profile data 1636 corresponding to the common point can be identified. The electronic device can merge the three-dimensional third vascular profile data 1626 and the three-dimensional fourth vascular profile data 1636 based on at least the fifth coordinate value to obtain three-dimensional fifth vascular profile data.

[0112] 17 is a diagram for explaining a method for generating three-dimensional vascular profile data according to another embodiment of the present disclosure. Referring to FIG. 17, a processor (e.g., processor 220 in FIGS. 2 and 3) of an electronic device (e.g., electronic device 100 in FIGS. 1 and 2) may acquire a plurality of images in step S1710. For example, the processor may acquire a plurality of images including blood vessels (e.g., a plurality of images 110 in FIG. 1). According to one embodiment, the plurality of images may include a plurality of angiographic images taken from different viewing angles.

[0113] In step S1720, the processor can generate three-dimensional first vascular profile data using a first set of images of the plurality of images, the first set including a first image and a second image, and in step S1730, the processor can generate three-dimensional second vascular profile data using a second set of images of the plurality of images, the second set including a third image and a fourth image.

[0114] The processor can identify a first coordinate value indicative of a common point based on at least one of the first set of images and at least one of the second set of images in step S1740. To this end, the processor can output at least one of the first set of images and at least one of the second set of images. The processor can then receive user input for the common point in the output at least one of the first set of images and at least one of the second set of images, and can identify the first coordinate value based on the received user input. Additionally or alternatively, the processor can input at least one of the first set of images and at least one of the second set of images into a CIP extraction model to automatically detect the common point and identify the first coordinate value based on the detected common point.

[0115] In step S1750, the processor can identify a second coordinate value in the first vascular profile data corresponding to the common point based on the identified first coordinate value. Also, in step S1760, the processor can identify a third coordinate value in the second vascular profile data corresponding to the common point based on the identified first coordinate value. Thereafter, in step S1770, the processor can merge at least the first vascular profile data and the second vascular profile data based on at least the second coordinate value and the third coordinate value to obtain three-dimensional third vascular profile data.

[0116] Additionally, the processor may generate three-dimensional fourth vascular profile data using a third set of images including a fifth image and a sixth image of the plurality of images, and may identify a fourth coordinate value indicative of the common point based on at least one of the images of the third set. The processor may then identify a fifth coordinate value in the fourth vascular profile data corresponding to the common point based on the identified fourth coordinate value. The processor may merge at least the third vascular profile data and the fourth vascular profile data based on at least the fifth coordinate value to obtain three-dimensional fifth vascular profile data.

[0117] The above flow chart and description are merely illustrative and may be implemented in different ways in some embodiments, for example, the order of steps may be changed, some steps may be repeated, some steps may be omitted, or some steps may be added.

[0118] The above-mentioned method may be provided as a computer program stored in a computer-readable recording medium for execution by a computer. The medium may be a medium for continuously storing a computer-executable program or a medium for temporarily storing the program for execution or download. The medium may be various recording means or storage means in the form of a single or multiple hardware components combined, and may be distributed over a network without being limited to a medium directly connected to a computer system. Examples of the medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROMs, RAMs, flash memories, etc., configured to store program instructions. Other examples of the medium include recording media or storage media managed by app stores that distribute applications, or sites, servers, etc. that supply or distribute various other software.

[0119] The methods, operations, or techniques of the present disclosure may be embodied in a variety of ways. For example, such techniques may be embodied in hardware, firmware, software, or a combination thereof. Those skilled in the art will appreciate that the various exemplary logic blocks, modules, circuits, and algorithm steps described in the present disclosure may be embodied in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, the various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is embodied as hardware or software will vary depending on the particular application and design requirements imposed on the overall system. Those skilled in the art may also embody the described functionality in a variety of ways for each particular application, but such embodying should not be interpreted as departing from the scope of the present disclosure.

[0120] In a hardware implementation, the processing units used to perform the techniques may be embodied as one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or any combination thereof.

[0121] Thus, the various example logic blocks, modules, and circuits described in this disclosure may be embodied or performed with a general purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination designed to perform the functions described herein. A general purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be embodied with a combination of computing devices, such as a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

[0122] In a firmware and / or software implementation, the techniques may be embodied with instructions stored on a computer-readable medium, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. The instructions are executable by one or more processors to cause the processors or others to perform certain aspects of the functions described in this disclosure.

[0123] If embodied as software, the techniques can be stored on or transferred via a computer-readable medium as one or more instructions or code. A computer-readable medium includes any medium that facilitates transfer of a computer program from one place to another, and includes both computer storage media and communication media. A storage medium can be any available medium that can be accessed by a computer. By way of non-limiting example, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to transport or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection can be properly termed a computer-readable medium.

[0124] For example, if the software is transferred from a website, server, or other remote source using coaxial cable, fiber optic cable, lead wire, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, lead wire, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. As used herein, disk and disc include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media, etc.

[0125] A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium may be coupled to the processor such that the processor reads information from, and writes information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0126] Although the above embodiments have been described as utilizing aspects of the presently disclosed subject matter on one or more stand-alone computer systems, the present disclosure is not limited thereto and may be embodied in any computing environment, such as a network or distributed computing environment. Additionally, aspects of the subject matter in the present disclosure may be embodied in multiple processing chips or devices, and storage may be similarly affected across multiple devices. Such devices may include PCs, network servers, and handheld devices.

[0127] Although the present disclosure has been described in the present specification by way of some embodiments, various modifications and alterations can be made without departing from the present disclosure, which can be understood by those of ordinary skill in the art to which the present disclosure pertains. In addition, such modifications and alterations should be understood to fall within the scope of the claims appended hereto. [Explanation of symbols]

[0128] 100 Electronic equipment 210 Memory 220 Processor 230 Communication Module 240 Input / Output Interface

Claims

1. 1. A method for generating three-dimensional vascular profile data performed by at least one processor, comprising: acquiring a plurality of images including a blood vessel; generating three-dimensional first vascular profile data using a first set of images of the plurality of images, the first set including a first image and a second image; generating three-dimensional second vascular profile data using a second set of images of the plurality of images, the second set including a third image and a fourth image; identifying a first coordinate value indicative of a common point based on at least one of the first set of images and at least one of the second set of images; identifying a second coordinate value in the first vascular profile data that corresponds to the common point based on the identified first coordinate value; identifying a third coordinate value in the second vascular profile data that corresponds to the common point based on the identified first coordinate value; and a step of combining at least the first vascular profile data and the second vascular profile data based on at least the second coordinate values ​​and the third coordinate values ​​to obtain three-dimensional third vascular profile data.

2. The method of claim 1 , wherein the plurality of images comprises a plurality of angiographic images taken from different viewing angles.

3. The step of identifying a first coordinate value indicative of the common point comprises: outputting at least one of the first set of images and at least one of the second set of images; receiving user input for the common point on at least one of the output first set of images and at least one of the output second set of images; and identifying the first coordinate value based on the received user input.

4. The step of identifying a first coordinate value indicative of the common point comprises: inputting at least one of the first set of images and at least one of the second set of images into a CIP extraction model to automatically detect the common points; and identifying the first coordinate value based on the detected common point.

5. generating three-dimensional fourth vascular profile data using a third set of images of the plurality of images, the third set including a fifth image and a sixth image; identifying a fourth coordinate value indicative of the common point based on at least one of the images of the third set; identifying a fifth coordinate value in the fourth vascular profile data that corresponds to the common point based on the identified fourth coordinate value; The method for generating three-dimensional vascular profile data of claim 1 , further comprising: merging at least the third vascular profile data and the fourth vascular profile data based on at least the fifth coordinate value to obtain three-dimensional fifth vascular profile data.

6. 1. A method for generating three-dimensional vascular profile data performed by at least one processor, comprising: acquiring a plurality of images including a blood vessel; generating three-dimensional first vascular profile data using a first image and a second image of the plurality of images; generating three-dimensional second vascular profile data using the second and third images of the plurality of images; identifying a first coordinate value from the second vessel profile data corresponding to a first point of the vessel; identifying a second coordinate value from the second image based on the first coordinate value; identifying a third coordinate value from the first vascular profile data corresponding to a first point of the blood vessel based on the second coordinate value; and a step of merging at least the first vascular profile data and the second vascular profile data based on at least the third coordinate values ​​to obtain three-dimensional third vascular profile data.

7. The method of claim 6 , wherein the plurality of images comprises a plurality of angiographic images taken from different viewing angles.

8. The method for generating three-dimensional vascular profile data of claim 6, wherein the first point of the blood vessel includes a boundary point where a value indicating the degree to which a particular region of the blood vessel is identified from the third image is less than a predetermined value between the proximal and distal portions of the blood vessel.

9. using the third vascular profile data to predict a blood flow characteristic value of the blood vessel; The blood flow characteristic value is a first blood flow feature value predicted using the second vascular profile data from a proximal portion of the blood vessel to a first point of the blood vessel; The method for generating three-dimensional vascular profile data as described in claim 6, wherein a portion of the blood vessel from a first point to a distal portion of the blood vessel is associated with a second blood flow feature value predicted using the first vascular profile data, with the first blood flow feature value corresponding to the first point of the blood vessel being used as an initial value.

10. generating three-dimensional fourth vascular profile data using the first image and a fourth image of the plurality of images; identifying a third three-dimensional fourth coordinate value from the third vessel profile data corresponding to a second point of the vessel; identifying a two-dimensional fifth coordinate value from the first image based on the fourth coordinate value; identifying a three-dimensional sixth coordinate value from the fourth vascular profile data corresponding to a second point of the blood vessel based on the fifth coordinate value; The method for generating three-dimensional vascular profile data of claim 6, further comprising: merging at least the third vascular profile data and the fourth vascular profile data based on at least the sixth coordinate value to obtain three-dimensional fifth vascular profile data.

11. generating three-dimensional sixth vascular profile data using the first image and a fifth image of the plurality of images; identifying a three-dimensional seventh coordinate value from the sixth vessel profile data corresponding to a third point of the vessel; identifying a two-dimensional eighth coordinate value from the first image based on the seventh coordinate value; and identifying a ninth three-dimensional coordinate value from the fourth vascular profile data corresponding to a third point of the blood vessel based on the eighth coordinate value; The method for generating three-dimensional vascular profile data of claim 10, wherein the step of acquiring the fifth vascular profile data includes a step of acquiring the fifth vascular profile data by merging at least the third vascular profile data, the fourth vascular profile data, and the sixth vascular profile data based on at least the sixth coordinate value and the ninth coordinate value.

12. generating three-dimensional fourth vascular profile data using the second image and a fourth image of the plurality of images; identifying a fourth three-dimensional coordinate value from the fourth vessel profile data corresponding to a second point of the vessel; identifying a two-dimensional fifth coordinate value from the second image based on the fourth coordinate value; and identifying a third coordinate value from the first vascular profile data corresponding to a second point of the blood vessel based on the fifth coordinate value; 7. The method of generating three-dimensional vascular profile data of claim 6, wherein the step of acquiring third vascular profile data includes a step of acquiring the third vascular profile data by merging at least the first vascular profile data, the second vascular profile data, and the fourth vascular profile data based on at least the third coordinate value and the sixth coordinate value.

13. generating three-dimensional fourth vascular profile data using the first image and a fourth image of the plurality of images; identifying a fourth three-dimensional coordinate value from the fourth vessel profile data corresponding to a second point of the vessel; identifying a two-dimensional fifth coordinate value from the first image based on the fourth coordinate value; and identifying a third coordinate value from the first vascular profile data corresponding to a second point of the blood vessel based on the fifth coordinate value; 7. The method of generating three-dimensional vascular profile data of claim 6, wherein the step of acquiring third vascular profile data includes a step of acquiring the third vascular profile data by merging at least the first vascular profile data, the second vascular profile data, and the fourth vascular profile data based on at least the third coordinate value and the sixth coordinate value.

14. A computer readable computer program for carrying out the method according to any one of claims 1 to 13 on a computer.

15. In an electronic device, Memory, at least one processor coupled to the memory and configured to execute at least one computer readable program contained in the memory; The at least one program comprises: Acquire multiple images including blood vessels, generating three-dimensional first vascular profile data using a first set of images of the plurality of images, the first set including a first image and a second image; generating three-dimensional second vascular profile data using a second set of images of the plurality of images, the second set including a third image and a fourth image; identifying a first coordinate value indicative of a common point based on at least one of the first set of images and at least one of the second set of images; identifying a second coordinate value in the first vascular profile data that corresponds to the common point based on the identified first coordinate value; identifying a third coordinate value in the second vascular profile data that corresponds to the common point based on the identified first coordinate value; An electronic device comprising instructions for merging at least the first vascular profile data and the second vascular profile data based on at least the second coordinate values ​​and the third coordinate values ​​to obtain three-dimensional third vascular profile data.

16. In an electronic device, Memory, at least one processor coupled to the memory and configured to execute at least one computer readable program contained in the memory; The at least one program comprises: Acquire multiple images including blood vessels, generating three-dimensional first vascular profile data using a first image and a second image of the plurality of images; generating three-dimensional second vascular profile data using the second image and a third image of the plurality of images; identifying a first coordinate value from the second vessel profile data corresponding to a first point of the vessel; identifying a second coordinate value from the second image based on the first coordinate value; identifying a third coordinate value from the first vessel profile data corresponding to a first point of the vessel based on the second coordinate value; and instructions for merging at least the first vascular profile data and the second vascular profile data based on at least the third coordinate values ​​to obtain three-dimensional third vascular profile data.

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