KR20230167194A - Method, device and program for generating 3d model of organ and blood vessel according to the type of surgery - Google Patents

Abstract

Disclosed is a method for generating 3D models of organs and blood vessels according to the type of surgery performed by a first device. The method includes obtaining one or more scanned images output by scanning the patient's body; Receiving a type of specific surgery to be performed on the patient through the first UI; Obtaining information related to organs and blood vessels related to the input specific type of surgery; Based on the obtained information, dividing and identifying images for each of the organs and blood vessels on the one or more scanned images through an AI model; Using images of each of the identified organs and blood vessels, generating 3D modeling data for each of the identified organs and blood vessels; And by matching the generated 3D modeling data, a 3D model for performing the specific surgery can be generated.

Description

Method, device and program for generating 3D models of organs and blood vessels according to the type of surgery {METHOD, DEVICE AND PROGRAM FOR GENERATING 3D MODEL OF ORGAN AND BLOOD VESSEL ACCORDING TO THE TYPE OF SURGERY}

This disclosure relates to methods, devices, and programs for creating 3D models. More specifically, the present disclosure relates to a method, device, and program for automatically generating 3D models for organs and blood vessels according to the type of surgery.

Recently, in the case of surgery in hospitals, before performing surgery, in order to check the patient's condition and surgical performance conditions and increase the probability of successful surgery, a 3D simulation (stereoscopic image) of the patient's surgical site is created and the actual surgery is performed. The surgery is being performed virtually under the same conditions.

In the case of this virtual mock surgery, it is possible to make a precise diagnosis of the patient's body and make a surgical plan in advance. Accordingly, by performing a virtual simulation surgery rather than relying on the intuition of a specialist, even the smallest errors related to surgery can be reduced.

However, simulations for existing virtual mock surgeries have been developed based on anatomical structures regardless of the type of surgery. Accordingly, there is a problem in that simulations for existing virtual mock surgeries do not reflect the characteristics of the type of surgery.

US Patent Registration No. 10178157 (2019.01.08) Japanese Patent Publication No. 2017-535337 (2017.11.30)

The present disclosure was created to solve the above-mentioned problems, and the problem that the present disclosure seeks to solve is to provide a method, device, and program for automatically generating 3D models of organs and blood vessels according to the type of surgery.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

According to an embodiment of the present disclosure to solve the above-described problem, a method of generating a 3D model of organs and blood vessels according to the type of surgery performed by a first device includes one output by scanning the patient's body. Acquiring one or more scanned images; Receiving a type of specific surgery to be performed on the patient through a first user interface (UI); Obtaining information related to organs and blood vessels related to the input specific type of surgery; Based on the obtained information, dividing and identifying images for each of the organs and blood vessels on the one or more scanned images through an artificial intelligence (AI) model; Using images of each of the identified organs and blood vessels, generating 3D modeling data for each of the identified organs and blood vessels; And it may include matching the generated 3D modeling data to generate a 3D model for performing the specific surgery.

And, the one or more scanned images include one or more computed tomography (CT) images of the patient's body, and the step of acquiring the one or more CT images includes a second scan of the patient's body. It may include receiving the one or more CT images from at least one of a device or a cloud server connected to the second device.

In addition, the step of acquiring information related to organs and blood vessels related to the type of specific surgery entered includes building or loading a database storing information on organs and blood vessels corresponding to each type of surgery. steps; And it may include obtaining information related to organs and blood vessels related to the input specific type of surgery from the database.

In addition, the AI model may include an algorithm module for each of the plurality of surgery types, capable of identifying images of organs and blood vessels corresponding to each of the plurality of surgery types.

And, the identifying step includes selecting an algorithm module capable of identifying images of organs and blood vessels corresponding to the type of the specific surgery input among the plurality of algorithm modules for each surgery type on the second UI; And it may include dividing and identifying images for each of the organs and blood vessels on the one or more CT images through the AI model using the selected algorithm module.

And, generating the 3D modeling data includes providing a third UI for inspecting the position, size, and shape of the image for each of the organs and blood vessels identified on the one or more scanned images; and modifying the image for each of the organs and blood vessels according to the inspection results input through the third UI.

And, the third UI includes at least one of a first tool for moving the position of the image for each of the identified organs and blood vessels, a second tool for deleting, or a third tool for creating an additional image. can do.

And, the matching step may include adjusting the position of the generated 3D modeling data based on the movement of the patient or the position of blood vessels commonly appearing on the one or more CT images.

A first device for generating a 3D model of organs and blood vessels according to the type of surgery according to an embodiment of the present disclosure to solve the above-described problem includes one or more communication modules; One or more memories; and one or more processors, wherein the one or more processors acquire one or more scanned images output by scanning the patient's body; Receiving the type of specific surgery to be performed on the patient through a first UI (user interface); Obtain information related to organs and blood vessels related to the type of specific surgery entered; Based on the obtained information, segment and identify images for each of the organs and blood vessels on the one or more scanned images through an artificial intelligence (AI) model; Using the images for each of the identified organs and blood vessels, 3D modeling data for each of the identified organs and blood vessels is generated; And it can be set to generate a 3D model for performing the specific surgery by matching the generated 3D modeling data.

In addition to this, a computer program stored in a computer-readable recording medium for execution to implement the present disclosure may be further provided.

In addition, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the above-described problem-solving means of the present disclosure, a method, device, and program for automatically generating 3D models of organs and blood vessels according to the type of surgery can be provided.

In addition, by creating a 3D simulation based on a 3D model in which the characteristics of each surgical procedure are reflected/combined by the means for solving the above-described problem of the present disclosure, it is possible to confirm the human body structure of the actual patient, which will be helpful in the actual surgery. You can.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the description below.

1 is a schematic diagram of a system for implementing a method for generating 3D models of organs and blood vessels according to the type of surgery, according to an embodiment of the present disclosure.
Figure 2 is a block diagram for explaining the configuration of a device that generates 3D models of organs and blood vessels according to the type of surgery, according to an embodiment of the present disclosure.
Figure 3 is a flowchart for explaining a method of generating 3D models of organs and blood vessels according to the type of surgery, according to an embodiment of the present disclosure.
Figure 4 is a flowchart for explaining a method of segmenting/identifying organs and blood vessels on a scanned image, according to an embodiment of the present disclosure.
5 to 7 are diagrams for explaining a method of generating 3D models of organs and blood vessels according to the type of surgery, according to an embodiment of the present disclosure.

Like reference numerals refer to like elements throughout this disclosure. The present disclosure does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present disclosure pertains is omitted. The term 'unit, module, member, block' used in the specification may be implemented as software or hardware, and depending on the embodiment, a plurality of 'unit, module, member, block' may be implemented as a single component, or It is also possible for one 'part, module, member, or block' to include multiple components.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary.

Throughout the specification, when a member is said to be located “on” another member, this includes not only cases where a member is in contact with another member, but also cases where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.

Singular expressions include plural expressions unless the context clearly makes an exception.

The identification code for each step is used for convenience of explanation. The identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless clearly specifically defined.

Hereinafter, the operating principle and embodiments of the present disclosure will be described with reference to the attached drawings.

In describing the present disclosure, “image” refers to multi-dimensional data consisting of discrete image elements (e.g., pixels in a two-dimensional image and voxels in a 3D image). You can. For example, the image may include a medical image of an object acquired by a CT imaging device.

In describing the present disclosure, an “object” may be a person (eg, a patient) or an animal, or a part or all of a person or an animal. For example, the object may include at least one of organs such as the liver, heart, uterus, brain, breast, abdomen, etc., blood vessels (eg, arteries or veins, etc.), fat tissue, etc. And, “target object” may refer to a part of a person that is actually the target of surgery.

In describing the present disclosure, a “user” is a medical professional and may be a doctor, nurse, clinical pathologist, medical imaging expert, etc., and may be a technician who repairs a medical device, but is not limited thereto.

In explaining the present disclosure, “3D modeling data” refers to data that represents a specific object in 3D, and “3D model” may refer to an element of a simulation created through the combination of one or more 3D modeling data. .

“3D modeling data” or/and “3D model” is provided in 2D on the display provided to the user, but is provided to be viewed in 3D form, or body parts appear in real space through the display, such as augmented reality. It can be presented to look like this.

In explaining the present disclosure, the “first device” (i.e., a device that performs a patient-customized 3D surgical simulation method) includes all various devices that can perform computational processing and provide results to the user.

For example, the devices include not only desktop PCs and laptops (Note Books), but also smart phones, tablet PCs, cellular phones, PCS phones (Personal Communication Service phones), synchronous/ Asynchronous IMT-2000 (International Mobile Telecommunication-2000) mobile terminals, Palm Personal Computers (Palm PCs), Personal Digital Assistants (PDAs), etc. may also apply. Additionally, if a Head Mounted Display (HMD) device includes computing functionality, the HMD device may be the device. Additionally, the device may be implemented as a separate server that receives requests from clients and performs information processing.

1 is a schematic diagram of a system 1000 for implementing a method for generating 3D models of organs and blood vessels according to the type of surgery, according to an embodiment of the present disclosure.

As shown in FIG. 1, the system 1000 for implementing a method of generating 3D models of organs and blood vessels according to the type of surgery includes a first device 100, a hospital server 200, and a database 300. ) and AI model 400.

Here, in FIG. 1, the first device 100 is shown to be implemented in the form of a single desktop, but it is not limited thereto. As described above, the first device 100 may refer to various types of devices or a device group in which one or more types of devices are connected.

The first device 100, hospital server 200, database 300, and artificial intelligence (AI) model 400 included in the system 1000 perform communication through the network (W). You can. Here, the network W may include a wired network and a wireless network. For example, the network may include various networks such as a local area network (LAN), a metropolitan area network (MAN), and a wide area network (WAN).

Additionally, the network W may include the known World Wide Web (WWW). However, the network (W) according to an embodiment of the present disclosure is not limited to the networks listed above, and may include at least some of a known wireless data network, a known telephone network, and a known wired and wireless television network.

The first device 100 may provide a method for generating 3D models of organs and blood vessels depending on the type of surgery.

For example, the first device 100 may obtain a scanned image by scanning the patient's body and receive input from the user about the type of specific surgery to be performed on the patient. The first device 100 may obtain information related to organs and blood vessels related to the type of specific surgery entered. Based on the acquired information, the first device 100 can segment and identify images for each organ and blood vessel on one or more scanned images through an AI model. The first device 100 uses images of each identified organ and blood vessel to generate 3D modeling data for each organ and blood vessel, and matches the generated 3D modeling data to create a 3D model for the progress of a specific surgery. can do.

The hospital server 200 (eg, cloud server, etc.) may store scanned images (eg, computer tomography (CT) images) obtained by scanning the patient's body. The hospital server 200 may transmit the saved scanned image to the first device 100, the database 300, or the AI model 400.

The hospital server 200 can protect personal information about the body by pseudonymizing or anonymizing the subject of the CT image. Additionally, the hospital server may encrypt and store information related to the age/gender/height/weight/parity of the patient who is the subject of the CT image input by the user.

The database 300 may store 3D modeling data and 3D models for various objects/surgical equipment generated by the first device 100. As another example, the database 300 may store information on organs and blood vessels corresponding to each type of surgery. Although FIG. 1 illustrates the case where the database 300 is implemented outside the first device 100, the database 300 may also be implemented as a component of the first device 100.

The AI model 400 is an artificial intelligence model learned to segment and identify images for each of the organs and blood vessels on one or more scanned images. The AI model 400 may be trained to segment and identify images for each of the organs and blood vessels on one or more scanned images through a data set constructed from actual surgical images, CT images, and anatomy-related data. Learning methods may include, but are not limited to, supervised training/unsupervised training.

1 illustrates a case where the AI model 400 is implemented outside of the first device 100 (e.g., implemented as cloud-based), but is not limited thereto and is implemented outside the first device 100. It can be implemented as a single component.

FIG. 2 is a block diagram for explaining the configuration of a first device 100 that generates 3D models of organs and blood vessels according to the type of surgery, according to an embodiment of the present disclosure.

As shown in FIG. 2, the first device 100 may include a memory 110, a communication module 120, a display 130, an input module 140, and a processor 150. However, it is not limited to this, and the software and hardware configuration of the first device 100 may be modified/added/omitted depending on the required operation within the range obvious to those skilled in the art.

The memory 110 can store data supporting various functions of the first device 100 and a program for the operation of the processor 150, and can store input/output data (e.g., music files, images, moving pictures, etc.), a plurality of application programs (application programs or applications) running on the device, data for operation of the first device 100, and commands can be stored. At least some of these applications may be downloaded from an external server via wireless communication.

The memory 110 is of a flash memory type, hard disk type, solid state disk type, SDD type (Silicon Disk Drive type), and multimedia card micro type. micro type), card type memory (e.g. SD or XD memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), EEPROM (electrically erasable) It may include at least one type of storage medium among programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk.

Additionally, the memory 110 is separate from the device, but may include a database connected by wire or wirelessly. That is, the database shown in FIG. 1 may be implemented as a component of the memory 110.

The communication module 120 may include one or more components that enable communication with an external device, for example, at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module. may include.

Wired communication modules include various wired communication modules such as Local Area Network (LAN) modules, Wide Area Network (WAN) modules, or Value Added Network (VAN) modules, as well as USB (Universal Serial Bus) modules. ), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).

In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, wireless communication modules include GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), and UMTS (universal mobile telecommunications system). ), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, 6G, etc. may include a wireless communication module that supports various wireless communication methods.

The display 130 displays (outputs) information processed by the first device 100 (eg, 3D modeling data/model or a simulation screen based on the 3D modeling data/model). For example, the display displays execution screen information of an application program (for example, an application) running on the first device 100, or UI (User Interface) and GUI (Graphic User Interface) information according to this execution screen information. It can be displayed. The types of UI output from the display 130 will be described later.

The input module 140 is for receiving information from the user. When information is input through the user input unit, the processor 150 can control the operation of the first device 100 to correspond to the input information.

The input module 140 includes hardware-type physical keys (e.g., buttons, dome switches, jog wheels, jog switches, etc. located on at least one of the front, back, and sides of the device) and software-type physical keys. May include touch keys. As an example, the touch key consists of a virtual key, soft key, or visual key displayed on the touch screen type display 130 through software processing, or the above It may consist of a touch key placed in a part other than the touch screen. Meanwhile, the virtual key or visual key can be displayed on the touch screen in various forms, for example, graphic, text, icon, video or these. It can be made up of a combination of .

The processor 150 has a memory that stores data for an algorithm for controlling the operation of the components in the first device 100 or a program that reproduces the algorithm, and performs the above-described operations using the data stored in the memory. It may be implemented with at least one processor (not shown). At this time, the memory and processor may each be implemented as separate chips. Alternatively, the memory and processor may be implemented as a single chip.

In addition, the processor 150 combines any one or a plurality of the above-described components to implement various embodiments according to the present disclosure described in FIGS. 3 to 7 below on the first device 100. You can control it.

3 shows that, according to an embodiment of the present disclosure, one or more scanned images output can be obtained by scanning the patient's body (S310).

For example, the first device may receive a computer tomography (CT) image obtained by scanning the patient's body from a hospital server. Specifically, the device may transmit a signal requesting a CT image of a specific patient including an image of a specific surgical object (eg, target object) to be performed on the patient to a hospital server (or a device controlled by the hospital). A second device controlled by the hospital may scan the patient's body to obtain one or more CT images. The hospital server (or cloud server) connected to the second device may transmit one or more CT images of a specific patient including the image of the specific surgical subject to the device according to a request signal from the first device.

However, this is only an example, and the first device may receive a CT image including an image of a specific surgical target from a CT imaging device connected wirelessly/wirely to the first device.

The first device can receive input of the type of specific surgery to be performed on the patient through the first UI (S320). For example, the first UI may be shown as shown in FIG. 5. The first device may select a specific type of surgery (i.e., a specific organ/blood vessel/fatty tissue, etc. where the surgery will be performed) through the UI element 500 on the first UI.

The first device may obtain information related to organs and blood vessels related to the type of specific surgery entered (S330). And, based on the acquired information, the first device can segment and identify images for each organ and blood vessel on one or more scanned images through an AI model (S340). S330 and S340 will be described in more detail with reference to FIG. 4.

The first device may generate 3D modeling data for each of the identified organs and blood vessels using images of each identified organ and blood vessel (S350).

Specifically, the first device may provide the user with a third UI for inspecting the position, size, and shape of the images for each of the organs and blood vessels identified on one or more scanned images. The first device can modify the images for each organ and blood vessel according to the inspection results input by the user through the third UI.

For example, the third UI can specify inspection items based on an organ/blood vessel database defined for each surgery type. For example, if the surgery type is stomach cancer, organs related to stomach cancer may include the liver, abdomen, spleen, etc., and the detailed blood vessels that make up the arteries related to stomach cancer include AORTA, CHA, and SA. , PHA, etc., and may include PV, SV, GCT, SMV, etc. as detailed blood vessels forming venous vessels related to stomach cancer.

And, the third UI includes a first tool to move the position of the image for each of the identified organs and blood vessels, a second tool to delete (e.g., eraser tool), or a third tool to create an additional image. It may include at least one of tools (eg, brush tool, etc.).

For example, the user may use an eraser tool to erase liver modeling data incorrectly predicted by the first device. As another example, the user can add omitted liver modeling data using the brush tool.

At this time, the user's inspection standard may be based on whether the inspection items defined for each type of surgery are properly confirmed and whether the structure is anatomically acceptable. As another example, the user's inspection criteria can refer to human anatomy to check whether the inspection items have the correct size and location. For example, the user can inspect the modeling by considering the various branch points of arterial blood vessels.

The first device can generate a 3D model for performing a specific surgery by matching the generated 3D modeling data (S360).

Specifically, the first device may adjust the location of the generated 3D modeling data based on the patient's movement or the location of blood vessels commonly appearing on one or more CT images.

For example, assume that the type of surgery to be applied to the patient is stomach cancer. The second device may acquire a plurality of CT images by scanning the locations of organs related to stomach cancer two or more times, and transmit the acquired plurality of CT images to the first device. The first device may match a plurality of 3D modeling data (i.e., 3D modeling data for a plurality of organs related to stomach cancer) generated based on blood vessels commonly identified in a plurality of CT images.

For example, assume that detailed blood vessels constituting a vein, shown in (a) of FIG. 7, are commonly identified in a plurality of CT images. If the location of the blood vessel commonly identified in a plurality of CT images has difference values based on x, y, z coordinates (-10mm, 3mm, -5mm) for each CT image, the first device The location (eg, detailed blood vessels constituting the artery shown in (b) of FIG. 7) can also be adjusted by applying the difference value. In addition, the first device can generate a 3D model for the progress of gastric cancer surgery (e.g., like the 3D model shown in (c) of FIG. 7) by matching the modeling data for each organ/blood vessel whose positions have been adjusted. there is.

The first device may generate a simulation for the progress of a specific surgery (i.e., virtual mock surgery) using the generated 3D model.

Figure 4 is a flowchart for explaining a method of segmenting/identifying organs and blood vessels on a scanned image, according to an embodiment of the present disclosure. That is, Figure 4 is a flowchart for specifically explaining S330 and S340.

The first device may build or load a database storing information on organs and blood vessels corresponding to a plurality of surgery types (S410).

For example, the first device may build a database by collecting information on organs/blood vessels/other tissues corresponding to a specific surgery. Alternatively, the first device may load (or receive) a database built based on information about organs/blood vessels/other tissues corresponding to a specific surgery (e.g., gastric cancer surgery) from an external device.

If the specific surgery is a stomach cancer surgery, the database includes organs related to the stomach cancer surgery (e.g., liver, abdomen, spleen, gallbladder, and pancreas), and detailed blood vessels forming the arteries of the organ (e.g., AORTA, A total of 16 blood vessels, including CT, CHA, SA, PHA, and GDA), detailed blood vessels forming the veins of the relevant organ (eg, a total of 12 blood vessels, including PV, SV, GCT, SMV, LGV, and LGEV), and other tissues (For example, it may include skin, Lib, abdominal wall, etc.).

The first device may obtain information related to organs and blood vessels related to the type of specific surgery entered in the database (S420).

The first device may select an algorithm module that can identify images of organs and blood vessels corresponding to the type of specific surgery input on the second UI (S430).

Specifically, the AI model may include a previously learned algorithm module for each type of surgery, capable of identifying images of organs and blood vessels corresponding to each type of surgery. Here, the algorithm module may refer to an algorithm learned by the AI model to perform a specific operation. The AI model is trained to identify images of organs and blood vessels corresponding to multiple types of surgery, and the AI model may include previously learned algorithm modules for each type of surgery.

The user can select an algorithm module corresponding to a specific surgery type among the algorithm modules previously learned for each surgery type on the second UI. Accordingly, the AI model can identify organs/blood vessels/other tissues related to a specific surgery type using the selected algorithm module.

As an example, the second UI may be implemented as shown in FIG. 6. The second UI may include UI elements 610 and 620 that can select a pre-learned algorithm module for each type of surgery. The user can select a specific algorithm module through UI elements 610 and 620.

The first device can segment and identify images for each organ and blood vessel on one or more CT images through an AI model using the selected algorithm module (S450).

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. Instructions may be stored in the form of program code, and when executed by a processor, may create program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

Computer-readable recording media include all types of recording media storing instructions that can be decoded by a computer. For example, there may be read only memory (ROM), random access memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, etc.

As described above, the disclosed embodiments have been described with reference to the attached drawings. A person skilled in the art to which this disclosure pertains will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

100: device
110: memory
120: communication module
130: display
140: input module
150: processor

Claims (10)

In a method of generating a 3D model of organs and blood vessels according to the type of surgery, performed by a first device,
Obtaining one or more scanned images output by scanning the patient's body;
Receiving a type of specific surgery to be performed on the patient through a first user interface (UI);
Obtaining information related to organs and blood vessels related to the input specific type of surgery;
Based on the obtained information, dividing and identifying images for each of the organs and blood vessels on the one or more scanned images through an artificial intelligence (AI) model;
Using images of each of the identified organs and blood vessels, generating 3D modeling data for each of the identified organs and blood vessels; and
A method comprising matching the generated 3D modeling data to generate a 3D model for performing the specific surgery. According to paragraph 1,
The one or more scanned images include one or more computer tomography (CT) images of the patient's body,
The step of acquiring one or more CT images includes:
A method comprising receiving the one or more CT images from at least one of a second device that scanned the patient's body or a cloud server connected to the second device. According to paragraph 2,
The step of acquiring information related to organs and blood vessels related to the type of specific surgery entered,
Building or loading a database storing information on organs and blood vessels corresponding to a plurality of surgery types; and
A method comprising obtaining information related to organs and blood vessels related to the input specific type of surgery from the database. According to paragraph 3,
The AI model is,
A method comprising a previously learned algorithm module for each of the plurality of surgery types, capable of identifying images of organs and blood vessels corresponding to each of the plurality of surgery types. According to paragraph 4,
The identification step is,
Selecting an algorithm module that can identify images of organs and blood vessels corresponding to the inputted specific surgery type among the algorithm modules previously learned for each surgery type on the second UI; and
A method comprising segmenting and identifying images for each of the organs and blood vessels on the one or more CT images through the AI model using the selected algorithm module. According to clause 5,
The step of generating the 3D modeling data is,
Providing a third UI for inspecting the position, size, and shape of the image for each of the organs and blood vessels identified on the one or more scanned images; and
A method comprising modifying images for each of the organs and blood vessels according to the inspection results input through the third UI. According to clause 6,
The third UI is,
A method comprising at least one of a first tool for moving the position of an image for each of the identified organs and blood vessels, a second tool for deleting, or a third tool for creating an additional image. In clause 7,
The matching step is,
Method comprising adjusting the position of the generated 3D modeling data based on the movement of the patient or the position of blood vessels commonly appearing on the one or more CT images. In the first device for generating 3D models of organs and blood vessels according to the type of surgery,
One or more communication modules;
One or more memories; and
Contains one or more processors,
The one or more processors:
Obtaining one or more scanned images output by scanning the patient's body;
Receiving the type of specific surgery to be performed on the patient through a first UI (user interface);
Obtain information related to organs and blood vessels related to the type of specific surgery entered;
Based on the obtained information, segment and identify images for each of the organs and blood vessels on the one or more scanned images through an artificial intelligence (AI) model;
Using the images for each of the identified organs and blood vessels, 3D modeling data for each of the identified organs and blood vessels is generated; and
A first device configured to generate a 3D model for performing the specific surgery by matching the generated 3D modeling data. A computer program combined with a first device, which is hardware, and stored in a computer-readable recording medium to execute a method of generating a 3D model of organs and blood vessels according to the type of surgery according to any one of claims 1 to 8.