KR20230049936A - Method and apparatus for detecting diaphragm using average figure - Google Patents

Abstract

The present disclosure relates to a diaphragm detection method using an average shape and a device therefor. According to an embodiment of the present disclosure, a diaphragm detection method comprises the steps of: receiving a first image generated corresponding to a continuous volume of a body part including the chest; obtaining information about the average shape of the diaphragm, wherein the diaphragm average shape is generated based on a plurality of second images generated corresponding to the continuous volume of the body part including the chest, and the information about the diaphragm average shape includes information about the coordinates of the diaphragm average shape; normalizing the first image to correspond to the diaphragm average shape and the second image used to generate the diaphragm average shape; matching and arranging the diaphragm average shape to the first image based on the information about the diaphragm average shape; and generating a diaphragm shape corresponding to the first image by transforming coordinates of at least a portion of the diaphragm average shape. The present invention can perform extraction of the diaphragm through the average shape generated by reflecting anatomical features.

Description

Diaphragm detection method using average figure and apparatus therefor

The technical spirit of the present disclosure relates to a diaphragm detection method and apparatus for detecting a diaphragm from a chest image using an average figure.

The diaphragm is a muscular tissue located below the lungs and separating the chest from the abdomen. When the human body breathes, when the diaphragm muscle goes down, the volume of the lungs increases and air is taken in, and when it goes up, the volume of the lungs decreases and air is expelled. In the case of diseases related to the lungs, especially Chronic Obstructive Pulmonary Disease (COPD), the shape of the diaphragm is modified according to the disease characteristics of the lungs shown on radiographs, and in the treatment process, invasive or non-invasive methods are used. This brings the diaphragm into a normal state. Therefore, extracting the diaphragm from a radiographic image and confirming its deformed shape can be helpful in confirming the progress of treatment for lung diseases.

In order to detect the diaphragm, a method of automatically detecting the boundary with the diaphragm using an image analysis algorithm has been used. However, the lungs and heart are located on the upper side of the diaphragm, and the liver and stomach are located on the lower side. Since these organs all correspond to soft tissues in contrast to the skeleton or air-filled part in a computed tomography (CT) image, these conventional The method of has a limitation that the detection accuracy is very low.

Recently, a method of extracting the diaphragm has been introduced through deep learning in which a part to be extracted is marked as 1 and a part other than the binary label is marked as 0. In this way, what is obtained and learned from the part indicated by 1 from the extraction target is the position of the extraction target and the surrounding pixel values. Therefore, errors due to the unclear boundary between the diaphragm and other organs, which are displayed with similar pixel values in the image, still exist, and in many cases, the boundary of the diaphragm divided by the algorithm invades other organs or the middle is unrecognizable. There is a problem that a cutting phenomenon occurs.

The technical task to be achieved by the diaphragm detection method and apparatus therefor according to the technical spirit of the present disclosure is to accurately extract the position and size of the diaphragm from a tomography image (CT) and display it on a user screen, thereby providing a diagnosis and treatment process of the diaphragm. It is to provide a method and apparatus capable of providing information on a deformed shape.

The technical task to be achieved by the diaphragm detection method and apparatus therefor according to the technical idea of the present disclosure is not limited to the above-mentioned tasks, and other tasks not mentioned will be clearly understood by those skilled in the art from the following description. .

According to one aspect of the technical concept of the present disclosure, a method for detecting a diaphragm may include receiving a first image generated corresponding to a continuous volume of a body part including a chest; Obtaining information about the average figure of the diaphragm - The average figure of the diaphragm is generated based on a plurality of second images generated corresponding to the continuous volume of the body part including the chest, and the information about the average figure of the diaphragm is Includes information about the coordinates of the diaphragm-averaged figure; normalizing the first image to correspond to the average diaphragm figure and the second image used to generate the average figure of the diaphragm; matching and arranging the average diaphragm figure to the first image based on the information about the average diaphragm figure; and generating a diaphragm figure corresponding to the first image by transforming coordinates of at least a portion of the average diaphragm figure.

According to an exemplary embodiment, the information on the diaphragm average figure includes information about a pixel gradient around the diaphragm average figure extracted based on a plurality of the second images, and the diaphragm figure corresponding to the first image The generating step may include measuring an output value of a loss function related to a pixel gradient around the diaphragm average figure in correspondence with the coordinate transformation of the diaphragm average figure; and generating the diaphragm figure by optimizing the coordinates of the average diaphragm figure based on the output value of the loss function.

According to an exemplary embodiment, the generating of the diaphragm figure by optimizing the coordinates of the diaphragm average figure may be performed by optimizing the coordinates of the diaphragm average figure to minimize an output value of the loss function.

According to an exemplary embodiment, the normalizing may include at least a partial region of the first image based on at least one of at least one diaphragmatic peripheral organ and at least one bone included in the first image and the second image. It can be performed by extracting, enlarging, reducing or rotating.

According to an exemplary embodiment, inversely transforming the generated diaphragm figure to correspond to the original first image; and rendering and displaying the diaphragm figure together with the first image.

According to one aspect of the technical concept of the present disclosure, a diaphragm detection device includes at least one processor; a memory for storing a program executable by the processor; And the processor, by executing the program, receives a first image generated corresponding to the continuous volume of the body part including the chest, and receives a plurality of images generated corresponding to the continuous volume of the body part including the chest. Receiving information about a diaphragm average figure generated based on a second image, normalizing the first image to correspond to the diaphragm average figure and the second image used to generate the diaphragm average figure, and the diaphragm average Based on the information about the figure, the diaphragm average figure is matched and arranged with the first image, and a diaphragm figure corresponding to the first image is generated by converting at least some coordinates of the diaphragm average figure, The information about the diaphragm average figure may include information about the coordinates of the diaphragm average figure.

According to the diaphragm detection method and apparatus therefor according to embodiments according to the technical idea of the present disclosure, by performing extraction of the diaphragm through an average figure generated by reflecting anatomical features, by extracting a part with an unclear boundary This has the effect of preventing the extraction target from deviating from an anatomically possible shape.

The effect that can be obtained by the device and the device according to the technical concept of the present disclosure is not limited to the above-mentioned effects, and other effects not mentioned above are based on common knowledge in the art to which the present disclosure belongs from the description below. It will be clearly understandable to those who have it.

A brief description of each figure is provided in order to more fully understand the figures cited in this disclosure.
1 is a flowchart illustrating a method for detecting a diaphragm according to an embodiment of the present disclosure.
FIG. 2 is a flowchart illustrating an embodiment of step S120 of FIG. 1 .
FIG. 3 is a flowchart illustrating an embodiment of step S150 of FIG. 1 .
4 to 6 exemplarily illustrate a process of detecting a diaphragm according to an embodiment of the present disclosure.
7 is a block diagram briefly illustrating the configuration of a diaphragm detection device according to an embodiment of the present disclosure.

Since the technical spirit of the present disclosure may be subject to various changes and may have various embodiments, specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the technical spirit of the present disclosure to specific embodiments, and should be understood to include all changes, equivalents, or substitutes included in the scope of the technical spirit of the present disclosure.

In describing the technical idea of the present disclosure, if it is determined that a detailed description of a related known technology may unnecessarily obscure the subject matter of the present disclosure, the detailed description will be omitted. In addition, numbers (eg, first, second, etc.) used in the description process of the present disclosure are only identifiers for distinguishing one component from another component.

In addition, in the present disclosure, when one component is referred to as "connected" or "connected" to another component, the one component may be directly connected or directly connected to the other component, but in particular Unless otherwise described, it should be understood that they may be connected or connected via another component in the middle.

In addition, terms such as "~ unit", "~ group", "~ character", and "~ module" described in the present disclosure mean a unit that processes at least one function or operation, which includes a processor, a micro Processor (Micro Processor), Micro Controller, CPU (Central Processing Unit), GPU (Graphics Processing Unit), APU (Accelerate Processor Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array) may be implemented by hardware or software or a combination of hardware and software.

In addition, it is intended to make it clear that the classification of components in the present disclosure is merely a classification for each main function in charge of each component. That is, two or more components to be described below may be combined into one component, or one component may be divided into two or more for each more subdivided function. In addition, each component to be described below may additionally perform some or all of the functions of other components in addition to its main function, and some of the main functions of each component may be performed by other components. Of course, it may be dedicated and performed by .

The method according to an embodiment of the disclosure may be performed in a personal computer having computing capability, a workstation, a computer device for a server, or a separate device for this purpose.

Also, the method may be performed on one or more computing devices. For example, at least one or more steps of the method 100 according to an embodiment of the present disclosure may be performed in a client device and other steps may be performed in a server device. In this case, the client device and the server device may be connected through a network to transmit and receive calculation results. Alternatively, method 100 may be performed by distributed computing technology.

Hereinafter, embodiments of the present disclosure will be described in detail in turn.

1 is a flowchart for explaining a diaphragm detection method according to an embodiment of the present disclosure, FIG. 2 is a flowchart for explaining an embodiment of step S120 of FIG. 1, and FIG. 3 is a flowchart for step S150 of FIG. 1 It is a flowchart for explaining one embodiment.

In step S110, the device may receive a first image generated corresponding to the continuous volume of the body part including the chest. Here, the first image is an input image for which detection of the diaphragm is requested, and may be received from an external database server or captured and input from a photographing device connected to the device through wired or wireless communication.

In an embodiment, the first image may be a computed tomography (CT) image generated by taking tomography of a body part including the chest of the subject. For example, the first image is composed of a plurality of 2D slice images (ie, image group) generated by continuously photographing a body part including the chest of a subject in one direction through a computed tomography method, or It may be a 3D image generated based on this.

In step S120, the device may acquire information about the diaphragm average shape. Here, the average figure of the diaphragm may be generated (or extracted) based on a plurality of second images generated with respect to the chest of a subject different from the subject of the first image.

In one embodiment, step S120 may include steps S121 to S123 as shown in FIG. 2 .

In step S121, the device may acquire a plurality of second images generated corresponding to the continuous volume of the body part including the chest.

For example, the second image may be received from an external database server or may be obtained by photographing from a photographing device CT connected to the device through wired or wireless communication.

In an embodiment, the second image may be a computed tomography (CT) image generated by taking a tomography scan of a body part including the chest of each of a plurality of subjects, and is composed of a plurality of 2D slice images (ie, image groups). Alternatively, it may be a 3D image generated based on this.

In step S122, the device may normalize the plurality of second images into a predetermined format suitable for extracting the average diaphragm shape.

For example, the device may perform normalization by extracting at least a partial area of the second image or by enlarging, reducing, and/or rotating the same.

In an embodiment, step S122 may be performed based on at least one of at least one organ around the diaphragm and at least one bone region.

For example, for each second image, the device extracts (cuts) a partial region of the image so that only the heart, liver, and stomach, which are organs around the diaphragm, are included, or enlarges, reduces, and enlarges the regions extracted from each second image. And/or by rotating, the second images may be normalized in a format corresponding to each other. Also, for example, the apparatus may normalize the second images into a format corresponding to each other by enlarging, reducing, and/or rotating the second images based on the spine and the ribs.

However, this is an example, and according to embodiments, the device may perform normalization based on various reference points.

Through step S122, predetermined organs, bone parts, etc. included in the plurality of second images may be transformed into sizes and angles corresponding to each other and placed on adjacent 3D space coordinates.

In step S123, the device may extract an average diaphragm figure corresponding to the diaphragm and a pixel gradient around the average diaphragm figure from the plurality of normalized second images.

In an embodiment, the extraction of the diaphragm average figure may be performed through a pre-learned network function. That is, the network function may have been previously trained on diaphragm extraction through training data (eg, a CT image including a diaphragm region labeled through an expert, examination, etc.).

Here, the network function may be used as the same meaning as a neural network and/or a neural network. A neural network (neural network) may be composed of a set of interconnected computational units, which may be generally referred to as nodes, and these nodes may be referred to as neurons. A neural network generally includes a plurality of nodes, and the nodes constituting the neural network may be interconnected by one or more links. In this case, some of the nodes constituting the neural network may configure one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The neural network may include a deep neural network (DNN) including a plurality of hidden layers in addition to an input layer and an output layer.

Also, according to embodiments, extraction of the diaphragm average shape may be performed through image processing based on a change in pixel value. However, this is an example, and is not limited thereto.

Meanwhile, although not shown, the device may store information about the averaged figure of the diaphragm. The diaphragm-averaged figure can be expressed as a surface with curvature in a 3-dimensional space, and the information on the diaphragm-averaged figure includes information on coordinates in the 3-dimensional space constituting the surface and information on pixel inclination around the diaphragm-averaged figure can include

In addition, although steps S122 and S123 are described as being performed in a 3-dimensional coordinate space based on a 3-dimensional second image, this is exemplary, and according to an embodiment, steps S122 and S123 are performed on a second image. It may be performed on each of a plurality of constituting 2D images (ie, slice images). In this case, in step S123, a 3-dimensional average diaphragm figure may be generated by extracting average diaphragm figures from each 2-dimensional image and stacking them.

In step S130, the device may normalize the first image to correspond to the second image. That is, the device may extract, enlarge, reduce, and/or rotate at least a partial region of the first image to correspond to the second image (ie, the normalized second image) used to extract the diaphragm average figure, and thus Accordingly, the first image may be normalized suitable for diaphragm extraction. In this case, the deformed first image may be mapped on the same 3D coordinate space to correspond to the second image used to extract the diaphragm average figure.

Step S130, like step S122 described above, may be performed based on at least one of at least one organ around the diaphragm (heart, liver, stomach, etc.) and at least one bone part (spine, ribs, etc.) .

In step S140, the device may arrange the average diaphragm shape by matching it to the first image based on the information about the average diaphragm shape. That is, based on the coordinates of the average diaphragm figure, the diaphragm average figure may be disposed at a predetermined position of the deformed first image.

According to an embodiment, the device may arrange the averaged diaphragm figure in each of a plurality of 2D images (ie, slice images) constituting the first image. In this case, step S150 described in detail below may be performed for each 2D image.

In operation S150, the apparatus may generate a diaphragm figure corresponding to the diaphragm included in the first image from the averaged diaphragm figure by transforming coordinates of at least a part of the averaged diaphragm figure disposed in the first image.

In one embodiment, step S150 may include steps S151 and S152, as shown in FIG. 3 .

In step S152, the device converts at least some coordinates of the diaphragm average figure in a vertical or horizontal direction in a three-dimensional coordinate space, and correspondingly measures an output value of a loss function for a pixel gradient around the diaphragm average figure.

Subsequently, in step S152, the device may generate a diaphragm figure corresponding to the diaphragm included in the first image by optimizing the coordinates of the average diaphragm figure based on the output value of the loss function.

That is, the loss function outputs a lower value as the diaphragm and diaphragm average figures included in the first image become more similar in position and shape to each other, and a repetitive process of reducing the output value of this loss function (coordinate conversion -> loss function) The diaphragm figure can be created by optimizing the coordinates of the average diaphragm figure through the measurement of the output value of .

To this end, stochastic gradient descent or the like may be applied, but is not limited thereto.

In step S160, the device may inversely transform the created diaphragm figure to correspond to the original first image, and render and display the diaphragm figure together with the first image.

That is, the device performs inverse transformation on the diaphragm figure in the same format as the original first image before performing step S130, and then renders on a 3D space along with at least a part of the first image based on the coordinates of the transformed diaphragm figure can be done Accordingly, the diaphragm included in the first image may be rendered in a 3D space together with surrounding organs (heart, liver, stomach, etc.) and bone parts (spine, ribs, etc.) and displayed to the user.

Meanwhile, in FIG. 1 , although step S120 is shown to be performed after step S110, it is not limited thereto, and step S120 may be performed first and then step S110 may be performed.

4 to 6 exemplarily illustrate a process of detecting a diaphragm according to an embodiment of the present disclosure.

First, referring to FIG. 4 , a plurality of second images may be normalized according to a predetermined format (or standard) to be suitable for extraction of a diaphragm average figure. This normalization process may be performed by extracting at least a partial region of the second image based on at least one peripheral organ and/or at least one bone region, or by enlarging, reducing, and/or rotating the extracted region.

For example, in a three-dimensional coordinate space, the x-axis (left, right) direction includes the entire ribcage including the lungs, and the y-axis (upper, lower) direction includes the 1st thoracic vertebrae (T1) to the 7th rib. Normalization may be performed by extracting a partial region of the second image to include all of the vertebrae and sternum in the z-axis (front and back) directions.

As described above, transformation may be performed to correspond to the normalized second image through a similar method for the first image to generate the diaphragm figure.

Referring to FIG. 5 , a diaphragm average figure 510 extracted or generated from a plurality of second images may be disposed in the first image in order to extract the diaphragm of the first image. Subsequently, the diaphragm figure 520 corresponding to the diaphragm of the first image can be generated by repeatedly performing the process of transforming the three-dimensional coordinates of the average diaphragm figure 510 and measuring the output value of the loss function according to the transformation. there will be

Referring to FIG. 6 , the created diaphragm figure may be displayed to the user by being 3D rendered together with at least one organ and/or bone included in the first image. The user can easily check the shape and position of the diaphragm 610 through the rendering screen.

7 is a block diagram briefly illustrating the configuration of a diaphragm detection device according to an embodiment of the present disclosure.

The communication unit 710 may receive input data (such as a chest CT image) for performing diaphragm detection. The communication unit 710 may include a wired/wireless communication unit. When the communication unit 710 includes a wired communication unit, the communication unit 710 may include a local area network (LAN), a wide area network (WAN), a value added network (VAN), and a mobile communication network ( mobile radio communication network), a satellite communication network, and one or more components that enable communication through a mutual combination thereof. In addition, when the communication unit 710 includes a wireless communication unit, the communication unit 710 transmits and receives data or signals wirelessly using cellular communication, a wireless LAN (eg, Wi-Fi), and the like. can In an embodiment, the communication unit may transmit/receive data or signals with an external device or an external server under the control of the processor 740 .

The input unit 720 may receive various user commands through external manipulation. To this end, the input unit 720 may include or connect one or more input devices. For example, the input unit 720 may be connected to an interface for various inputs such as a keypad and a mouse to receive user commands. To this end, the input unit 720 may include an interface such as a thunderbolt as well as a USB port. In addition, the input unit 720 may receive an external user command by including or combining various input devices such as a touch screen and buttons.

The memory 730 may store programs and/or program commands for operation of the processor 740 and may temporarily or permanently store input/output data. The memory 730 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM , SRAM, ROM (ROM), EEPROM, PROM, magnetic memory, a magnetic disk, it may include at least one type of storage medium.

In addition, the memory 730 may store various network functions and algorithms, and may store various data, programs (one or more instructions), applications, software, commands, codes, etc. for driving and controlling the device 700. there is.

The processor 740 may control the overall operation of the device 700 . Processor 740 may execute one or more programs stored in memory 730 . The processor 740 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to the technical idea of the present disclosure are performed.

In an embodiment, the processor 740 receives a first image generated corresponding to the continuous volume of the body part including the chest, and receives a plurality of second images generated corresponding to the continuous volume of the body part including the chest. Receiving information about the diaphragm average figure generated based on the image, normalizing the first image to correspond to the diaphragm average figure and the second image used for generating the diaphragm average figure, and A diaphragm figure corresponding to the first image may be generated by matching and arranging the average diaphragm figure to the first image and converting at least some coordinates of the average diaphragm figure based on the information about the diaphragm. Here, the information about the average diaphragm figure may include information about the coordinates of the average diaphragm figure and information about pixel gradients around the average diaphragm figure extracted based on the plurality of second images.

In an embodiment, the processor 740 measures an output value of a loss function related to a pixel gradient around the diaphragm average figure in response to the coordinate transformation of the diaphragm average figure, and obtains the diaphragm average figure based on the output value of the loss function. By optimizing the coordinates of , the diaphragm figure can be created.

In an embodiment, the processor 740 may generate the diaphragm figure by optimizing the coordinates of the average diaphragm figure so that the output value of the loss function is minimized.

In an embodiment, the processor 740 extracts and enlarges at least a portion of the first image based on at least one of at least one diaphragm peripheral organ and at least one bone included in the first image and the second image. , it is possible to normalize the first image by reducing or rotating it.

In an embodiment, the processor 740 converts the input first image to correspond to the normalized second image, matches and arranges the diaphragm average figure with respect to the transformed first image, and arranges the diaphragm figure can create

In an embodiment, the processor 740 may inversely transform the generated diaphragm figure to correspond to the original first image, and render and display the diaphragm figure together with the first image.

The method according to an embodiment of the present disclosure may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the present disclosure, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler.

In addition, the method according to the disclosed embodiments may be provided by being included in a computer program product. Computer program products may be traded between sellers and buyers as commodities.

A computer program product may include a S/W program and a computer-readable storage medium in which the S/W program is stored. For example, a computer program product may include a product in the form of a S/W program (eg, a downloadable app) that is distributed electronically through a manufacturer of an electronic device or an electronic marketplace (eg, Google Play Store, App Store). there is. For electronic distribution, at least a part of the S/W program may be stored in a storage medium or temporarily generated. In this case, the storage medium may be a storage medium of a manufacturer's server, an electronic market server, or a relay server temporarily storing SW programs.

A computer program product may include a storage medium of a server or a storage medium of a client device in a system composed of a server and a client device. Alternatively, if there is a third device (eg, a smart phone) that is communicatively connected to the server or the client device, the computer program product may include a storage medium of the third device. Alternatively, the computer program product may include a S/W program itself transmitted from the server to the client device or the third device or from the third device to the client device.

In this case, one of the server, the client device and the third device may execute the computer program product to perform the method according to the disclosed embodiments. Alternatively, two or more of the server, the client device, and the third device may execute the computer program product to implement the method according to the disclosed embodiments in a distributed manner.

For example, a server (eg, a cloud server or an artificial intelligence server) may execute a computer program product stored in the server to control a client device communicatively connected to the server to perform a method according to the disclosed embodiments.

Although the embodiments have been described in detail above, the scope of the present disclosure is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present disclosure defined in the following claims are also within the scope of the present disclosure. belongs to

Claims (7)

In the diaphragm detection method using the average figure,
receiving a first image generated corresponding to the continuous volume of a body part including the chest;
Obtaining information about the average figure of the diaphragm - The average figure of the diaphragm is generated based on a plurality of second images generated corresponding to the continuous volume of the body part including the chest, and the information about the average figure of the diaphragm is Includes information about the coordinates of the diaphragm-averaged figure;
normalizing the first image to correspond to the average diaphragm figure and the second image used to generate the average figure of the diaphragm;
matching and arranging the average diaphragm figure to the first image based on the information about the average diaphragm figure; and
and generating a diaphragm figure corresponding to the first image by transforming coordinates of at least a portion of the average diaphragm figure. According to claim 1,
The information about the diaphragm average figure includes information about a pixel gradient around the diaphragm average figure extracted based on a plurality of the second images,
The step of generating a diaphragm figure corresponding to the first image,
measuring an output value of a loss function related to a pixel inclination around the diaphragm average figure in correspondence with the coordinate transformation of the diaphragm average figure; and
and generating the diaphragm figure by optimizing the coordinates of the average diaphragm figure based on the output value of the loss function. According to claim 1,
The step of generating the diaphragm figure by optimizing the coordinates of the diaphragm average figure,
The method is performed by optimizing the coordinates of the diaphragm average figure so that the output value of the loss function is minimized. According to claim 1,
The normalization step is
Extracting, enlarging, reducing, or rotating at least a partial region of the first image based on at least one of at least one diaphragm peripheral organ and at least one bone included in the first image and the second image. . According to claim 1,
inversely transforming the generated diaphragm figure to correspond to the original first image; and
The method further comprising rendering and displaying the diaphragm figure together with the first image. In the diaphragm detection device using the average figure,
at least one processor;
a memory for storing a program executable by the processor; and
The processor, by executing the program, receives a first image generated corresponding to the continuous volume of the body part including the chest, and receives a plurality of first images generated corresponding to the continuous volume of the body part including the chest. Receiving information about the diaphragm average figure generated based on 2 images, normalizing the first image to correspond to the diaphragm average figure and the second image used for generating the diaphragm average figure, and the diaphragm average figure Based on the information about, the diaphragm average figure is matched and arranged with the first image, and a diaphragm figure corresponding to the first image is generated by converting at least some coordinates of the diaphragm average figure,
Wherein the information about the diaphragm-averaged figure includes information about the coordinates of the diaphragm-averaged figure. A computer program stored in a recording medium to execute the method of any one of claims 1 to 5.

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