KR20200131737A - Method for aiding visualization of lesions in medical imagery and apparatus using the same - Google Patents

Abstract

The present disclosure relates to a method for assisting visualization of a lesion in a medical image and a device using the same. Specifically, according to the method of the present disclosure, the method for assisting visualization of a lesion may receive a user input for selecting one point of a medical image to modify a lesion mask indicating a lesion area shown in the medical image, generate the modified lesion mask in response to the received user input, and provide the medical image and the modified lesion mask together.

Description

A method of assisting the visualization of lesions in a medical image, and an apparatus using the same {METHOD FOR AIDING VISUALIZATION OF LESIONS IN MEDICAL IMAGERY AND APPARATUS USING THE SAME}

The present disclosure relates to a method for assisting visualization of a lesion in a medical image and an apparatus using the same.

The lesion mask represents a region of a lesion within a medical image, and may be displayed overlapping with the medical image. According to the doctor's opinion, since the lesion area may be determined differently even for the same medical image, a means for modifying the lesion mask based on the doctor's judgment is required. In the method of modifying the existing lesion mask, the user who reads the lesion of the medical image can modify the lesion mask by directly coloring the area determined as an additional lesion area using a mouse pointer provided as a'coloring tool'. there was. In addition, the user may modify the lesion mask by directly erasing an area determined to be not a lesion area in the provided lesion mask through a mouse pointer provided as an'erasing tool'.

In addition, when the medical image corresponds to a three-dimensional image such as a computed tomography (CT) image, since all related tomography images must be modified to correct the lesion mask, the existing area to be modified is directly colored or erased with the mouse pointer. There is a problem that the method is less efficient in correcting the lesion mask.

Accordingly, in the present specification, a method and an apparatus using the same can be proposed to more easily modify a lesion mask in a medical image.

An object of the invention described in the present disclosure, that is, the present invention, is to provide an interface in which a lesion mask representing a lesion area in a medical image can be easily modified based on a single user input such as a click.

Specifically, in the lesion mask in which frequent correction is performed according to the opinion of the doctor, the present invention can perform the correction more efficiently than the existing method of modifying the lesion mask by individually coloring or erasing the area of the lesion mask to be corrected. It aims to provide a means of being able to exist.

The characteristic configuration of the present invention for achieving the object of the present invention as described above and realizing the characteristic effects of the present invention described later is as follows.

According to an aspect of the present invention, a method of assisting visualization of a lesion in a medical image is provided, and according to the method, one of the medical images is used to modify a lesion mask indicating a lesion area shown in the medical image. Receiving a user input for selecting a point; Generating a modified lesion mask in response to the received user input; And providing the medical image and the modified lesion mask together.

According to another aspect of the present invention, there is also provided a computer program, stored on a machine-readable non-transitory recording medium, comprising instructions embodied to perform the method of assisting lesion visualization of the present invention.

According to another aspect of the present invention, a computing device for assisting visualization of a lesion in a medical image is provided, the computing device for modifying the lesion mask in a medical image to which a lesion mask representing a lesion area is combined. A communication unit sensing a user input for selecting a point of the medical image; And a processor for generating a modified lesion mask corresponding to the received user input, wherein the processor may support an output device linked through the communication unit to provide a medical image to which the modified lesion mask is combined. .

According to the present invention, a lesion mask can be modified through a simple user input such as a single click, and through this, the reader can directly color or delete a desired area using a mouse point, which is more efficient than the conventional method of modifying the lesion mask. You can modify the lesion mask.

Accordingly, according to the present invention, there is an ultimate effect of improving the speed and quality of reading in a reading field where different judgments of the shape of a lesion are frequent depending on the reader, thereby enabling innovation in the workflow in the medical field.

Further, the present invention is usefully applied not only to a medical field for reading lesions of a medical image of an actual patient, but also to a training process for enhancing image reading ability, thereby improving the efficiency of the training process of a trainee.

The accompanying drawings, which are attached for use in the description of the embodiments of the present invention, are only some of the embodiments of the present invention, and for those skilled in the art to which the present invention belongs, Other drawings can be obtained.
1 is a diagram schematically illustrating a method for assisting visualization of a lesion according to the present disclosure.
2 is a conceptual diagram schematically showing an exemplary configuration of a computing device that performs a method for assisting lesion visualization according to the present disclosure.
3 is an exemplary block diagram illustrating hardware or software components of a computing device that performs a method of assisting lesion visualization according to the present disclosure.
4 is a flowchart illustrating an exemplary method of assisting lesion visualization according to the present disclosure.
5 is a diagram illustrating an example of an artificial neural network used in a method for assisting lesion visualization according to the present disclosure.
6 is a diagram illustrating an example to which a method for visualizing a lesion according to the present disclosure is applied.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description of the present invention to be described later refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced in order to clarify the objects, technical solutions and advantages of the present invention. These embodiments are described in detail enough to enable a person skilled in the art to practice the present invention.

The term "image" or "image data" used throughout the detailed description and claims of the present disclosure refers to multidimensional data composed of discrete image elements (eg, pixels in a 2D image and voxels in a 3D image). Refers to.

For example, "image" is collected by (cone-beam) computed tomography, magnetic resonance imaging (MRI), ultrasound, or any other medical imaging system known in the art. It may be a medical image of the subject. Also, an image may be provided in a non-medical context, such as a remote sensing system, an electron microscopy, and the like.

Throughout the detailed description and claims of the present disclosure, an'image' refers to an image visible to the eye (e.g., displayed on a video screen) or an image (e.g., a file corresponding to a pixel output such as a CT, MRI detector). It is a term referring to the digital representation of

In the drawings presented for convenience of explanation, it is sometimes shown that cone-beam computed tomography (CBCT) image data is an exemplary image modality. However, those of ordinary skill in the art are aware that the image formats used in various embodiments of the present invention include X-ray images, MRI, CT, positron emission tomography (PET), PET-CT, SPECT, SPECT-CT, MR-PET, 3D ultrasound images, etc. It will be appreciated that including but not limited to the types listed by way of example.

Throughout the detailed description and claims of the present disclosure, the'DICOM (Digital Imaging and Communications in Medicine; Medical Digital Imaging and Communication)' standard is a generic term for various standards used for digital image expression and communication in medical devices. DICOM standards are published by an allied committee formed by the American Radiological Society (ACR) and the American Electrical Association (NEMA).

In addition, throughout the detailed description and claims of the present disclosure,'medical image storage and transmission system (PACS; Picture Archiving and Communication System)' refers to a system that stores, processes, and transmits in accordance with the DICOM standard, X-ray, Medical image images acquired using digital medical imaging equipment such as CT and MRI are stored in DICOM format and can be transmitted to terminals inside and outside the hospital through a network, and reading results and medical records can be added to this.

And throughout the detailed description and claims of the present disclosure,'learning' or'learning' is a term that refers to performing machine learning through computing according to procedures, and mental actions such as human educational activities It will be appreciated by those skilled in the art that it is not intended to refer to.

And throughout the detailed description and claims of this disclosure, the word'comprise' and its variations are not intended to exclude other technical features, additions, elements or steps. In addition,'one' or'one' is used in more than one meaning, and'another' is limited to at least a second or more.

Other objects, advantages, and features of the present invention to those skilled in the art will appear, partly from the present disclosure, and partly from the practice of the present invention. The examples and drawings below are provided by way of example and are not intended to limit the invention. Accordingly, the details disclosed in the present disclosure with respect to a specific structure or function are not to be construed in a limiting sense, but merely providing guidelines for a person skilled in the art to variously implement the present invention with any suitable detailed structures. It should be interpreted as representative basic data.

Moreover, the present invention covers all possible combinations of the embodiments shown in this disclosure. It should be understood that the various embodiments of the present invention are different from each other, but need not be mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the present invention in relation to one embodiment. In addition, it is to be understood that the location or arrangement of individual components in each disclosed embodiment may be changed without departing from the spirit and scope of the present invention. Accordingly, the detailed description to be described below is not intended to be taken in a limiting sense, and the scope of the present invention, if properly described, is limited only by the appended claims, along with all scopes equivalent to those claimed by the claims. Like reference numerals in the drawings refer to the same or similar functions over several aspects.

Unless otherwise indicated in this disclosure or clearly contradicting the context, items referred to in the singular encompass the plural unless otherwise required by that context. In addition, in describing the present invention, if it is determined that a detailed description of a related known configuration or function may obscure the subject matter of the present invention, a detailed description thereof will be omitted.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings in order to allow those skilled in the art to easily implement the present invention.

1 is a diagram schematically illustrating a method for assisting visualization of a lesion according to the present disclosure.

1A is an example of an interface 110 that provides a lung image in which a lesion mask indicating a lesion area is overlapped, and the interface 110 corresponds to various viewpoints through sub-screens 120, 130, 140, 150 Provides medical images that can be used. In the medical image provided through each of the sub-screens 120, 130, 140, and 150, the lesion area may be visualized through the lesion masks 121, 131, 141, and 151. A reader who reads a medical image can observe the lesion area from various angles through the sub-screens 120, 130, 140, and 150, and through this, can more accurately read the lesion diameter and volume.

The method of assisting lesion visualization according to an embodiment may provide an interface for more simply modifying a lesion mask through a single user input such as a click or a touch.

Specifically, as shown in (b), when receiving a user input 162 for adding a region to the lesion mask 161 in the medical image 160 to which the lesion mask 161 is applied, image lesion visualization assistance The apparatus may generate a lesion mask 171 in which a region selected by the user input 162 is added to the lesion mask 161 and provide a medical image 170 in which the generated lesion mask 171 is overlapped.

In addition, when receiving a user input 182 for removing a region from the lesion mask 181 in the medical image 180 to which the lesion mask 181 has been applied, the image lesion visualization assistant device uses the user input 182. A medical image 190 in which the lesion mask 191 from which the selected region has been removed may be overlapped.

In the process of modifying the lesion mask, compared to the conventional method in which a user input of directly coloring or erasing all areas to be corrected is performed, the lesion visualization assist method of the present invention provides a lesion through simple user input such as single click and touch. It is possible to provide a means to efficiently modify the mask.

FIG. 2 is a conceptual diagram schematically illustrating an exemplary configuration of a computing device that performs a method of assisting visualization of a lesion according to the present disclosure.

The computing device 200 according to an embodiment of the present invention includes a communication unit 210 and a processor 220, and may directly or indirectly communicate with an external computing device (not shown) through the communication unit 210. .

Specifically, the computing device 200 is a device that may include components of typical computer hardware (eg, computer processor, memory, storage, input device and output device, and other conventional computing devices; routers, switches, etc.) Electronic communication devices; electronic information storage systems such as network-attached storage (NAS) and storage area networks (SANs) and computer software (i.e., allowing computing devices to function in a specific way) Instructions) to achieve the desired system performance.

The communication unit 210 of such a computing device can transmit and receive requests and responses to and from other computing devices to which it is linked. As an example, such requests and responses may be made by the same transmission control protocol (TCP) session. However, the present invention is not limited thereto, and may be transmitted/received as, for example, a user datagram protocol (UDP) datagram. In addition, in a broad sense, the communication unit 210 may include a keyboard for receiving commands or instructions, a pointing device such as a mouse, other external input devices, printers, displays, and other external output devices.

In addition, the processor 220 of the computing device includes a micro processing unit (MPU), a central processing unit (CPU), a graphics processing unit (GPU) or a tensile processing unit (TPU), a cache memory, and a data bus. ), and the like. In addition, it may further include an operating system and a software configuration of an application that performs a specific purpose.

3 is an exemplary block diagram illustrating a hardware or software component of a computing device that performs a method for assisting lesion visualization according to the present disclosure.

A brief overview of the configuration of the method and apparatus according to the present disclosure with reference to FIG. 3, the computing device 200 may include an image acquisition module 310 as its component. The image acquisition module 310 is configured to acquire a medical image to which the method according to the present disclosure is applied. The individual modules shown in FIG. 3 are, for example, the communication unit 210 included in the computing device 200 or It will be understood by those of ordinary skill in the art that it may be implemented by interlocking the processor 220 or the communication unit 210 and the processor 220. The medical image may be obtained from an external image storage system such as an imaging device or a medical image storage and transmission system (PACS) interlocked through the communication unit 210, but is not limited thereto. For example, a medical image may be captured by a (medical) imaging device, transmitted to PACS according to the DICOM standard, and then acquired by the image acquisition module 310 of the computing device 200.

Next, the acquired medical image is transmitted to the lesion determination module 320, and the lesion determination module 320 is configured to determine a region of the suspected lesion, which is an area suspected as a lesion in the medical image. The lesion determination module 320 may be a lesion determination model for determining whether each pixel of the medical image corresponds to a lesion region, or at least a module associated therewith.

An example of the lesion determination module or the lesion determination model 320 may be a deep learning model, which can be briefly described in the form of stacking artificial neural networks in multiple layers. In other words, it is expressed as a deep neural network (deep neural network) in the sense of a deep network, and automatically learns the features of each image by learning a large amount of data in a structure consisting of a multi-layered network. It is a form of learning the network in a way that minimizes the error of the objective function. This is compared to the connections between neurons in the human brain, and such deep neural networks are becoming the next generation model of AI. Among these deep learning models, in particular, the convolutional neural network (CNN) is a model suitable for image classification, and is a convolutional layer that creates a feature map using a plurality of filters for each region of the image. layer) and a sub-sampling layer that reduces the size of the feature map to extract features that are invariant to changes in position or rotation, from low-level features such as points, lines, and planes to complex and meaningful Since it is possible to extract features of various levels up to the high-level features that are present, it is possible to extract suspicious lesions, which are parts with specific features, from medical images.

However, one of ordinary skill in the art may understand that the lesion determination module or the lesion determination model is not limited to such a CNN, and various kinds of machine learning models or statistical models may be used. Specifically, the lesion determination module 320 may perform any one of a rule-based neural network, a learning-based neural network, or a method in which a user directly labels a lesion area. It may be to determine the lesion through.

Based on the result of the lesion determination by the lesion determination module 320, the lesion mask generation module 330 may generate a lesion mask corresponding to the lesion area. The lesion mask may be an element that enables a reader to easily identify the lesion area by displaying a lesion area included in the medical image as an element distinct from the medical image. For example, the lesion mask may be one in which a predetermined lesion area is displayed with a predetermined color as shown in FIG. 1, but the shape of the lesion mask is not limited to the presented example, and a medical image and a lesion area can be distinguished. Can be created in any way.

The output module 340 may provide, for example, display a lesion mask superimposed on a medical image through a predetermined output device to be connected through the communication unit 210.

The lesion mask provided through the output module 340 may be modified according to the opinion of the reader, and the reader may exclude some regions of the lesion mask from the lesion mask or add adjacent regions to the lesion mask through a single click or touch. have.

The user input acquisition module 350 may receive a user input for correction of a lesion mask through the communication unit 210, and the lesion mask correction module 360 may be configured to the lesion mask generation module 330 based on the received user input. The lesion mask created by it can be modified.

The output module 340 may superimpose and output a medical image and a lesion mask modified through the lesion mask correction module 360 on an external entity through an output device connected through the communication unit 210.

Here, the external entity includes a user of the computing device 200, an administrator, a medical professional in charge of the subject, etc., but in addition to this, any subject that needs to view or read the medical image It should be understood to be included.

Detailed functions and effects of each of the components shown in FIG. 3 will be described later in detail. Although the components shown in FIG. 3 have been illustrated to be realized in one computing device for convenience of description, it will be understood that the computing device 200 performing the method of the present invention may be configured such that a plurality of devices are interlocked with each other.

Now, an embodiment of a method of assisting lesion visualization according to the present disclosure will be described in more detail with reference to FIGS. 4 to 6.

4 is a flowchart schematically illustrating a method for assisting lesion visualization according to the present disclosure, and FIG. 4 is a conceptual diagram schematically illustrating an embodiment of a method for assisting lesion visualization according to the present disclosure.

Referring to FIG. 4, in the method of assisting lesion visualization according to the present disclosure as a whole, when a user input for modifying a lesion mask provided through the lesion mask generation module 330 is detected (S100), the computing device may modify the lesion mask. Using the module 360, it may be determined whether the point where the detected user input is performed corresponds to the existing lesion mask area.

If the point where the user input is performed in step S200 corresponds to the inner area of the lesion mask, the lesion mask correction module 360 is based on the lesion mask excluding the selected area determined based on the user input through step S300. Thus, a modified lesion mask can be created. For example, the selection area may be determined as an area corresponding to a preset radius based on the point where the user input is performed, but the method of determining the selection area based on the user input is selected based on the point where the user input is performed. It can include any way of determining the area.

According to an embodiment, the lesion mask modification module 360 is based on a lesion mask from which a selection region is excluded from among a plurality of lesion masks generated by inputting information on an existing lesion mask and a selection region into a previously learned artificial neural network. A modified lesion mask can be created. More specifically, the lesion mask modification module 360 is a pre-modified lesion among a plurality of lesion masks generated by inputting information on an existing lesion mask and information on a 3D patch corresponding to a selected area to a previously learned artificial neural network. A modified lesion mask may be generated based on a lesion mask including a region of the mask and not including a selection region determined based on a user input. The computing device may provide, for example, display, to an external entity, a medical image in which the lesion mask, which is modified through the output module 340 in operation S500, is superimposed.

According to another embodiment, the lesion mask modification module 360 includes a plurality of lesion masks from among a plurality of lesion masks generated by inputting information on an existing lesion mask and information on a selection region into a previously learned artificial neural network. A candidate lesion mask of may be provided to the user, and any one candidate lesion mask selected based on a user input may be determined as a final lesion mask. For example, the lesion mask correction module 360 may determine a selection region based on different radii based on a point where a user input is performed, and determine a lesion mask from which each selection region is excluded as a candidate lesion mask. . The method of selecting a candidate lesion mask is not limited to the presented example, and may include an arbitrary method of determining a plurality of candidate lesion masks from which a selection region according to a user input is excluded among lesion masks generated through an artificial neural network. .

When the point where the user input is performed in step S200 corresponds to the outer area of the lesion mask, the lesion mask correction module 360 further includes a selection area determined based on the user input through the step S300. A modified lesion mask can be created based on the mask.

According to an embodiment, the lesion mask modification module 360 includes a lesion mask including a selection region among a plurality of lesion masks generated by inputting information on an existing lesion mask and information on a selection region into a previously learned artificial neural network. A modified lesion mask can be created based on. More specifically, the lesion mask modification module 360 is a lesion mask before modification among a plurality of lesion masks generated by inputting information on a medical image to which an existing lesion mask is applied and information on a selection area to a previously learned artificial neural network. A modified lesion mask may be generated based on the lesion mask including the region and the selected region. In operation S500, the computing device may provide a medical image in which the modified lesion mask is overlapped through the output module 340 to the external entity.

According to another embodiment, the lesion mask modification module 360 inputs information on a medical image to which an existing lesion mask is applied and information on a selection area to a pre-learned artificial neural network, and before modification among a plurality of lesion masks generated A plurality of candidate lesion masks including a region of the lesion mask and a selection region may be provided to a user, and any one candidate lesion mask selected based on a user input may be determined as a final lesion mask.

5 is a diagram illustrating an example of an artificial neural network used in the method for assisting lesion visualization according to the present disclosure.

The artificial neural network 510 shown in FIG. 5 outputs a plurality of lesion masks 530 through a 3D patch 521 corresponding to a selection region determined based on information on the existing lesion mask 522 and a user input. can do.

The artificial neural network 510 may be generated based on a combination of the U-Net 511 used for medical image segmentation and the conditional variational auto encoder (CVAE) 512. The CVAE 512 may include a map 513 in which a segmentation variant is encoded at each location and a dictionary network 514 that estimates a probability for each segmentation variant based on an input.

,

,

, ...)을 추출하고, 인공 신경망(510)은 추출된 샘플 및 U-Net(511)로부터의 출력에 기초하여 병변 마스크(530)를 생성할 수 있다.In the process of generating each lesion mask 530, the CVAE 512 of the artificial neural network 510 is a sample encoded in the map 513 (

,

,

, ...), and the artificial neural network 510 may generate the lesion mask 530 based on the extracted sample and the output from the U-Net 511.

The artificial neural network 510 may be trained by determining an optimal embedding for a segmentation variant based on ground truth data and a lesion mask predicted through training data.

6 is a diagram illustrating an example to which a lesion visualization method according to the present disclosure is applied.

Referring to FIG. 6A, a reader may perform a user input 612 including a predetermined region in the lesion mask in the medical image 610 in which the lesion mask 611 is overlapped. The computing device detecting the user input 612 uses a pre-learned artificial neural network to determine the reader's intention based on a predetermined condition among a lesion mask 611 and a plurality of lesion masks generated through the user input 612. A medical image 620 in which a lesion mask determined to be best reflected may be displayed. The lesion mask that best reflects the reader's intention may correspond to a lesion mask in which a selection region determined based on the user input 612 is added or excluded without missing. If the displayed medical image 620 does not conform to the intention, the reader may perform an additional user input for correction to the medical image 6230, and the computing device may perform a modified lesion mask based on the additional user input and the lesion mask. Can provide. Modification of the lesion mask may be repeated until the reader does not proceed with further modification requests.

6(b) shows an example to which a lesion visualization assist method according to another embodiment is applied. When a reader performs a user input 632 for correcting the lesion mask 631 of the provided medical image 630, the computing device detecting the user input 632 uses a pre-learned artificial neural network to input the user input ( A plurality of lesion masks in which the 632) is reflected may be generated, and predetermined candidate lesion masks 641, 642, 643 in which the reader's intention is best reflected among the generated lesion masks may be provided. The computing device may display a medical image 650 in which a lesion mask 641 selected by a reader among the candidate lesion masks 641, 642, and 643 is overlapped.

Based on the description of the above embodiment, one of ordinary skill in the art can realize that the method and/or processes of the present invention, and the steps thereof, can be implemented in hardware, software, or any combination of hardware and software suitable for a specific application. Can understand the point clearly. The hardware may include a general-purpose computer and/or a dedicated computing device, or a specific computing device or special features or components of a specific computing device. The processes may be realized by one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, with internal and/or external memory. In addition, or as an alternative, the processes can be configured to process application specific integrated circuits (ASICs), programmable gate arrays, programmable array logic (PAL) or electronic signals. May be implemented with any other device or combination of devices. Furthermore, the objects of the technical solution of the present invention or parts contributing to the prior art may be implemented in the form of program instructions that can be executed through various computer components and recorded in a machine-readable recording medium. The machine-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the machine-readable recording medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in the computer software field. Examples of machine-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROM, DVD, Blu-ray, and magnetic-optical media such as floptical disks. (magneto-optical media), and hardware devices specially configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include a processor, a processor architecture, or a heterogeneous combination of different hardware and software combinations, as well as any one of the aforementioned devices, or storage and compilation or interpreting to be executed on a machine capable of executing any other program instructions. Can be made using a structured programming language such as C, an object-oriented programming language such as C++, or a high-level or low-level programming language (assembly, hardware description languages and database programming languages and technologies), machine code, This includes not only bytecode but also high-level language code that can be executed by a computer using an interpreter or the like.

Thus, in an aspect according to the present disclosure, when the above-described method and combinations thereof are performed by one or more computing devices, the method and combinations of methods may be implemented as executable code that performs each step. In another aspect, the method may be implemented as systems that perform the steps, and the methods may be distributed in several ways across devices or all functions may be integrated into one dedicated, standalone device or other hardware. In yet another aspect, the means for performing the steps associated with the processes described above may include any hardware and/or software described above. All such sequential combinations and combinations are intended to be within the scope of this disclosure.

For example, the hardware device may be configured to operate as one or more software modules to perform processing according to the present disclosure, and vice versa. The hardware device may include a processor such as MPU, CPU, GPU, TPU, which is coupled with a memory such as ROM/RAM for storing program instructions and configured to execute instructions stored in the memory, and may include an external device and a signal It may include a communication unit that can send and receive. In addition, the hardware device may include a keyboard, a mouse, and other external input devices for receiving commands written by developers.

In the above, the present invention has been described by specific matters such as specific components and limited embodiments and drawings, but this is provided only to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments, Anyone with ordinary knowledge in the technical field to which the present invention pertains can make various modifications and variations from this description.

Therefore, the spirit of the present invention is limited to the above-described embodiments and should not be defined, and all modifications equivalently or equivalently to the claims as well as the claims to be described later fall within the scope of the spirit of the present invention. I would say.

Such equivalently or equivalently modified ones will include, for example, a logically equivalent method capable of producing the same result as that of carrying out the method according to the present disclosure. The scope should not be limited by the examples described above, but should be understood in the broadest possible sense by law.

Claims (1)

In a method of assisting the visualization of lesions in a medical image,
Receiving a user input for selecting a point of the medical image in order to modify a lesion mask indicating a lesion area shown in the medical image;
Generating a modified lesion mask in response to the received user input; And
Providing the medical image and the modified lesion mask together
Containing, lesion visualization auxiliary method.

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