KR101943011B1 - Method for facilitating medical image reading and apparatus using the same - Google Patents

Abstract

The present invention relates to a method for supporting medical image reading on a subject and an apparatus using the same in order to increase the reading efficiency. Specifically, according to the present invention, the method comprises the steps of: obtaining a medical image of a subject by a computing apparatus; generating reading assist information on the medical image based on a lesion determination model from an input of the medical image; providing a lesion item in which at least one of sorting and filtering processes is performed to an external entity; and repeating a process of re-performing at least one of the sorting and filtering processes in response to a predetermined operation.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a medical image reading method,

The present invention relates to a method for supporting reading of a medical image of a subject and an apparatus using the same. Specifically, according to the method of the present invention, the computing device acquires a medical image of a subject and, based on a lesion determination model from the input of the medical image, Providing an external entity with a lesion item having at least one of sorting and filtering and performing at least one of the sorting and filtering in response to a predetermined operation, Repeat.

Currently, medical images such as CT (computed tomography) are widely used for diagnosis by analyzing lesions. For example, chest CT images are frequently used for reading because they can observe the internalities of the body, such as lungs, bronchi, and heart.

Some of the findings that can be read through chest CT images can be easily overlooked by a human surgeon because the radiologist can not distinguish the features and types of the radiologist through years of training. In particular, if the degree of difficulty of reading is high, such as a lung nodule, a doctor may fail to observe even if he / she takes a high level of attention, which may cause a problem.

The need for computer aided diagnosis (CAD) has aroused the need to assist in the reading of images that humans can easily overlook, and conventional CAD technology aids physician judgment in very limited areas . For example, Korean Patent Laid-Open Publication No. 10-2014-0091176 discloses an apparatus and a method for assisting a conventional lesion diagnosis.

Reading a lesion using computer assisted diagnosis can be done by first identifying a suspected lesion site and evaluating the score (e.g., confidence, malignity, etc.) for that lesion. For example, when a plurality of nodules are found in the lung, it may be necessary to identify a nodule that is expected to have a high degree of malignancy to determine the treatment plan.

However, because there are a plurality of nodules, it is not possible to know which nodule is most malignant before the reading, and when the diagnosis is performed from the nodule which is not high in actual malignancy or expected to be malignant, It is sporadic. In addition, even if diagnosis is performed from a part where it is predicted that a certain nodule is not actually a nodule because it is difficult to know before the actual nodule is read and the reliability is not high, the reading efficiency is low.

Accordingly, in order to solve such a problem, the present invention introduces a scoring system for a conventional lesion detection system and allows reading out from lesions having the highest score (for example, reliability, malignancy, etc.) And a device using the same.

KR 10-2014-0091176 A

An object of the present invention is to make it possible to read out lesions having the highest score according to a score evaluation method when a plurality of lesions are detected in a medical image, thereby improving the read efficiency.

Specifically, the present invention aims to provide a user interface that can conveniently control a threshold value for a score, so that it can concentrate on lesions that are important for reading.

As a result, the present invention aims at enhancing the accuracy of analysis by assisting the doctor in deriving a more accurate diagnosis result in less time.

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 below is as follows.

According to an aspect of the present invention, there is provided a method of supporting reading of a medical image of a subject, the method comprising: (a) the computing device acquiring a medical image of the subject, To acquire another device associated with the first device; (b) supporting the computing device to generate or generate read assistance information on the medical image based on a lesion determination model from input of the medical image, The location of the lesion as a lesion item for the lesion, and at least one score for the lesion; (c) when the read assistance information is generated, supporting the computing device to provide or provide an external entity with a lesion item comprising at least one of sorting and filtering; And (d) in response to a predetermined operation, the computing device repeating step (c) by performing at least one of the sorting and the filtering again.

According to another aspect of the present invention, there is also provided a computer program stored in a machine readable non-transitory medium, comprising instructions embodied to perform a medical image read support method in accordance with the present invention.

According to still another aspect of the present invention, there is provided a computing device for supporting reading of a medical image of a subject, the computing device comprising: a communication unit for acquiring a medical image of the subject; And (i) a process of generating read assistance information on the medical image based on a lesion determination model from the input of the medical image or assisting generation of other assistant devices through the communication unit, ii) a process for providing or providing an external entity with a lesion item in which at least one of sorting and filtering is generated when said read assistance information is generated, and iii) (Ii) repeating the process by performing at least one of the alignment and the filtering, wherein the read assistance information comprises a position of the lesion as a lesion item for the individual lesion, And includes at least one score.

According to the present invention, when a plurality of lesions are detected in a medical image, it is possible to read out the lesions having the highest score according to the scoring system without significantly changing the existing reading process, So that it is possible to concentrate on the lesion that is important for the reading, thereby improving the reading efficiency.

Therefore, according to the present invention, the efficiency of reading is increased and the doctor can obtain a more accurate diagnosis result in less time. As a result, the speed and quality of reading are improved and the workflow in the medical field is innovated There is an ultimate effect that can be achieved.

In addition, the present invention can be applied to systems such as medical images used in hospitals in the past, for example, three-dimensionally acquired ultrasound images, MRI images, and the like. Of course, it is not dependent.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, The figures can be obtained.
1 is a conceptual diagram schematically illustrating an exemplary configuration of a computing device that performs a method for supporting reading of a medical image of a subject according to the present invention (hereinafter referred to as " method for assisting reading of a medical image ").
2 is an exemplary block diagram illustrating the hardware or software components of a computing device that performs a method of supporting medical image reading in accordance with the present invention.
3 is a conceptual view illustrating a medical image reading support method according to the present invention.
4 is a diagram illustrating a user interface provided in a medical image reading support method according to the present invention.
FIG. 5 is a conceptual diagram illustrating an appearance in which components such as a lesion item are arranged in an exemplary user interface provided according to the present invention.
6 is a conceptual diagram illustrating an example of changing alignment and filtering of a lesion item through components provided in an exemplary user interface provided in accordance with the present invention.

The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of example, specific embodiments in which the invention may be practiced in order to clarify the objects, technical solutions and advantages of the invention. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention.

The term " image " or " image data " used throughout the description and claims of the present invention refers to multidimensional data composed of discrete image elements (e.g., pixels in a two-dimensional image and voxels in a three- Quot; For example, " imaging " may be computed by (cone-beam) computed tomography, magnetic resonance imaging (MRI), ultrasound, or any other medical imaging system known in the art I.e., a medical image of a subject. The images may also be provided in a non-medical context, for example, a remote sensing system, an electron microscopy, and the like.

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

For convenience of illustration, cone-beam computed tomography (CBCT) image data is sometimes shown as an exemplary image modality in the drawings. However, those skilled in the art will appreciate that image formats used in various embodiments of the present invention may be used in various imaging formats such as X-ray imaging, MRI, CT, positron emission tomography (PET), PET-CT, SPECT, SPECT-CT, MR- But it should be understood that the invention is not limited to the examples enumerated.

Throughout the detailed description and claims of the present invention, the term 'DICOM' (Digital Imaging and Communications in Medicine) is a generic term for various standards used in digital image representation and communication in medical devices, The standard is presented at the Joint Committee composed of the American Radiation Medical Association (ACR) and the American Electrical Manufacturers Association (NEMA).

In addition, the term 'Picture Archiving and Communication System (PACS)' refers to a system for storing, processing and transmitting according to the DICOM standard throughout the detailed description and claims of the present invention, , And MRI can be stored in the DICOM format and transmitted to a terminal inside or outside the hospital through the network, and the result of reading and the medical record can be added to the terminal.

Throughout the detailed description and claims of the present invention, 'learning' or 'learning' refers to performing machine learning through computing according to a procedure, It will be understood by those of ordinary skill in the art that the present invention is not intended to be so-called.

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

Other objects, advantages and features of the present invention will become apparent to those skilled in the art from this description, and in part from the practice of the invention. The following examples and figures are provided by way of illustration and are not intended to limit the invention. Accordingly, the details disclosed herein with respect to a particular structure or function are not to be construed in a limiting sense, but merely as being representative of the general inventive concept providing a guideline for carrying out the invention in various detail structures, It should be interpreted as basic data.

Moreover, the present invention encompasses all possible combinations of embodiments shown herein. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with one embodiment. It should also be understood that the position or arrangement of individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly explained. In the drawings, like reference numerals refer to the same or similar functions throughout the several views.

Unless otherwise indicated herein or clearly contradicted by context, items referred to in the singular are intended to encompass a plurality unless otherwise specified in the context. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily carry out the present invention.

1 is a conceptual diagram schematically illustrating an exemplary configuration of a computing device that performs a medical image reading support method according to the present invention.

1, a computing device 100 according to an embodiment of the present invention includes a communication unit 110 and a processor 120. The communication unit 110 communicates with an external computing device (not shown) Communication is possible.

In particular, the computing device 100 may be implemented as a computer-readable medium, such as conventional computer hardware (e.g., a computer processor, memory, storage, input and output devices, Electronic communication devices, electronic information storage systems such as network-attached storage (NAS) and storage area networks (SAN), and computer software (i.e., computing devices that enable a computing device to function in a particular manner) Commands) to achieve the desired system performance.

The communication unit 110 of the computing device can send and receive requests and responses to and from other interworking computing devices. As an example, such requests and responses can be made by the same transmission control protocol (TCP) session But not limited to, a user datagram protocol (UDP) datagram, for example. In addition, in a broad sense, the communication unit 110 may include a keyboard, a mouse, an external input device, a printer, a display, and other external output devices for receiving commands or instructions.

The processor 120 of the computing device may also be a micro processing unit (MPU), a central processing unit (CPU), a graphics processing unit (GPU), or a tensor processing unit (TPU), a cache memory, a data bus ). ≪ / RTI > It may further include a software configuration of an operating system and an application that performs a specific purpose.

2 is an exemplary block diagram illustrating the hardware or software components of a computing device that performs a method of supporting medical image reading in accordance with the present invention.

2, the computing device 100 may include an image acquisition module 210 as a component thereof. The individual modules shown in FIG. 2 may be implemented, for example, by a communication unit 110 included in the computing device 100, a processor 210 included in the computing device 100, The communication unit 110 and the processor 120 may be implemented by interlocking the communication unit 110 or the communication unit 120 or the communication unit 110 and the processor 120, respectively. The medical image may be obtained from, for example, an external image storage system such as a radiographic imaging apparatus or a medical image storage transmission system (PACS) linked through the communication unit 110, but is not limited thereto. For example, the medical image may be captured through a medical imaging device, transmitted to the PACS according to the DICOM standard, and then acquired by the image acquisition module 210 of the computing device 100.

Next, the obtained medical image may be transmitted to the lesion judgment module 220. The lesion judgment module 220 judges, based on the lesion determination model, the read assistance information on the medical image Respectively.

An example of a lesion judgment model is a deep learning model, which can be briefly described as a stack of artificial neural networks. In other words, it is expressed as a deep neural network (deep neural network) in the sense of a network of deep structure. By learning a large amount of data in a structure composed of a multi-layer network, the characteristics of each image are automatically learned, It is a form that learns the network in a way that minimizes the error of the objective function, that is, the accuracy of judgment of the lesion. This is compared to the connections between neurons in the human brain, and such a neural network is becoming a next generation model of AI. Among these deep learning models, CNN (Convolutional Neural Network) is a model suitable for classification of images. It is a convolutional neural network that generates a feature map by using a plurality of filters for each region of an image. layer and feature map By reducing the size of the map and repeating the sub-sampling layer to extract features that are invariant to changes in position or rotation, it is possible to search for features ranging from low-level features such as points, lines and surfaces to complex and meaningful high-level features It is possible to extract features of various levels and using the finally extracted feature as an input value of the existing judgment model, it is possible to construct a judgment model with higher accuracy.

However, it will be understood by those of ordinary skill in the art that a lesion judgment model is not limited to such CNN, and various types of lesion judgment models can be used.

Once the read assistance information is generated, it may be communicated to the sorting and filtering module 230, wherein the read assistance information may be at least one of sorting and filtering for the read assistance information and then storing the resulting lesion item, (240), and the like to an external entity. When provided to an external entity, the user interface module 240 may pass through a predetermined display device or the like, and the reading assistance information or the storage of the lesion item may be performed by another device linked to the computing device 100, for example, a PACS .

The user interface module 240, in response to a predetermined operation, causes the sorting and filtering module 230 to perform at least one of the sorting and filtering again. The read assistance information, the lesion item, the predetermined operation, the sorting and filtering, and the like will be described later in detail.

Although the components shown in FIG. 2 are illustrated as being realized in one computing device for convenience of explanation, it will be understood that a plurality of computing devices 100 performing the method of the present invention may be configured to be interlocked with each other.

An embodiment of a medical image reading support method according to the present invention will now be described in more detail with reference to FIGS.

FIG. 3 is a conceptual view of a medical image reading support method according to the present invention, and FIG. 4 is a view illustrating a user interface provided in a medical image reading support method according to the present invention.

3 and 4, a method for supporting a medical image reading according to the present invention includes a step of acquiring a medical image of a subject or acquiring a medical image of the subject through the communication unit 110 (S100) for receiving input from other apparatuses connected to the computing device 100 via the wireless communication interface (not shown), such as in the user interface exemplified in FIG. 4, image of chest CT 410).

In the present invention, detection of a lung-related lesion such as a nodule is exemplified for convenience of explanation, but it is not limited to this, and it can be understood that it is generally applicable to various lesions.

Next, a medical image reading support method according to the present invention is characterized in that the lesion judgment module 220 implemented by the computing device 100 detects a medical image based on a lesion judgment model from input of the medical image, And generating (S200) generating the read assistance information or causing the other device to generate the read assistance information. Here, the read assistance information may include the position of the lesion as a lesion item for the individual lesion, and at least one score for the lesion. For example, the score may include at least one of the confidence that the lesion is the actual lesion and the malignancy of the lesion. Or the score may be calculated based on a number of score factors including at least one of reliability and maliciousness. These scores are for ordering or ranking the readings for lesions.

3, a medical image read support method according to the present invention includes a sorting and filtering module 230 implemented by the computing device 100 when the read assistance information is generated, (S300) of providing or providing to the external entity the lesion item in which at least one of alignment and filtering has been performed.

Here, the sorting is performed based on a predetermined ranking, and the predetermined ranking may be a ranking reflecting the ranking according to the score. In addition, the filtering may be performed based on a predetermined threshold value, and a lesion having a score that does not reach a predetermined threshold value may be excluded by filtering.

In step S300, the lesion item is stored in a storage and transmission module 250 (not shown) and / or provided to an external entity through a user interface module 240 utilizing a predetermined display device or the like And / or may be provided to other devices, such as PACS, that are interfaced to the computing device 100.

Here, the external entity includes a user of the computing device 100, a manager, a medical specialist in charge of the subject, and the like, but may also include a medical image, a read assistance information, a lesion item, The subject should be understood to include any subject. For example, the external entity may be an external AI device comprising separate AI hardware and / or software modules. Also, 'external' in an external entity is intended to exclude embodiments in which AI hardware and / or software modules utilizing the medical image, read assistance information, lesion items, etc. are integrated into the computing device 100 But is used to suggest that the output of the hardware and / or software modules performing the method of the present invention may be utilized as input data of other methods. That is, the external entity may be the computing device 100 itself.

The information thus generated can be used to facilitate the reading and diagnosis of a doctor.

Meanwhile, the lesion item generated in this step S300 can be provided as a lesion list as indicated by reference numeral 420 on the user interface illustrated in FIG. 4. As shown in FIG. 4, the lesion item is illustrated in an upper right portion. However, .

FIG. 5 is a conceptual diagram more specifically illustrating an appearance in which components such as a lesion item are arranged in an exemplary user interface provided according to the present invention.

Referring to FIG. 5, in step S300, a region of interest (ROI) 424 of a medical image corresponding to each of the lesion items together with each of the lesion items 422 may be provided through a user interface have. Such a region of interest may be provided, for example, as a cross-sectional photograph of a lesion. As illustrated in FIG. 5, the lesion item 422 includes a physical quantity of the lesion, for example, information such as volume and diameter, a serial number of the axial slice image (when composed of a plurality of slice images such as a CT image) Etc.), and the like.

In FIG. 5, scores 426 corresponding to each of the lesion items 422 are also illustrated, and are arranged in descending order so that the user can easily view the score. A scroll bar 428 may also be provided for viewing a large number of lesion items. Regardless of the total number of lesions detected, the number of lesion items displayed on a screen can be set to maintain a fixed width of the lesion list. For example, FIG. 5 shows an example in which the number of the lesion items displayed on one screen is 5, and operations such as scrolling and dragging can be performed to view more than 5 lesion items detected.

Step S300 Next, the medical image reading support method according to the present invention is a method in which, in response to a predetermined operation, the user interface module 240 causes the sorting and filtering module 230 to perform at least one of the sorting and the filtering (S400) by repeating the step (S300) by causing the computer to perform the operation again.

Figure 6 is a conceptual diagram illustrating an example of changing the alignment and filtering of a lesion item in step S400 through the components provided in an exemplary user interface provided in accordance with the present invention.

Step S400 may be performed as the threshold value is changed in response to a predetermined operation. In FIG. 6, a slider 430 capable of changing the threshold is illustrated as a component of the user interface. This is to allow the threshold value to be increased or decreased through the drag operation of the mouse or the like. The predetermined operation is not limited to the drag operation of the mouse but may include various operations such as a scrolling operation of an input device such as a mouse and an operation of inputting a predetermined hot key. For example, there may be an operation of rotating the wheel as a scrolling operation of the mouse. At this time, the threshold value can be increased or decreased according to the rotation angle of the wheel.

As such, as the threshold value changes, the filtering will be performed again based on the threshold value, e.g., a lesion item having a score that does not reach a threshold value may be excluded from the lesion list.

The threshold value may be continuously or discontinuously (i.e., discrete) increased or decreased in accordance with the predetermined operation, and in the case of discontinuous increase or decrease, the interval of the increase or decrease may be a predetermined value.

Also, as exemplarily shown in FIG. 6, the number of lesion items 440 may be displayed as one component of the user interface, which means the number of lesion items included in the lesion list provided through sorting and filtering .

In step S400, the number of the lesion items may be changed in response to a predetermined operation, and filtering may be performed again. For example, the filtering may be to select a predetermined number of lesion items from the order of highest score, wherein the predetermined number (the number of lesion items) may be increased, decreased, or changed according to a predetermined operation have.

As an example, if the number of lesion items 440 is forced by the user, a threshold value corresponding to the number of lesion items may be automatically selected. In this case, the illustrated slider 430 indicates its automatically selected threshold value. A fixing operation for preventing the threshold value or the number of the lesion items from being changed by the operation mistake can be made by a predetermined operation.

Preferably, the number (440) of these lesions can be set to a value that is less than or equal to a minimum of five, and the maximum number of lesion items, which can generally be determined accurately by examining five lesions with the highest malignancy , And the total number of lesions may be less than 5 (among the papers published in NEJM, " Probability of Cancer in Pulmonary Nodules Detected on First Screening CT ", web page http://www.nejm.org/doi/full/ 10.1056 / NEJMoa1214726}. The present invention can be applied to other natural numbers other than 5.

Preferably, the medical image reading support method according to the present invention further comprises a step of, in response to a predetermined operation, causing the computing device 100 to change at least one of (i) the lesion determination model and (ii) , And repeating the above-described steps (S200) to (S400) (S500 (not shown)).

For example, a well-known approach such as Lung-RADS ™ can be used to generate maliciousness as a score, and other methods can be used to facilitate readability. In addition, there may be lesion judgment models developed by various developers, so that it is possible to reduce the labor required to operate different software if the user can select the models according to the preference of the user. In Figure 6, radio buttons are illustratively provided as components of a user interface for such selection. Using the radio button illustrated in FIG. 6, the user may select a method of generating scores using either the Lung-RADS ™ score or the VUNO Malignancy Risk Calculator. Of course, such a choice would be illustrative and would allow selection of a variety of scoring schemes and lesion judgment models.

As such, the present invention has the advantage of being able to filter and sort lesions conveniently and quickly when a human (e.g., a reader) uses a computer readiness assistance system over all of the embodiments and variations described above. It is possible to exclude the image of the part of the lesion which is not suspected to be a lesion, and it is possible to reduce the trouble of reading and to make a quick and accurate diagnosis. As a result, the quality of the medical treatment can be enhanced You can do it.

Based on the description of the embodiments above, those skilled in the art will recognize that the methods and / or processes of the present invention and their steps may be implemented in hardware, software, or any combination of hardware and software suitable for the particular application Points can be clearly understood. The hardware may include special features or components of a general purpose computer and / or a dedicated computing device or a specific computing device or a particular computing device. The processes may be realized by one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices having internal and / or external memory. Additionally or alternatively, the processes can be configured to process application specific integrated circuits (ASICs), programmable gate arrays, programmable array logic (PAL) Or any other device or combination of devices. Furthermore, the objects of the technical solution of the present invention, or portions contributing to the prior art, may be implemented in the form of program instructions that can be executed through various computer components and recorded on a machine-readable recording medium. The machine-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination. The program instructions recorded on the machine-readable recording medium may be those specially designed and constructed for the present invention or may be those known to those of ordinary skill in the computer software arts. Examples of the machine-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROM, DVD, Blu-ray, magneto-optical media such as floptical disks magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include, but are not limited to, any of the above devices, as well as a heterogeneous combination of processors, processor architectures or combinations of different hardware and software, Which may be constructed using a structured programming language such as C, an object-oriented programming language such as C ++ or an advanced or low-level programming language (assembly language, hardware description languages and database programming languages and techniques) 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 one aspect of the present invention, when the methods and combinations described above are performed by one or more computing devices, combinations of the methods and methods may be implemented as executable code that performs each of the steps. In another aspect, the method may be implemented as systems for performing the steps, and the methods may be distributed in various ways throughout the devices, or all functions may be integrated into one dedicated, stand-alone device, or other hardware. In yet another aspect, the means for performing the steps associated with the processes described above may include any of the 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 in accordance with the present invention, and vice versa. The hardware device may include a processor, such as an MPU, CPU, GPU, TPU, coupled to a memory, such as ROM / RAM, for storing program instructions and configured to execute instructions stored in the memory, And a communication unit capable of receiving and sending data. In addition, the hardware device may include a keyboard, a mouse, and other external input devices for receiving commands generated by the developers.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, Those skilled in the art will appreciate that various modifications and changes may be made thereto without departing from the scope of the present invention.

Therefore, the spirit of the present invention should not be construed as being limited to the above-described embodiments, and all of the equivalents or equivalents of the claims, as well as the following claims, I will say.

Such equally or equivalently modified means include, for example, a logically equivalent method which can produce the same result as the method according to the present invention, Should not be limited by the foregoing examples, but should be understood in the broadest sense permissible by law.

Claims (11)

A method of supporting reading of a medical image of a subject,
(a) supporting a computing device to acquire a medical image of the subject or acquire another device associated with the computing device;
(b) providing a user interface for providing the medical image and receiving a predetermined operation from a user or supporting the other device to provide the medical image, the method comprising the steps of: (a) A graphic object is provided, and on the other side of the user interface,
A list of lesion items,
A first interface component configured to adjust a predetermined threshold value,
A second interface component configured to display a predetermined number appearing in the list of lesion items, and
A third interface component configured to select any one of at least two lesion determination models for generating read assistance information about the medical image from input of the medical image;
(Ii) a partial image of the lesion; (iii) a serial number of the slice image to which the lesion belongs; and (iii) iv) information of a physical quantity including any one of volume, diameter and radius of the lesion, and (v) at least one score for the lesion;
(c) supporting the computing device to generate or generate the read assistance information based on a lesion determination model;
(d) when the read assistance information is generated, the computing device is operable to cause the at least one of the sorting and filtering at least one of the external entities through the list of lesion items on the user interface, To provide or provide to the user; And
(e) in response to a first predetermined operation on at least one of the first interface component and the second interface component, causing the computing device to perform at least one of the sorting and the filtering again, Repeating the steps;
, ≪ / RTI &
Wherein the filtering is to select only the predetermined number of lesion items from the order of highest score, the predetermined number being increased, decreased or changed in accordance with the predetermined first operation,
(f) modifying, in response to a predetermined second operation on the third interface component, the computing device to change at least one of (i) the lesion determination model and (ii) repeating steps b) to e)
Further comprising:
In the step (d)
Wherein said external entity is provided with said predetermined number of said filtering through said second interface component with said lesion item,
Wherein the step (e) is performed as the predetermined threshold value is changed in response to the predetermined first operation, and the threshold value is continuously or discontinuously increased or decreased in accordance with the predetermined first operation, , The interval of the increase / decrease is a predetermined value,
Wherein the filtering is performed based on the threshold value. delete delete delete delete A computer program stored in a machine-readable non-transitory medium, comprising instructions embodied in a computer-readable medium for causing a computing device to perform the method of any preceding claim. A computing device for supporting reading of a medical image of a subject,
A communication unit for acquiring a medical image of the subject; And
(i) providing a user interface for providing the medical image through a predetermined output device and receiving a predetermined operation from a user, or for supporting other devices interlocked through the communication unit to provide the user interface , A graphical object representing the medical image is provided on one side of the user interface, a first interface component configured to adjust a list of the lesion items, a predetermined threshold value, on the other side of the user interface, A second interface component configured to display a predetermined number of appearances, and at least two lesion determination models for generating read assistance information regarding the medical image from an input of the medical image The configured third interface Wherein the read assistance information comprises a position of a lesion as a lesion item for an individual lesion, a partial image of the lesion, a serial number of the slice image to which the lesion belongs, a volume, diameter and radius of the lesion , And at least one score for the lesion; (ii) generating the read assistance information from an input of the medical image based on the selected lesion determination model, or supporting other devices interlocked through the communication unit to generate, (iii) A process for providing or providing an external entity with a lesion item having at least one of sorting and filtering through the list of lesion items on the user interface when the auxiliary information is generated; iv) repeating the process (iii) by again performing at least one of the sorting and the filtering in response to a predetermined first operation on at least one of the first interface component and the second interface component Processor
, ≪ / RTI &
Wherein the filtering is to select only the predetermined number of lesion items from the highest order of the scores, the predetermined number being increased, decreased, or varied in accordance with the predetermined first operation,
The processor comprising:
(I) to (iv) by changing at least one of (1) the lesion judgment model and (2) the method of generating the score, in response to a predetermined second operation on the third interface component Repeat,
In the process (iii)
Wherein said external entity is provided with said predetermined number of said filtering through said second interface component with said lesion item,
Wherein the step (iv) is performed as the predetermined threshold value is changed in response to the predetermined first operation, and the threshold value is continuously or discontinuously increased or decreased in accordance with the predetermined first operation, , The interval of the increase / decrease is a predetermined value,
Wherein the filtering is performed based on the threshold value. delete 8. The method of claim 7,
Wherein the predetermined first operation is a scrolling operation of the input device. 8. The method of claim 7,
The predetermined first operation is an operation for inputting a predetermined hot key,
Wherein the filtering selects only a predetermined number of lesion items from the highest order of the score. delete

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