KR20220142563A - BRAIN DISEASES DIAGNOSING METHOD USING fMRI - Google Patents

Abstract

According to one embodiment of the present invention, provided is a brain disease diagnosing method using fMRI. The method includes the steps of: obtaining an fMRI image; quantifying the obtained fMRI image; and applying an automatic analysis algorithm to the quantified fMRI image. The step of applying an automatic analysis algorithm includes the steps of: learning a network function through a quantified fMRI image; and analyzing a patient's fMRI image.

Description

Brain disease reading method using fMRI {BRAIN DISEASES DIAGNOSING METHOD USING fMRI}

The present invention relates to a method for reading a brain disease using fMRI.

In general, dementia is a neurodegenerative brain disease that attacks neurotransmitters, brain cells and nerves, affecting brain function, memory and behavior. According to a recent report, around 50 million people worldwide suffer from dementia, and this number is expected to increase to about 152 million by 2050.

Dementia is not easily recognized by changes in the brain until symptoms appear. Most of the time when direct symptoms, such as memory loss or language disorder, appear on the outside, after brain changes have already progressed to some extent. Therefore, accurate diagnosis of dementia is very important for patients.

In general, in diagnosing dementia, changes in brain function along with structural changes in the brain are important for diagnosis. Therefore, for the diagnosis of dementia, PET (Positron Emission Tomography), in which an image is taken by administering a radioactive isotope-labeled tracer into the body, and the distribution of gamma (γ) rays emitted after administering a small amount of radioisotope into the body Image analysis such as SPECT (Single Photon Emission Computed Tomography) to image the brain and fMRI (Functional Magnetic Resonance Imaging) to measure brain activity by detecting changes related to blood flow are used.

Of these, fMRI does not use radioactive isotopes compared to PET or SPECT, so it is possible to take repeated imaging, and although imaging conditions such as fasting and meal control are much easier, it usually takes 2 hours to analyze after image recombination. It has been recognized as a very inconvenient test because it takes time.

Therefore, a method capable of solving such a problem is required.

An object of the present invention is to solve the above problems, and an object of the present invention is to provide a brain disease reading method using fMRI.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

According to an embodiment of the present invention, there is provided a method of reading a brain disease using fMRI. The method comprises: acquiring an fMRI image; quantifying the acquired fMRI image; and applying an automatic analysis algorithm to the quantified fMRI image, wherein the applying the automatic analysis algorithm includes learning a network function through the quantified fMRI image and analyzing the patient's fMRI image can do.

According to the present invention, it is possible to reduce the time consumed for brain image analysis by analyzing the brain image through artificial intelligence.

In addition, according to the present invention, it is possible to assist the diagnosis of dementia by analyzing brain images through artificial intelligence and providing the contents of analysis of brain structures and brain functions to a specialist.

In addition, according to the present invention, by providing a brain disease diagnosis method through artificial intelligence to a picture archiving and communication system (PACS) environment, brain images can be automatically analyzed and provided to a specialist.

In order to more fully understand the drawings recited in the Detailed Description of the Invention, a brief description of each drawing is provided.
1 is a view for explaining a brain disease diagnosis method according to an embodiment of the present invention.
FIG. 2 is a diagram for explaining a method in which the brain disease diagnosis method according to an embodiment of the present invention is applied to a medical image information system environment.
3 is a view for explaining a state in which the brain disease diagnosis method according to an embodiment of the present invention is applied to a medical image information system environment.
4 is a diagram illustrating an example of subdividing a standard brain image according to a method for diagnosing a brain disease according to an embodiment of the present invention.
5 is a diagram illustrating an example of automatically subdividing a brain image for each lobe region according to a method for diagnosing a brain disease according to an embodiment of the present invention.
6 is a diagram illustrating a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied.
7 is a diagram illustrating a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied.
8 is a diagram illustrating a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied.
9 is a diagram illustrating a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied.
10 is a diagram illustrating a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied.
11 is a diagram illustrating an example of automatically subdividing a brain image into subregions according to a method for diagnosing a brain disease according to an embodiment of the present invention.

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

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

In addition, in the present invention, when a component is referred to as “connected” or “connected” with another component, the component may be directly connected or directly connected to the other component, but in particular It should be understood that, unless there is a description to the contrary, it may be connected or connected through another element in the middle.

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

And it is intended to clarify that the classification of the constituent parts in the present invention is merely a division for each main function that each constituent unit is responsible for. 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 of the constituent units to be described below may additionally perform some or all of the functions of other constituent units in addition to the main function it is responsible for. Of course, it can also be performed by being dedicated to it.

Throughout this specification, a network function may be used synonymously with a neural network and/or a neural network. Here, a neural network (neural network) may be composed of a set of interconnected computational units that may be generally referred to as nodes, and these nodes may be referred to as neurons. A neural network is generally configured to include a plurality of nodes. Nodes constituting the neural network may be interconnected by one or more links.

Some of the nodes constituting the neural network may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance of n from the initial input node may constitute n layers.

The neural network described herein may include a deep neural network (DNN) including a plurality of hidden layers in addition to an input layer and an output layer. The deep neural network may include a convolutional neural network (CNN), a recurrent neural network (RNN), and the like.

1 is a diagram for explaining a method for diagnosing a brain disease according to an embodiment of the present invention, and FIG. 2 is a diagram for explaining a method for diagnosing a brain disease according to an embodiment of the present invention to be applied to a medical image information system environment 3 is a diagram for explaining a state in which the brain disease diagnosis method according to an embodiment of the present invention is applied to a medical image information system environment, and FIG. 4 is a brain disease diagnosis according to an embodiment of the present invention. It is a diagram showing an example of subdividing a standard brain image according to a method, and FIG. 5 is a diagram showing an example of automatically subdividing a brain image into lobe regions according to a brain disease diagnosis method according to an embodiment of the present invention, FIG. is a diagram illustrating a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied, and FIG. 7 is a diagram showing a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied. 8 is a view showing a UI operation screen of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied, and FIG. 9 is a UI operation of software to which the brain disease diagnosis method according to an embodiment of the present invention is applied. It is a view showing a screen, and Fig. 10 is a view showing a UI operation screen of software to which a brain disease diagnosis method according to an embodiment of the present invention is applied, and Fig. 11 is a view showing a brain disease diagnosis method according to an embodiment of the present invention. It is a diagram showing an example of automatically subdividing a brain image into subregions.

1 to 11 , the brain disease diagnosis method according to an embodiment of the present invention includes acquiring an fMRI image, quantifying the acquired fMRI image, and applying an automatic analysis algorithm to the quantified fMRI image. can do.

According to an embodiment, the step of acquiring the fMRI image may include fMRI from an image database (DB, Data Base) of an existing dementia or cognitive impairment patient in each hospital, or a medical image information system (PACS, Picture Archiving and Communication System) environment. This can be done by acquiring an image. The medical image information system is a system that integrally processes functions necessary to acquire and store images diagnosed in a digital state through a radiology image diagnosis device, and to transmit and retrieve the readings and medical records together to each terminal. it means.

According to an embodiment, quantifying the acquired fMRI image may be performed by reconstructing the acquired fMRI image. Specifically, it can be performed by subdividing the acquired fMRI image into parts that can be learned, grouping similar parts with each other, but removing unnecessary redundant data during grouping. In this case, the quantified fMRI image may be used as training data of a network function through a digitization process.

According to an embodiment, the quantified fMRI image may be provided to a medical image information system. Through this, the fMRI image data for each hospital can be standardized, and the learning norm of the artificial intelligence network function can be provided. Therefore, each hospital can analyze the brain image through the brain disease diagnosis method learned through the standardized fMRI image data and utilize it for examination. In addition, even if fMRI image data is increased for each hospital, the standardized fMRI image may be continuously automatically updated through the medical image information system.

In some embodiments, applying the automatic analysis algorithm to the quantified fMRI image may include learning a network function through the quantified fMRI image and analyzing the fMRI image of the patient.

Learning the network function may be performed by receiving a quantified fMRI image and learning the network function through this. In this case, the network function may be a plurality of CNNs. In more detail, the network function may be learned through an ensemble learning method in which a plurality of CNNs are learned through a mesh network. In this way, compared to using a learning algorithm separately, the automatic analysis algorithm can exhibit better predictive performance.

The step of analyzing the fMRI image of the patient may be performed by inputting the captured fMRI image of the patient into a learned network function to determine whether or not dementia, the severity, and the like. In more detail, the network function may compare the quantified fMRI image with the received fMRI image of the patient to determine whether the patient has dementia, the severity, cognitive impairment, etc., and may generate and output the patient's fMRI analysis result.

In some embodiments, analyzing the fMRI image of the patient may include obtaining clinical data and generating a diagnosis result of the patient based on the clinical data and the fMRI analysis result.

The step of receiving the clinical data may be performed by receiving the patient's signs, symptoms, psychological examination items, and the like.

The generating of the patient's diagnosis result based on the clinical data and the fMRI analysis result may include generating the patient's diagnosis result based on the acquired clinical data (patient's signs, symptoms, psychological test items, etc.) and the patient's fMRI analysis result. and output, it is possible to assist the diagnosis of a specialist.

According to an embodiment, the analyzed fMRI image of the patient may be provided to a medical image information system and used as re-learning data for re-learning a network function. For example, a diagnosis result generated in parallel with the analyzed fMRI image and clinical data of the patient is input to the medical image information system, and the network function acquires it as learning data from the medical image information system, so that learning can be performed. Through this, even if brain image-related data for each hospital increases, the network function can continuously acquire updated learning data and relearn.

According to an embodiment, the brain disease diagnosis method may be applied to a medical image information system environment. To this end, the brain disease diagnosis method can be applied to the medical image information system environment by being implemented as a platform for diagnosing dementia. Specifically, the dementia diagnosis platform may be learned from the fMRI images acquired from the medical image information system by being connected to the medical image information system and/or the medical image information system, and may analyze each fMRI image and output the results. In the dementia diagnosis platform, whenever the fMRI image of a patient visiting the hospital is updated in the medical image information system, the dementia diagnosis platform standardizes the patient's fMRI image, learns and analyzes it at the same time, and provides the analysis result to the specialist. .

The brain disease diagnosis method according to an embodiment may be implemented in the form of a program command 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. The program instructions recorded on the medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - Includes 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 not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

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

The 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, computer program products may include products (eg, downloadable apps) in the form of S/W programs distributed electronically through manufacturers of electronic devices or electronic markets (eg, Google Play Store, App Store). have. For electronic distribution, at least a portion of the S/W program may be stored in a storage medium or may be temporarily generated. In this case, the storage medium may be a server of a manufacturer, a server of an electronic market, or a storage medium of a relay server temporarily storing a S/W program.

The computer program product, in a system consisting of a server and a client device, may include a storage medium of a server or a storage medium of a client device. Alternatively, when 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 the S/W program itself transmitted from the server to the client device or the third device, or transmitted 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 a server, a client device, and a third device may execute a computer program product to distribute the method according to the disclosed embodiments.

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 with the server to perform the method according to the disclosed embodiments.

As described above, the best embodiment has been disclosed in the drawings and the specification. Although specific terms are used herein, they are used only for the purpose of describing the present invention, and are not used to limit the meaning or scope of the present invention described in the claims. Therefore, it will be understood by those skilled in the art that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (1)

As a brain disease diagnosis method using fMRI,
acquiring an fMRI image;
quantifying the acquired fMRI image; and
Comprising the step of applying an automatic analysis algorithm to the quantified fMRI image,
The step of applying the automatic analysis algorithm comprises learning a network function through a quantified fMRI image and analyzing the patient's fMRI image.

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