EP-4736769-A1 - METHOD FOR CALCULATING DEMENTIA-RELATED INFORMATION USING VOLUME PREDICTED BY BRAIN CT AND ANALYSIS DEVICE THEREOF

EP4736769A1EP 4736769 A1EP4736769 A1EP 4736769A1EP-4736769-A1

Abstract

A method for deriving dementia-related information using volume predicted from brain CT includes: a step of an analysis apparatus receiving a brain CT (Computed Tomography) image of a subject; a step of the analysis apparatus inputting the brain CT image into a pre-trained segmentation model to extract regions of interest; a step of the analysis apparatus inputting pixel information of the regions of interest into a pre-trained first learning model to predict the volume of at least one region among the regions of interest; and a step of the analysis apparatus inputting the volume of the at least one region into a pre-trained second learning model to derive dementia-related information of the subject.

Inventors

PARK, CHAE JUNG
KIM, SOO JONG
GU, Yun A
SEO, SANG WON
JANG, HYE MIN

Assignees

Samsung Life Public Welfare Foundation

Dates

Publication Date: 20260506
Application Date: 20240311

Claims (10)

A method of deriving dementia-related information using volume predicted from brain CT, comprising: receiving, by an analysis apparatus, a brain CT (Computed Tomography) image of a subject; extracting, by the analysis apparatus, regions of interest by inputting the brain CT image into a trained segmentation model; predicting, by the analysis apparatus, a volume of at least one region among the regions of interest by inputting pixel information of the regions of interest into a pre-trained first learning model; and calculating, by the analysis apparatus, dementia-related information of the subject by inputting the volume of the at least one region into a pre-trained second learning model, wherein the regions of interest include a plurality of regions selected from frontal cerebrospinal fluid region, temporal cerebrospinal fluid region, parietal cerebrospinal fluid region, occipital cerebrospinal fluid region, anterior lateral ventricle region, posterior lateral ventricle region, and peri-hippocampal cerebrospinal fluid region.
The method of claim 1, wherein the first learning model comprises a plurality of learning models prepared in advance for each of the regions of interest.
The method of claim 1, wherein the second learning model further receives at least one information among age of the subject, gender of the subject, and APOE4 genotype of the subject to derive dementia-related information of the subject.
The method of claim 1, wherein the dementia-related information is one of dementia onset status, dementia risk, dementia probability, dementia-related score, dementia prognosis prediction, degree of brain atrophy, beta-amyloid (amyloid-β, Aβ) positivity, tau protein positivity, and brain age.
The method of claim 1, wherein the segmentation model comprises a plurality of models prepared in advance for each of the regions of interest.
An analysis apparatus for deriving dementia-related information using volume predicted from brain CT, comprising: an interface device for receiving a brain CT (Computed Tomography) image of a subject; a storage device for storing a segmentation model for extracting regions of interest from a brain CT image, a first learning model for receiving brain region of interest information and predicting volume, and a second learning model for calculating dementia-related information; and a computing device for extracting regions of interest by inputting the received brain CT image into the segmentation model, predicting a volume of at least one region among the regions of interest by inputting pixel information of the extracted regions of interest into the first learning model, and calculating dementia-related information of the subject by inputting the volume of the at least one region into the second learning model, wherein the regions of interest include a plurality of regions selected from frontal cerebrospinal fluid region, temporal cerebrospinal fluid region, parietal cerebrospinal fluid region, occipital cerebrospinal fluid region, anterior lateral ventricle region, posterior lateral ventricle region, and peri-hippocampal cerebrospinal fluid region.
The analysis apparatus of claim 6, wherein the segmentation model comprises a plurality of models prepared in advance for each of the regions of interest.
The analysis apparatus of claim 6, wherein the first learning model comprises a plurality of learning models prepared in advance for each of the regions of interest.
The analysis apparatus of claim 6, wherein the second learning model further receives at least one information among age of the subject, gender of the subject, and APOE4 genotype of the subject to derive dementia-related information of the subject.
The analysis apparatus of claim 6, wherein the dementia-related information is one of dementia onset status, dementia risk, dementia probability, dementia-related score, dementia prognosis prediction, degree of brain atrophy, beta-amyloid (amyloid-β, Aβ) positivity, tau protein positivity, and brain age.

Description

[Technical Field] The following description relates to a technique for predicting dementia-related information of a subject using brain CT images. [Background Art] Dementia refers to a syndrome that causes cognitive function impairment such as memory, language, and judgment. Alzheimer's disease is the most common type of dementia. Brain imaging such as MRI (Magnetic Resonance Imaging) and PET is used for Alzheimer's diagnosis. [Detailed Description of Invention] [Technical Problem] 3D (dimensional) MRI and PET-CT (positron emission tomography-Computed Tomography) require high time and cost for imaging. In contrast, CT has no restrictions such as the presence of metal substances in the human body, can be acquired much faster than MRI, and is relatively inexpensive. The technology described below aims to provide a technique for calculating dementia-related information such as dementia status, dementia risk, and brain atrophy using only brain CT images. [Technical Solution] In one general aspect, there is provided a method of calculating dementia-related information using volume predicted from brain CT including: a step of an analysis apparatus receiving a brain CT image of a subject; a step of the analysis apparatus inputting the brain CT image into a pre-trained segmentation model to extract regions of interest; a step of the analysis apparatus inputting pixel information of the regions of interest into a pre-trained first learning model to predict the volume of at least one region among the regions of interest; and a step of the analysis apparatus inputting the volume of the at least one region into a pre-trained second learning model to derive dementia-related information of the subject. In another general aspect, there is provided analysis apparatus for calculating dementia-related information using volume predicted from brain CT including: an interface device for receiving a brain CT image of a subject; a storage device storing a segmentation model for extracting regions of interest from brain CT images, a first learning model for receiving brain region of interest information and predicting volume, and a second learning model for calculating dementia-related information; and a computing device that inputs the received brain CT image into the segmentation model to extract regions of interest, inputs pixel information of the extracted regions of interest into the first learning model to predict the volume of at least one region among the regions of interest, and inputs the volume of the at least one region into the second learning model to derive dementia-related information of the subject. [Effects of Invention] The technology described below extracts regions of interest from CT images and derives dementia-related information based on the volume of the regions of interest. The technology described below can quickly and inexpensively derive significant dementia-related information using only CT images. [Brief Description of Drawings] FIG. 1 is an example of a system for calculating dementia-related information based on CT images.FIG. 2 is an example of a process for calculating dementia-related information using brain CT images.FIG. 3 is an example of a process for training a segmentation model that extracts regions of interest from brain CT images.FIG. 4 is an example of a training process for a learning model that predicts the volume of regions of interest based on extracted regions of interest.FIG. 5 is an example of a training process for a learning model that derives dementia-related information based on brain region of interest volume.FIG. 6 is an example of an analysis apparatus that derives dementia-related information of a subject using brain CT images. [Modes of the Invention] The technology described below can be modified in various ways and can have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the technology described below to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and technical scope of the technology described below. Terms such as first, second, A, B, etc. may be used to describe various components, but the components are not limited by the terms and are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the technology described below, a first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. The term and/or includes a combination of a plurality of related described items or any of a plurality of related described items. In the terms used in this specification, singular expressions should be understood to include plural expressions unless clearly interpreted differently in context, and terms such as "comprises" mean that the described features, numbers,