KR20230120551A - Dementia prediction method based on 2-dimensional magnetic resonance imaging and analysis apparatus - Google Patents

Abstract

A method for predicting dementia using 2D MRI includes the steps of receiving 2D MRI slices of a subject by an analysis device, inputting the 2D MRI slices into a segmentation model by the analysis device and extracting regions of interest, and the analysis device using the region of interest. Calculating the summed number of pixels of , wherein the analysis device inputs the regions of interest to first learning models pre-learned to calculate the cortical thickness of some of the regions of interest and the volume of some of the regions of interest Predicting and classifying whether or not the subject has dementia by inputting, by the analysis device, the thickness of the cerebral cortex of the partial regions and the volume of the partial regions to a second learning model trained in advance.

Description

Dementia prediction method and analysis device using 2-dimensional MRI {DEMENTIA PREDICTION METHOD BASED ON 2-DIMENSIONAL MAGNETIC RESONANCE IMAGING AND ANALYSIS APPARATUS}

The technique to be described below is a technique for predicting dementia using 2D MRI.

Dementia refers to a syndrome that causes the future of cognitive functions such as memory, language, and judgment. There are many types of dementia, but Alzheimer's disease is the most common form of dementia.

Brain imaging such as Magnetic Resonance Imaging (MRI) and positron emission tomography (PET) is used to diagnose dementia. Typically, cortical thickness is used as a factor related to dementia.

Korean Patent Publication No. 10-2020-0062589

Conventionally, brain imaging such as 3D (dimension) MRI and PET-CT was used for early diagnosis of dementia. However, 3D MRI requires high-spec MRI equipment and is time-consuming and expensive. Furthermore, 2D MRI scanners used in general health examination centers or primary hospitals generate about 20 images, making accurate diagnosis difficult.

The technology to be described below is intended to provide a technique for diagnosing or predicting dementia using a small number of 2D MRI images.

A method for predicting dementia using 2D MRI includes the steps of receiving 2D MRI slices of a subject by an analysis device, inputting the 2D MRI slices into a segmentation model by the analysis device and extracting regions of interest, and the analysis device using the region of interest. Calculating the summed number of pixels of the regions of interest, wherein the analysis device inputs the summed number of pixels of the regions of interest to first learning models trained in advance to obtain cortical thicknesses of some of the regions of interest and among the regions of interest Predicting the volume of some regions, and classifying whether the subject has dementia by inputting, by the analysis device, the thickness of the cerebral cortex of the partial regions and the volume of the partial regions to a second learning model trained in advance. .

An analysis device for predicting dementia using 2D MRI includes an interface device that receives 2D MRI slices of a subject, a segmentation model that extracts a region of interest from a 2D MRI slice, and a region of interest information that predicts cerebral cortical thickness and volume. A storage device for storing the first learning models and a second learning model for classifying dementia by receiving the thickness and volume of the cerebral cortex, inputting the received 2D MRI slices to the segmentation model to extract regions of interest, and extracting the extracted The summed number of pixels of the regions of interest is input to the first learning models to predict the cortical thickness of some of the regions of interest and the volume of some of the regions of interest, and the cortical thickness of the partial regions and the and an arithmetic device that classifies whether or not the subject has dementia by inputting the volume of some regions to the second learning model. The regions of interest include frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricle and hippocampus.

The technology to be described below accurately predicts dementia by using a learning model that analyzes a small number of 2D MRI images. The technology described below can provide useful information for diagnosing dementia based on brain MRI calculated by a low-end 2D MRI scanner.

1 is an example of the entire process of predicting whether or not a subject has dementia using brain images.
2 is an example of a process of learning a segmentation model for extracting a region of interest from a brain image.
3 is an example of a learning process of a learning model that predicts cortical thickness and brain region volume based on regions of interest.
4 is an example of a learning process of a learning model predicting dementia based on cortical thickness and brain region volume.
5 is an example of an analysis device for predicting whether or not a subject has dementia using brain images.

Since the technology to be described below can have various changes and various embodiments, 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 it should be understood to include all modifications, equivalents, or substitutes included in the spirit and scope of the technology described below.

Terms such as first, second, A, B, etc. may be used to describe various elements, but the elements are not limited by the above terms, and are merely used to distinguish one element from another. used only as For example, without departing from the scope of the technology described below, a first element may be referred to as a second element, and similarly, the second element may be referred to as a first element. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

In terms used in this specification, singular expressions should be understood to include plural expressions unless clearly interpreted differently in context, and terms such as “comprising” refer to the described features, numbers, steps, operations, and components. , parts or combinations thereof, but it should be understood that it does not exclude the possibility of the presence or addition of one or more other features or numbers, step-action components, parts or combinations thereof.

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

In addition, in performing a method or method of operation, each process constituting the method may occur in a different order from the specified order unless a specific order is clearly described in context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

The technology described below is a technology for diagnosing or predicting dementia. The following dementia includes Alzheimer's dementia.

The technique described below is a technique for calculating the degree of brain atrophy using brain MRI. Typically, the degree of brain atrophy can be calculated based on the thickness or volume of the cerebral cortex.

Hereinafter, a device that analyzes a brain image and predicts whether a subject has dementia is called an analysis device. The analysis device may take the form of a computer device such as a PC, a smart device, a network server, and a data processing dedicated chipset.

The analysis device can predict dementia based on brain images using a plurality of learning models. The learning model is a machine learning model. Thus, the classification model can be any of various types of models. For example, the learning model is any one of a decision tree, a random forest (RF), a K-nearest neighbor (KNN), a Naive Bayes, a support vector machine (SVM), a deep neural network (DNN), and a regression model. It may be a model implemented in the manner of There are various types of DNN models. For example, DNN includes a segmentation model for extracting a region of interest from an image, a classification model for performing classification based on features of an image, and the like.

1 is an example of the entire process 100 of predicting whether or not a subject has dementia using brain images.

The analysis device may predict whether or not the subject has dementia based on a small number of 2D MRI slices. In this case, the number of 2D MRI slices is generally calculated by a 2D MRI scanner. The researcher built a learning model using 20 2D MRI slices. Therefore, it is assumed that the number of 2D MRI slices is 20 for convenience of description below. That is, it is assumed that the 2D MRI scanner has generated 2D MRI slices of the subject.

The analysis device receives an MRI of the subject's brain (110). For example, the analysis device may receive 20 2D MRI slices of the subject.

The analysis device extracts Regions of Interest (ROIs) from the received 2D MRI slices (120). The analysis device may extract a plurality of set regions of interest using a previously learned segmentation model. Regions of interest may include reference regions for calculating cortical thickness. Also, regions of interest may include regions for calculating brain volume. For example, regions of interest include frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricle and hippocampus. can do.

The analysis device may calculate a certain brain image index using the extracted regions of interest (130). Here, the brain image index may include the number of extracted pixels of the region of interest. Accordingly, the region of interest may be normalized to a 2D MRI slice or a size set in advance. In addition, the brain image index may include the total brain size, which can be calculated as the size of regions of the brain image. The brain size can be calculated by summing the sizes of the extracerebral cerebrospinal fluid, the six regions of interest described above, and the white matter of the regions of interest. Accordingly, the analysis device may separately extract a region of interest for brain size calculation from brain MRI. The analysis device may use a separate segmentation model that calculates regions of interest that are criteria for brain size calculation. Meanwhile, since various methodologies for brain size determination have been studied, the analysis device may calculate the subject's brain size using one of various image processing techniques.

The analysis device may estimate the thickness of the cerebral cortex and the volume of the brain region using a pre-learned learning model (140). The analysis device may predict the thickness of the cerebral cortex by inputting the brain image index to the learning model. The analysis device may predict cortical thickness in each region of interest using a plurality of learning models. For example, the analysis device may calculate the thickness of the cerebral cortex of each region using individual learning models for the frontal region, the temporal region, the parietal region, and the occipital region. In addition, the analysis device can predict the volume of the brain region using a separate learning model. For example, the analyzer may calculate the volume of each region by separately using a learning model for the lateral ventricle and a learning model for the hippocampus.

Meanwhile, the analysis device may calculate the thickness of the cerebral cortex or the volume of a brain region by additionally using the subject's clinical information (gender, age, etc.) in addition to the brain image. In this case, each learning model must be trained in advance to calculate the thickness of the cerebral cortex or the volume of the brain region using clinical information in addition to brain MRI.

The analysis device may finally predict whether or not the subject has dementia based on the thickness of the cerebral cortex and the volume of the brain region of the regions of interest (150). The analysis device may predict whether or not the subject has dementia by using a separate learning model that receives cortical thicknesses and brain region volumes of regions of interest.

The dementia prediction process of FIG. 1 uses multiple learning models to predict the subject's dementia. The researcher built a number of learning models for the above-mentioned process, and verified the performance and dementia prediction performance of individual models. Hereinafter, the construction process of the learning models used by the researcher to predict the subject's dementia will be described.

The researcher collected data of the population who visited the affiliated medical institution (Samsung Seoul Hospital). The population was selected as subjects who had both MRI and PET scans taken within a certain period of time. Table 1 below shows information about the population. The researcher used data from a total of 1,120 people. The population consisted of 529 patients with Alzheimer's disease dementia (ADD) and 591 normal controls (NC). Table 1 shows clinical information on population composition and cortical thickness and brain region volume by region of interest. Clinical information includes gender, age and education level. The thickness of the cerebral cortex includes the thickness of the frontal, temporal, parietal and occipital regions. The brain region volume includes the volume of the lateral ventricle and the hippocampus.

total population ADD (Patients with Dementia) NC (Normal) average Standard Deviation(%) average Standard Deviation(%) average Standard Deviation(%) Subjects (persons) 1,120 (100.0%) 529 (47.2%) 591 (52.8%) Female (person) 659 (58.8%) 306 (57.8%) 353 (59.7%) Age (years) 69.0 10.9 69.9 10.2 68.2 11.5 Education (years) 11.7 4.8 11.3 4.9 12.1 4.7 Frontal cortical thickness (mm) 3.087 0.152 3.007 0.154 3.158 0.108 Parietal cortical thickness (mm) 2.984 0.192 2.877 0.200 3.080 0.120 Temporal cortical thickness (mm) 3.186 0.189 3.068 0.185 3.291 0.117 Occipital cortical thickness (mm) 2.897 0.204 2.793 0.186 2.991 0.171 Lateral ventricle volume (mm ³ ) 18,063 10,107 22,318 10,493 14,254 8,028 Hippocampus volume (mm ³ ) 2,627 632 2,177 523 3,031 411

The researcher built a segmentation model using the data of 980 people among the data of the population. The data of 908 persons are 3D MRI data generated using a 3.0 T MRI scanner (Philips 3.0T Achieva).

The researcher performed structural image analysis using the 3D segmentation mask of the CIVET pipeline. In CIVET, cortical thickness is computed as the Euclidean distance between the inner and outer surfaces of the cerebral cortex. Regions of interest include (i) frontal, temporal, parietal and occipital gray matter, (ii) lateral ventricle and (iii) hippocampus. The researcher used the regions of interest extracted from the data of 980 people as the ground truth for learning the segmentation model.

2 is an example of a process 200 of learning a segmentation model for extracting a region of interest from a brain image. Hereinafter, the learning process of the model will be described as being performed by the learning device. The learning device refers to a computer device capable of processing image data and performing a learning process of a machine learning model.

The learning device learns a segmentation model using MRI images of multiple subjects. However, FIG. 2 illustrates a process of learning a segmentation model using MRI of one subject as an example.

The learning device receives a 3D MRI of a specific subject belonging to the aforementioned population (210).

The learning device prepares a learning data set (220). The training data set consists of an input slice (original slice) and an answer image for the corresponding slice for each slice. The learning device selects 20 slices from 480 slices of 3D MRI. The learning device may select slices matching 20 slices obtained from the 2D MRI scanner among 480 slices. In addition, the learning device receives the correct answer images for the selected 20 slices. The answer image is an image obtained by dividing regions of interest with respect to the input slice. As the correct answer image, an image displayed (annotated) by a medical imaging expert by dividing the region of interest may be used.

The learning device performs learning of the segmentation model using the learning data (230). The learning device may learn a segmentation model using input slices and answer images for 20 slices. The segmentation model receives an input slice and learns to extract (separate) regions of interest of an answer image.

The segmentation model may be a semantic segmentation model. For example, the segmentation model may be a fully convolutional network (FCN)-based model such as U-net. The researcher extracted a region of interest from a 2D MRI slice using a segmentation model of a U-net structure.

The segmentation model extracts multiple regions of interest. The researcher performed curriculum learning to accurately extract various areas of interest with different characteristics. Curriculum learning is a learning strategy that imitates the human learning process to learn data with easy difficulty first and gradually learns difficult data.

The researcher built a segmentation model and verified its performance according to the following five learning sequences.

turn order of learning One All ROIs 2 Hippocampus→Hippocampus and LV→All regions of interest 3 Hippocampus and lateral ventricles → all regions of interest 4 Lateral ventricle → hippocampus and lateral ventricle → all regions of interest 5 Lateral ventricles → all regions of interest

The performance of the segmentation model was measured by IoU (intersection over union) and DSC (dice similarity coefficient) calculated based on the correct answer and the region of interest extracted by the segmentation model. IoU and DSC may be expressed by Equations 1 and 2 below, respectively.

In the above equation, A _GT is the correct region of interest, A _pred is the region of interest (prediction region) identified by the segmentation model, and A _overlap is the overlapping region between A _GT and A _pred .

The segmentation model showed IoU and DSC as shown in Table 3 below for the six regions of interest. The segmentation model showed the highest performance in lateral ventricle extraction.

area of interest IoU DSC frontal area 0.701 0.741 temporal region 0.660 0.702 parietal region 0.644 0.651 occipital 0.560 0.626 lateral ventricle 0.848 0.884 hippocampus 0.691 0.734 average 0.689 0.728

Table 4 below is the result of analyzing the performance of the learning sequence in Table 2. The learning method in the order of learning the lateral ventricle first showed slightly higher performance than the learning method in the other order. Therefore, it can be seen that the technique of performing curriculum learning by dividing the region of interest is significant in the performance of the segmentation model.

turn order of learning IoU One All ROIs 0.653 2 Hippocampus→Hippocampus and LV→All regions of interest 0.565 3 Hippocampus and lateral ventricles → all regions of interest 0.665 4 Lateral ventricle → hippocampus and lateral ventricle → all regions of interest 0.683 5 Lateral ventricles → all regions of interest 0.689

3 is an example of a learning process 300 of a learning model that predicts cortical thickness and brain region volume based on regions of interest. In FIG. 3 , the learning model predicts the thickness of the cerebral cortex and the volume of the brain region based on the above-mentioned region of interest.

The learning of FIG. 3 is based on a state in which the aforementioned region of interest is extracted from MRI of the brain of a subject belonging to a population. That is, the learning device may use the region of interest calculated using the segmentation model of FIG. 2 .

The learning device extracts input data based on regions of interest extracted from brain MRI (310). The input data may include brain image indexes and clinical information extracted from regions of interest extracted by the segmentation model.

The brain imaging index includes size information for six regions of interest (frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricle, and hippocampus). The size of the six regions of interest may be determined by the number of pixels in the region of interest image. That is, the size of the region of interest may be the sum of all pixels belonging to the region. In addition, the brain image index may further include brain size. The brain size may be calculated by summing the sizes of the extracerebral cerebrospinal fluid region, the six regions of interest described above, and the white matter of the regions of interest.

The summed number of pixels of a specific region of interest may be the sum of all pixels of the corresponding region of interest for each of the 20 slices. For example, the summed number of pixels of the frontal gray matter may be a value obtained by summing all pixels belonging to the frontal gray matter region in each of 20 slices. Assume that the indexes of 20 slices are 0 to 19. The summed number of pixels in the frontal gray matter is the number of pixels belonging to the frontal gray matter region of slice 0, the number of pixels belonging to the frontal gray matter region of slice 1, ..., the number of pixels belonging to the frontal gray matter region of slice 19. It may be the sum of all values.

Clinical information may include age and gender of the subject.

The researcher used the combined number of pixels, brain size, and clinical information of the six regions of interest as input data.

The learning device performs a process of learning a learning model using the extracted input data (320). The learning device repeats the learning process of the learning model using input data extracted from brain MRIs of a plurality of subjects.

The learning device inputs the extracted input data to the learning model and performs learning while comparing the value (predicted value) output by the learning model with the correct answer. The trained model is trained to predict cortical thickness and volume of specific brain regions. The correct answer is the information calculated by MRI and PET-CT of subjects belonging to the population (see Table 1).

A learning model may be built as an individual model according to the information to be predicted. A learning model is a machine learning model and can be implemented as one of many types of models. The researcher used a regression model. The researcher developed a model that predicts cortical thickness in the frontal region (anterior temporal model), a model that predicts cortical thickness in the temporal region (temporal region model), a model that predicts cortical thickness in the parietal region (parietal region model), and a model that predicts cortical thickness in the occipital region (parietal region model). A model to predict cortical thickness (occipital model), a model to predict volume of the lateral ventricle (lateral ventricle model), and a model to predict volume of the hippocampus (hippocampal model) were separately constructed.

For example, the frontal model may predict the thickness of the cerebral cortex in the frontal region by receiving the summed number of pixels of the frontal gray matter and clinical information. In addition, the frontal model can predict the thickness of the cerebral cortex in the frontal region by receiving the summed number of pixels of the frontal gray matter, brain size, and clinical information.

The researcher verified the performance of the model by constructing a model using brain size as input data and a model not using it. Table 5 below is a model that evaluated the performance of the learning model built by the researcher. The performance indicator is the Pearson Correlation Coefficient. In Table 5, model 1 is a model that does not use brain size, and model 2 is a model that uses even brain size. It can be seen that the performance of model 1 or model 2 is slightly higher depending on the area of interest. Accordingly, Model 1 or Model 2 may be selectively used as a model for predicting cortical thickness and brain region volume according to a region of interest. However, there was no significant difference between model 1 and model 2 in terms of performance.

ROI model 1 model 2 frontal gray matter 0.774 0.779 temporal gray matter 0.786 0.788 parietal gray matter 0.801 0.799 occipital gray matter 0.649 0.638 lateral ventricle 0.880 0.869 hippocampus 0.663 0.667

4 is an example of a learning process 400 of a learning model predicting dementia based on cortical thickness and brain region volume. Finally, a learning model that predicts dementia can be implemented as any one of various machine learning model types. The researcher implemented a dementia prediction model with a deep learning model.

The learning of FIG. 4 is based on the premise of acquiring cortical thickness, specific brain region volume, and clinical information for subjects belonging to the population. For example, the learning device may use the thickness of the cerebral cortex and the volume of a specific brain region for each region of the subject calculated using the learning model of FIG. 3 .

The learning device extracts the thickness of the cerebral cortex and the volume of a specific brain region extracted from brain MRI of subjects belonging to the population. The cortical thickness may include frontal, temporal, parietal, and occipital cortical thicknesses. The volume of a specific brain region may include the volume of the lateral ventricle and hippocampal region. In addition, the learning device acquires clinical information of subjects. Clinical information may include age, gender and education level. The learning data may include the thickness of the cerebral cortex and the volume of a specific brain region calculated using the learning model of FIG. 3 . Alternatively, the learning data may include the thickness of the cerebral cortex and the volume of a specific brain region calculated from brain images of actual subjects. The researcher used the thickness of the cerebral cortex and the volume of a specific brain region calculated from the 3D MRI analysis results of actual subjects as learning data.

The learning device performs a process of learning a learning model using the extracted input data (420). The learning device repeats the learning process of the learning model using input data extracted from a plurality of subjects.

The learning device inputs the extracted input data to the learning model and performs learning while comparing the value (predicted value) output by the learning model with the correct answer. The learning model is trained to predict whether or not the subject has dementia. The learning model outputs a binary classification result of whether or not the subject has dementia. The correct answer is the information on whether or not the subjects belonging to the population have dementia (see Table 1).

The researcher built three learning models using different input data sets. The researcher used the data of 924 people (80%) of the population as learning data, and the data of 196 people (20%) as verification data. The input data used to construct the three learning models are shown in Table 6 below.

model type Training data (input data) learning model 1 (i) Frontal cortical thickness, temporal cortical thickness, parietal cortical thickness, occipital cortical thickness, lateral ventricular volume and hippocampal volume learning model 2 (i)+ (ii) Clinical information (age, sex) learning model 3 (i)+ (ii) Clinical information (age, sex) + (iii) Clinical information (educational level)

The researcher verified the built learning model. Meanwhile, in the verification process, the researcher used the cortical thickness and brain region volume predicted by the learning model of FIG. 3 as input data for the learning model of FIG. 4 . Researchers performed 10-fold cross-validation. The verification results are shown in Table 7 below. Verification compared the result predicted by the learning model with the correct answer. As performance indicators, AUC (area under the receiver operating characteristic curve) and AUPRC (area under the precision-recall curve) were used.

The researcher compared the performance of the conventional 3D MRI-based classification model and the aforementioned 2D MRI-based learning model. Conventional 3D MRI-based classification models used previously studied models (Rebsamen, M., et al., Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation. Human Brain Mapping, 2020. 41(17) : p. 4804-4814.).

Among the 2D MRI-based learning models, learning model 2 showed the highest performance. Furthermore, the performance difference between the 3D MRI-based model (AUC: 0.936) and the 2D MRI-based model (AUC: 0.922) was not significant. Therefore, it can be said that the 2D MRI-based model developed by the researcher is also sufficiently significant in predicting dementia.

model type Conventional 3D MRI-based learning model 2D MRI-based learning model AUC AUPRC AUC AUPRC learning model 1 0.938 0.933 0.913 0.883 learning model 2 0.936 0.935 0.922 0.900 learning model 3 0.936 0.942 0.917 0.890

5 is an example of an analysis device 500 that predicts whether or not a subject has dementia using brain images. The analysis device 500 may be physically implemented in various forms. For example, the analysis device 500 may have a form of a computer device such as a PC, a smart device, a network server, and a chipset dedicated to data processing. Meanwhile, the analysis device 500 may be connected to brain imaging equipment or may be an integral device.

The analysis device 500 may include a storage device 510, a memory 520, an arithmetic device 530, an interface device 540, a communication device 550, and an output device 560.

The storage device 510 may store the 2D MRI of the subject generated by the medical imaging equipment.

The storage device 510 may store clinical information of the subject. Clinical information may include at least one of age, gender, and level of education.

The storage device 510 may store a segmentation model for extracting an ROI from a brain image (2D MRI slice). A segmentation model is a pretrained model.

The storage device 510 may store a learning model for predicting the thickness of the cerebral cortex and the volume of a specific brain region based on information of the region of interest and clinical information (age, gender). A learning model that predicts the thickness of the cerebral cortex and the volume of a specific brain region is called a first learning model. As described above, the first learning model uses the summed number of pixels of the region of interest (frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricle, and hippocampus) and clinical information as input data. Furthermore, the first learning model may further use brain size as input data. Accordingly, the first learning model may be divided into a model that does not use brain size and a model that uses even brain size, and may be prepared in advance. Also, the first learning model may be prepared in advance for each area of interest.

The storage device 510 may store a learning model for predicting dementia based on the thickness of the cerebral cortex and the volume of a specific brain region. Finally, a learning model predicting dementia is called a second learning model. As described above, the second learning model may be implemented in various models according to the type of input data. The second learning model is (i) a model using only cortical thickness and volume of a specific brain region as input data, (ii) a model using cortical thickness and volume of specific brain region, and clinical information (age and gender) as input data. and (iii) models used as input data for cortical thickness, volume of specific brain regions, and clinical information (age, gender, and level of education).

Codes or programs for predicting dementia in brain images may be stored.

The memory 520 may store data and information generated in the course of the analysis device 500 predicting dementia in a brain image.

The interface device 540 is a device that receives certain commands and data from the outside. The interface device 540 may receive the subject's 2D MRI and clinical information from a physically connected input device or external storage device. The number of input 2D MRI slices is less than 20. The interface device 540 may transmit dementia information predicted based on 2D MRI to an external object.

The communication device 550 refers to a component that receives and transmits certain information through a wired or wireless network. The communication device 550 may receive 2D MRI and clinical information of a subject from an external object. The number of 2D MRI slices is less than 20. The communication device 550 may transmit dementia information predicted based on 2D MRI to an external object such as a user terminal.

The interface device 540 may be a device that internally receives data received from the communication device 550 .

The output device 560 is a device that outputs certain information. The output device 560 may output an interface necessary for a data processing process, a brain image, a region of interest divided from a brain image, a dementia-related indicator calculated based on a region of interest, a dementia prediction result, and the like.

The arithmetic device 530 may pre-process the subject's brain image (2D MRI) at regular intervals. The arithmetic device 530 may generate a mask for segmenting the entire brain region through a data pre-processing process. The computing device 530 may segment the entire brain region from the brain image using a mask for the entire brain region.

The arithmetic unit 530 may normalize the size and resolution of the subject's 2D MRI to a constant.

The computing device 530 may extract a region of interest by inputting the 2D MRI slice of the subject to the learned segmentation model. Regions of interest include frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricle and hippocampus.

The computing device 530 may extract size information of the regions of interest. The calculator 530 may calculate the summed number of pixels for each of the regions of interest. The calculation device 530 may calculate the summed number of pixels by summing the number of pixels identified in the entire 2D slice for each region of interest (eg, the frontal part).

The computing device 530 may calculate the size of the entire brain based on the sizes of the region of interest and other regions in the brain MRI. The brain size can be determined by summing the size of the extracerebral cerebrospinal fluid region, the six regions of interest described above, and the white matter of the regions of interest.

The computing device 530 may predict the thickness or volume of the cerebral cortex of the region of interest by inputting the summed number of pixels and clinical information (age and gender) of the region of interest to the first learning model. The computing device 530 may predict the thickness or volume of the cerebral cortex of the region of interest by inputting the summed number of pixels and clinical information (age and gender) for each region of interest to an individual learning model.

In addition, the calculation device 530 may predict the thickness or volume of the cerebral cortex of the region of interest by inputting the summed number of pixels, brain size, and clinical information (age and gender) of the region of interest to the first learning model. The computing device 530 may predict the thickness or volume of the cerebral cortex of the region of interest by inputting the summed number of pixels, brain size, and clinical information (age and gender) for each region of interest into an individual learning model.

The arithmetic device 530 may predict whether or not the subject has dementia by inputting the thickness or volume of the cerebral cortex predicted in the first learning model to the second learning model.

The calculation device 530 inputs the thickness of the cerebral cortex of the frontal region, the thickness of the cerebral cortex of the temporal region, the thickness of the cerebral cortex of the parietal region, the thickness of the cerebral cortex of the occipital region, the volume of the lateral ventricle, and the volume of the hippocampus into the second learning model to determine whether the subject has dementia. can predict

Alternatively, the computing device 530 may include (i) frontal cortical thickness, temporal cortical thickness, parietal cortical thickness, occipital cortical thickness, lateral ventricle volume and hippocampus volume, and (ii) clinical information ( age and gender) can be input into the second learning model to predict whether or not the subject has dementia.

Alternatively, the computing device 530 may include (i) frontal cortical thickness, temporal cortical thickness, parietal cortical thickness, occipital cortical thickness, lateral ventricle volume and hippocampus volume, and (ii) clinical information ( Age, gender, and education level) can be input into the second learning model to predict whether or not the subject has dementia.

The arithmetic device 530 may be a device such as a processor, an AP, or a chip in which a program is embedded that processes data and performs certain arithmetic operations.

In addition, the brain image analysis method or the 2D MRI-based dementia prediction method as described above may be implemented as a program (or application) including an executable algorithm that can be executed on a computer. The program may be stored and provided in a temporary or non-transitory computer readable medium.

A non-transitory readable medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and can be read by a device. Specifically, the various applications or programs described above are CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM (read-only memory), PROM (programmable read only memory), EPROM (Erasable PROM, EPROM) Alternatively, it may be stored and provided in a non-transitory readable medium such as EEPROM (Electrically EPROM) or flash memory.

Temporary readable media include static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), and enhanced SDRAM (Enhanced SDRAM). SDRAM, ESDRAM), Synchronous DRAM (Synclink DRAM, SLDRAM) and Direct Rambus RAM (DRRAM).

This embodiment and the drawings accompanying this specification clearly represent only a part of the technical idea included in the foregoing technology, and those skilled in the art can easily understand it within the scope of the technical idea included in the specification and drawings of the above technology. It will be obvious that all variations and specific examples that can be inferred are included in the scope of the above-described technology.

Claims (12)

receiving 2D (dimension) Magnetic Resonance Imaging (MRI) slices of the subject by an analysis device;
extracting regions of interest by inputting the 2D MRI slices to a segmentation model by the analysis device;
calculating, by the analysis device, the number of summed pixels of the ROI;
predicting, by the analysis device, cortical thicknesses of some of the regions of interest and volumes of some of the regions of interest by inputting the summed number of pixels of the regions of interest to previously trained first learning models; and
Classifying whether or not the subject has dementia by inputting, by the analysis device, the thickness of the cerebral cortex of the partial regions and the volume of the partial regions to a pre-learned second learning model,
The regions of interest include frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricles, and the hippocampus. According to claim 1,
The first learning models are a dementia prediction method using 2D MRI that predicts the thickness of the cerebral cortex of the partial regions and the volume of the partial regions by receiving the sum number of pixels of the regions of interest, the age of the subject, and the gender of the subject. . According to claim 2,
Dementia prediction method using 2D MRI, wherein the first learning models further receive brain sizes calculated from the 2D MRI slices. According to claim 1,
The first learning models include a plurality of learning models prepared in advance for each region of interest, and the plurality of learning models are input by inputting the sum of pixels for the corresponding region of interest, the age of the target, and the gender of the target. Dementia prediction method using two-dimensional MRI for predicting the thickness or volume of the cerebral cortex of the corresponding region of interest. According to claim 1,
The second learning model receives the thickness of the cerebral cortex of the frontal region, the thickness of the cerebral cortex of the temporal region, the thickness of the cerebral cortex of the parietal region, the thickness of the cerebral cortex of the occipital region, the volume of the lateral ventricle and the volume of the hippocampus, and classifies whether the metabolite has dementia 2 Dementia prediction method using dimensional MRI. According to claim 5,
The second learning model is a dementia prediction method using a two-dimensional MRI further receiving at least one information of the subject's age, the subject's gender, and the subject's education level. According to claim 1,
The 2D MRI slices are a dementia prediction method using 2-dimensional MRI including 20 or less slices. An interface device for receiving 2D (dimension) Magnetic Resonance Imaging (MRI) slices of a subject;
A segmentation model that extracts a region of interest from a 2D MRI slice, first learning models that predict cortical thickness and volume by receiving region of interest information, and a second learning model that classifies dementia by receiving cortical thickness and volume a storage device to store; and
The received 2D MRI slices are input to the segmentation model to extract regions of interest, and the summed number of pixels of the extracted regions of interest is input to the first learning models to obtain cortical thicknesses of some of the regions of interest and the An arithmetic device for predicting the volume of some of the regions of interest and inputting the cortical thickness of the partial region and the volume of the partial region to the second learning model to classify whether the subject has dementia,
The regions of interest include frontal gray matter, temporal gray matter, parietal gray matter, occipital gray matter, lateral ventricles, and the hippocampus, an analysis device for predicting dementia using 2-dimensional MRI. According to claim 8,
The first learning models use 2D MRI to predict the thickness of the cerebral cortex of the partial regions and the volume of the partial regions by receiving the sum of the number of pixels of the regions of interest, the age of the subject, and the gender of the subject, thereby preventing dementia. predictive analytics. According to claim 8,
The first learning models include a plurality of learning models prepared in advance for each region of interest, and the plurality of learning models are input by inputting the sum of pixels for the corresponding region of interest, the age of the target, and the gender of the target. An analysis device for predicting dementia using a two-dimensional MRI that predicts the thickness or volume of the cerebral cortex of the corresponding region of interest. According to claim 8,
The second learning model receives the thickness of the cerebral cortex of the frontal region, the thickness of the cerebral cortex of the temporal region, the thickness of the cerebral cortex of the parietal region, the thickness of the cerebral cortex of the occipital region, the volume of the lateral ventricle and the volume of the hippocampus, and classifies whether the metabolite has dementia 2 An analysis device that predicts dementia using dimensional MRI. According to claim 11,
The second learning model is an analysis device for predicting dementia using a two-dimensional MRI further receiving at least one information of the subject's age, the subject's gender, and the subject's education level.

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