WO2020111534A2 - Procédé et système pour estimer l'âge vasculaire cérébral à partir d'une image médicale - Google Patents

Procédé et système pour estimer l'âge vasculaire cérébral à partir d'une image médicale Download PDF

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WO2020111534A2
WO2020111534A2 PCT/KR2019/014530 KR2019014530W WO2020111534A2 WO 2020111534 A2 WO2020111534 A2 WO 2020111534A2 KR 2019014530 W KR2019014530 W KR 2019014530W WO 2020111534 A2 WO2020111534 A2 WO 2020111534A2
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cerebrovascular
image
age
cnn
neural network
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Korean (ko)
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WO2020111534A3 (fr
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장진희
남윤호
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가톨릭대학교 산학협력단
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present invention relates to the estimation of cerebrovascular age, and more particularly, to a method and system for estimating cerebrovascular age from medical images using machine learning.
  • the medical image acquires the anatomical and functional information of the object for medical purposes.
  • Medical images can be obtained in various ways from various parts of the human body.
  • various cerebrovascular images can be obtained for the diagnosis of cerebrovascular disease, and one of them is a technique called time-of-flight MR angiography (TOF MRA).
  • TOF MRA time-of-flight MR angiography
  • This technique uses a magnetic resonance image (Magnetic resonance image) to make the signal of blood with a strong flow rate appear strong and the signal of the stationary blood weakly extract only the images of blood vessels from each section and extract the blood vessel images obtained from each section. It is a technical method to obtain images of blood vessels without using contrast agents by combining and reconstructing in two or three dimensions.
  • TOF MRA is a blood vessel imaging technique that is widely used in the medical field because it can present cerebral vessel structures in detail with excellent image resolution.
  • complex blood flow phenomena such as vortices can be diagnosed, and recently, microvascular images can also be obtained.
  • CT cerebral angiography which reconstructs cerebrovascular images by obtaining a computed tomography image after injecting a contrast agent, is widely used for high spatial resolution and fast examination time, and can obtain precise cerebrovascular images and hemodynamic information.
  • aging of the brain is well known and important. Since the brain is rich in blood supply to support high metabolic activity, it is not surprising that age-dependent changes include brain parenchyma as well as brain vasculatures. Histopathological studies have shown changes in cerebral arteries with aging, and microscopic changes have been associated with large morphological changes in the arteries of the head and neck.
  • Patent Document 1 Registered Patent Publication No. 10-1620302 (2016.05.04)
  • the problem to be solved by the present invention was created to solve the limitations and inconveniences of structural analysis of cerebrovascular images using the above-described conventional hand-crafted features, machine learning. For example, by using deep learning to analyze changes in the shape of cerebral blood vessels appearing on cerebrovascular images, medical knowledge that can predict the cerebrovascular age by extracting knowledge-based cerebrovascular features It provides a method and system for estimating cerebrovascular age from imaging.
  • a method of estimating cerebrovascular age from a medical image according to the present invention for achieving the above technical problem includes: collecting cerebrovascular images with age information of a plurality of subjects; Performing pre-processing to detect the location of the middle cerebral artery (MCA) for machine learning through a convolutional neural network (CNN) having a plurality of learning layers on the cerebral blood vessel image having the age information; Consists of a plurality of convolutional layers for calculating feature maps by applying a weight to the input value using the pre-processed cerebrovascular image as an input value of the input layer Machine learning through a convolutional neural network (CNN); And estimating the age of cerebral blood vessels through the machine-learned convolutional neural network (CNN).
  • MCA middle cerebral artery
  • CNN convolutional neural network
  • the pre-processing comprises interpolating cerebral blood vessel images into an isotropic space and normalizing signal strength; Detecting the position of the middle cerebral artery (MCA) of the z-axis to match the coverage of the top and bottom (z-axis) of the head of the cerebrovascular image; Cutting an image of a predetermined size around a position of the detected middle cerebral artery (MCA); And interpolating the cut-sized image slab to adjust the data size.
  • MCA middle cerebral artery
  • MCA middle cerebral artery
  • the method for estimating cerebrovascular age from a medical image before cutting data to reduce sensitivity to spatial variation of cerebrovascular image data in the learning data set of the convolutional neural network (CNN) It is characterized by increasing data by performing shifting, rotation and flipping to reflect the diversity that may occur during medical imaging.
  • CNN convolutional neural network
  • the method for estimating cerebrovascular age from a medical image according to the present invention may further include extracting features of the cerebral blood vessels through the machine-learned convolutional neural network (CNN).
  • the input data of the convolutional neural network (CNN) may further include risk factors that induce cerebrovascular diseases including hypertension, diabetes, and hyperlipidemia.
  • the system for estimating cerebrovascular age from medical images includes: a cerebrovascular imaging DB that collects and stores a 3D TOF MRA image with age information of a subject; A pre-processing unit for performing pre-processing to detect the position of the middle cerebral artery (MCA) to machine learn a 3D cerebrovascular image with age information obtained from the cerebrovascular image DB through a convolutional neural network (CNN); An input unit for inputting age and cerebrovascular disease risk factors of images pre-processed by the pre-processing unit; A plurality of convolutional layers and a plurality of convolutional layers for calculating feature maps by applying a weight to the input value using the cerebral blood vessel image having the pre-processed age information as an input value of the input layer It comprises a plurality of pooling (pooling) layer to reduce the size of the feature map, and includes a convolutional neural network (CNN) that estimates the age of the cerebral blood vessels through regression analysis.
  • CNN convolutional neural network
  • the pre-processing unit is an isotropic standardization unit for interpolating cerebral blood vessel images into an isotropic space and standardizing signal intensity;
  • An MCA detection unit that detects the position of the middle cerebral artery (MCA) on the z-axis in order to align the top-down (z-axis) coverage of the cerebrovascular image;
  • a three-dimensional slab generating unit for cutting the image of a predetermined size based on the detected position of the middle cerebral artery (MCA), interpolating the cut-sized image (slab), adjusting the data size, and inputting machine learning. do.
  • the system for estimating cerebrovascular age from medical images moves before cutting data to reduce sensitivity to spatial variation of cerebrovascular image data during learning and testing of the convolutional neural network (CNN) ( It further includes an image enhancement unit that increases training data by performing shifting, rotation, and flipping.
  • CNN convolutional neural network
  • the convolutional neural network uses the TOF MRA image having the pre-processed age information as an input value of an input layer to apply a weight to the input value to calculate feature maps. hierarchy; A plurality of pooling layers that reduce the size of the feature maps of the plurality of convolution layers through sub-sampling; A full connection layer connected to the pooling layer and all activations; And a regression analysis unit for estimating the age of cerebral blood vessels through regression analysis using the output value of the complete connection layer.
  • the input unit may further include risk factors and sex information for causing cerebrovascular diseases including hypertension, diabetes, and hyperlipidemia.
  • the overall aging state of cerebral blood vessels is quantified by quantifying morphological changes in cerebral blood vessels through machine learning, for example, deep learning.
  • a single measure or index can be provided to evaluate.
  • brain vascular age can be predicted by extracting a knowledge-based cerebrovascular feature by analyzing changes in the shape of cerebral blood vessels appearing on cerebrovascular images using machine learning. It can be used as an imaging biomarker for aging.
  • knowledge-based cerebrovascular characteristics and risk factor information of a patient can be added to provide cerebrovascular information that cannot be previously measured.
  • new cerebrovascular information to check the health status of each individual and utilize it to promote health, early diagnosis and prevention of specific diseases can be prevented.
  • 1 is a block diagram showing the technical idea of the present invention.
  • FIG. 2 shows an example of a change in morphological characteristics of brain blood vessels with age.
  • Figure 3 is a block diagram showing an embodiment of the configuration of the cerebrovascular age estimation system from a medical image according to the present invention.
  • FIG. 4 is a block diagram showing a more specific configuration of the pre-processing unit 320.
  • FIG. 5 shows an embodiment of the pre-processing unit 320 as an MRA image.
  • FIG. 6 shows an example of the image enhancement unit 330.
  • CNN convolutional neural network
  • CNN convolutional neural network
  • FIG. 9 is a flowchart illustrating an embodiment of a method for estimating cerebrovascular age from a medical image according to the present invention.
  • FIG. 11 shows an example of providing services such as risk and lifestyle advice for risk factors using the cerebrovascular age estimated by the system for estimating cerebrovascular age from the medical image according to the present invention.
  • FIG. 1 is a block diagram showing the technical idea of the present invention, as a brain blood vessel image, machine learning through a convolutional neural network (Convolution Neural Network), for example, deep learning, deep learning Indicates that it is estimated.
  • FIG. 2 shows an example of changes in the morphological characteristics of cerebral blood vessels according to age, and it can be seen that the morphological characteristics of the cerebral blood vessels shown in the brain MRA image change with age.
  • Convolution Neural Network convolution Neural Network
  • MRA Magnetic Resonance Angiography
  • the age of the cerebral blood vessels is estimated by deep learning the change of the morphological characteristics of the (130).
  • FIG. 3 is a block diagram showing an embodiment of the configuration of the cerebral vascular age estimation system from the medical image according to the present invention
  • the cerebral vascular age estimation system from the medical image according to an embodiment of the present invention is a cerebrovascular image DB ( 310, a pre-processing unit 320, an input unit 340, and a convolutional neural network 350, and may further include an image enhancement unit 330.
  • the cerebrovascular image DB 310 may be constructed as a database for collecting and storing cerebrovascular images, for example, three-dimensional TOF MRA images, with age information of a subject.
  • the pre-processing unit 320 performs machine learning through the convolutional neural network (CNN, 350), for example, a cerebral blood vessel image having the age information obtained from the cerebrovascular imaging DB 310, for example, a 3D TOF MRA image. Pre-treatment is performed for deep learning.
  • 4 is a block diagram showing a more specific configuration of the pre-processing unit 320, the pre-processing unit 320 includes an isotropic standardization unit 410, an MCA detection unit 420 and a SLAB generation unit 430. Referring to FIG. 4, the isotropic standardization unit 410 interpolates a cerebrovascular image, for example, a TOP MRA image into an isotropic space and normalizes signal strength.
  • the MCA detection unit 420 detects the position of the middle cerebral artery (MCA) on the z-axis in order to match the coverage in the upper and lower range (z-axis) of the brain vessel image, for example, the 3D TOF MRA image.
  • MCA middle cerebral artery
  • the SLAB generation unit 430 cuts an image of a predetermined size based on the detected position of the middle cerebral artery (MCA) and interpolates the cut-sized image (slab) to reduce the data size.
  • FIG. 5 shows an embodiment of the pre-processing unit 320 as an MRA image.
  • MRA data from cerebral blood vessel imaging with age from specific hospital databases (Our DB, 511) and public databases CASILab (513) and IXI (515) Collected.
  • CASILab 515
  • IXI 515
  • the 3D TOF image is interpolated into a 0.5 mm isotropic space for the MRA DB 510 including three databases 511, 513, and 515, and signal strength is normalized. (510)
  • the position of the middle cerebral artery (MCA) on the z-axis is estimated as shown in Fig. B 520 to match the coverage of the MRA image data in the upper and lower range (z-axis) direction.
  • MCA middle cerebral artery
  • a 6.4 cm thick slab (SLAB, 1.7 cm to 4.7 cm above) is extracted.
  • the central part of the slab (256x256) is cut off and the SLAB (256x256x128), cut off due to limitations in the computational environment, is interpolated into a 1.0mm isotropic space (128x128x64) ( Figure C, 530).
  • the input unit 340 inputs risk factors for cerebrovascular disease including gender, age and hypertension, diabetes, and hyperlipidemia of the image preprocessed in the preprocessing unit 320.
  • the convolutional neural network applies feature maps by applying a weight to the input value using a brain blood vessel image with pre-processed age information, for example, a TOF MRA image as an input value of an input layer. It comprises a plurality of convolutional layers to calculate and a plurality of pooling layers to reduce the size of the feature maps of the plurality of convolutional layers, and the age of cerebral blood vessels is estimated through regression analysis.
  • the image augmentation unit 330 shifts the MRA image before cutting the data to reduce sensitivity to spatial variation of cerebrovascular image data of the data set in the course of learning and testing of the convolutional neural network (CNN, 350). , Rotation and flipping to increase training data.
  • 6 illustrates an example of the image enhancement unit 330, and shows that 652 images can be increased to 20,000 or more images by performing shifting, rotation, and flipping.
  • FIG. 7 shows an example of a convolutional neural network (CNN, 350) used as a component of a cerebrovascular age estimation system from a medical image according to the present invention, a plurality of convolutional layers (720, 740), a plurality of pooling It comprises a (pooling) layer (730, 750), a fully connected (fully connected) layer 760 and a regression (regression) analysis unit 770.
  • CNN convolutional neural network
  • the plurality of convolutional layers 720 and 740 is a feature map by applying a weight to the input value using a TOF MRA image 710 as an input value of an input layer as an example of a cerebrovascular image having pre-processed age information. (feature maps).
  • the plurality of pooling layers 730 and 750 reduce the size of the feature maps of the plurality of convolutional layers 720 and 740 through sub-sampling.
  • the fully connected layer 760 is connected to all activations of the pooling layer 750.
  • the regression analysis unit 770 estimates the age of the cerebral blood vessels through regression analysis using the output value of the complete connection layer 760.
  • FIG. 8 shows an example of an implementation of a convolutional neural network (CNN, 350) used as a component of a cerebrovascular age estimation system from a medical image according to the present invention.
  • CNN convolutional neural network
  • FIG. 8 shows a scheme of 3D CNN, a representative image of an activation map of each layer is shown.
  • the 3D CNN architecture has five weight layers (not shown), three 3D convolutional layers 810, 820, and 830, and two fully connected layers 840.
  • a 3x3x3 convolution kernel 850 with a stride of 1 and a 2x2x2 maximum pooling with a stride of 1 (860) were used for all layers.
  • ReLU rectified linear unit
  • the mean square error was used as a loss function in regression (880) and the batch size was 8.
  • a convolution operation when a convolution operation is performed using a 3x3x3 convolution kernel 850 having a stride of 1 for 3D input data 805 of 128x128x64, a plurality of 128x128x64 Feature maps can be constructed.
  • a subsampling map of 64x64x32 may be constructed by applying 2x2x2 maximum pooling 860 with a stride length of 1 from a feature map of each 128x128x64 in the convolution layer 810.
  • activation can be seen primarily in vascular voxels.
  • Network parameters including learning rate and number of epochs can be adjusted empirically to improve performance for validation through repeated experiments.
  • FIG. 9 is a flowchart illustrating an embodiment of a method for estimating cerebrovascular age from a medical image according to the present invention. 3, 4, 7 and 9, an embodiment of a method for estimating cerebrovascular age from a medical image according to the present invention will be described.
  • a medical image with age information of a plurality of subjects (subject), for example, TOF MRA images are collected to build a cerebrovascular image DB 310 (step S910).
  • the pre-processing unit 320 performs pre-processing to detect the position of the middle cerebral artery (MCA) in order to machine-learn a cerebral blood vessel image with age information through a convolutional neural network (CNN). It shows the process in more detail.
  • the isotropic standardization unit 410 interpolates a cerebrovascular image, for example, a TOF MRA image into an isotropic space and normalizes signal strength.
  • the MCA detection unit 420 heads the TOF MRA image
  • the position of the middle cerebral artery (MCA) of the z-axis is detected in order to match the coverage in the upper and lower range (z-axis).
  • Step S1020 The SLAB generation unit 430 detects the position of the detected middle cerebral artery (MCA). The image of a predetermined size is cut out at the center. (Step S1030) Then, the size of the data is reduced by interpolating the cut image of the predetermined size (Step S1040).
  • the CNN unit 350 applies the TOF MRA image having the pre-processed age information as an input value of an input layer and applies a weight to the input value.
  • Machine learning is performed through a convolutional neural network (CNN) including a plurality of convolutional layers for calculating feature maps (step S930).
  • the regression analysis unit of the CNN unit 350 (step S930) 770) estimates the age of cerebral blood vessels through the machine-learned convolutional neural network (CNN) (step S940).
  • step S920 after pre-processing a cerebrovascular image, for example, a TOF MRA image, data to reduce sensitivity to spatial variation of cerebrovascular image data in the learning data set of the convolutional neural network (CNN).
  • the data can be increased by performing shifting, rotation, and flipping before cutting.
  • the characteristics of the cerebral blood vessels can be extracted through the machine-learned convolutional neural network (CNN).
  • the input data of the convolutional neural network may further include a risk factor that causes cerebrovascular diseases including hypertension, diabetes, and hyperlipidemia.
  • 11 shows an example of providing an analysis of cerebrovascular age and risk factors using a system for estimating cerebrovascular age from a medical image according to the present invention.
  • a 3D cerebral blood vessel image, a patient's gender and age, and risk factors for cerebrovascular disease such as hypertension, diabetes, hyperlipidemia, and cardiovascular disease are input to estimate cerebral vascular age, and estimated cerebrovascular disease
  • it can be used for personal health check and health promotion. Through this, you can improve your lifestyle and diagnose and prevent certain diseases early.
  • the present invention can be embodied as computer readable code on a computer readable recording medium (including all devices having information processing functions).
  • the computer-readable recording medium includes all kinds of recording devices in which data readable by a computer system are stored. Examples of computer-readable recording devices include ROM, RAM, CD-ROM, magnetic tape, floppy disks, and optical data storage devices.
  • the “unit” may be a hardware component such as a processor or circuit, and/or a software component executed by a hardware component such as a processor.

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Abstract

La présente invention concerne un procédé et un système permettant d'estimer l'âge vasculaire cérébral à partir d'une image médicale, le procédé permettant d'estimer l'âge vasculaire cérébral à partir d'une image médicale comprenant les étapes consistant à : collecter des images vasculaires cérébrales comportant des informations d'âge d'une pluralité de patients ; prétraiter une image vasculaire cérébrale comportant des informations d'âge pour détecter la position d'une artère cérébrale moyenne (MCA) pour un apprentissage machine par l'intermédiaire d'un réseau neuronal convolutif (CNN) comportant une pluralité de couches d'apprentissage ; effectuer un apprentissage machine par l'intermédiaire du réseau neuronal convolutif (CNN) comprenant une pluralité de couches convolutives pour calculer des cartes de caractéristiques à l'aide de l'image vasculaire cérébrale prétraitée comportant les informations d'âge en tant que valeur d'entrée d'une couche d'entrée et appliquer une pondération à la valeur d'entrée ; et estimer un âge vasculaire cérébral par l'intermédiaire du réseau neuronal convolutif (CNN) à apprentissage machine.
PCT/KR2019/014530 2018-11-27 2019-10-31 Procédé et système pour estimer l'âge vasculaire cérébral à partir d'une image médicale WO2020111534A2 (fr)

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