WO2019098399A1 - Procédé d'estimation de la densité minérale osseuse et appareil l'utilisant - Google Patents

Procédé d'estimation de la densité minérale osseuse et appareil l'utilisant Download PDF

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WO2019098399A1
WO2019098399A1 PCT/KR2017/012911 KR2017012911W WO2019098399A1 WO 2019098399 A1 WO2019098399 A1 WO 2019098399A1 KR 2017012911 W KR2017012911 W KR 2017012911W WO 2019098399 A1 WO2019098399 A1 WO 2019098399A1
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image
bone
bone density
learning
processor
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Korean (ko)
Inventor
박마이클용
김상기
김현준
정규환
이예하
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Vuno Inc
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Vuno Inc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

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  • the present invention relates to a method for estimating bone density and an apparatus using the same. More particularly, the present invention relates to a method of estimating a bone density based on an image obtained by a computed tomography (CT) and an apparatus using the same.
  • CT computed tomography
  • Osteoporosis is a skeletal disorder in which the strength of the bones is weakened and fractures are easily broken even at a slight impact.
  • the causes of osteoporosis include aging, drugs, hormone deprivation, and lifestyle habits.
  • bone density is used to measure the amount of bone.
  • the World Health Organization (WHO) provides criteria for the diagnosis of osteoporosis based on differences in the mean age of healthy young adults from the same sex.
  • Osteoporosis has few symptoms of its own. Therefore, there is a problem that the bone is broken and discovered after the disease progresses without early diagnosis or treatment. There is a need to prevent premature osteoporosis and fracture or vertebral deformity through treatment. However, there is a problem in that patients are not aware of osteoporosis and can not find it if they do not receive bone density test separately.
  • the conventional technique of predicting the bone density using the noise of the image has a problem that the noise can be changed according to the kind of the CT device or the X-ray irradiation amount at the time of photographing, and the accurate bone density prediction result can not be obtained.
  • Bone Density Diagnosis Device Using Distributed Image Noise of Medical Image and Method of Providing Diagnostic Information of Bone Mineral Density (Korean Patent No. 10-1587720)
  • the present invention has been proposed in order to solve the above problems.
  • the present invention provides a method and an apparatus for accurately estimating a bone density using a CT image without a patient performing a bone density test separately.
  • a method of estimating a bone density using a computed tomography (CT) image comprising: And a processor coupled to the input unit, the method comprising: an input step of the input unit of the BMD estimating apparatus obtaining the CT image; and a processor of the BMD estimating apparatus, And an analysis step of generating a bone density estimate for the patient based on a deep learning algorithm.
  • CT computed tomography
  • the deep learning algorithm executed by the processor includes a plurality of synthesis layers in which learning of optimized features for the estimation of bone density is performed, The learning can be performed using the corresponding bone density test value.
  • the method may further include the step of the processor of the bone mineral density estimating apparatus calculating the risk of osteoporosis based on the bone mineral density estimated value and at least one of the patient's race, sex, weight, and age.
  • the method may further include the step of calculating the risk of osteoporosis based on the bone mineral density estimated value and at least one of race, gender, body weight, and age of the patient, and calculating the risk of osteoporosis And transmitting the report data to at least one of a Picture Archiving and Communication System (PACS), an Electronic Medical Record (EMR), and a Web inquiry system.
  • PACS Picture Archiving and Communication System
  • EMR Electronic Medical Record
  • the analysis step may include a step of determining an analysis target image to be used for generating a bone density estimation value from the CT image, wherein the analysis target image includes a preset formula for selecting an image including a bone part, Lt; / RTI > can be determined by an algorithm that recognizes the shape of the object.
  • the deep learning algorithm may be performed by analyzing a correlation between a selected part of the CT images for learning and a corresponding part of the bone density value, And the accuracy of the deep learning algorithm can be determined by comparing the bone density estimated value with the bone density test value corresponding to the remaining image.
  • the deep learning algorithm can perform learning using a modulated image generated by modulating the CT image for learning.
  • the inputting step may further include the step of the BMD estimating apparatus receiving the CT image from the DICOM server.
  • the inputting step may include receiving a CT image from a DICOM server by a Picture Archiving and Communication System (PACS), transmitting a request signal for the CT image to the PACS, Receiving the CT image according to the request signal from the PACS.
  • PACS Picture Archiving and Communication System
  • an apparatus for estimating a bone density using a computed tomography (CT) image includes an input unit for acquiring the CT image, and an input unit for acquiring the bone density And a processor for generating an estimate based on a Deep Learning algorithm.
  • CT computed tomography
  • the deep learning algorithm includes a plurality of synthesis layers (conv. Layers) in which learning of an optimized feature is performed for estimating the bone density, and the deep learning algorithm includes a learning CT image and a bone density test value corresponding to the learning CT image Learning can be carried out using the above.
  • the processor may also calculate the risk of osteoporosis based on the bone mineral density estimate and at least one of the patient's race, gender, weight, and age.
  • the processor may calculate the risk of osteoporosis based on the bone mineral density estimated value and at least one of race, gender, body weight, and age of the patient, and generate report data using the bone density estimated value or the risk of osteoporosis And a communication unit for transmitting the report data to at least one of a Picture Archiving and Communication System (PACS), an Electronic Medical Record (EMR), and a Web inquiry system.
  • PACS Picture Archiving and Communication System
  • EMR Electronic Medical Record
  • Web inquiry system a Web inquiry system
  • the processor may determine an analysis target image to be used for generating a bone density estimation value from the CT image, and the analysis target image may include a predetermined formula for selecting an image including a bone part, Lt; / RTI >
  • the deep learning algorithm may be performed by analyzing a correlation between a selected part of the CT images for learning and a corresponding part of the bone density value, And the accuracy of the deep learning algorithm can be determined by comparing the bone density estimated value with the bone density test value corresponding to the remaining image.
  • the deep learning algorithm can perform learning using a modulated image generated by modulating the CT image for learning.
  • the apparatus may further include a communication unit for receiving the CT image from the DICOM server.
  • the apparatus may further include a communication unit for transmitting a request signal for the CT image to the PACS and receiving the CT image according to the request signal from the PACS.
  • the method and apparatus for estimating a bone density according to an embodiment of the present invention have an effect of accurately predicting a bone density using a CT image even if a patient does not perform a bone density test separately.
  • an optimal image for predicting the bone density is selected and the bone density can be predicted quickly and conveniently.
  • more accurate bone density prediction can be performed by using the deep learning algorithm.
  • 1 and 2 are flowcharts of a method of estimating a bone density according to an embodiment of the present invention.
  • FIGS. 3 to 4 are reference views showing a step of determining an analysis object image according to an embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating a part of the operation of the deep learning algorithm according to an embodiment of the present invention.
  • FIG. 6 is a reference diagram showing a CNN (Convolution Neural Network) of a deep learning algorithm according to an embodiment of the present invention.
  • CNN Convolution Neural Network
  • FIG. 7 is a flowchart illustrating a part of the operation of the deep learning algorithm according to an embodiment of the present invention.
  • FIG. 8 is a block diagram briefly showing a configuration of a bone mineral density estimating apparatus according to an embodiment of the present invention.
  • 9 to 10 are reference views showing an example of a data flow in an apparatus and method for estimating a bone mineral density according to an embodiment of the present invention.
  • first, second, etc. may be used to describe various elements, but the elements should not be limited by the terms.
  • the terms may be named for the purpose of distinguishing one element from another, for example, without departing from the scope of the right according to the concept of the present invention, the first element may be referred to as a second element,
  • the component may also be referred to as a first component.
  • FIG. 1 is a flowchart of a method of estimating a bone density according to an embodiment of the present invention.
  • the BMD estimation apparatus can acquire a CT image (S110).
  • the CT image means an image taken from a CT (Computed Tomography) apparatus. It may be an image taken for the examination of other diseases, not for the image taken for the bone density test.
  • the CT image according to an embodiment of the present invention may be an image directly received from a CT device or an image received directly from a DICOM server.
  • the PACS may have received a CT image from the DICOM server.
  • the BMD estimating apparatus transmits a request signal for the CT image to the PACS
  • the PACS transmits the CT image according to the request signal to the BMD estimating apparatus, so that the BMD estimating apparatus can receive the CT image.
  • the CT image used in the method of estimating a bone density may be an image including a bone part such as a lumbar region or a thigh.
  • a bone part such as a lumbar region or a thigh.
  • the CT image of the abdomen is used, since the lumbar region is included in the CT image, it can be used in the method of estimating the bone density of the present invention.
  • the method of estimating the bone mineral density according to the embodiment of the present invention can be applied as long as the images include only the part of the bone.
  • the BMD estimating apparatus can generate a BMD estimation value for a patient to be a target of the CT image based on the deep learning algorithm (S120).
  • the deep learning algorithm is a method of estimating a bone density using a CT image, in which a plurality of data are collected and used as key features for directly estimating a bone density,
  • the artificial intelligence that learns by itself.
  • the model used in this deep learning algorithm can be briefly described as a stack of artificial neural networks. In other words, it is expressed as a deep neural network (deep neural network) in the sense of a network of deep structure. By learning a large amount of data in a structure composed of a multi-layer network, the characteristics of each image are automatically learned, It is a type that learns the network in a way that minimizes the error of the objective function.
  • the deep learning algorithm can use, for example, a CNN (Convolution Neural Network) based regression model.
  • This CNN is a model suitable for classification of images, especially two-dimensional images. It is composed of a convolution layer which creates feature maps by using a plurality of filters, By repeating the sub-sampling layer to extract features that are invariant to changes in rotation, it is possible to extract various levels of features from low-level features such as points, lines, and surfaces to complex and meaningful high-level features , And using the finally extracted feature as an input value of the existing classification model, it is possible to construct a classification model with higher accuracy.
  • a CNN Convolution Neural Network
  • the risk of osteoporosis can be calculated (S130).
  • the risk of osteoporosis according to one embodiment of the present invention can be calculated based on at least one of the race, sex, weight, and age of the patient, which is the object of the bone density estimation value and the corresponding bone density estimation value.
  • the risk of osteoporosis may mean the degree of risk of osteoporosis, the current status of osteoporosis, or the progress of osteoporosis.
  • the risk of osteoporosis can be a T-value, a Z-value, etc. generally used in determining osteoporosis.
  • the T-value is the standard deviation from the mean BMD of the young adult population in the same gender, which means a difference from healthy adults.
  • the Z-value means the difference from the mean value of bone mineral density of the same age group.
  • the race, gender, weight, and age of the patient may be data entered together at the time of CT image acquisition or data entered at the PACS. Whether or not osteoporosis is recognized can be determined according to a predetermined calculation formula or a value selected by a doctor or the like manager, and the criterion of judgment can be adjusted.
  • the BMD estimation apparatus can generate the report data (S140).
  • the report data refers to data processed by using the bone mineral density estimation value or the risk of osteoporosis obtained through the above-mentioned process, and this means meaningful information to a doctor or a patient.
  • a graph or a report showing the position of the patient in the distribution of bone density of a yellow female young woman can be given.
  • reporting data may include a bone density estimate or a value of the risk itself of osteoporosis.
  • the bone mineral density estimating apparatus may transmit the report data to at least one of PACS, EMR (Electronic Medical Record), and Web inquiry system (S150).
  • the web inquiry system can refer to a system in which a computer or a terminal device can inquire necessary information through an online connection and confirm or download the inquired information.
  • the present invention may refer to a web inquiry system used in the field related to medical records.
  • the doctor or the patient can confirm the above-described report data through the conventional PACS, EMR or web inquiry system which is conventionally used. Therefore, it is possible to confirm the results of the risk of osteoporosis by performing CT scan by examining other diseases even if bone density is not separately measured.
  • the physician or patient can check the reporting data and decide whether to proceed with additional bone density testing.
  • data can be sent together with EMR and Web inquiry systems to enable viewing, querying and statistics.
  • FIG. 2 is a flowchart illustrating a process of determining an analysis target image for generating a bone density estimation value in the method of estimating a bone density according to an embodiment of the present invention.
  • FIGS. 3 to 4 are reference views showing a step of determining an analysis object image according to an embodiment of the present invention.
  • a process of determining an analysis target image will be described with reference to FIG. 2 to FIG.
  • An analysis object image refers to an image used for bone density estimation from among the CT images acquired by the bone density estimation apparatus.
  • the lumbar or femur may be an image.
  • an image more suitable for the bone density estimation can be the analysis target image.
  • the analysis target image is not selected by an expert such as a doctor, but is automatically determined from among all the CT images stored after imaging, thereby making it possible to generate the bone density estimation value more quickly and efficiently.
  • Step S210 is the same as step S110 in FIG.
  • the BMD estimation apparatus can determine the analysis target image (S220). Through the process of S220, among the 37 obtained abdominal CT images 31 as shown in Fig. 3, five images in which the lumbar spine is well-revealed can be determined as the analysis subject images 32. Fig.
  • the method of determining the image to be analyzed can be determined by a preset formula.
  • the predetermined formula may be designed to empirically select an image containing a bone part of the CT image. For example, when the obtained original CT image is N sheets and five analysis target images are determined, the following equation can be used.
  • the method of determining the image to be analyzed may be performed by an algorithm for recognizing the shape of the bone part. For example, in the case of the abdominal CT image, the image in which the shape of the most important part of the lumbar spine is most appropriately recognized can be recognized and determined as the analysis target image.
  • the algorithm for recognizing the shape of the bone part may be performed by a deep learning algorithm or by a separate image recognition algorithm.
  • a CNN-based model can be used to recognize the shape of the bone part.
  • the bone density estimation device can receive the analysis target image determination signal from a doctor or the like (not shown).
  • the analysis target image signal means a signal indicating that the analysis target image is to be used for establishing or approving the use of the bone density estimation value.
  • the analysis target image determination signal may be a signal input by a physician or the like.
  • the doctor can confirm whether the image to be analyzed is properly determined, and an opportunity to finally determine the image is provided.
  • the doctor or other specialist can perform the correction work such as replacing the analysis target image with another image without inputting the determination target image signal.
  • the bone density estimation apparatus can adjust the analysis target image (S230).
  • the deep learning algorithm receives the formatted data, it may be necessary to generate the formatted data. Therefore, you can adjust the size if different sizes are used for different CT devices or shooting options, or you can trim the image so that the bone part is located at the center of the image to be analyzed.
  • the image to be analyzed 41 is an image having a size of 512 (h) * 512 (w) * 5 (c) (w) * 5 (c).
  • FIG. 5 is a flowchart illustrating a part of the operation of the deep learning algorithm according to an embodiment of the present invention.
  • the deep learning algorithm may acquire a bone density test value corresponding to the learning CT image and the learning CT image (S510).
  • the learning CT image is a CT image used for learning a deep learning algorithm, and the shape of the image itself may be the same as the CT image described above. Also, the CT image described above may be used as a learning CT image.
  • the bone density test value may mean a bone density measurement value obtained by a generally performed bone density testing method.
  • Typical bone mineral density (BMD) tests include dual energy X-ray absorptiometry (DXA), ultrasound, and quantitative computed tomography (CT). DXA is the method used in most hospitals.
  • the corresponding bone density test value of the learning CT image may mean a bone density test value in a period adjacent to the date when the CT image is taken by the patient who is the subject of the learning CT image.
  • the adjacent period can be around a certain period based on the CT imaging date. For example, it may be a bone density test value about one month after the CT image is taken.
  • the data of the CT image and CT image taken and the results of the bone density test of adjacent seals can be seen as one data.
  • the accuracy of the estimated bone density can be increased.
  • the deep learning algorithm can perform the learning by analyzing the correlation between the learning CT image and the corresponding bone density test value (S520). Deep learning algorithms can find and learn the most optimized features for bone density estimation by themselves. In addition, the principle of determining the analysis target image described above can be similarly applied to the learning CT image.
  • the deep learning algorithm may perform learning for a selected part of the CT images for learning and determine the accuracy of the deep learning algorithm for the remaining unselected CT images.
  • 90% of the learning CT images can be used for learning of the deep learning algorithm, and the remaining 10% of the learning CT images can be used for accuracy determination.
  • the selection of images can be made randomly.
  • the method of determining the accuracy can be performed by generating a bone density estimation value for the remaining non-selected images, and comparing the bone density test values corresponding to the selected images to determine how much the difference has occurred.
  • the bone density estimation value can be generated through the deep learning algorithm, and the bone density estimation value and accuracy can be determined.
  • FIG. 6 is a reference diagram showing a CNN (Convolution Neural Network) of a deep learning algorithm according to an embodiment of the present invention.
  • CNN Convolution Neural Network
  • a learning process and a bone density estimation value generation process for generating a bone density estimation value approximating a bone density test value by the deep learning algorithm can be performed by CNN.
  • Deep learning algorithms can be performed by CNN to extract key features for bone density estimation in CT images.
  • FIG. 7 is a flowchart illustrating a part of the operation of the deep learning algorithm according to an embodiment of the present invention.
  • the deep learning algorithm can acquire a learning CT image (S710).
  • the process of step S710 may be the same as that of step S510. That is, it is possible to obtain the bone density test value corresponding to the learning CT image.
  • the deep learning algorithm can generate a modulated image by modulating the CT image for learning (S720).
  • the modulated image according to an exemplary embodiment of the present invention may be an image obtained by artificially modifying or modulating a CT image for learning.
  • As a method of generating a modulated image it is possible to change the size or angle of the CT image for learning or insert noise.
  • Deep learning algorithms can generate modulated images for various learning CT images in various ways.
  • the deep learning algorithm can perform learning using the modulated image (S730).
  • the method of performing the learning in step S730 may be the same as the learning method described above.
  • the depth learning algorithm extracts the key features for bone density estimation from the CT image, and extracts far more various features than when extracting features using only the original CT image of learning. Therefore, it is possible to generate a more accurate bone density estimation value.
  • FIG. 8 is a block diagram briefly showing a configuration of a bone mineral density estimating apparatus according to an embodiment of the present invention.
  • the apparatus 10 for estimating a bone mineral density may include an input unit 11 and a processor 12.
  • the input unit 11 can acquire a CT image.
  • the processor 12 may generate a bone density estimate for the subject of the CT image based on a deep learning algorithm.
  • the processor 12 may also calculate the risk of osteoporosis based on the bone mineral density estimate and at least one of the patient's race, gender, weight, and age.
  • the processor 12 may also calculate the risk of osteoporosis based on the bone mineral density estimate and at least one of the patient's race, gender, weight, and age, and generate the report data using the bone density estimate or the risk of osteoporosis .
  • the processor 12 may further include a communication unit for transmitting the report data to at least one of a Picture Archiving and Communication System (PACS), an Electronic Medical Record (EMR), and a Web inquiry system.
  • PACS Picture Archiving and Communication System
  • EMR Electronic Medical Record
  • Web inquiry system a communication unit for transmitting the report data to at least one of a Picture Archiving and Communication System (PACS), an Electronic Medical Record (EMR), and a Web inquiry system.
  • PACS Picture Archiving and Communication System
  • EMR Electronic Medical Record
  • the processor 12 can determine an analysis target image to be used for generating a bone density estimation value in the CT image.
  • the analysis target image may be determined by a predetermined formula for selecting an image including a bone part or an algorithm for recognizing the shape of the bone part.
  • the apparatus 10 for estimating a bone density may further include a communication unit (not shown).
  • the communication unit can receive the CT image from the DICOM server.
  • the communication unit may transmit a request signal for the CT image to the PACS, and may receive the CT image according to the request signal from the PACS.
  • 9 to 10 are reference views showing an example of a data flow in an apparatus and method for estimating a bone mineral density according to an embodiment of the present invention.
  • 9 to 10 illustrate an example of a method of transmitting and receiving data to and from another server, device, or apparatus, in the flow of the method of estimating a bone density according to the first embodiment of the present invention.
  • the BMD estimation apparatus can directly receive a CT image from a CT apparatus or a DICOM server.
  • the DICOM server can simultaneously transmit the CT image to the PACS and the bone density estimating device.
  • the BMD estimation apparatus may receive and view the stored image from the PACS.
  • the PACS may have received a CT image from the DICOM server.
  • the PACS transmits the CT image according to the request signal to the BMD estimating apparatus, so that the BMD estimating apparatus can receive the CT image.
  • the BMD estimator may transmit the BMD analysis results to at least one of a PACS, an EMR, or a Web query system.
  • the analysis result can be generated by DICOM. 9 and 10 illustrate the case where the analysis result is generated as DICOM and transmitted to the PACS.

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Abstract

L'invention concerne un procédé par lequel un appareil d'estimation de la densité minérale osseuse estime la densité minérale osseuse à l'aide d'une image de tomodensitométrie (CT), le procédé, mis en œuvre par un appareil d'estimation de la densité minérale osseuse, comprenant : une étape d'entrée pour acquérir une image CT ; et une étape d'analyse pour générer une valeur d'estimation de la densité minérale osseuse pour le patient de l'image CT, sur la base d'un algorithme d'apprentissage profond, l'algorithme d'apprentissage profond s'entraînant en utilisant une image CT pour l'apprentissage et une valeur de densitométrie osseuse correspondant à l'image CT pour l'apprentissage.
PCT/KR2017/012911 2017-11-15 2017-11-15 Procédé d'estimation de la densité minérale osseuse et appareil l'utilisant Ceased WO2019098399A1 (fr)

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CN116458909A (zh) * 2023-04-10 2023-07-21 清华大学 使用锥形束dr设备测量三维骨密度分布的方法及装置
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KR20170046105A (ko) * 2015-09-24 2017-04-28 주식회사 뷰노코리아 의료 영상 판독 과정에서 사용자의 시선 정보를 이용한 판독 효율 증대 방법 및 그 장치
JP2017118985A (ja) * 2015-12-28 2017-07-06 朝日レントゲン工業株式会社 骨粗鬆症診断支援装置、骨粗鬆症診断支援プログラム、及び骨粗鬆症診断支援方法
JP2017164412A (ja) * 2016-03-18 2017-09-21 メディア株式会社 骨粗鬆症診断支援装置
WO2017177182A1 (fr) * 2016-04-07 2017-10-12 Icahn School Of Medicine At Mount Sinai Appareil, procédé et système pour fournir des implants osseux personnalisables

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US20240225581A9 (en) * 2020-09-09 2024-07-11 Promedius Inc. Medical image processing apparatus, medical image learning method, and medical image processing method
US12533099B2 (en) * 2020-09-09 2026-01-27 Promedius Inc. Medical image processing apparatus, medical image learning method, and medical image processing method
CN113869443A (zh) * 2021-10-09 2021-12-31 新大陆数字技术股份有限公司 基于深度学习的颌骨密度分类方法、系统及介质
CN116458909A (zh) * 2023-04-10 2023-07-21 清华大学 使用锥形束dr设备测量三维骨密度分布的方法及装置
CN116458909B (zh) * 2023-04-10 2024-05-07 清华大学 使用锥形束dr设备测量三维骨密度分布的方法及装置

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