WO2022177044A1 - Appareil et procédé pour générer une image radiologique thoracique haute résolution à l'aide d'un réseau neuronal antagoniste génératif conditionnel multi-échelle basé sur un mécanisme d'attention - Google Patents

Appareil et procédé pour générer une image radiologique thoracique haute résolution à l'aide d'un réseau neuronal antagoniste génératif conditionnel multi-échelle basé sur un mécanisme d'attention Download PDF

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WO2022177044A1
WO2022177044A1 PCT/KR2021/002628 KR2021002628W WO2022177044A1 WO 2022177044 A1 WO2022177044 A1 WO 2022177044A1 KR 2021002628 W KR2021002628 W KR 2021002628W WO 2022177044 A1 WO2022177044 A1 WO 2022177044A1
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chest
ray image
discriminator
branch
generating
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Korean (ko)
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장혁재
장영걸
안경진
맹신희
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연세대학교 산학협력단
주식회사 온택트헬스
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • 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

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  • the present invention relates to an apparatus and method for generating a high-resolution chest X-ray image using a multi-scale conditional adversarial generating neural network based on a mechanism of interest.
  • a common test for early detection of heart and lung diseases is chest X-ray, and the X-ray is transmitted through the chest area to determine the presence or absence of heart and lung-related diseases.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • chest X-ray examination is relatively inexpensive, can be taken in a short time, and has the advantage of low exposure dose.
  • a balanced data set is very important for learning convergence in learning the current supervised learning-based AI model well.
  • most of the medical data collected due to the difference in disease prevalence is numerically unbalanced, and when used as training data without pre-processing, it may overfit to a specific disease, resulting in undesirable convergence results.
  • learning progresses well for frequently occurring diseases (atelectasis, pleural effusion, infiltration, etc.), whereas other diseases (pneumonia, cardiac hypertrophy, etc.) have relatively poor learning performance.
  • Korean Patent Registration No. 10-2119056 discloses a method and apparatus for learning a medical image based on a generative adversarial neural network.
  • the present invention provides an apparatus and method for generating a chest X-ray image that can generate a high-resolution image that reflects the characteristics of each disease well with only one network for the purpose of solving the problem of numerical imbalance of chest X-ray image data sets for multiple diseases. would like to provide
  • an embodiment of the present invention receives a latent variable and a condition variable for a characteristic of a specific disease and up-sampling for each branch to perform up-sampling for each of the specific resolutions of the specific
  • a model building unit for constructing a multi-scale conditional adversarial generation neural network including a generator generating a plurality of chest X-ray images indicating a disease and a discriminator for discriminating whether the plurality of chest X-ray images are authentic, and the multi-scale conditional adversarial generation
  • the apparatus for generating a chest X-ray image comprising an image generator for generating a chest X-ray image representing the specific disease with a resolution greater than or equal to a preset value by inputting the latent variable and the condition variable for the characteristic of a specific disease into a neural network can provide
  • another embodiment of the present invention receives a latent variable and a condition variable for the characteristic of a specific disease, and up-sampling for each quarter to obtain a plurality of chest X-ray images representing the specific disease with different resolutions.
  • generating a multi-scale conditionally adversarial generating neural network including a generating generator and a discriminator for discriminating whether or not the plurality of chest X-ray images are authentic; It is possible to provide a method for generating a chest X-ray image, which includes generating a chest X-ray image indicating the specific disease by inputting a condition variable and having a resolution greater than or equal to a preset value.
  • any one of the above-described problem solving means of the present invention it is possible to control the target disease of the chest X-ray image generated by adding a condition variable as an input, thereby eliminating the inefficiency of learning as well as learning when the learning is extremely unbalanced. This impossible problem can be solved.
  • FIG. 1 is a diagram illustrating an apparatus for generating a chest X-ray image according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating a network structure of a multi-scale conditional adversarial generating neural network according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating a loss graph of a generator and a discriminator according to an embodiment of the present invention.
  • FIG. 5 is a view showing a chest X-ray image generated as a result of comparing the performance of the experimental example and the comparative example of the present invention.
  • FIG. 6 is a flowchart illustrating a method for generating a chest X-ray image according to an embodiment of the present invention.
  • a "part" includes a unit realized by hardware, a unit realized by software, and a unit realized using both.
  • one unit may be implemented using two or more hardware, and two or more units may be implemented by one hardware.
  • Some of the operations or functions described as being performed by the terminal or device in this specification may be instead performed by a server connected to the terminal or device. Similarly, some of the operations or functions described as being performed by the server may also be performed in a terminal or device connected to the corresponding server.
  • FIG. 1 is a diagram illustrating an apparatus for generating a chest X-ray image according to an embodiment of the present invention
  • FIG. 2 is a diagram illustrating a network structure of a multi-scale conditional adversarial generation neural network according to an embodiment of the present invention.
  • the X-ray image generating apparatus 1 may generate a high-resolution image by well reflecting detailed characteristics of the ribs, diaphragm, lung, heart, etc. in the chest X-ray image through a multi-scale conditional adversarial generating neural network.
  • a high-resolution image is essential to reflect detailed characteristics of organs in a chest X-ray image.
  • a high-resolution image can be generated by learning the multi-scale image distribution from a low-resolution image to a high-resolution image.
  • the multi-scale conditional adversarial generation neural network can generate images for multiple diseases with only one network by adding a condition variable, which is a disease condition control factor.
  • the shape of the skeleton or organs is different for each patient (patient-specific), and the distribution of the intensity level is unclear due to peripheral organs such as blood vessels and noise, so it is difficult to determine the disease by only considering regional characteristics. .
  • the problem of long-distance dependence within the image is essential because the diseased region may span the entire image or several may be distributed far apart.
  • An example of the X-ray image generating apparatus 1 may include a mobile terminal capable of wired/wireless communication as well as a personal computer such as a desktop or a notebook computer.
  • a mobile terminal is a wireless communication device that guarantees portability and mobility, and includes not only smartphones, tablet PCs, and wearable devices, but also Bluetooth (BLE, Bluetooth Low Energy), NFC, RFID, Ultrasonic, infrared, and Wi-Fi ( WiFi) and Li-Fi (LiFi) may include various devices equipped with a communication module.
  • BLE Bluetooth Low Energy
  • NFC NFC
  • RFID Ultrasonic, infrared
  • WiFi Wi-Fi
  • Li-Fi Li-Fi
  • the X-ray image generating apparatus 1 is not limited to the shape shown in FIG. 1 or those exemplified above.
  • the chest X-ray image generating apparatus 1 may include a model building unit 100 and an image generating unit 110 .
  • the model building unit 100 may build a multi-scale conditional adversarial generating neural network 200 including a generator 201 and discriminators 203 , 205 , and 207 .
  • the multi-scale conditional adversarial generating neural network 200 may be, for example, a network for learning multi-scale image distribution from a low-resolution image to a high-resolution image by extending StackGAN++ and LSGAN.
  • the multi-scale conditional adversarial generation neural network 200 is composed of one generator 201 and a plurality of discriminators 203, 205, and 207 to form a tree-structured network.
  • the generator 201 of the multi-scale conditional adversarial generative neural network 200 receives a latent variable and a condition variable for the characteristic of a specific disease, and up-sampling it for each branch to obtain a plurality of units representing a specific disease with different resolution.
  • a chest X-ray image can be generated.
  • the generator 201 may learn a multi-scale image distribution from a low-resolution image to a high-resolution image for each branch in the generator 201 to gradually generate a high-quality chest X-ray image.
  • condition variable allows control of the chest X-ray image to be generated on behalf of a part of the latent variable for each disease.
  • the condition variable may be a one-hot encoding value for any one of a plurality of classes in order to generate a plurality of chest X-ray images indicating a plurality of chest diseases.
  • one-hot encoding values for 8 classes may be assigned to the condition variable in order to generate chest X-ray images corresponding to 8 representative chest diseases.
  • the initial distribution expressed by the standard normal distribution may be approximated to data within the standard data distribution of the corresponding scale for each quarter while passing through several hidden layers of the generator 201 together with the condition variable.
  • the discriminators 203 , 205 , and 207 of the multi-scale conditional adversarial generation neural network 200 may discriminate whether a plurality of chest X-ray images are authentic or not.
  • the discriminators 203 , 205 , and 207 can further discriminate whether the plurality of chest X-ray images generated by the generator 201 satisfy the condition variable, unlike the discriminator of the conventional adversarial generative neural network. .
  • the discriminator 203, 205, 207 determines whether the multi-scale image generated by the generator 201 is well made by the discriminator 203, 205, 207 corresponding to each branch of the generator 201. By evaluating, it guides the constructor 201 to be optimized.
  • the generator 201 may be divided into three branches to generate a chest X-ray image having a high resolution at a low resolution.
  • the generator 201 includes a first branch 209 generating a chest X-ray image having a first resolution, a second branch 211 generating a chest X-ray image having a second resolution higher than the first resolution, and A third branch 213 for generating a chest X-ray image having a third resolution higher than the second resolution may be included.
  • the generator 201 generates a chest X-ray image having the primary color and structure by approximating the image distribution of the first resolution in the first branch, and the chest X-ray image having the primary color and structure in the second branch
  • a chest X-ray image expressing detailed information may be generated by approximating the image distribution of the second resolution higher than the first resolution by receiving the gradient of .
  • the first branch 209 is a sub-generator for a low-resolution image 64x64, including four up-block layers, an attention layer, and a 3*3 convolution layer. ) may be included.
  • a nearest-neighbor method may be used for upsampling in order to mitigate the occurrence of checkerboard-artifact.
  • the sub-generators of the second branch 211 and the third branch 213 may include a joining layer and two residual layers and an up-block layer.
  • a 128 ⁇ 128 chest X-ray image and a 256 ⁇ 256 chest X-ray image may be output through 3 ⁇ 3 convolution after passing through the four layers.
  • Each quarter discriminator (203, 205, 207) consists of a down-sampling layer, and for calculating conditional and unconditional loss functions, the input part of the previous step of the last layer is divided into two parts and the condition variable is combined in only one place. can do it
  • the discriminators 203 , 205 and 207 are the first discriminator 203 , which determines whether the chest X-ray image generated in the first branch 209 is authentic or not, and the chest X-ray image generated in the second branch 211 .
  • Equation 1 the loss function of the discriminator of the i-th generator 201 branch is It can be defined as Equation 1.
  • the values of a and b are set to 0 and 1, respectively.
  • the generator 201 is expressed as the sum of the discriminator loss functions for each branch, and the loss function may be defined as in Equation (2).
  • the value of d is set to 1.
  • the generator 201 is trained in a direction of approximating the distribution of the non-conditional image and the conditional image with respect to images of high resolution from low resolution.
  • modeling the image distribution in multi-scale has the advantage of transferring the gradient to the initial layer well because the gradient for each scale is generated and transmitted.
  • these features play a key role in stabilizing network learning, making it possible to generate high-resolution images.
  • FIG. 3 is a diagram illustrating a loss graph of a generator and a discriminator according to an embodiment of the present invention.
  • Figure 3 (a) shows the loss graph of the generator 201 in the learning phase
  • Figure 3 (b) shows the loss graph of the discriminators (203, 205, 207).
  • the x-axis means iteration
  • the y-axis means loss.
  • FIG. 4 is a view showing the Frechet Densnet distance as a performance comparison result of the Experimental Example and Comparative Example of the present invention
  • FIG. 5 is a view showing a chest X-ray image generated as a performance comparison result of the Experimental Example and Comparative Example of the present invention. to be.
  • the present applicant used 112,120 chest X-ray image data for 14 heart and lung diseases provided by the NIH Clinical Center for the chest X-ray image data augmentation experiment. Among them, only 8 representative diseases (atelectasis, cardiac hypertrophy, pleural effusion, infiltration, mass, nodule, pneumonia, and pneumothorax) were used.
  • the present applicant conducted two experiments for 8 representative diseases of the chest for performance comparison.
  • the first experiment consists of 1) a conventional deep convolutional adversarial generating neural network and 2) generating high-resolution images (256x256) for each disease with our multi-scale conditional adversarial generating neural network.
  • the performance of the network is compared with the Fre'chet DenseNet distance (FDD) (FIG. 4).
  • the number of images used in the experiment is about 100,000, and when the batch size is 16, 7,000 repetitions per epoch are made.
  • the discriminator was gradually stabilized after 5 epochs, and the function fluctuation was stabilized in the generator around 20 epochs (repeat 140,000).
  • both models lost their balance and both models collapsed and the learning was stopped.
  • the Fre'chet Inception Distance is a measure of the difference between two normal distributions, and it was proposed to compensate for the disadvantage that the Inception Score (IS) does not use the distribution of actual data.
  • the FID is calculated as in Equation 3, and a smaller value means better quality.
  • (m, C) and (m w , C w ) is the mean and covariance of the generated data and the actual data.
  • DenseNet-121 was trained with 78,468 training data and 11,219 validation data. And based on the learned model, the performance of the model was quantitatively evaluated by obtaining the FDD values for the real image, cDCGAN, and the real image and the proposed model.
  • the actual video means the remaining part that is not used for DenseNet-121 training.
  • the number of image data for each class is uneven. Therefore, the number of actual images and generated images by class is 1,000 attelectasis, 575 cardiomegaly, 1,000 effusion, 1,000 infiltration, 729 mass, 774 nodule, 242 pneumonia, and 539 pneumothorax, for a total of 5,859 images. The test was performed in the same way.
  • the FDD value calculated by calculating the distance between the real image and the image generated through the multi-scale conditional adversarial generating neural network of the present application is the distance between the real image and the image generated by cDCGAN, a conventional deep convolutional adversarial generating neural network. Since it is generally lower than the calculated FDD value, it can be confirmed that the multi-scale conditional adversarial generating neural network of the present application is superior.
  • the second experiment is a qualitative comparison of the experimental results from the conventional adversarial generative neural network, DCGAN, and our multi-scale conditionally adversarial neural network, with actual images. The location was found and marked.
  • FIG. 5 shows the results of real data and the conventional deep convolutional adversarial neural network, and the multi-scale conditional adversarial neural network of the present application.
  • the specific method for distinguishing the disease is as follows.
  • Atelectasis causes air loss of all or part of the lungs, usually accompanied by a decrease in lung volume, biased respiratory tract (airway), or whitening of the lungs.
  • Cardiomegaly is defined as a condition in which the ventricular wall (muscle) thickens and the weight of the myocardium increases, and the length of the cardiac shadow occupies more than half of the internal length of the thoracic shadow.
  • Effusion is a stagnant fluid in the pleural cavity, which shows an unbalanced increase in shading in the left and right thoracic cavities.
  • Infiltration is a form of inflammatory cells gathered in normal tissue, and it shows an increase in alveolar shade and is well seen around the lungs.
  • a mass is a large mass with a diameter of 3 cm or more and increased shading. It can be described anywhere in the chest, including the lungs, pleura, mediastinum (sinus), and chest wall.
  • a nodule is a round lung shade with a border of 3 cm or less.
  • Pneumonia is an inflammatory reaction in the lungs, mainly caused by bacterial infection, and shadows such as mesh or honeycomb are found in the image.
  • pneumothorax a hole in the pleural cavity is formed and a large air sac is visible.
  • the results of the conventional deep convolutional adversarial neural network have significantly lowered image resolution compared to the actual data. That is, the shape of the ribs or the shape of the lung bronchus is not clearly visible, so it is difficult to say that the main features have been properly learned.
  • the results of our multi-scale conditional adversarial neural network showed image features and resolution that were indistinguishable from the actual images, and the characteristics of each disease were also learned so well that there was no significant difference from the actual images. For example, in the case of cardiac hypertrophy, compared to other diseases, the shape of the heart is significantly increased, which is a result of well learning the characteristic of the disease, 'increase in heart size'.
  • FIG. 6 is a flowchart illustrating a method for generating a chest X-ray image according to an embodiment of the present invention.
  • the method for generating a chest X-ray image according to an embodiment illustrated in FIG. 6 includes operations that are time-series processed by the apparatus 1 for generating a chest X-ray image illustrated in FIG. 1 . Therefore, even if omitted below, it is also applied to the method for generating a chest X-ray image performed according to the embodiment shown in FIG. 6 .
  • a multi-scale conditional adversarial generation neural network including a generator and a discriminator may be generated.
  • the generator may generate a plurality of chest X-ray images representing specific diseases having different resolutions by receiving the latent variable and the condition variable for the characteristic of a specific disease and performing up-sampling for each branch.
  • the discriminator may discriminate whether the plurality of chest X-ray images are authentic or not.
  • a chest X-ray image may be generated through the multi-scale conditional adversarial generation neural network. For example, by inputting a latent variable and a condition variable for a characteristic of a specific disease into a multi-scale conditional adversarial generating neural network, a chest X-ray image representing a specific disease with a resolution greater than or equal to a preset value may be generated.
  • the chest X-ray image generating method described with reference to FIG. 6 may be implemented in the form of a computer program stored in a medium, or in the form of a recording medium including instructions executable by a computer, such as a program module executed by a computer.
  • Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media.
  • Computer-readable media may include computer storage media.
  • Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

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Abstract

L'invention concerne un appareil pour générer une image radiologique thoracique haute résolution à l'aide d'un réseau neuronal antagoniste génératif conditionnel multi-échelle basé sur un mécanisme d'attention pouvant comprendre : une unité de construction de modèle pour construire un réseau neuronal antagoniste génératif conditionnel multi-échelle comprenant un générateur, qui reçoit une variable potentielle et une variable conditionnelle pour la caractéristique d'une maladie spécifique et effectue un suréchantillonnage à chaque quart pour générer une pluralité d'images radiologiques thoraciques indiquant la maladie spécifique dans différentes résolutions, et un discriminateur pour identifier si la pluralité d'images radiologiques thoraciques est vraie ou non ; et une unité de génération d'image, qui entre la variable potentielle et la variable conditionnelle pour la caractéristique de la maladie spécifique dans le réseau neuronal antagoniste génératif conditionnel multi-échelle pour générer une image radiologique thoracique indiquant la maladie spécifique dans une résolution supérieure ou égale à une valeur prédéfinie.
PCT/KR2021/002628 2021-02-18 2021-03-03 Appareil et procédé pour générer une image radiologique thoracique haute résolution à l'aide d'un réseau neuronal antagoniste génératif conditionnel multi-échelle basé sur un mécanisme d'attention WO2022177044A1 (fr)

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