WO2024259613A1 - Procédés pour détecter et afficher des pixels de calcification dans des images radiologiques à l'aide de réseaux neuronaux artificiels - Google Patents

Procédés pour détecter et afficher des pixels de calcification dans des images radiologiques à l'aide de réseaux neuronaux artificiels Download PDF

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WO2024259613A1
WO2024259613A1 PCT/CN2023/101579 CN2023101579W WO2024259613A1 WO 2024259613 A1 WO2024259613 A1 WO 2024259613A1 CN 2023101579 W CN2023101579 W CN 2023101579W WO 2024259613 A1 WO2024259613 A1 WO 2024259613A1
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image
calcification
neural network
scattered
normal tissue
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Xuran ZHAO
Peng Chen
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Shenzhen Xpectvision Technology Co Ltd
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Shenzhen Xpectvision Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/0475—Generative networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30068—Mammography; Breast
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/031—Recognition of patterns in medical or anatomical images of internal organs

Definitions

  • Breast calcification is one of the main signs of breast cancer, and mammography is the most commonly used imaging method in breast cancer screening and diagnosis.
  • a method comprising: obtaining a training data set which comprises multiple actual normal tissue images and multiple calcification scattered tissue images; and configuring a computer system that implements a generative neural network, using the training data set and a discriminator neural network.
  • An output of the generative neural network is a processed normal tissue image generated from an image input into the generative neural network.
  • An output of the discriminator neural network is a probability of an image input into the discriminator neural network being an actual normal tissue image.
  • a region of interest is outlined which comprises multiple calcification pixels and multiple normal tissue pixels.
  • a number of all pixels of the region of interest of said each image is at least 10 times a number of all calcification pixels of the region of interest of said each image.
  • a number of all pixels of the region of interest of said each image is at least 50%a number of all pixels of said each image.
  • said configuring the computer system comprises: computing a first loss function of parameters of the computer system, the parameters representing associations among nodes of the generative neural network, the first loss function representing (A) deviation between the image input into the generative neural network and a generated image output by the generative neural network when the image input into the generative neural network is an actual normal tissue image, and (B) the probability, determined by the discriminator neural network, of the generated image being an actual normal tissue image, when the image input into the generative neural network is a calcification scattered tissue image; and upon determination that a first termination condition is not satisfied, adjusting the values of the parameters.
  • said configuring the computer system comprises: computing a second loss function of associations among nodes of the discriminator neural network, the second loss function representing the probability, determined by the discriminator neural network, of the generated image being an actual normal tissue image, when the image input into the generative neural network is a calcification scattered tissue image; and upon determination that a second termination condition is not satisfied, adjusting the associations among nodes of the discriminator neural network.
  • each of the multiple actual normal tissue images and the multiple calcification scattered tissue images is an X-ray image.
  • Also disclosed herein is a method, comprising: generating with a neural network a processed normal tissue image based on a calcification scattered tissue image; and generating a calcification image based on (A) the calcification scattered tissue image and (B) the processed normal tissue image.
  • said generating the calcification image is based on a difference between (A) the calcification scattered tissue image and (B) the processed normal tissue image.
  • said generating the calcification image comprises subtracting the processed normal tissue image from the calcification scattered tissue image resulting in a residual image.
  • said generating the calcification image further comprises, for each pixel of the residual image whose value is less than 0, setting a pixel value of said each pixel to 0.
  • the method further comprises generating a calcification enhanced image based on (A) the calcification scattered tissue image and (B) the calcification image.
  • said generating the calcification enhanced image comprises: multiplying the calcification image by a coefficient resulting in a multiply image; and adding the multiply image to the calcification scattered tissue image.
  • the method further comprises filtering out single pixel bright spots in the calcification image.
  • the method further comprises generating the calcification scattered tissue image, which comprises: creating a foreground mask for an input image; and cropping the input image based on the foreground mask, resulting in the calcification scattered tissue image.
  • the processed normal tissue image and the calcification scattered tissue image are of the same size.
  • the corresponding pixel of the processed normal tissue image is at a different degree of brightness; and for each normal tissue pixel of the calcification scattered tissue image, the corresponding pixel of the processed normal tissue image is at the same degree of brightness.
  • the calcification scattered tissue image is an X-ray image.
  • Also disclosed herein is a computer program product comprising a non-transitory computer readable medium having instructions recorded thereon, the instructions when executed by a computer implementing a method above.
  • Fig. 1A –Fig. 1D show X-ray images of human breast regions.
  • Fig. 2 shows a flowchart generalizing a method for training artificial neural networks, according to an embodiment.
  • Fig. 3A –Fig. 3D show X-ray images of human breast regions.
  • Fig. 4 shows a flowchart generalizing a method for detecting and displaying calcification pixels in X-ray images, according to an embodiment.
  • a normal tissue pixel in an X-ray image of a specimen is a pixel caused by X-rays that have interacted with normal tissues in the specimen.
  • a threshold value may be specified, and a pixel may be considered a normal tissue pixel if its degree of brightness is at most the specified threshold value.
  • a calcification pixel in an X-ray image of a specimen is a pixel caused by X-rays that have interacted with calcification in the specimen.
  • a pixel may be considered a calcification pixel if its degree of brightness exceeds the specified threshold value.
  • calcifications are more absorptive to X-rays than normal tissues and therefore appear as brighter spots on X-ray images.
  • calcification pixels have higher degrees of brightness than normal tissue pixels.
  • An actual normal tissue image is an X-ray image that has normal tissue pixels but no calcification pixels.
  • the actual normal tissue image is captured by an X-ray detector.
  • a calcification scattered tissue image is an X-ray image that has both normal tissue pixels and calcification pixels.
  • the calcification scattered tissue image is captured by an X-ray detector.
  • a processed normal tissue image is an image that has normal tissue pixels but no calcification pixels.
  • the processed normal tissue image is generated by an artificial neural network based on a calcification scattered tissue image.
  • a calcification image is an image that has (A) calcification pixels and (B) the remaining pixels having degrees of brightness way below the specified threshold value and near zero. As a result, the calcification image displays bright calcification pixels on a dark background.
  • a training data set may be collected from hospitals; the training data set may include (A) multiple actual normal tissue images of human breast regions and (B) multiple calcification scattered tissue images of human breast regions.
  • Fig. 1A shows an actual normal tissue image 110 of the training data set. Note that there are only normal tissue pixels and no calcification pixels in the actual normal tissue image 110.
  • Fig. 1B shows a calcification scattered tissue image 120 of the training data set. Note that there are both normal tissue pixels and calcification pixels (e.g., calcification pixels 123) in the calcification scattered tissue image 120.
  • the training data set may be obtained by (i.e., sent to or loaded to) a computer system (not shown) .
  • a generative neural network and a discriminator neural network may be constructed and trained by the computer system using the training data set.
  • the computer system is configured to implement the generative neural network, using the training data set and the discriminator neural network.
  • the generative neural network may be used to (A) receive as input a calcification scattered tissue image of a human breast region and (B) generate as output a processed normal tissue image based on the received calcification scattered tissue image.
  • the generative neural network may receive as input a calcification scattered tissue image 130 (Fig. 1C) and generate as output a processed normal tissue image 140 (Fig. 1D) based on the calcification scattered tissue image 130.
  • a calcification scattered tissue image 130 e.g., calcification pixels 133
  • the processed normal tissue image 140 e.g., calcification pixels 133
  • the discriminator neural network may (A) receive as input an input image, and (B) generate as output a probability of that input image being an actual normal tissue image.
  • Fig. 2 shows a flowchart 200 generalizing the method for training the generative neural network and the discriminator neural network, using the training data set, according to an embodiment.
  • the method may include obtaining a training data set which comprises multiple actual normal tissue images and multiple calcification scattered tissue images.
  • the computer system obtains the training data set which includes (A) multiple actual normal tissue images and (B) multiple calcification scattered tissue images.
  • the method may include configuring a computer system that implements a generative neural network, using the training data set and a discriminator neural network.
  • the computer system constructs and trains the generative neural network and the discriminator neural network, using the training data set.
  • an output of the generative neural network is a processed normal tissue image generated from an image input into the generative neural network.
  • the generative neural network (A) receives as input a calcification scattered tissue image of a human breast region and (B) generates as output a processed normal tissue image based on the received calcification scattered tissue image.
  • an output of the discriminator neural network is a probability of an image input into the discriminator neural network being an actual normal tissue image.
  • the discriminator neural network (A) receives as input an input image, and (B) generate as output a probability of that input image being an actual normal tissue image.
  • a region of interest in each of the multiple calcification scattered tissue images of the training data set, may be outlined which includes multiple calcification pixels and multiple normal tissue pixels.
  • a region of interest 127 in the calcification scattered tissue image 120, a region of interest 127 may be outlined using a polygon 125.
  • the region of interest 127 includes multiple calcification pixels (e.g., the calcification pixels 123) and multiple normal tissue pixels.
  • the region of interest of said each image does not have to include all the calcification pixels of said each image.
  • the region of interest 127 of the calcification scattered tissue image 120 does not include calcification pixels 129.
  • the regional outlining e.g., drawing the polygon 125 of Fig. 1B
  • the regional outlining may be performed manually by radiologists or other professionals before the training data set is sent to the computer system for training the generative neural network and the discriminator neural network.
  • the number of all pixels of the region of interest of said each image may be at least 10 times the number of all calcification pixels of the region of interest of said each image.
  • the number of all pixels of the region of interest 127 may be at least 10 times the number of all calcification pixels (including the calcification pixels 123) of the region of interest 127.
  • the number of all normal tissue pixels of the region of interest 127 is at least 9 times the number of all calcification pixels of the region of interest 127.
  • the number of all pixels of the region of interest of said each image may be at least 50%the number of all pixels of said each image.
  • the number of all pixels of the region of interest 127 may be at least 50%the number of all pixels of the calcification scattered tissue image 120.
  • a first loss function for training the generative neural network may be computed based on 2 components: (A) deviation between the image input into the generative neural network and a generated image output by the generative neural network based on the input image, when the image input into the generative neural network is an actual normal tissue image, and (B) the probability, determined by the discriminator neural network, of the generated image being an actual normal tissue image, when the image input into the generative neural network is a calcification scattered tissue image.
  • the image output by the generative neural network may include calcification pixels, and therefore is called “generated image” instead of “processed normal tissue image” .
  • the output of the generative neural network no longer includes calcification pixels and therefore can be called “processed normal tissue image” .
  • a processed normal tissue image includes no calcification pixels.
  • component (A) above may be computed using the following formula:
  • component (B) above may be computed using the following formula:
  • ⁇ is a model hyperparameter defined empirically, and ⁇ is a judgment function.
  • the training of the generative neural network may include, upon determination that a first termination condition is not satisfied, adjusting the values of the parameters representing associations among nodes of the generative neural network.
  • the first termination condition may include one or more scenarios selected from the following: minimization of the first loss function; maximization of the first loss function; reaching a certain number of iterations; reaching a value of the first loss function equal to or beyond a certain threshold value; reaching a certain computation time; and/or reaching a value of the first loss function within an acceptable error limit.
  • a second loss function for training the discriminator neural network may be computed based on the probability, determined by the discriminator neural network, of the generated image being an actual normal tissue image, when the image input into the generative neural network is a calcification scattered tissue image.
  • this probability may be computed using the following formula:
  • the training of the discriminator neural network may include, upon determination that a second termination condition is not satisfied, adjusting the associations among nodes of the discriminator neural network.
  • the second termination condition may include one or more scenarios selected from the following: minimization of the second loss function; maximization of the second loss function; reaching a certain number of iterations; reaching a value of the second loss function equal to or beyond a certain threshold value; reaching a certain computation time; and/or reaching a value of the second loss function within an acceptable error limit.
  • the computer system both (A) obtains the training data set and (B) trains the generative neural network and the discriminator neural network.
  • a first computer system may obtain the training data set; and a second computer system may train the generative neural network and the discriminator neural network, using the training data set which the second computer system may receive from the first computer system.
  • the generative neural network may be used to generate a processed normal tissue image 320 (Fig. 3B) based on a calcification scattered tissue image 310 (Fig. 3A) .
  • the pixels of the processed normal tissue image 320 (Fig. 3B) corresponding to the calcification pixels (bright spots) of the calcification scattered tissue image 310 (Fig. 3A) are normal tissue pixels. By appearance, it almost looks as if the calcification pixels of the calcification scattered tissue image 310 (Fig. 3A) were replaced by pixels similar to the neighboring normal tissue pixels, resulting in the processed normal tissue image 320 (Fig. 3B) .
  • a calcification image 330 may be generated based on (A) the calcification scattered tissue image 310 (Fig. 3A) and (B) the processed normal tissue image 320 (Fig. 3B) .
  • the calcification pixels of the calcification scattered tissue image 310 Fig. 3A
  • the remaining pixels were blackened, resulting in the calcification image 330 (Fig. 3C) .
  • Fig. 4 shows a flowchart 400 generalizing the method for detecting and displaying the calcification pixels in a calcification scattered tissue image.
  • the method may include generating with a neural network a processed normal tissue image based on a calcification scattered tissue image.
  • the generative neural network generates the processed normal tissue image 320 (Fig. 3B) based on the calcification scattered tissue image 310 (Fig. 3A) .
  • the method may include generating a calcification image based on (A) the calcification scattered tissue image and (B) the processed normal tissue image.
  • the calcification image 330 (Fig. 3C) is generated based on (A) the calcification scattered tissue image 310 (Fig. 3A) and (B) the processed normal tissue image 320 (Fig. 3B) .
  • the calcification image 330 may be generated based on the difference between (A) the calcification scattered tissue image 310 (Fig. 3A) and (B) the processed normal tissue image 320 (Fig. 3B) .
  • the generation of the calcification image 330 may include subtracting the processed normal tissue image 320 (Fig. 3B) from the calcification scattered tissue image 310 (Fig. 3A) , resulting in a residual image (not shown) .
  • the pixel value of said each pixel may be set to 0.
  • single pixel bright spots in the calcification image 330 may be filtered out.
  • a calcification enhanced image 340 may be generated based on (A) the calcification scattered tissue image 310 (Fig. 3A) and (B) the calcification image 330 (Fig. 3C) .
  • the generation of the calcification enhanced image 340 may include multiplying the calcification image 330 (Fig. 3C) by a coefficient resulting in a multiply image (not shown) ; and then adding (pixel by pixel) the multiply image to the calcification scattered tissue image 310 (Fig. 3A) .
  • the calcification scattered tissue image 310 may be generated by (A) creating a foreground mask for an input image; and (B) cropping the input image based on the foreground mask, resulting in the calcification scattered tissue image 310.
  • the calcification scattered tissue image 310 (Fig. 3A) and the processed normal tissue image 320 (Fig. 3B) may be of the same size.
  • each calcification pixel of the calcification scattered tissue image 310 (Fig. 3A)
  • the corresponding pixel of the processed normal tissue image 320 (Fig. 3B) may be at a different (e.g., lower) degree of brightness.
  • each pixel in the processed normal tissue image 320 (Fig. 3B) corresponding to a calcification pixel in the calcification scattered tissue image 310 may be inferred from the normal tissue pixels of the processed normal tissue image 320 (Fig. 3B) in the vicinity of said each pixel.
  • the corresponding pixel of the processed normal tissue image 320 may be at the same degree of brightness.
  • all pixels in the processed normal tissue image 320 (Fig. 3B) corresponding to normal tissue pixels in the calcification scattered tissue image 310 (Fig. 3A) may be very similar (e.g., within 5%of the full dynamic range) .
  • the calcification scattered tissue image 310 may be an X-ray image.

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Abstract

L'invention concerne un procédé consistant à obtenir un ensemble de données d'apprentissage qui comprend de multiples images de tissu normal réelles et de multiples images de tissu à calcifications diffuses, et à configurer un système informatique qui met en œuvre un réseau neuronal génératif à l'aide de l'ensemble de données d'apprentissage et d'un réseau neuronal discriminateur. Une sortie du réseau neuronal génératif est une image de tissu normal traitée générée à partir d'une entrée d'image dans le réseau neuronal génératif. Une sortie du réseau neuronal discriminateur est une probabilité qu'une image entrée dans le réseau neuronal discriminateur soit une image tissulaire normale réelle.
PCT/CN2023/101579 2023-06-21 2023-06-21 Procédés pour détecter et afficher des pixels de calcification dans des images radiologiques à l'aide de réseaux neuronaux artificiels Ceased WO2024259613A1 (fr)

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CN202380035848.5A CN119137620A (zh) 2023-06-21 2023-06-21 利用人工神经网络检测和显示x射线图像中的钙化像素的方法

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