WO2023182702A1 - Dispositif et procédé de traitement de données de diagnostic par intelligence artificielle pour des images numériques de pathologie - Google Patents

Dispositif et procédé de traitement de données de diagnostic par intelligence artificielle pour des images numériques de pathologie Download PDF

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WO2023182702A1
WO2023182702A1 PCT/KR2023/003136 KR2023003136W WO2023182702A1 WO 2023182702 A1 WO2023182702 A1 WO 2023182702A1 KR 2023003136 W KR2023003136 W KR 2023003136W WO 2023182702 A1 WO2023182702 A1 WO 2023182702A1
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image
polygon
artificial intelligence
images
converting
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PCT/KR2023/003136
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English (en)
Korean (ko)
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윤길중
김승종
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주식회사 몰팩바이오
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    • 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
    • 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 converts a pathology slide image into matrix data, corrects the converted matrix data according to a preset threshold, converts the obtained corrected image into a polygon image, and calculates the polygon area from the converted polygon image. After that, the extracted polygons extracted by filtering according to the calculated polygon area are converted to a file format for artificial intelligence diagnosis and returned, thereby providing artificial intelligence diagnosis data of digital pathology images that can effectively process pathology images for artificial intelligence learning. It relates to a processing device and method.
  • an artificial intelligence (AI) system is a computer system that implements human-level intelligence, and refers to a system in which machines learn and make decisions on their own and become smarter.
  • Machine learning refers to an algorithm that classifies and learns the characteristics of input data on its own
  • element technologies include machine learning such as deep learning. It is a technology that utilizes algorithms and can consist of technical fields such as linguistic understanding, visual understanding, inference/prediction, knowledge expression, and motion control.
  • fields where artificial intelligence technology is applied may include, for example, linguistic understanding, visual understanding, reasoning and prediction, knowledge expression, and motion control.
  • fields where artificial intelligence technology is applied may include, for example, linguistic understanding, visual understanding, reasoning and prediction, knowledge expression, and motion control.
  • visual understanding for example, object recognition and object tracking
  • technologies such as image search, person recognition, scene understanding, spatial understanding, and image improvement.
  • CNN convolutional neural network
  • This CNN is a model that mimics the human brain function created based on the assumption that when a person recognizes an object, he or she extracts the basic features of the object and then performs complex calculations in the brain to recognize the object based on the results.
  • Various filters to extract image features through convolution operations and pooling or non-linear activation functions to add non-linear characteristics can be used.
  • the present invention converts a pathology slide image into matrix data, corrects the converted matrix data according to a preset threshold, converts the obtained correction image into a polygon image, and calculates the polygon area from the converted polygon image, Artificial intelligence diagnosis data processing of digital pathology images that can effectively process artificial intelligence diagnosis data of pathology images by converting and returning the extracted polygons extracted by filtering according to the calculated polygon area into a file format for artificial intelligence diagnosis.
  • the purpose is to provide a device and method.
  • an image pre-processing unit for converting a pathology slide image into matrix data; a smoothing processor that corrects the converted matrix data according to a preset threshold; a polygon generator that converts the correction image obtained through the smoothing processing unit into a polygon image; an area calculation unit that calculates the polygon area from the converted polygon image; And a result output unit that converts the extracted polygon extracted by filtering according to the calculated polygon area into a file format for artificial intelligence diagnosis and returns it.
  • An artificial intelligence diagnostic data processing device for a digital pathology image including a.
  • the image pre-processing unit divides the pathology slide image into patch images of a preset size, and then divides the pathology slide image into patch images of a preset size, and then divides the pathological slide image into patch images of a preset size, and then outputs binary data results according to the diagnosis results of each patch image.
  • An artificial intelligence diagnostic data processing device for converting digital pathology images into matrix data may be provided.
  • the smoothing processor applies a Gaussian blur effect (Gaussian smoothing, Gaussian blur) to the matrix data and then corrects the artificial intelligence diagnostic data of the digital pathology image according to the preset threshold.
  • Gaussian blur effect Gaussian smoothing, Gaussian blur
  • a processing device may be provided.
  • the polygon generator converts the coordinates of the correction image into points and then converts them into the polygon image using a concave hull algorithm.
  • An intelligent diagnostic data processing device may be provided.
  • the result output unit may be provided with an artificial intelligence diagnostic data processing device for a digital pathology image that calculates the polygon area using a shoelace formula in the polygon image. .
  • converting a pathology slide image into matrix data Obtaining a corrected image by correcting the converted matrix data according to a preset threshold; Converting the obtained correction image into a polygon image; Calculating a polygon area from the converted polygon image; and converting and returning the extracted polygons extracted by filtering according to the calculated polygon area into a file format for artificial intelligence diagnosis.
  • a method of processing artificial intelligence diagnostic data of a digital pathology image may be provided, including:
  • the step of converting to matrix data includes dividing the pathology slide image into patch images of a preset size, and then binary data results output according to the diagnosis results of each patch image.
  • An artificial intelligence diagnostic data processing method of a digital pathology image that converts the value into the matrix data may be provided.
  • the step of acquiring the corrected image includes applying Gaussian smoothing (Gaussian blur) to the matrix data and then correcting the digital pathology image according to the preset threshold.
  • Gaussian smoothing Gaussian blur
  • An artificial intelligence diagnostic data processing method may be provided.
  • the step of converting into a polygon image includes converting the coordinates of the correction image into points and then converting the digital image into the polygon image using a concave hull algorithm.
  • a method of processing artificial intelligence diagnostic data of pathology images may be provided.
  • the step of calculating the polygon area includes an artificial intelligence diagnostic data processing method of a digital pathology image that calculates the polygon area using a shoelace formula in the polygon image.
  • the present invention converts a pathology slide image into matrix data, corrects the converted matrix data according to a preset threshold, converts the obtained correction image into a polygon image, and calculates the polygon area from the converted polygon image, By converting and returning the extracted polygons extracted by filtering according to the calculated polygon area into a file format for artificial intelligence diagnosis, the processing of artificial intelligence diagnosis data of pathology images can be effectively performed.
  • FIG. 1 is a block diagram showing an artificial intelligence diagnostic data processing device for digital pathology images according to an embodiment of the present invention
  • FIGS. 2 to 10 are diagrams for explaining the processing of pathology images in an artificial intelligence diagnostic data processing device for digital pathology images according to an embodiment of the present invention
  • Figure 11 is a flow chart showing the process of processing digital pathology images into artificial intelligence diagnosis data according to another embodiment of the present invention.
  • 12 to 22 are diagrams for explaining a process of processing digital pathology images into artificial intelligence diagnosis data according to another embodiment of the present invention.
  • Figure 1 is a block diagram showing an artificial intelligence diagnosis data processing device of a digital pathology image according to an embodiment of the present invention
  • Figures 2 to 10 are artificial intelligence diagnosis data of a digital pathology image according to an embodiment of the present invention. This is a diagram to explain processing a pathology image in a processing device.
  • the artificial intelligence diagnostic data processing device for digital pathology images includes an image pre-processing unit 110, a smoothing processing unit 120, a polygon generating unit 130, and area calculation. It may include a unit 140, a result output unit 150, etc.
  • the image preprocessing unit 110 converts the pathology slide image into matrix data. After dividing the pathology slide image into patch images of a preset size, the binary data result value is output according to the diagnosis result of each patch image. It can be converted to matrix data.
  • the slide image when diagnosing a pathology slide using artificial intelligence, the slide image exceeds tens of thousands to hundreds of thousands of pixels, so it must be divided according to a preset size for diagnosis.
  • the divided patch images can be analyzed, learned, and diagnosed using artificial intelligence.
  • misdiagnosis may occur in a local area during the analysis, learning, and diagnosis process of artificial intelligence, and there was a problem in clearly recognizing the boundary of the diagnosis area when diagnosing each part on a patch image basis, which will be described later.
  • the image processing process must be performed.
  • a classification method using CNN convolution neural network
  • binary data of 0 or 1 is output for each patch image as a result
  • the entire pathology slide image is divided into patches of a preset size.
  • each patch image is diagnosed and output as binary data, and then the output binary data can be converted into matrix data.
  • a converted image as shown in FIG. 4 can be obtained.
  • the smoothing processing unit 120 corrects the converted matrix data according to a preset threshold. After applying Gaussian blurring (Gaussian blur) to the matrix data converted through the image preprocessing unit 110, the preset It can be corrected according to the threshold.
  • Gaussian blurring Gaussian blur
  • the matrix data converted through the image pre-processing unit 110 has a size of (existing image resolution/patch size), and is spaced apart from the main area (area of a certain size or more) of the converted image in the form of a small dot.
  • correction can be made by readjusting the resulting value of the matrix data using a preset threshold to dichotomize it based on a certain value.
  • the Gaussian blur effect emphasizes the main area of the converted image or the surrounding background by smoothing, blurring, blurring, crushing, or distorting a specific area or the entire conversion image of matrix data.
  • blurring noise in the converted image can be primarily removed as shown in FIG. 5.
  • the converted image to which the Gaussian blur effect is applied can be obtained as a corrected image as shown in FIG. 6 by applying a preset threshold.
  • the polygon generator 130 converts the corrected image obtained through the smoothing processing unit 120 into a polygon image. After converting the coordinates of the corrected image into points, a polygon is created using the concave hull algorithm. It can be converted to an image.
  • a polygon can be drawn using an algorithm and converted into a polygon image with a plurality of polygons as shown in FIG. 8.
  • the Concave Hull algorithm creates a convex polygon (polygon) using some of the points when there are multiple points on a two-dimensional plane, and creates a polygon to include all points inside the convex polygon (polygon). This can be done by setting one reference point with the smallest Y coordinate in the point conversion image, aligning other points in a counterclockwise direction based on the set reference point, and sequentially connecting the outer points according to the angle, as shown in FIG. You can create a polygon image like this.
  • the area calculation unit 140 calculates the polygon area from the converted polygon image, and can calculate the polygon area using a shoelace formula from the polygon image generated through the polygon generation unit 130.
  • polygon image generated through the polygon generator 130
  • area of each polygon can be calculated using the new root formula for each polygon.
  • the shoelace formula is a formula for calculating the area of a polygon when the coordinates of the vertices on the coordinate plane are known.
  • the area of the polygon can be calculated by crossing and multiplying the coordinate values of each vertex of the polygon.
  • the area calculation unit 140 can use the area calculated from the corresponding pathology slide image to specify the main area of each polygon as shown in FIG. 9.
  • the result output unit 150 can convert the extracted polygons extracted by filtering according to the calculated polygon area into a file format for artificial intelligence diagnosis and return it.
  • the result output unit 150 can compare the polygon area calculated through the area calculation unit 140 with a preset size condition.
  • the preset size condition may be based on 1/2 of the size (i.e., area) of the largest polygon.
  • polygons whose size is relatively smaller than the preset size condition are removed, polygons that are the same as the preset size condition or relatively large are left, and then only the extracted polygons remain, and the extracted polygon image can be saved as a polygon. It can be converted to a set file format (e.g. xml, json, etc.), and the converted extracted polygon image can be returned for artificial intelligence learning, diagnostic data visualization, etc.
  • a set file format e.g. xml, json, etc.
  • the extracted polygon image can be converted into an image in which a plurality of extracted polygons are applied to the input pathology slide image as shown in FIG. 10.
  • one embodiment of the present invention converts the pathology slide image into matrix data, corrects the converted matrix data according to a preset threshold, converts the obtained correction image into a polygon image, and converts the polygon image into a polygon image. After calculating the area, the extracted polygons filtered according to the calculated polygon area are converted to a file format for artificial intelligence diagnosis and returned, thereby effectively processing artificial intelligence diagnosis data of pathology images.
  • Figure 11 is a flow chart showing the process of processing digital pathology images into artificial intelligence diagnosis data according to another embodiment of the present invention
  • Figures 12 to 22 are flow charts showing the process of processing digital pathology images into artificial intelligence diagnosis data according to another embodiment of the present invention. This is a diagram to explain the process of processing data.
  • the image pre-processing unit 110 can convert the pathology slide image into matrix data (step 1110).
  • the image pre-processing unit 110 divides the pathology slide image into patch images of a preset size, and then converts the pathological slide image into a binary data result value output according to the diagnosis result of each patch image. This can be converted to matrix data.
  • a classification method using CNN convolution neural network
  • binary data of 0 or 1 for each patch image is set to be output as a result, and the overall result as shown in FIG. 12
  • a patch image is created by dividing the pathology slide image into a specific image size, and as shown in Figure 13, artificial intelligence converts the patch images for which the diagnosis has been completed into binary data, and then uses the location information and diagnosis information of the patch image.
  • a converted image as shown in FIG. 14 can be obtained.
  • (label 0) indicates positive and (label 1) indicates negative
  • the diagnosis is made as 0 or 1
  • the location information By backtracking, diagnostic information can be output as binary data of 0 or 1 at the corresponding location.
  • the smoothing processing unit 120 can obtain a corrected image by correcting the matrix data converted through the image pre-processing unit 110 according to a preset threshold (step 1120).
  • the smoothing unit 120 may apply a Gaussian blur effect to the matrix data and then correct it according to a preset threshold.
  • the matrix data converted through the image pre-processing unit 110 has a size of (existing image resolution/patch size), and is spaced apart from the main area (area of a certain size or more) of the converted image as shown in Figure 14.
  • the resulting value of the matrix data is obtained by using a preset threshold to dichotomize based on a certain value. By correcting by readjusting , a corrected image as shown in FIG. 16 can be obtained.
  • the polygon generator 130 can convert the corrected image obtained through the smoothing processor 120 into a polygon image (1130).
  • the polygon generator 130 may convert the coordinates of the correction image into points and then convert them into a polygon image using the concave hull algorithm.
  • the correction image shown in FIG. 16 can be displayed as shown in FIG. 17 when applied to a pathology slide image, where the coordinates of each midpoint of the correction image obtained through the smoothing processing unit 120 are points.
  • polygons are drawn using the concave hull algorithm for the positive part of the result, and a plurality of polygons as shown in FIG. 19 are generated.
  • a polygon image It can be converted to .
  • the area calculation unit 140 can calculate the polygon area from the polygon image converted through the polygon generation unit 130 (step 1140).
  • the area calculation unit 140 can calculate the polygon area using the shoelace formula in the polygon image.
  • the polygon image generated through the polygon generation unit 130 includes a plurality of There may be polygons, and the area of each polygon can be calculated using the new root formula for each polygon.
  • the area calculation unit 140 can use the area calculated from the corresponding pathology slide image to specify the main area of each polygon as shown in FIG. 20.
  • the result output unit 150 can convert the extracted polygons extracted by filtering according to the polygon area calculated through the area calculation unit 140 into a file format for artificial intelligence diagnosis and return it (step 1150).
  • the result output unit 150 applies the polygon image in which a plurality of polygons are generated to the input pathology slide image, as shown in FIG. 20.
  • the main area of each polygon can be expressed as an original application image that is specified, and the polygon area calculated through the area calculation unit 140 can be compared with a preset size condition.
  • the preset size condition may be based on 1/2 of the size (i.e., area) of the largest polygon.
  • another embodiment of the present invention converts the pathology slide image into matrix data, corrects the converted matrix data according to a preset threshold, converts the obtained correction image into a polygon image, and converts the polygon image into a polygon image. After calculating the area, the extracted polygons filtered according to the calculated polygon area are converted to a file format for artificial intelligence diagnosis and returned, thereby effectively processing artificial intelligence diagnosis data of pathology images.

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Abstract

La présente invention se réfère à un dispositif et à un procédé de traitement de données de diagnostic par intelligence artificielle pour des images numériques de pathologie. Le dispositif de traitement de données de diagnostic par intelligence artificielle comprend : une unité de prétraitement d'image pour convertir des images de coupe de pathologie en données de matrice ; une unité de génération de lissage pour corriger les données de matrice converties selon une valeur de seuil prédéfinie, une unité de génération de polygone pour convertir une image de correction obtenue par l'intermédiaire de l'unité de traitement de lissage en une image de polygone ; une unité de calcul de surface pour calculer une surface de polygone dans l'image de polygone convertie ; et une unité de sortie de résultat qui convertit un polygone d'extraction, qui est extrait par filtrage selon la surface du polygone calculé, en un format de fichier en vue d'un diagnostic par intelligence artificielle et le renvoie. L'invention permet ainsi de traiter efficacement des données de diagnostic par intelligence artificielle d'images de pathologie.
PCT/KR2023/003136 2022-03-25 2023-03-08 Dispositif et procédé de traitement de données de diagnostic par intelligence artificielle pour des images numériques de pathologie WO2023182702A1 (fr)

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KR102237696B1 (ko) 2019-04-29 2021-04-08 계명대학교 산학협력단 자궁경부암 세포 영상 자동 분석을 위한 인공지능 컴퓨터 보조 진단 시스템 및 그 제어 방법

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102476888B1 (ko) * 2022-03-25 2022-12-13 주식회사 몰팩바이오 디지털 병리이미지의 인공지능 진단 데이터 처리 장치 및 그 방법

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
BANDI, P. ET AL.: "Comparison of different methods for tissue segmentation in histopathological whole- slide images", 2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017, 18 April 2017 (2017-04-18), Melbourne, VIC, Australia, pages 591 - 595, XP055606693, DOI: 10.1109/ ISBI.2017.7950590 *
BEJNORDI, B. E. ET AL.: "Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images", IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 35, no. 9, September 2016 (2016-09-01), pages 2141 - 2150, XP011621364, DOI: 10.1109/TMI.2016.2550620 *
LI GAOHE, ZHANG YANLI, XU BO, LI XINHAO: "Image Analysis and Processing of Skin Cell Injury Based on OpenCV", JOURNAL OF PHYSICS: CONFERENCE SERIES, INSTITUTE OF PHYSICS PUBLISHING, GB, vol. 1237, no. 3, 1 June 2019 (2019-06-01), GB , pages 032003, XP093095786, ISSN: 1742-6588, DOI: 10.1088/1742-6596/1237/3/032003 *
PRATONDO, A. ET AL.: "Integrating machine learning with region-based active contour modelsin medical image segmentation", JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, vol. 43, February 2017 (2017-02-01), pages 1 - 9, XP029888052, DOI: http://dx.doi.org/10.1016/j.jvcir. 2016.11.01 9 *

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