WO2022164214A1 - Appareil et procédé de détection de zones à l'intérieur d'une image de résultat de kit de diagnostic - Google Patents

Appareil et procédé de détection de zones à l'intérieur d'une image de résultat de kit de diagnostic Download PDF

Info

Publication number
WO2022164214A1
WO2022164214A1 PCT/KR2022/001444 KR2022001444W WO2022164214A1 WO 2022164214 A1 WO2022164214 A1 WO 2022164214A1 KR 2022001444 W KR2022001444 W KR 2022001444W WO 2022164214 A1 WO2022164214 A1 WO 2022164214A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
diagnostic kit
reference point
test result
spot
Prior art date
Application number
PCT/KR2022/001444
Other languages
English (en)
Korean (ko)
Inventor
김원태
강신욱
이명재
김동민
홍호탁
김소연
한아름
Original Assignee
(주) 제이엘케이
피씨엘 (주)
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020220011274A external-priority patent/KR20220109334A/ko
Application filed by (주) 제이엘케이, 피씨엘 (주) filed Critical (주) 제이엘케이
Publication of WO2022164214A1 publication Critical patent/WO2022164214A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/576—Immunoassay; Biospecific binding assay; Materials therefor for hepatitis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • 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
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering

Definitions

  • the present invention relates to an apparatus and method for detecting a spot in a result image of a diagnostic kit, and more particularly, to an apparatus and method for detecting a spot in a result image of a diagnostic kit that can be determined more quickly and precisely by automating the negative or positive determination of a disease. it's about
  • IVD In Vitro Diagnostics
  • the diagnostic kit test result image consists of a reference point and a spot, and as shown in FIG. 1, according to the diagnostic test method of acquiring an image of a diagnostic kit of the prior art and reading the result, sample collection, sample input, It proceeds through the process of acquiring an image of the test result of the diagnostic kit, identifying a spot with the naked eye, and determining whether or not there is an infection.
  • the diagnosis is made qualitatively only by identifying spots with the naked eye of the examiner to determine whether or not the infection is present, and the test accuracy differs depending on the skill of the examiner.
  • in vitro diagnostic equipment requires a technology that prevents errors from occurring even when performed by general inspectors, not skilled personnel, including disease tests that require quick test results.
  • the present invention has been devised to solve the above problem, and according to the present invention, a spot that is difficult to identify with the naked eye in a short time using image processing and machine learning in the diagnostic kit test result image acquired through the image scanner of the screening equipment. to automatically detect and quantitatively quantify the degree of the reaction expressed in the detected spot, so that the negative or positive determination of the disease can be determined more quickly and precisely.
  • An apparatus for detecting a spot in a diagnostic kit result image for solving the above-described problem includes: an image acquisition unit configured to acquire a diagnostic kit test result image through an image scanner of a screening device; a reference point detector for detecting a reference point on the acquired diagnostic kit test result image; an image pre-processing unit for extracting a region of interest (ROI) by aligning the diagnostic kit test result image based on the detected reference point; a spot detector for detecting a spot on the extracted ROI using an image denoising technique and a blob detection algorithm; a negative/positive reading unit that quantitatively quantifies the degree of a positive reaction occurring in the detected spots to determine negative or positive; and a read result output unit for displaying the determined result value.
  • ROI region of interest
  • the reference point detection unit detects a reference point on the acquired diagnostic kit test result image using image binarization that binarizes the diagnostic kit test result image based on a set threshold value. can do.
  • the image pre-processing unit aligns the diagnostic kit test result image by performing Image Affine Transformation to send the center of the reference points to three new corners to align the image of the interest A region of interest (ROI) may be extracted.
  • the image denoising technique uses a median filter, an average filter, or a Gaussian filter, and the blob detection ) algorithm may be configured using a Maximally Stable External Region (MSER) algorithm and a Spatial Transformer Network (STN).
  • MSER Maximally Stable External Region
  • STN Spatial Transformer Network
  • the method for detecting a spot in a diagnostic kit result image includes: an image acquisition step in which an image acquisition unit acquires a diagnostic kit test result image through an image scanner of a screening device; a reference point detection step in which a reference point detection unit detects a reference point on the acquired diagnostic kit test result image; an image pre-processing step in which an image pre-processing unit extracts a region of interest (ROI) by aligning the diagnostic kit test result image based on the detected reference point; a spot detection step in which a spot detector detects a spot on the extracted ROI using an image denoising technique and a blob detection algorithm; a negative/positive reading step in which a negative/positive reading unit quantitatively quantifies the extent to which a positive reaction has occurred in the detected spots to determine negative or positive; and a read result output step in which the read result output unit displays the determined result value.
  • ROI region of interest
  • the step of detecting the reference point includes the obtained diagnostic kit test result using image binarization, which binarizes the diagnostic kit test result image based on the threshold value set by the reference point detector.
  • a reference point can be detected on the image.
  • the image pre-processing unit aligns the diagnostic kit test result image by performing Image Affine Transformation by sending the center of the reference points to three new corners ( Alignment) to extract the region of interest (ROI).
  • the present invention automatically detects spots that are difficult to identify with the naked eye in a short time using image processing and machine learning in the diagnostic kit test result image acquired through the image scanner of the screening equipment, and the reaction expressed in the detected spot is By quantitatively quantifying the degree, it is possible to more quickly and precisely determine whether a disease is negative or positive.
  • FIG. 1 is a view for explaining a diagnostic test method of acquiring an image of a diagnostic kit according to the prior art and reading the result.
  • FIG. 2 is a diagram illustrating an image of a diagnostic kit test result according to an embodiment of the present invention.
  • FIG. 3 is a view for explaining an apparatus for detecting a spot in a diagnostic kit result image according to an embodiment of the present invention.
  • FIG. 4 is a diagram illustrating a spot where a positive reaction occurs in an image of a test result of a diagnostic kit according to an embodiment of the present invention.
  • 5 is a diagram quantitatively quantifying the degree of a positive reaction according to an embodiment of the present invention.
  • FIG. 6 is a flowchart illustrating a method for detecting a spot in a diagnostic kit result image according to an embodiment of the present invention.
  • the present invention relates to HIV 1/2 (Human immunodeficiency virus 1/2), HCV (Hepatitis C virus) and HTLV 1/2 (Human T-lymphotropic virus 1/2) in plasma or serum using the principle of fluorescence immunoassay.
  • Antibodies formed when infected HBV surface antigen (HBsAg) formed when infected with HTLV 1/2 (Human T-lymphotropic virus 1/2) and HBV (Hepatitic B virus), HIV p24 antigen, HCV core antigen
  • HBV surface antigen HBV surface antigen
  • HBV Hepatitic B virus
  • HIV p24 antigen HCV core antigen
  • the purpose of the in-vitro diagnostic kit that detects the disease at the same time is to acquire an image of the test result of the diagnostic kit and automatically detect the spot so that the disease can be diagnosed more quickly and accurately than the existing diagnostic method.
  • FIG. 2 is a diagram illustrating a diagnostic kit test result image according to an embodiment of the present invention
  • FIG. 3 is a diagram for explaining an apparatus for detecting a spot in a diagnostic kit result image according to an embodiment of the present invention.
  • the diagnostic kit test result image according to an embodiment of the present invention includes a reference point 101 and a spot 102
  • the spot detection device in the diagnostic kit result image according to an embodiment of the present invention includes The spot 102 may be detected using the reference point 101 .
  • the spot detection device in the diagnostic kit result image is a device used in a medical facility, and may be configured in an on-premise type that can operate in an environment where an external network is not connected. .
  • the spot detection device in the diagnostic kit result image may be composed of a computer terminal, a server, or a dedicated device, and a component providing each function may be composed of hardware or software.
  • the spot detection apparatus in the diagnostic kit result image includes an image acquisition unit 110, a reference point detection unit 120, an image preprocessing unit 130, a spot detection unit 140, and a negative/positive reading unit ( 150) and a read result output unit 160 may be included.
  • the image acquisition unit 110 acquires a diagnostic kit test result image through an image scanner of a screening device.
  • the reference point detection unit 120 detects a reference point on the acquired diagnostic kit test result image.
  • the reference point detector 120 detects a reference point on the acquired diagnostic kit test result image using image binarization, which binarizes the diagnostic kit test result image based on a set threshold as an image processing technique. can do.
  • the reference point detector 120 may detect the reference point using Equation 1 below.
  • f(p(x, y)) represents a reference point
  • x and y of p(x, y) represent pixel coordinates in the image
  • p represents a pixel value
  • the reference points are 3 to 4, which are generally present in the upper left, upper right, and lower right corners of the diagnostic kit test result image, so you just need to find three reference points.
  • the image preprocessor 130 extracts a region of interest (ROI) by aligning the diagnostic kit test result image based on the detected reference point.
  • ROI region of interest
  • the image preprocessor 130 aligns the diagnostic kit test result image by applying Image Affine Transformation, which sends the center of the reference points to three new corners.
  • a region of interest (ROI) may be extracted.
  • the spot detector 140 detects a spot on the extracted ROI using an image denoising technique and a blob detection algorithm.
  • the spot detector 140 may reduce the image operation range on the extracted region of interest and remove noise present in the image by capturing an image denoising technique.
  • a median filter, an average filter, or a Gaussian filter used in the field of noise reduction and denoising is applied to the image. Existing noise can be removed.
  • the spot detection unit 140 may detect a spot in the image from which the noise has been removed by using a blob detection algorithm.
  • the above blob detection algorithm is an algorithm that supplements the disadvantages of MSER by utilizing the Maximally Stable External Region (MSER) algorithm and the Spatial Transformer Network (STN) by utilizing the optimization technique of STN.
  • MSER Maximally Stable External Region
  • STN Spatial Transformer Network
  • the MSER (Maximally Stable External Region) algorithm refers to an algorithm for detecting an aggregation region of pixels having different intensity from neighboring pixels as a blob.
  • This MSER algorithm is one of the best area detection algorithms and is widely used to find the blob as a preprocessing.
  • the MSER algorithm has the advantage of being able to detect blobs faster than the SWT algorithm, but has a disadvantage in that it is somewhat lacking in accuracy, such as detecting tickles or noise as blobs.
  • the negative/positive reading unit 150 quantitatively quantifies the extent to which a positive reaction has occurred in the detected spots to determine negative or positive.
  • FIG. 4 is a diagram illustrating a spot where a positive reaction occurs in an image of a diagnostic kit test result according to an embodiment of the present invention
  • FIG. 5 is a diagram quantitatively quantifying the degree of a positive reaction according to an embodiment of the present invention.
  • the number of white pixels having a pixel value greater than or equal to a specified threshold is obtained in the spot area, and the area of the spot area is divided thereto to quantitatively quantify the degree of a positive reaction.
  • the negative/positive reading unit 150 finally determines negative/positive according to whether the quantified positive reaction value exceeds the positive determination reference value, and the reading result output unit 160 outputs the determined result value.
  • FIG. 6 is a flowchart illustrating a method for detecting a spot in a diagnostic kit result image according to an embodiment of the present invention.
  • an image acquisition unit acquires an image of a diagnostic kit test result through an image scanner of a screening device (S210).
  • the reference point detection unit detects the reference point on the obtained diagnostic kit test result image (S220).
  • the reference point may be detected on the acquired diagnostic kit test result image using image binarization, which binarizes the diagnostic kit test result image based on the threshold value set by the reference point detector.
  • the image preprocessor aligns the diagnostic kit test result image based on the detected reference point to extract a region of interest (ROI) (S230).
  • the image pre-processing unit performs Image Affine Transformation to send the center of the reference points to three new corners to align the diagnostic kit test result image to the region of interest (ROI: Region of Interest) can be extracted.
  • ROI Region of Interest
  • the spot detector detects a spot on the extracted ROI using an image denoising technique and a blob detection algorithm (S240).
  • the spot detection unit uses a median filter, an average filter, or a Gaussian filter as the image denoising technique, and MSER as the blob detection algorithm.
  • a spot can be detected on the extracted ROI using a (Maximally Stable External Region) algorithm and a Spatial Transformer Network (STN).
  • STN Spatial Transformer Network
  • the negative/positive reading unit can quantitatively quantify the degree of positive reaction in the detected spots to determine negative or positive (S250), and the reading result output unit can display the determined result value (S260).
  • a spot that is difficult to identify with the naked eye is automatically detected in a short time using image processing and machine learning from the diagnostic kit test result image obtained through the image scanner of the screening equipment, and expressed in the detected spot.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Urology & Nephrology (AREA)
  • Public Health (AREA)
  • Chemical & Material Sciences (AREA)
  • Hematology (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Epidemiology (AREA)
  • Pathology (AREA)
  • Primary Health Care (AREA)
  • Biotechnology (AREA)
  • Medicinal Chemistry (AREA)
  • Microbiology (AREA)
  • Theoretical Computer Science (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Food Science & Technology (AREA)
  • Cell Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Databases & Information Systems (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Virology (AREA)
  • Communicable Diseases (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

La présente invention concerne un appareil et un procédé de détection de zones dans une image de résultat de kit de diagnostic. L'appareil pour détecter des zones dans une image de résultat de kit de diagnostic, selon la présente invention, comprend : une unité d'acquisition d'image qui acquiert une image de résultat de test de kit de diagnostic par l'intermédiaire d'un dispositif d'analyse d'image d'un équipement de dépistage ; une unité de détection de point de référence qui détecte un point de référence sur l'image de résultat de test de kit de diagnostic acquise ; une unité de prétraitement d'image qui extrait une région d'intérêt (ROI) en alignant l'image de résultat de test de kit de diagnostic sur la base du point de référence détecté ; une unité de détection de zone qui détecte des zones sur la région d'intérêt extraite à l'aide d'une technique de débruitage d'image et d'un algorithme de détection de blob ; une unité de lecture négative/positive qui détermine un négatif ou un positif en numérisant quantitativement le degré auquel une réaction positive s'est produite dans les zones détectées ; et une unité de sortie de résultat de lecture qui affiche une valeur du résultat déterminé.
PCT/KR2022/001444 2021-01-28 2022-01-27 Appareil et procédé de détection de zones à l'intérieur d'une image de résultat de kit de diagnostic WO2022164214A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR10-2021-0012128 2021-01-28
KR20210012128 2021-01-28
KR1020220011274A KR20220109334A (ko) 2021-01-28 2022-01-26 진단키드 결과 이미지 내 스팟 검출 장치 및 방법
KR10-2022-0011274 2022-01-26

Publications (1)

Publication Number Publication Date
WO2022164214A1 true WO2022164214A1 (fr) 2022-08-04

Family

ID=82653639

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2022/001444 WO2022164214A1 (fr) 2021-01-28 2022-01-27 Appareil et procédé de détection de zones à l'intérieur d'une image de résultat de kit de diagnostic

Country Status (1)

Country Link
WO (1) WO2022164214A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101900200B1 (ko) * 2017-08-30 2018-11-05 (주)인스페이스 딥러닝 기반 결핵 검사 방법
KR102005597B1 (ko) * 2012-02-06 2019-07-30 더 리전트 오브 더 유니버시티 오브 캘리포니아 휴대용 신속 진단 테스트 리더기
KR102005621B1 (ko) * 2017-06-21 2019-07-30 주식회사 나노바이오라이프 면역 크로마토크래피 신속 진단 키트 및 이를 이용한 검사 방법
WO2020038461A1 (fr) * 2018-08-23 2020-02-27 Sanwa Biotech Ltd Appareil de diagnostic portable et procédé associé
JP2020535924A (ja) * 2017-10-05 2020-12-10 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 診断撮像における画像特徴のアノテーション

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102005597B1 (ko) * 2012-02-06 2019-07-30 더 리전트 오브 더 유니버시티 오브 캘리포니아 휴대용 신속 진단 테스트 리더기
KR102005621B1 (ko) * 2017-06-21 2019-07-30 주식회사 나노바이오라이프 면역 크로마토크래피 신속 진단 키트 및 이를 이용한 검사 방법
KR101900200B1 (ko) * 2017-08-30 2018-11-05 (주)인스페이스 딥러닝 기반 결핵 검사 방법
JP2020535924A (ja) * 2017-10-05 2020-12-10 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 診断撮像における画像特徴のアノテーション
WO2020038461A1 (fr) * 2018-08-23 2020-02-27 Sanwa Biotech Ltd Appareil de diagnostic portable et procédé associé

Similar Documents

Publication Publication Date Title
CN107328776B (zh) 一种免疫层析试纸卡的快速检测方法
WO2020141812A1 (fr) Dispositif de marquage de région d'intérêt pour analyse de moelle osseuse et système de détection de région d'intérêt comprenant ce dernier
WO2019107666A1 (fr) Bandelette de test d'urine comprenant un minuteur et méthode de détection et d'analyse de bandelette de test d'urine
WO2019132589A1 (fr) Dispositif de traitement d'images et procédé de détection d'objets multiples
WO2017065358A1 (fr) Procédé et dispositif pour le diagnostic de maladie infectieuse par l'intermédiaire d'image de kit de réactifs
WO2016159726A1 (fr) Dispositif pour détecter automatiquement l'emplacement d'une lésion à partir d'une image médicale et procédé associé
WO2023063646A1 (fr) Procédé de dérivation d'une densité osseuse pour un diagnostic de fracture de la hanche basé sur un apprentissage machine, et programme de dérivation d'une densité osseuse l'utilisant
WO2017181723A1 (fr) Procédé et système de détection de rupture de fil
CN108395986A (zh) 基于深度学习的人类乳头状瘤病毒自动分型检测装置
JPH10506462A (ja) 自動化された細胞学的スコアリングのために不適切な状態を検出するための方法および装置
JPWO2019065105A1 (ja) 画像解析装置、方法およびプログラム
CN111815614A (zh) 基于人工智能的寄生虫检测方法、系统及终端设备
WO2022164214A1 (fr) Appareil et procédé de détection de zones à l'intérieur d'une image de résultat de kit de diagnostic
WO2017150894A1 (fr) Procédé et dispositif d'analyse de vaisseau sanguin à l'aide d'une image angiographique
WO2012121488A2 (fr) Procédé de traitement d'une image médicale de vaisseaux sanguins
KR20220109335A (ko) 인공지능 기반 진단키트 검사 결과 이미지에서 비정형 이물질 배제를 통한 진단 검사 장치 및 방법
WO2022164215A1 (fr) Dispositif et procédé pour test diagnostique utilisant l'exclusion d'un matériau étranger atypique d'une image de résultat de test de kit de diagnostic basé sur l'intelligence artificielle
WO2022158843A1 (fr) Procédé d'affinage d'image d'échantillon de tissu, et système informatique le mettant en œuvre
KR20220109334A (ko) 진단키드 결과 이미지 내 스팟 검출 장치 및 방법
WO2015137542A1 (fr) Dispositif de traitement d'images médicales pour diagnostic médical et procédé associé
WO2021177799A1 (fr) Procédé et système de classification quantitative d'une infection à coronavirus à base d'image
WO2014204126A2 (fr) Appareil de capture d'images ultrasonores en 3d et procédé pour le faire fonctionner
CN110243823A (zh) 基于支持向量机的小儿佝偻病自动筛查装置及其使用方法
WO2021112436A1 (fr) Dispositif et procédé de calcul automatique de la propreté des intestins
WO2022250460A1 (fr) Procédé à base d'images pour le comptage de plaquettes, et procédé de sortie pour informations de comptage

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22746240

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22746240

Country of ref document: EP

Kind code of ref document: A1