JP7726218B2 - 医用画像のセット内の異常画像を識別する方法及びシステム - Google Patents
医用画像のセット内の異常画像を識別する方法及びシステムInfo
- Publication number
- JP7726218B2 JP7726218B2 JP2022565566A JP2022565566A JP7726218B2 JP 7726218 B2 JP7726218 B2 JP 7726218B2 JP 2022565566 A JP2022565566 A JP 2022565566A JP 2022565566 A JP2022565566 A JP 2022565566A JP 7726218 B2 JP7726218 B2 JP 7726218B2
- Authority
- JP
- Japan
- Prior art keywords
- image
- images
- medical
- abnormal
- score
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
Links
Classifications
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4053—Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- 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
- 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
- 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
-
- 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
- G06T2207/20081—Training; Learning
-
- 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
-
- 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
- G06T2207/30061—Lung
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Radiology & Medical Imaging (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Quality & Reliability (AREA)
- Pathology (AREA)
- Data Mining & Analysis (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Image Analysis (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP20173286.4A EP3907696A1 (en) | 2020-05-06 | 2020-05-06 | Method and system for identifying abnormal images in a set of medical images |
| EP20173286.4 | 2020-05-06 | ||
| PCT/EP2021/061525 WO2021224162A1 (en) | 2020-05-06 | 2021-05-03 | Method and system for identifying abnormal images in a set of medical images |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2023524947A JP2023524947A (ja) | 2023-06-14 |
| JP2023524947A5 JP2023524947A5 (OSRAM) | 2024-03-25 |
| JP7726218B2 true JP7726218B2 (ja) | 2025-08-20 |
Family
ID=70613665
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2022565566A Active JP7726218B2 (ja) | 2020-05-06 | 2021-05-03 | 医用画像のセット内の異常画像を識別する方法及びシステム |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US12450736B2 (OSRAM) |
| EP (2) | EP3907696A1 (OSRAM) |
| JP (1) | JP7726218B2 (OSRAM) |
| CN (1) | CN115943426A (OSRAM) |
| WO (1) | WO2021224162A1 (OSRAM) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3786880A1 (en) * | 2019-08-29 | 2021-03-03 | Koninklijke Philips N.V. | Methods for analyzing and reducing inter/intra site variability using reduced reference images and improving radiologist diagnostic accuracy and consistency |
| CN114529544B (zh) * | 2022-04-22 | 2022-07-19 | 武汉大学 | 医学图像分析方法、计算机设备及存储介质 |
| CN118447280B (zh) * | 2024-05-17 | 2025-02-11 | 华中科技大学 | 一种考虑多层级特征的多类别点云异常检测方法及系统 |
| WO2025240997A1 (en) * | 2024-05-19 | 2025-11-27 | 4DMedical Limited | Method and system for detecting lung disease |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016007270A (ja) | 2014-06-23 | 2016-01-18 | 東芝メディカルシステムズ株式会社 | 医用画像処理装置 |
| US20200105394A1 (en) | 2018-09-28 | 2020-04-02 | Varian Medical Systems International Ag | Methods and systems for adaptive radiotherapy treatment planning using deep learning engines |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7054473B1 (en) | 2001-11-21 | 2006-05-30 | R2 Technology, Inc. | Method and apparatus for an improved computer aided diagnosis system |
| AU2003216295A1 (en) * | 2002-02-15 | 2003-09-09 | The Regents Of The University Of Michigan | Lung nodule detection and classification |
| EP2880625B1 (en) * | 2012-08-06 | 2019-12-11 | Koninklijke Philips N.V. | Image noise reduction and/or image resolution improvement |
| WO2015154206A1 (en) | 2014-04-11 | 2015-10-15 | Xiaoou Tang | A method and a system for face verification |
| US9536293B2 (en) | 2014-07-30 | 2017-01-03 | Adobe Systems Incorporated | Image assessment using deep convolutional neural networks |
| CN104933711B (zh) * | 2015-06-10 | 2017-09-29 | 南通大学 | 一种肿瘤病理图像自动快速分割方法 |
| CN111095261A (zh) * | 2017-04-27 | 2020-05-01 | 视网膜病答案有限公司 | 眼底图像自动分析系统和方法 |
| EP3584742A1 (en) * | 2018-06-19 | 2019-12-25 | KPIT Technologies Ltd. | System and method for traffic sign recognition |
| CN109101994B (zh) * | 2018-07-05 | 2021-08-20 | 北京致远慧图科技有限公司 | 一种眼底图像筛查方法、装置、电子设备及存储介质 |
| US10929708B2 (en) * | 2018-12-10 | 2021-02-23 | International Business Machines Corporation | Deep learning network for salient region identification in images |
| CN110209859B (zh) * | 2019-05-10 | 2022-12-27 | 腾讯科技(深圳)有限公司 | 地点识别及其模型训练的方法和装置以及电子设备 |
| CN110472676A (zh) | 2019-08-05 | 2019-11-19 | 首都医科大学附属北京朝阳医院 | 基于深度神经网络的胃早癌组织学图像分类系统 |
| CN110504029B (zh) * | 2019-08-29 | 2022-08-19 | 腾讯医疗健康(深圳)有限公司 | 一种医学图像处理方法、医学图像识别方法及装置 |
| US11776117B2 (en) * | 2020-07-22 | 2023-10-03 | Siemens Healthcare Gmbh | Machine learning from noisy labels for abnormality assessment in medical imaging |
| EP4060609A1 (en) * | 2021-03-18 | 2022-09-21 | Koninklijke Philips N.V. | Detecting abnormalities in an x-ray image |
| US11797647B2 (en) * | 2021-03-30 | 2023-10-24 | Nano-X Ai Ltd. | Two stage detector for identification of a visual finding in a medical image |
| KR102650919B1 (ko) * | 2021-10-27 | 2024-03-22 | 사회복지법인 삼성생명공익재단 | 패치 단위 대조 학습 모델을 이용한 의료 영상 분석 방법 및 분석 장치 |
| EP4235566A1 (en) * | 2022-02-25 | 2023-08-30 | Siemens Healthcare GmbH | Method and system for determining a change of an anatomical abnormality depicted in medical image data |
-
2020
- 2020-05-06 EP EP20173286.4A patent/EP3907696A1/en not_active Withdrawn
-
2021
- 2021-05-03 EP EP21722236.3A patent/EP4147197B1/en active Active
- 2021-05-03 WO PCT/EP2021/061525 patent/WO2021224162A1/en not_active Ceased
- 2021-05-03 US US17/922,809 patent/US12450736B2/en active Active
- 2021-05-03 JP JP2022565566A patent/JP7726218B2/ja active Active
- 2021-05-03 CN CN202180033722.5A patent/CN115943426A/zh active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016007270A (ja) | 2014-06-23 | 2016-01-18 | 東芝メディカルシステムズ株式会社 | 医用画像処理装置 |
| US20200105394A1 (en) | 2018-09-28 | 2020-04-02 | Varian Medical Systems International Ag | Methods and systems for adaptive radiotherapy treatment planning using deep learning engines |
Also Published As
| Publication number | Publication date |
|---|---|
| US12450736B2 (en) | 2025-10-21 |
| EP3907696A1 (en) | 2021-11-10 |
| CN115943426A (zh) | 2023-04-07 |
| WO2021224162A1 (en) | 2021-11-11 |
| EP4147197B1 (en) | 2025-02-12 |
| US20230334656A1 (en) | 2023-10-19 |
| JP2023524947A (ja) | 2023-06-14 |
| EP4147197A1 (en) | 2023-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US12450736B2 (en) | Identifying abnormal images in set of medical images based on combined feature set with global features extracted using first CNN and local features extracted using second CNN analyzing high resolution image patches | |
| Ma et al. | A multitask deep learning approach for pulmonary embolism detection and identification | |
| Bartholmai et al. | Quantitative computed tomography imaging of interstitial lung diseases | |
| JP5054252B1 (ja) | 類似症例検索装置、類似症例検索方法、類似症例検索装置の作動方法およびプログラム | |
| JP2014029644A (ja) | 類似症例検索装置および類似症例検索方法 | |
| US12340895B2 (en) | Method and systems for the automated detection of free fluid using artificial intelligence for the focused assessment sonography for trauma (FAST) examination for trauma care | |
| CN118429665B (zh) | 基于ai模型识别冠状cta粥样斑块及易损斑块的方法 | |
| JP2024528381A (ja) | 医用画像データの自動追跡読影方法及びシステム | |
| Chen et al. | Automated unruptured cerebral aneurysms detection in TOF MR angiography images using dual-channel SE-3D UNet: a multi-center research | |
| Moosavi et al. | Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model | |
| Ali et al. | Improving classification accuracy for prostate cancer using noise removal filter and deep learning technique | |
| Vogado et al. | A ensemble methodology for automatic classification of chest X-rays using deep learning | |
| Ribeiro et al. | Explainable artificial intelligence in deep learning–based detection of aortic elongation on chest X-ray images | |
| Rajaraman et al. | Data characterization for reliable AI in medicine | |
| US12125204B2 (en) | Radiogenomics for cancer subtype feature visualization | |
| Ou et al. | A novel structure fusion attention model to detect architectural distortion on mammography | |
| Wu et al. | Enhancing placental pathology detection with GAMatrix-YOLOv8 model | |
| CN114631116A (zh) | 用于在医学图像中自动传播分割的方法和系统 | |
| US20180060501A1 (en) | System and method for generating clinical actions in a healthcare domain | |
| Mgbole et al. | Machine learning techniques for diagnosis of rare diseases from medical images | |
| Ghashghaei et al. | Grayscale image statistical attributes effectively distinguish the severity of lung abnormalities in ct scan slices of covid-19 patients | |
| CN115458109A (zh) | 一种基于卷积神经网络的医疗辅助诊断方法及系统 | |
| Singh et al. | Overview of image processing technology in healthcare systems | |
| Vadhera et al. | Optimizing Pulmonary Embolism detection through diverse UNET architectural variations | |
| US20240177454A1 (en) | Methods and systems for classifying a medical image dataset |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20240311 |
|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20240311 |
|
| A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20250124 |
|
| A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20250128 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20250414 |
|
| TRDD | Decision of grant or rejection written | ||
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20250708 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20250721 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 7726218 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |