EP3619631A1 - Verbesserungen bei der radiologischen erkennung chronisch thromboembolischer pulmonaler hypertonie - Google Patents
Verbesserungen bei der radiologischen erkennung chronisch thromboembolischer pulmonaler hypertonieInfo
- Publication number
- EP3619631A1 EP3619631A1 EP18720250.2A EP18720250A EP3619631A1 EP 3619631 A1 EP3619631 A1 EP 3619631A1 EP 18720250 A EP18720250 A EP 18720250A EP 3619631 A1 EP3619631 A1 EP 3619631A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- cteph
- human
- computer system
- images
- features
- 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.)
- Withdrawn
Links
- 208000002815 pulmonary hypertension Diseases 0.000 title claims abstract description 96
- 208000026151 Chronic thromboembolic pulmonary hypertension Diseases 0.000 title claims abstract description 91
- 238000001514 detection method Methods 0.000 title abstract description 7
- 238000000034 method Methods 0.000 claims abstract description 34
- 238000004590 computer program Methods 0.000 claims abstract description 20
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- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 206010013975 Dyspnoeas Diseases 0.000 description 2
- 208000019693 Lung disease Diseases 0.000 description 2
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Classifications
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/507—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for determination of haemodynamic parameters, e.g. perfusion CT
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
- A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/032—Transmission computed tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30061—Lung
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Definitions
- the present invention is concerned with the radiological detection of chronic thromboembolic pulmonary hypertension (CTEPH).
- CTEPH chronic thromboembolic pulmonary hypertension
- the invention relates to a method, a computer system and a computer program product for the automated detection of indications of the presence of CTEPH in a human.
- Chronic thromboembolic pulmonary hypertension is a special form of pulmonary hypertension (PH, pulmonary hypertension). It is characterized by the influx of thrombi into the pulmonary arteries. These clog and constrict the vessels in whole or in part and can transform connective tissue. In rare cases, pulmonary hypertension develops with poor prognosis.
- CTEPH chronic myeloea
- the symptoms of CTEPH are nonspecific. Dyspnoea and fatigue may occur in the early stages. The duration of the first symptoms of the diagnosis is on average 14 months, with some patients already in an advanced stage of the disease. This underlines the need for accurate and timely diagnostics.
- CTEPH chronic pulmonary hypertension
- the gold standard for diagnosing or excluding CTEPH is ventilation / perfusion scintigraphy.
- the negative predictive value of perfusion scintigraphy is close to 100, which means that a proper perfusion distribution excludes a CTEPH with almost certainty.
- the problem is that CTEPH is comparatively rare. The rarity and the complex diagnostics and differential diagnostics lead to CTEPH being underdiagnosed.
- a first subject of the present invention is a method for detecting evidence of the presence of CTEPH in a human comprising the following steps:
- Another object of the present invention is a computer system for detecting evidence for the presence of CTEPH in a human, comprising - means for automatically receiving or retrieving one or more computed tomographic images of the human thorax
- the present invention is directed to the automated image analysis of computed tomographic images of the human thorax.
- a CT image produced by the human thorax is immediately and automatically subjected to an image analysis according to the invention after it has been generated.
- a computer system aligned to produce a corresponding CT scan may be configured to supply the CT scan to the image analysis of the present invention.
- the components that perform the image analysis receive the CT scan.
- FIG 3 shows schematically an embodiment of the computer system 100 according to the invention.
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Radiology & Medical Imaging (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Physics & Mathematics (AREA)
- Pathology (AREA)
- Animal Behavior & Ethology (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Molecular Biology (AREA)
- Heart & Thoracic Surgery (AREA)
- Optics & Photonics (AREA)
- High Energy & Nuclear Physics (AREA)
- Biophysics (AREA)
- Epidemiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Primary Health Care (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Theoretical Computer Science (AREA)
- Dentistry (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physiology (AREA)
- Vascular Medicine (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Cardiology (AREA)
- Pulmonology (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP17169079 | 2017-05-02 | ||
PCT/EP2018/060732 WO2018202541A1 (de) | 2017-05-02 | 2018-04-26 | Verbesserungen bei der radiologischen erkennung chronisch thromboembolischer pulmonaler hypertonie |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3619631A1 true EP3619631A1 (de) | 2020-03-11 |
Family
ID=58672386
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP18720250.2A Withdrawn EP3619631A1 (de) | 2017-05-02 | 2018-04-26 | Verbesserungen bei der radiologischen erkennung chronisch thromboembolischer pulmonaler hypertonie |
Country Status (6)
Country | Link |
---|---|
US (1) | US20200237331A1 (de) |
EP (1) | EP3619631A1 (de) |
JP (1) | JP2020518396A (de) |
CN (1) | CN110574070A (de) |
CA (1) | CA3061988A1 (de) |
WO (1) | WO2018202541A1 (de) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11727571B2 (en) | 2019-09-18 | 2023-08-15 | Bayer Aktiengesellschaft | Forecast of MRI images by means of a forecast model trained by supervised learning |
US11915361B2 (en) | 2019-09-18 | 2024-02-27 | Bayer Aktiengesellschaft | System, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics |
US12002203B2 (en) | 2019-03-12 | 2024-06-04 | Bayer Healthcare Llc | Systems and methods for assessing a likelihood of CTEPH and identifying characteristics indicative thereof |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102018222606A1 (de) | 2018-12-20 | 2020-06-25 | Siemens Healthcare Gmbh | Verfahren und Vorrichtung zur Detektion eines anatomischen Merkmals eines Blutgefäßabschnittes |
EP3813017A1 (de) | 2019-10-21 | 2021-04-28 | Bayer AG | Segmentierung der herzregion in ct-aufnahmen |
JP7394588B2 (ja) * | 2019-11-07 | 2023-12-08 | キヤノン株式会社 | 情報処理装置、情報処理方法、および撮像システム |
WO2022106302A1 (en) | 2020-11-20 | 2022-05-27 | Bayer Aktiengesellschaft | Representation learning |
US20240185577A1 (en) | 2021-04-01 | 2024-06-06 | Bayer Aktiengesellschaft | Reinforced attention |
WO2022268656A1 (en) | 2021-06-25 | 2022-12-29 | Bayer Aktiengesellschaft | Federated representation learning with consistency regularization |
CN113702482B (zh) * | 2021-08-30 | 2022-08-23 | 中国医学科学院北京协和医院 | 一种IgG N-糖链特征组合及其应用 |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090012382A1 (en) * | 2007-07-02 | 2009-01-08 | General Electric Company | Method and system for detection of obstructions in vasculature |
JP2010104581A (ja) * | 2008-10-30 | 2010-05-13 | Canon Inc | X線撮影装置及びx線撮影方法 |
WO2013036842A2 (en) * | 2011-09-08 | 2013-03-14 | Radlogics, Inc. | Methods and systems for analyzing and reporting medical images |
RU2545927C1 (ru) * | 2014-03-13 | 2015-04-10 | Федеральное государственное бюджетное учреждение "Научно-исследовательский институт кардиологии" Сибирского отделения Российской академии медицинских наук | Способ дифференциальной диагностики острой тромбоэмболии легочной артерии и хронической постэмболической легочной гипертензии |
WO2015163098A1 (ja) * | 2014-04-22 | 2015-10-29 | 国立大学法人東北大学 | 肺高血圧症の検査方法 |
-
2018
- 2018-04-26 US US16/609,138 patent/US20200237331A1/en not_active Abandoned
- 2018-04-26 JP JP2019560392A patent/JP2020518396A/ja active Pending
- 2018-04-26 CN CN201880029130.4A patent/CN110574070A/zh active Pending
- 2018-04-26 WO PCT/EP2018/060732 patent/WO2018202541A1/de unknown
- 2018-04-26 CA CA3061988A patent/CA3061988A1/en active Pending
- 2018-04-26 EP EP18720250.2A patent/EP3619631A1/de not_active Withdrawn
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US12002203B2 (en) | 2019-03-12 | 2024-06-04 | Bayer Healthcare Llc | Systems and methods for assessing a likelihood of CTEPH and identifying characteristics indicative thereof |
US11727571B2 (en) | 2019-09-18 | 2023-08-15 | Bayer Aktiengesellschaft | Forecast of MRI images by means of a forecast model trained by supervised learning |
US11915361B2 (en) | 2019-09-18 | 2024-02-27 | Bayer Aktiengesellschaft | System, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics |
Also Published As
Publication number | Publication date |
---|---|
JP2020518396A (ja) | 2020-06-25 |
US20200237331A1 (en) | 2020-07-30 |
CN110574070A (zh) | 2019-12-13 |
CA3061988A1 (en) | 2019-10-30 |
WO2018202541A1 (de) | 2018-11-08 |
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