BR112022005437A2 - Método implementado por computador e sistema para segmentação de hipersensibilidades da substância branca presentes nas imagens cerebrais decorrentes da ressonância magnética - Google Patents
Método implementado por computador e sistema para segmentação de hipersensibilidades da substância branca presentes nas imagens cerebrais decorrentes da ressonância magnéticaInfo
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- BR112022005437A2 BR112022005437A2 BR112022005437A BR112022005437A BR112022005437A2 BR 112022005437 A2 BR112022005437 A2 BR 112022005437A2 BR 112022005437 A BR112022005437 A BR 112022005437A BR 112022005437 A BR112022005437 A BR 112022005437A BR 112022005437 A2 BR112022005437 A2 BR 112022005437A2
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- present
- hypersensitivities
- white matter
- computer
- magnetic resonance
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- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
- G06N3/0455—Auto-encoder networks; Encoder-decoder networks
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
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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
- G06N3/08—Learning methods
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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
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
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- 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
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- 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
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- 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
- G06T2207/10088—Magnetic resonance imaging [MRI]
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- 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
- G06T2207/20081—Training; Learning
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- 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
- G06T2207/20084—Artificial neural networks [ANN]
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- 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
- G06T2207/30016—Brain
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- 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
- G06T2207/30096—Tumor; Lesion
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
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- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
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- Biomedical Technology (AREA)
- Biophysics (AREA)
- General Physics & Mathematics (AREA)
- Molecular Biology (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Medical Informatics (AREA)
- Radiology & Medical Imaging (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Heart & Thoracic Surgery (AREA)
- Pathology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- High Energy & Nuclear Physics (AREA)
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- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
método implementado por computador e sistema para segmentação de hipersensibilidades da substância branca presentes nas imagens cerebrais decorrentes da ressonância magnética. a presente invenção se refere a um método e um sistema para a segmentação de hipersensibilidades da substância branca (wmhs) presentes na imagens cerebrais da ressonância magnética, compreendendo: provisão de uma matriz de redes neurais convolucionais (cnns) treinadas com uma imagem cerebral da ressonância magnética; determinação, para cada uma das cnns e para cada voxel, da probabilidade de que o determinado voxel corresponda a uma hipersensibilidade patológica; cálculo da média de todas as probabilidades determinadas para cada voxel; comparação das probabilidades médias para cada voxel com um limiar; geração de uma imagem máscara com os voxels que excedem o limiar.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
ES201930818A ES2813777B2 (es) | 2019-09-23 | 2019-09-23 | Metodo y sistema para la segmentacion automatica de hiperintensidades de sustancia blanca en imagenes de resonancia magnetica cerebral |
PCT/ES2020/070069 WO2021058843A1 (es) | 2019-09-23 | 2020-01-30 | Método y sistema para la segmentación automática de hiperintensidades de sustancia blanca en imágenes de resonancia magnética cerebral |
Publications (1)
Publication Number | Publication Date |
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BR112022005437A2 true BR112022005437A2 (pt) | 2022-06-21 |
Family
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
BR112022005437A BR112022005437A2 (pt) | 2019-09-23 | 2020-01-30 | Método implementado por computador e sistema para segmentação de hipersensibilidades da substância branca presentes nas imagens cerebrais decorrentes da ressonância magnética |
Country Status (7)
Country | Link |
---|---|
US (1) | US20220343142A1 (pt) |
EP (1) | EP4020322A4 (pt) |
JP (1) | JP7462055B2 (pt) |
AU (1) | AU2020352676A1 (pt) |
BR (1) | BR112022005437A2 (pt) |
ES (1) | ES2813777B2 (pt) |
WO (1) | WO2021058843A1 (pt) |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11495353B2 (en) | 2018-05-10 | 2022-11-08 | Mohamed Anver Jameel | Method, apparatus, and computer readible media for artificial intelligence-based treatment guidance for the neurologically impaired patient who may need neurosurgery |
ES2828728A1 (es) * | 2019-11-27 | 2021-05-27 | Fundacion Para La Investigacion Del Hospital Univ La Fe De La Comunidad Valenciana | Metodo para obtener un biomarcador de imagen que cuantifica la calidad de la estructura trabecular de los huesos |
CN113159147B (zh) * | 2021-04-08 | 2023-09-26 | 平安科技(深圳)有限公司 | 基于神经网络的图像识别方法、装置、电子设备 |
CN113096142B (zh) * | 2021-04-30 | 2022-12-30 | 北京理工大学 | 基于联合嵌入空间的白质神经束自动分割方法 |
CN114419066B (zh) * | 2022-01-14 | 2022-12-13 | 深圳市铱硙医疗科技有限公司 | 脑白质高信号分割方法、装置、设备及存储介质 |
CN115514343B (zh) * | 2022-05-13 | 2023-08-11 | 浙江腾腾电气有限公司 | 电网波形滤波系统及其滤波方法 |
CN115115628B (zh) * | 2022-08-29 | 2022-11-22 | 山东第一医科大学附属省立医院(山东省立医院) | 一种基于三维精细化残差网络的腔隙性脑梗死识别系统 |
CN117556715B (zh) * | 2024-01-12 | 2024-03-26 | 湖南大学 | 基于信息融合的典型环境下智能电表退化分析方法及系统 |
Family Cites Families (14)
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US10210246B2 (en) * | 2014-09-26 | 2019-02-19 | Oracle International Corporation | Techniques for similarity analysis and data enrichment using knowledge sources |
EP3573520A4 (en) * | 2017-01-27 | 2020-11-04 | Arterys Inc. | AUTOMATED SEGMENTATION USING FULLY CONVOLUTIVE NETWORKS |
US11357398B2 (en) * | 2017-01-31 | 2022-06-14 | Nidek Co., Ltd. | Image processing device and non-transitory computer-readable recording medium |
US10366491B2 (en) * | 2017-03-08 | 2019-07-30 | Siemens Healthcare Gmbh | Deep image-to-image recurrent network with shape basis for automatic vertebra labeling in large-scale 3D CT volumes |
GB201709672D0 (en) * | 2017-06-16 | 2017-08-02 | Ucl Business Plc | A system and computer-implemented method for segmenting an image |
US10223610B1 (en) * | 2017-10-15 | 2019-03-05 | International Business Machines Corporation | System and method for detection and classification of findings in images |
CN109886992A (zh) * | 2017-12-06 | 2019-06-14 | 深圳博脑医疗科技有限公司 | 用于分割mri图像中异常信号区的全卷积网络模型训练方法 |
CN108171711A (zh) * | 2018-01-17 | 2018-06-15 | 深圳市唯特视科技有限公司 | 一种基于完全卷积网络的婴幼儿脑部磁共振图像分割方法 |
CN109410167B (zh) * | 2018-08-31 | 2021-11-09 | 深圳大学 | 一种3d乳腺图像的分析方法及系统、介质 |
CN109872328B (zh) * | 2019-01-25 | 2021-05-07 | 腾讯科技(深圳)有限公司 | 一种脑部图像分割方法、装置和存储介质 |
CN109886273B (zh) * | 2019-02-26 | 2022-12-16 | 四川大学华西医院 | 一种cmr图像分割分类系统 |
CN109993809B (zh) * | 2019-03-18 | 2023-04-07 | 杭州电子科技大学 | 基于残差U-net卷积神经网络的快速磁共振成像方法 |
CN109993735A (zh) * | 2019-03-29 | 2019-07-09 | 成都信息工程大学 | 基于级联卷积的图像分割方法 |
CN110189334B (zh) * | 2019-05-28 | 2022-08-09 | 南京邮电大学 | 基于注意力机制的残差型全卷积神经网络的医学图像分割方法 |
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2019
- 2019-09-23 ES ES201930818A patent/ES2813777B2/es active Active
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2020
- 2020-01-30 EP EP20867430.9A patent/EP4020322A4/en active Pending
- 2020-01-30 WO PCT/ES2020/070069 patent/WO2021058843A1/es unknown
- 2020-01-30 AU AU2020352676A patent/AU2020352676A1/en active Pending
- 2020-01-30 BR BR112022005437A patent/BR112022005437A2/pt unknown
- 2020-01-30 JP JP2022543800A patent/JP7462055B2/ja active Active
- 2020-01-30 US US17/762,628 patent/US20220343142A1/en active Pending
Also Published As
Publication number | Publication date |
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EP4020322A1 (en) | 2022-06-29 |
US20220343142A1 (en) | 2022-10-27 |
ES2813777B2 (es) | 2023-10-27 |
WO2021058843A1 (es) | 2021-04-01 |
EP4020322A4 (en) | 2023-10-25 |
JP2023514964A (ja) | 2023-04-12 |
AU2020352676A1 (en) | 2022-04-21 |
ES2813777A1 (es) | 2021-03-24 |
JP7462055B2 (ja) | 2024-04-04 |
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