WO2021058843A1 - Método y sistema para la segmentación automática de hiperintensidades de sustancia blanca en imágenes de resonancia magnética cerebral - Google Patents
Método y sistema para la segmentación automática de hiperintensidades de sustancia blanca en imágenes de resonancia magnética cerebral Download PDFInfo
- Publication number
- WO2021058843A1 WO2021058843A1 PCT/ES2020/070069 ES2020070069W WO2021058843A1 WO 2021058843 A1 WO2021058843 A1 WO 2021058843A1 ES 2020070069 W ES2020070069 W ES 2020070069W WO 2021058843 A1 WO2021058843 A1 WO 2021058843A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- layers
- image
- convolutional
- neural network
- convolutional neural
- Prior art date
Links
- 206010072731 White matter lesion Diseases 0.000 title claims abstract description 35
- 210000004556 brain Anatomy 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000011218 segmentation Effects 0.000 title abstract description 17
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 83
- 230000001575 pathological effect Effects 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims description 30
- 238000013528 artificial neural network Methods 0.000 claims description 17
- 208000037170 Delayed Emergence from Anesthesia Diseases 0.000 claims description 16
- 230000009467 reduction Effects 0.000 claims description 16
- 230000004913 activation Effects 0.000 claims description 12
- 230000017105 transposition Effects 0.000 claims description 12
- 238000002595 magnetic resonance imaging Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 2
- 238000007670 refining Methods 0.000 claims description 2
- 238000012935 Averaging Methods 0.000 claims 1
- 238000004513 sizing Methods 0.000 claims 1
- 230000006870 function Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 230000003902 lesion Effects 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 208000037451 Leukoaraiosis Diseases 0.000 description 5
- 210000004885 white matter Anatomy 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000002790 cross-validation Methods 0.000 description 2
- 230000010339 dilation Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 238000002075 inversion recovery Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000011176 pooling Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- 208000024827 Alzheimer disease Diseases 0.000 description 1
- 101100400452 Caenorhabditis elegans map-2 gene Proteins 0.000 description 1
- 206010012289 Dementia Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 208000029028 brain injury Diseases 0.000 description 1
- 210000003710 cerebral cortex Anatomy 0.000 description 1
- 210000004289 cerebral ventricle Anatomy 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000013434 data augmentation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000036449 good health Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000002610 neuroimaging Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000003625 skull Anatomy 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Classifications
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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]
-
- 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
-
- 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
-
- 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
-
- 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
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
-
- 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]
-
- 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
-
- 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]
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- 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)
- Veterinary Medicine (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
Description
Claims
Priority Applications (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
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 |
JP2022543800A JP7462055B2 (ja) | 2019-09-23 | 2020-01-30 | 脳の磁気共鳴画像における白質高信号域の自動セグメント化のための方法およびシステム |
US17/762,628 US20220343142A1 (en) | 2019-09-23 | 2020-01-30 | Method and system for the automatic segmentation of white matter hyperintensities in magnetic resonance brain images |
AU2020352676A AU2020352676A1 (en) | 2019-09-23 | 2020-01-30 | Method and system for the automatic segmentation of white matter hyperintensities in brain magnetic resonance images |
EP20867430.9A EP4020322A4 (en) | 2019-09-23 | 2020-01-30 | METHOD AND SYSTEM FOR AUTOMATIC SEGMENTATION OF WHITE SUBSTANCE HYPERINTENSITIES IN BRAIN MAGNETIC RESONANCE IMAGES |
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 |
ESP201930818 | 2019-09-23 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021058843A1 true WO2021058843A1 (es) | 2021-04-01 |
Family
ID=75109007
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
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 |
Country Status (7)
Country | Link |
---|---|
US (1) | US20220343142A1 (es) |
EP (1) | EP4020322A4 (es) |
JP (1) | JP7462055B2 (es) |
AU (1) | AU2020352676A1 (es) |
BR (1) | BR112022005437A2 (es) |
ES (1) | ES2813777B2 (es) |
WO (1) | WO2021058843A1 (es) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113096142A (zh) * | 2021-04-30 | 2021-07-09 | 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 |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
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 |
Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
WO2018140596A2 (en) * | 2017-01-27 | 2018-08-02 | Arterys Inc. | Automated segmentation utilizing fully convolutional networks |
US20180260951A1 (en) * | 2017-03-08 | 2018-09-13 | Siemens Healthcare Gmbh | Deep Image-to-Image Recurrent Network with Shape Basis for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes |
WO2018229490A1 (en) * | 2017-06-16 | 2018-12-20 | Ucl Business Plc | A system and computer-implemented method for segmenting an image |
CN109410167A (zh) * | 2018-08-31 | 2019-03-01 | 深圳大学 | 一种3d乳腺图像的分析方法及相关产品 |
CN109872328A (zh) * | 2019-01-25 | 2019-06-11 | 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 |
WO2019109410A1 (zh) * | 2017-12-06 | 2019-06-13 | 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 |
CN109886273A (zh) * | 2019-02-26 | 2019-06-14 | 四川大学华西医院 | 一种cmr图像分割分类系统 |
CN109993809A (zh) * | 2019-03-18 | 2019-07-09 | 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 |
CN110189334A (zh) * | 2019-05-28 | 2019-08-30 | 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 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10210246B2 (en) | 2014-09-26 | 2019-02-19 | Oracle International Corporation | Techniques for similarity analysis and data enrichment using knowledge sources |
US11357398B2 (en) | 2017-01-31 | 2022-06-14 | Nidek Co., Ltd. | Image processing device and non-transitory computer-readable recording medium |
US10223610B1 (en) | 2017-10-15 | 2019-03-05 | International Business Machines Corporation | System and method for detection and classification of findings in images |
-
2019
- 2019-09-23 ES ES201930818A patent/ES2813777B2/es active Active
-
2020
- 2020-01-30 WO PCT/ES2020/070069 patent/WO2021058843A1/es unknown
- 2020-01-30 EP EP20867430.9A patent/EP4020322A4/en active Pending
- 2020-01-30 AU AU2020352676A patent/AU2020352676A1/en active Pending
- 2020-01-30 JP JP2022543800A patent/JP7462055B2/ja active Active
- 2020-01-30 BR BR112022005437A patent/BR112022005437A2/pt unknown
- 2020-01-30 US US17/762,628 patent/US20220343142A1/en active Pending
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018140596A2 (en) * | 2017-01-27 | 2018-08-02 | Arterys Inc. | Automated segmentation utilizing fully convolutional networks |
US20180260951A1 (en) * | 2017-03-08 | 2018-09-13 | Siemens Healthcare Gmbh | Deep Image-to-Image Recurrent Network with Shape Basis for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes |
WO2018229490A1 (en) * | 2017-06-16 | 2018-12-20 | Ucl Business Plc | A system and computer-implemented method for segmenting an image |
WO2019109410A1 (zh) * | 2017-12-06 | 2019-06-13 | 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 |
CN109410167A (zh) * | 2018-08-31 | 2019-03-01 | 深圳大学 | 一种3d乳腺图像的分析方法及相关产品 |
CN109872328A (zh) * | 2019-01-25 | 2019-06-11 | 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 |
CN109886273A (zh) * | 2019-02-26 | 2019-06-14 | 四川大学华西医院 | 一种cmr图像分割分类系统 |
CN109993809A (zh) * | 2019-03-18 | 2019-07-09 | 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 |
CN110189334A (zh) * | 2019-05-28 | 2019-08-30 | 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 |
Non-Patent Citations (4)
Title |
---|
BERNAL JOSE ET AL., DEEP CONVOLUTIONAL NEURAL NETWORKS FOR BRAIN IMAGE ANALYSIS ON MAGNETIC RESONANCE IMAGING: A REVIEW, XP085638002 * |
KARIMI DAVOOD ET AL.: "Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks", IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 39, no. 2, 19 July 2019 (2019-07-19), Piscataway, Nj, Us, pages 499 - 513, XP011769481, ISSN: 0278-0062, DOI: 10.1109/TMI.2019.2930068 * |
See also references of EP4020322A4 * |
XU BOTIAN ET AL.: "Orchestral fully convolutional networks for small lesion segmentation in brain MRI", IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018, 4 April 2018 (2018-04-04), pages 889 - 892, XP033348292, DOI: 10.1109/ISBI.2018.8363714 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113096142A (zh) * | 2021-04-30 | 2021-07-09 | 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 |
Also Published As
Publication number | Publication date |
---|---|
US20220343142A1 (en) | 2022-10-27 |
BR112022005437A2 (pt) | 2022-06-21 |
EP4020322A1 (en) | 2022-06-29 |
AU2020352676A1 (en) | 2022-04-21 |
JP7462055B2 (ja) | 2024-04-04 |
EP4020322A4 (en) | 2023-10-25 |
ES2813777A1 (es) | 2021-03-24 |
ES2813777B2 (es) | 2023-10-27 |
JP2023514964A (ja) | 2023-04-12 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
ES2813777B2 (es) | Metodo y sistema para la segmentacion automatica de hiperintensidades de sustancia blanca en imagenes de resonancia magnetica cerebral | |
Mendrik et al. | MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans | |
ES2701095T3 (es) | Un sistema y método para anotar imágenes propagando información | |
Milošević et al. | Automated estimation of chronological age from panoramic dental X-ray images using deep learning | |
Klein et al. | Early diagnosis of dementia based on intersubject whole-brain dissimilarities | |
KR102458324B1 (ko) | 학습 모델을 이용한 데이터 처리 방법 | |
CN111932492B (zh) | 一种医学图像处理方法、装置及计算机可读存储介质 | |
CN113096137B (zh) | 一种oct视网膜图像领域适应分割方法及系统 | |
Ellingsen et al. | Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling | |
Li et al. | An efficient approach for differentiating Alzheimer's disease from normal elderly based on multicenter MRI using gray-level invariant features | |
Jain et al. | A novel method for differential prognosis of brain degenerative diseases using radiomics-based textural analysis and ensemble learning classifiers | |
JP6785976B2 (ja) | 脳画像正規化装置、方法およびプログラム | |
Dai et al. | Bagging ensembles for the diagnosis and prognostication of Alzheimer's disease | |
KR102043829B1 (ko) | 병변 발생 시점 추정 방법, 장치 및 프로그램 | |
KR20220133834A (ko) | 학습 모델을 이용한 데이터 처리 방법 | |
Shetty et al. | Detection and Prediction of Alzheimer's disease using Deep learning: A review | |
Jain et al. | A review of deep learning-based disease detection in Alzheimer's patients | |
Rachmadi et al. | Evaluation of four supervised learning schemes in white matter hyperintensities segmentation in absence or mild presence of vascular pathology | |
Baydargil et al. | A parallel deep convolutional neural network for Alzheimer's disease classification on PET/CT brain images | |
Abd Algani et al. | Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN | |
Dey | A subpixel residual U-Net and feature fusion preprocessing for retinal vessel segmentation | |
Mukhopadhyay | Task-Agnostic Continual Hippocampus Segmentation for Smooth Population Shifts | |
Jiménez Mesa | Enhancing diagnostic accuracy in neuroimaging through machine learning: advancements in statistical classification and mapping | |
Yee | 3D convolutional neural networks for Alzheimer’s disease classification | |
Danielsson | Multimodal Brain Age Estimation |
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: 20867430 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2022543800 Country of ref document: JP Kind code of ref document: A |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
ENP | Entry into the national phase |
Ref document number: 2020867430 Country of ref document: EP Effective date: 20220322 |
|
REG | Reference to national code |
Ref country code: BR Ref legal event code: B01A Ref document number: 112022005437 Country of ref document: BR |
|
ENP | Entry into the national phase |
Ref document number: 2020352676 Country of ref document: AU Date of ref document: 20200130 Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 112022005437 Country of ref document: BR Kind code of ref document: A2 Effective date: 20220323 |