JP6993371B2 - ディープラーニングに基づいたコンピュータ断層撮影肺結節検出法 - Google Patents

ディープラーニングに基づいたコンピュータ断層撮影肺結節検出法 Download PDF

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JP6993371B2
JP6993371B2 JP2019049066A JP2019049066A JP6993371B2 JP 6993371 B2 JP6993371 B2 JP 6993371B2 JP 2019049066 A JP2019049066 A JP 2019049066A JP 2019049066 A JP2019049066 A JP 2019049066A JP 6993371 B2 JP6993371 B2 JP 6993371B2
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ルングァオ ジャン
モンモン スン
シャオカン ワン
クァン チェン
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JP2019049066A 2018-03-16 2019-03-15 ディープラーニングに基づいたコンピュータ断層撮影肺結節検出法 Active JP6993371B2 (ja)

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Families Citing this family (68)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11589834B2 (en) * 2018-03-07 2023-02-28 Rensselaer Polytechnic Institute Deep neural network for CT metal artifact reduction
CN109255782A (zh) * 2018-09-03 2019-01-22 图兮深维医疗科技（苏州）有限公司 一种肺结节图像的处理方法、装置、设备及存储介质
CN109523521B (zh) * 2018-10-26 2022-12-20 复旦大学 基于多切片ct图像的肺结节分类和病灶定位方法和系统
CN109636817B (zh) * 2018-11-30 2020-10-30 华中科技大学 一种基于二维卷积神经网络的肺结节分割方法
CN109886967A (zh) * 2019-01-16 2019-06-14 成都蓝景信息技术有限公司 基于深度学习技术的肺部解剖学位置定位算法
CN110189307B (zh) * 2019-05-14 2021-11-23 慧影医疗科技（北京）有限公司 一种基于多模型融合的肺结节检测方法及系统
JP7313192B2 (ja) * 2019-05-27 2023-07-24 キヤノンメディカルシステムズ株式会社 診断支援装置、及び、ｘ線ｃｔ装置
CN111062947B (zh) * 2019-08-14 2023-04-25 深圳市智影医疗科技有限公司 一种基于深度学习的x光胸片病灶定位方法及系统
CN110739049A (zh) * 2019-10-10 2020-01-31 上海联影智能医疗科技有限公司 图像勾画方法、装置、存储介质及计算机设备
CN110942446A (zh) * 2019-10-17 2020-03-31 付冲 一种基于ct影像的肺结节自动检测方法
CN110570425B (zh) * 2019-10-18 2023-09-08 北京理工大学 一种基于深度强化学习算法的肺结节分析方法及装置
CN110852350B (zh) * 2019-10-21 2022-09-09 北京航空航天大学 一种基于多尺度迁移学习的肺结节良恶性分类方法和系统
US11605164B2 (en) 2019-10-22 2023-03-14 Shanghai United Imaging Intelligence Co., Ltd. Systems and methods for lung nodule evaluation
CN110992312B (zh) * 2019-11-15 2024-02-27 上海联影智能医疗科技有限公司 医学图像处理方法、装置、存储介质及计算机设备
CN110782446B (zh) * 2019-10-25 2022-04-15 杭州依图医疗技术有限公司 一种确定肺结节体积的方法及装置
CN112365504B (zh) * 2019-10-29 2024-11-29 杭州脉流科技有限公司 Ct左心室分割方法、装置、设备和存储介质
CN110853011B (zh) * 2019-11-11 2022-05-27 河北工业大学 用于肺结节检测的卷积神经网络模型的构建方法
CN110992377B (zh) * 2019-12-02 2020-12-22 推想医疗科技股份有限公司 图像分割方法、装置、计算机可读存储介质和设备
KR102322995B1 (ko) * 2019-12-10 2021-11-09 (주)헬스허브 다이나믹 윈도우 기반 인공지능 노듈 세그먼테이션 방법 및 그 장치
CN111080625B (zh) * 2019-12-18 2020-12-29 推想医疗科技股份有限公司 肺部影像条索检测模型的训练方法及其训练装置
CN111127482B (zh) * 2019-12-20 2023-06-30 广州柏视医疗科技有限公司 基于深度学习的ct影像肺气管的分割方法及系统
CN111369623B (zh) * 2020-02-27 2022-11-15 复旦大学 一种基于深度学习3d目标检测的肺部ct图像识别方法
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CN111539918B (zh) * 2020-04-15 2023-05-02 复旦大学附属肿瘤医院 基于深度学习的磨玻璃肺结节风险分层预测系统
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CN111862001B (zh) * 2020-06-28 2023-11-28 微医云(杭州)控股有限公司 Ct影像的半自动标注方法及装置、电子设备、存储介质
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CN111798425B (zh) * 2020-06-30 2022-05-27 天津大学 基于深度学习的胃肠道间质瘤中核分裂象智能检测方法
CN111815608B (zh) * 2020-07-13 2023-08-25 北京小白世纪网络科技有限公司 基于深度学习的新冠肺炎患者康复时间预测方法及系统
US20230289957A1 (en) * 2020-07-23 2023-09-14 Deep Bio Inc. Disease diagnosis method using neural network trained by using multi-phase biometric image, and disease diagnosis system performing same
CN112116558A (zh) * 2020-08-17 2020-12-22 您好人工智能技术研发昆山有限公司 一种基于深度学习的ct影像肺结节检测系统
CN111986189B (zh) * 2020-08-27 2022-12-27 上海市公共卫生临床中心 一种基于ct影像的多类别肺炎筛查深度学习装置
CN112241948B (zh) * 2020-09-23 2024-11-01 深圳视见医疗科技有限公司 一种自适应层厚的肺结节检测分析方法及系统
CN112184657A (zh) * 2020-09-24 2021-01-05 上海健康医学院 一种肺结节自动检测方法、装置及计算机系统
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CN112365498B (zh) * 2020-12-10 2024-01-23 南京大学 一种针对二维图像序列中多尺度多形态目标的自动检测方法
CN112634210B (zh) * 2020-12-10 2024-04-09 重庆大学 一种基于三维ct影像的肺结节检测方法
US11776128B2 (en) * 2020-12-11 2023-10-03 Siemens Healthcare Gmbh Automatic detection of lesions in medical images using 2D and 3D deep learning networks
CN113077427B (zh) * 2021-03-29 2023-04-25 北京深睿博联科技有限责任公司 一种类别预测模型的生成方法及装置
CN113012144A (zh) * 2021-04-08 2021-06-22 湘南学院附属医院 一种肺部肿瘤的自动勾画方法、勾画系统、计算设备和存储介质
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CN113658105A (zh) * 2021-07-21 2021-11-16 杭州深睿博联科技有限公司 一种3d肝脏病灶检测方法及装置
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CN113658174B (zh) * 2021-09-02 2023-09-19 北京航空航天大学 基于深度学习和图像处理算法的微核组学图像检测方法
US11367191B1 (en) * 2021-10-07 2022-06-21 Qure.Ai Technologies Private Limited Adapting report of nodules
KR102710766B1 (ko) * 2021-10-08 2024-09-27 주식회사 피맥스 폐 질환 경과 추적을 위한 정보 제공 방법 및 이를 위한 장치
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CN113971728B (zh) * 2021-10-25 2023-04-21 北京百度网讯科技有限公司 图像识别方法、模型的训练方法、装置、设备及介质
CN113962984A (zh) * 2021-11-15 2022-01-21 北京航空航天大学 基于深度学习算法的质子ct成像方法、装置和电子设备
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CN115100156B (zh) * 2022-07-01 2024-12-03 江苏康思宁医疗科技有限公司 一种深度学习级联ct肺结节检测方法及装置
CN115393321A (zh) * 2022-08-26 2022-11-25 南通大学 一种基于深度学习的多层螺旋ct对肺结核多分类检出方法
CN117635519A (zh) * 2022-08-29 2024-03-01 杭州堃博生物科技有限公司 基于ct图像的病灶检测方法、装置及计算机可读存储介质
US20240152747A1 (en) * 2022-11-08 2024-05-09 UnitX, Inc. Three-dimensional spatial-channel deep learning neural network
CN116012355B (zh) * 2023-02-07 2023-11-21 重庆大学 一种基于深度学习的自适应假阳性肺结节剔除方法
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CN116258717B (zh) * 2023-05-15 2023-09-08 广州思德医疗科技有限公司 病灶识别方法、装置、设备和存储介质
CN116664953B (zh) * 2023-06-28 2024-09-13 北京大学第三医院（北京大学第三临床医学院） 2.5d肺炎医学ct影像分类装置及设备
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003225231A (ja) 2001-11-20 2003-08-12 General Electric Co <Ge> 肺疾患検出のための方法及びシステム
US20170046616A1 (en) 2015-08-15 2017-02-16 Salesforce.Com, Inc. Three-dimensional (3d) convolution with 3d batch normalization
CN106504232A (zh) 2016-10-14 2017-03-15 北京网医智捷科技有限公司 一种基于3d卷积神经网络的肺部结节自动检测方法

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5881124A (en) * 1994-03-31 1999-03-09 Arch Development Corporation Automated method and system for the detection of lesions in medical computed tomographic scans
US6141437A (en) * 1995-11-22 2000-10-31 Arch Development Corporation CAD method, computer and storage medium for automated detection of lung nodules in digital chest images
US7130457B2 (en) * 2001-07-17 2006-10-31 Accuimage Diagnostics Corp. Systems and graphical user interface for analyzing body images
CN103186703A (zh) * 2011-12-30 2013-07-03 无锡睿影信息技术有限公司 一种基于胸片的计算机辅助检测肺结节的方法
CN106780460B (zh) * 2016-12-13 2019-11-08 杭州健培科技有限公司 一种用于胸部ct影像的肺结节自动检测系统
CA3053487A1 (en) * 2017-02-22 2018-08-30 The United States Of America, As Represented By The Secretary, Department Of Health And Human Services Detection of prostate cancer in multi-parametric mri using random forest with instance weighting & mr prostate segmentation by deep learning with holistically-nested networks
US10580131B2 (en) * 2017-02-23 2020-03-03 Zebra Medical Vision Ltd. Convolutional neural network for segmentation of medical anatomical images
CN106940816B (zh) * 2017-03-22 2020-06-09 杭州健培科技有限公司 基于3d全卷积神经网络的ct图像肺结节检测系统
CN107274402A (zh) * 2017-06-27 2017-10-20 北京深睿博联科技有限责任公司 一种基于胸部ct影像的肺结节自动检测方法及系统

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003225231A (ja) 2001-11-20 2003-08-12 General Electric Co <Ge> 肺疾患検出のための方法及びシステム
US20170046616A1 (en) 2015-08-15 2017-02-16 Salesforce.Com, Inc. Three-dimensional (3d) convolution with 3d batch normalization
CN106504232A (zh) 2016-10-14 2017-03-15 北京网医智捷科技有限公司 一种基于3d卷积神经网络的肺部结节自动检测方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Arnaud Arindra Adiyoso Setio et al.，"Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks，IEEE TRANSACTIONS ON MEDICAL IMAGING，Vol.35, No.5，米国，2016年04月29日，pp.1160-1169，https://geertlitjens.nl/publication/seti-16/seti-16.pdf
Gustavo Perez et al.，"Automated Detection of Lung Nodules with Three-dimensional Convolutional Neural Networks"，Proceeding of SPIE vol.10572，米国，2017年11月17日，Vol.10572，https://biomedicalcomputervision.uniandes.edu.co/images/papers/pa_sipaim2017.pdf

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