JP6816255B2 - 高度な病理診断 - Google Patents
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Description
Claims (11)
- 処理装置の作動方法であって、
顕微鏡に光学的に結合されたデジタルカメラから病理資料の拡大病理画像を受信すること、
前記処理装置が前記拡大病理画像内の1つまたは複数の対象領域を特定するために、前記拡大病理画像を病理データベース内に含まれる参照病理画像と比較することであって、前記1つまたは複数の対象領域を特定するのに機械学習アルゴリズムが使用され、前記機械学習アルゴリズムは、前記病理データベース内の前記参照病理画像を使用して前記1つまたは複数の対象領域を特定するように訓練される、前記拡大病理画像を前記参照病理画像と比較すること、
前記処理装置が前記顕微鏡のユーザに前記拡大病理画像内の前記1つまたは複数の対象領域に注意するように警告すること
を含み、前記ユーザに警告することは、前記ユーザに、前記1つまたは複数の対象領域内の構造の診断と、前記診断の信頼区間とを知らせることを含む、方法。 - 前記病理データベースはさらに、前記病理データベース内の前記参照病理画像に対応する病理医からの注釈を含み、前記機械学習アルゴリズムは、前記参照病理画像と共に前記病理医からの前記注釈を使用して前記拡大病理画像内の前記1つまたは複数の対象領域を特定する、請求項1に記載の方法。
- 前記病理データベースはさらに、前記病理データベース内の前記参照病理画像に関するスライド位置情報および倍率情報を含み、前記機械学習アルゴリズムは、前記スライド位置情報および前記倍率情報を使用して前記拡大病理画像内の前記1つまたは複数の対象領域を特定する、請求項2に記載の方法。
- 前記処理装置が前記機械学習アルゴリズムを使用して前記ユーザの確認が不十分であると考えられる前記病理試料の部位を特定すること、および、
前記処理装置が前記ユーザの確認が不十分であると考えられる前記部位内の注意対象である可能性がある1つまたは複数の領域を前記ユーザに報告すること
をさらに含む、請求項1に記載の方法。 - 前記機械学習アルゴリズムは、前記ユーザの確認が不十分であると考えられる前記病理試料の前記部位について、前記ユーザが閾値時間の間、前記部位を見ていることに基づいて特定するように訓練される、請求項4に記載の方法。
- 確認が不十分であると考えられる前記部位はさらに、凝視検出、前記病理試料上の位置、または前記顕微鏡の倍率レベルの少なくとも1つによって決定される、請求項5に記載の方法。
- 前記ユーザに警告することは、前記1つまたは複数の対象領域の特定に応答して、音声、視覚的、または触覚的通知を出力することを含む、請求項1に記載の方法。
- 少なくとも1つの機械アクセス可能記憶媒体であって、処理装置によって実行されたときに、
顕微鏡に光学的に結合されたデジタルカメラから病理試料の拡大病理画像を受信する動作、
前記拡大病理画像内の1つまたは複数の対象領域を特定するために、前記拡大病理画像を病理データベース内に含まれる参照病理画像と比較する動作であって、前記拡大病理画像を前記参照病理画像と比較する動作は、機械学習アルゴリズムを使用して前記1つまたは複数の対象領域を特定する動作を含み、前記機械学習アルゴリズムは、前記病理データベースを使用して対象領域を特定するように訓練される、前記拡大病理画像を前記参照病理画像と比較する動作、
前記顕微鏡のユーザに前記拡大病理画像内の1つまたは複数の対象領域に注意するように警告する動作
前記1つまたは複数の対象領域内の構造の診断と、前記病理データベース内の前記参照病理画像に基づいて前記診断の信頼区間とを出力する動作
を含む動作を前記処理装置に実行させる命令を備える少なくとも1つの機械アクセス可能記憶媒体。 - 前記病理データベースはさらに、前記病理データベース内の前記参照病理画像に対応する病理医からの注釈を含み、前記機械学習アルゴリズムは、前記参照病理画像と共に前記病理医からの前記注釈を使用して前記拡大病理画像内の前記1つまたは複数の対象領域を特定する、請求項8に記載の少なくとも1つの機械アクセス可能記憶媒体。
- 前記病理データベースはさらに、倍率情報および前記参照病理画像上の位置を特定するための位置情報を含み、前記機械学習アルゴリズムは、前記参照病理画像に関する前記位置情報および前記倍率情報を使用して前記拡大病理画像内の前記1つまたは複数の対象領域を特定する、請求項8に記載の少なくとも1つの機械アクセス可能記憶媒体。
- 前記病理データベースはさらに、前記機械学習アルゴリズムを訓練するために同じ病気の複数の参照病理画像を含む、請求項8に記載の少なくとも1つの機械アクセス可能記憶媒体。
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US15/235,541 US10025902B2 (en) | 2016-08-12 | 2016-08-12 | Enhanced pathology diagnosis |
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PCT/US2017/046125 WO2018031674A1 (en) | 2016-08-12 | 2017-08-09 | Enhanced pathology diagnosis |
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Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2022158908A1 (ko) * | 2021-01-22 | 2022-07-28 | 주식회사 루닛 | 병리 이미지 분석 방법 및 시스템 |
Families Citing this family (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP6333871B2 (ja) * | 2016-02-25 | 2018-05-30 | ファナック株式会社 | 入力画像から検出した対象物を表示する画像処理装置 |
US10025902B2 (en) * | 2016-08-12 | 2018-07-17 | Verily Life Sciences Llc | Enhanced pathology diagnosis |
US11010610B2 (en) | 2017-06-13 | 2021-05-18 | Google Llc | Augmented reality microscope for pathology |
DK3642743T3 (da) | 2017-06-19 | 2021-12-06 | Viz Ai Inc | Fremgangsmåde og system til computerunderstøttet triage |
US10733730B2 (en) | 2017-06-19 | 2020-08-04 | Viz.ai Inc. | Method and system for computer-aided triage |
JP2021515240A (ja) * | 2018-04-12 | 2021-06-17 | グーグル エルエルシーGoogle LLC | 定量的バイオマーカデータのオーバレイを有する病理学用拡張現実顕微鏡 |
US10706328B2 (en) | 2018-05-07 | 2020-07-07 | Google Llc | Focus-weighted, machine learning disease classifier error prediction for microscope slide images |
CN112714886B (zh) * | 2018-09-28 | 2023-03-21 | 仪景通株式会社 | 显微镜系统、投影单元以及图像投影方法 |
JP6912788B2 (ja) | 2018-11-09 | 2021-08-04 | ルニット・インコーポレイテッドLunit Inc. | アノテーション作業の管理方法、それを支援する装置およびシステム |
US10936160B2 (en) * | 2019-01-11 | 2021-03-02 | Google Llc | System, user interface and method for interactive negative explanation of machine-learning localization models in health care applications |
WO2020208091A1 (en) * | 2019-04-08 | 2020-10-15 | Leica Instruments (Singapore) Pte. Ltd. | Self-teaching microscope |
FR3095878B1 (fr) * | 2019-05-10 | 2021-10-08 | Univ De Brest | Procédé d'analyse automatique d'images pour reconnaître automatiquement au moins une caractéristique rare |
JP2021124861A (ja) * | 2020-02-04 | 2021-08-30 | ソニーグループ株式会社 | 解析装置、解析方法、解析プログラム及び診断支援システム |
US11211160B2 (en) | 2020-03-13 | 2021-12-28 | PAIGE.AI, Inc. | Systems and methods of automatically processing electronic images across regions |
US20230122392A1 (en) * | 2020-03-30 | 2023-04-20 | Verily Life Sciences Llc | Artificial Intelligence-Based Assistant For Concurrent Review Of Needle Core Prostate Biopsies |
CN115552309A (zh) * | 2020-03-31 | 2022-12-30 | 仪景通株式会社 | 显微镜系统、投影单元以及检卵辅助方法 |
WO2021230000A1 (ja) * | 2020-05-15 | 2021-11-18 | ソニーグループ株式会社 | 情報処理装置、情報処理方法及び情報処理システム |
WO2022044095A1 (ja) * | 2020-08-24 | 2022-03-03 | オリンパス株式会社 | 情報処理装置、学習装置、及び学習済みモデル |
JP6908806B1 (ja) * | 2020-09-16 | 2021-07-28 | BonBon株式会社 | プログラム、情報処理装置、方法 |
US11694807B2 (en) * | 2021-06-17 | 2023-07-04 | Viz.ai Inc. | Method and system for computer-aided decision guidance |
CN113241184B (zh) * | 2021-06-24 | 2022-07-29 | 华侨大学 | 一种儿童肺炎辅助诊断模型及其训练方法 |
WO2023022871A1 (en) * | 2021-08-18 | 2023-02-23 | PAIGE.AI, Inc. | Systems and methods for processing electronic images with metadata integration |
DE102021121635A1 (de) * | 2021-08-20 | 2023-02-23 | Carl Zeiss Microscopy Gmbh | Automatisiertes trainieren eines maschinengelernten algorithmus basierend auf der überwachung einer mikroskopiemessung |
WO2023128059A1 (en) * | 2021-12-28 | 2023-07-06 | Lunit Inc. | Method and apparatus for tumor purity based on pathological slide image |
DE102022116407A1 (de) * | 2022-06-30 | 2024-01-04 | Ali Eissing-Al-Mukahal | System zur Unterstützung eines Nutzers bei der bildbasierten Erkennung einer Gewebeentartung |
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JP2697372B2 (ja) | 1991-06-25 | 1998-01-14 | 日本電気株式会社 | 高電力半導体集積回路冷却試験治具 |
JP3263111B2 (ja) * | 1992-01-29 | 2002-03-04 | 株式会社東芝 | 画像保管通信システム及びその端末装置 |
US6005964A (en) | 1996-01-24 | 1999-12-21 | The Board Of Trustees Of The University Of Illinois | Automatic machine vision microscope slide inspection system and method |
US6396941B1 (en) * | 1996-08-23 | 2002-05-28 | Bacus Research Laboratories, Inc. | Method and apparatus for internet, intranet, and local viewing of virtual microscope slides |
CA2342414A1 (en) | 2001-03-29 | 2002-09-29 | Motic Instruments Inc. | Digital imaging microscope |
JP2003093380A (ja) * | 2001-09-25 | 2003-04-02 | Hitachi Medical Corp | 読影支援装置 |
JP2004135868A (ja) | 2002-10-17 | 2004-05-13 | Fuji Photo Film Co Ltd | 異常陰影候補検出処理システム |
US20040122705A1 (en) * | 2002-12-18 | 2004-06-24 | Sabol John M. | Multilevel integrated medical knowledge base system and method |
US7187790B2 (en) * | 2002-12-18 | 2007-03-06 | Ge Medical Systems Global Technology Company, Llc | Data processing and feedback method and system |
US7116440B2 (en) | 2003-02-28 | 2006-10-03 | Aperio Technologies, Inc. | Image processing and analysis framework |
US7529394B2 (en) | 2003-06-27 | 2009-05-05 | Siemens Medical Solutions Usa, Inc. | CAD (computer-aided decision) support for medical imaging using machine learning to adapt CAD process with knowledge collected during routine use of CAD system |
JP2005182670A (ja) | 2003-12-24 | 2005-07-07 | Icc Computer System:Kk | 遠隔画像閲覧システムおよび遠隔画像閲覧用表示方法 |
WO2005121863A1 (en) | 2004-06-11 | 2005-12-22 | Nicholas Etienne Ross | Automated diagnosis of malaria and other infections |
US7958063B2 (en) * | 2004-11-11 | 2011-06-07 | Trustees Of Columbia University In The City Of New York | Methods and systems for identifying and localizing objects based on features of the objects that are mapped to a vector |
US7573439B2 (en) * | 2004-11-24 | 2009-08-11 | General Electric Company | System and method for significant image selection using visual tracking |
EP1839264A2 (en) * | 2005-01-18 | 2007-10-03 | Trestle Corporation | System and method for creating variable quality images of a slide |
JP4624841B2 (ja) * | 2005-04-13 | 2011-02-02 | オリンパスメディカルシステムズ株式会社 | 画像処理装置および当該画像処理装置における画像処理方法 |
CN1891155A (zh) * | 2006-05-26 | 2007-01-10 | 北京思创贯宇科技开发有限公司 | 一种基于ct图像的组织成分分析方法 |
US8189855B2 (en) * | 2007-08-31 | 2012-05-29 | Accenture Global Services Limited | Planogram extraction based on image processing |
JP4558047B2 (ja) | 2008-01-23 | 2010-10-06 | オリンパス株式会社 | 顕微鏡システム、画像生成方法、及びプログラム |
JP2010035756A (ja) * | 2008-08-04 | 2010-02-18 | Fujifilm Corp | 診断支援装置及び診断支援方法 |
JP5321145B2 (ja) | 2009-03-04 | 2013-10-23 | 日本電気株式会社 | 画像診断支援装置、画像診断支援方法、画像診断支援プログラム、及びその記憶媒体 |
BRPI1006388A2 (pt) * | 2009-04-15 | 2020-02-04 | Koninl Philips Electronics Nv | sistema de apoio à decisão clínica (adc) e método (adc) de apoio à decisão clínica implementado por um sistema adc |
JP4676021B2 (ja) * | 2009-04-16 | 2011-04-27 | 富士フイルム株式会社 | 診断支援装置、診断支援プログラムおよび診断支援方法 |
EP2425401A2 (en) * | 2009-04-28 | 2012-03-07 | Koninklijke Philips Electronics N.V. | Microdissection method and information processing system |
JP6174860B2 (ja) * | 2009-12-18 | 2017-08-02 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | 取得画像とオブジェクトとの関連付け |
DE102010008243B4 (de) * | 2010-02-17 | 2021-02-11 | Siemens Healthcare Gmbh | Verfahren und Vorrichtung zur Ermittlung der Vaskularität eines sich in einem Körper befindlichen Objektes |
US8311279B2 (en) | 2010-02-23 | 2012-11-13 | Fuji Xerox Co., Ltd. | System and method for improved image analysis through gaze data feedback |
CN102253922B (zh) * | 2010-05-18 | 2013-07-03 | 北京普利生仪器有限公司 | 远程分析病理切片的方法 |
US8600143B1 (en) | 2010-05-20 | 2013-12-03 | Kla-Tencor Corporation | Method and system for hierarchical tissue analysis and classification |
US8315812B2 (en) * | 2010-08-12 | 2012-11-20 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
JP5556674B2 (ja) * | 2011-01-12 | 2014-07-23 | コニカミノルタ株式会社 | 医用画像表示装置及びプログラム |
JP5652227B2 (ja) * | 2011-01-25 | 2015-01-14 | ソニー株式会社 | 画像処理装置および方法、並びにプログラム |
JP6514892B2 (ja) | 2011-07-13 | 2019-05-15 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | デジタル病理画像の焦点面を自動調節する方法 |
EP2845045B1 (en) | 2012-05-02 | 2023-07-12 | Leica Biosystems Imaging, Inc. | Real-time focusing in line scan imaging |
US9383347B2 (en) * | 2012-05-24 | 2016-07-05 | Nec Corporation | Pathological diagnosis results assessment system, pathological diagnosis results assessment method, and pathological diagnosis results assessment device |
US9575304B2 (en) | 2012-06-25 | 2017-02-21 | Huron Technologies International Inc. | Pathology slide scanners for fluorescence and brightfield imaging and method of operation |
JP6438395B2 (ja) * | 2012-08-22 | 2018-12-12 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | 効果的な表示及び報告のための画像資料に関連する以前の注釈の自動検出及び取り出し |
WO2014144103A1 (en) * | 2013-03-15 | 2014-09-18 | Sony Corporation | Characterizing pathology images with statistical analysis of local neural network responses |
FR3024540B1 (fr) * | 2014-07-29 | 2018-02-16 | L'air Liquide, Societe Anonyme Pour L'etude Et L'exploitation Des Procedes Georges Claude | Debitmetre utilise pour le dosage de l'energie apportee a un procede utilisant un fluide cryogenique |
EP3207499A4 (en) * | 2014-10-17 | 2018-09-19 | Cireca Theranostics, LLC | Methods and systems for classifying biological samples, including optimization of analyses and use of correlation |
JP6514486B2 (ja) | 2014-10-29 | 2019-05-15 | ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー | 医用システム及び医用装置並びにプログラム |
CN105550651B (zh) * | 2015-12-14 | 2019-12-24 | 中国科学院深圳先进技术研究院 | 一种数字病理切片全景图像自动分析方法及系统 |
US10025902B2 (en) * | 2016-08-12 | 2018-07-17 | Verily Life Sciences Llc | Enhanced pathology diagnosis |
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Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2022158908A1 (ko) * | 2021-01-22 | 2022-07-28 | 주식회사 루닛 | 병리 이미지 분석 방법 및 시스템 |
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US11501871B2 (en) | 2022-11-15 |
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