JP2019518581A5 - - Google Patents

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JP2019518581A5
JP2019518581A5 JP2019516914A JP2019516914A JP2019518581A5 JP 2019518581 A5 JP2019518581 A5 JP 2019518581A5 JP 2019516914 A JP2019516914 A JP 2019516914A JP 2019516914 A JP2019516914 A JP 2019516914A JP 2019518581 A5 JP2019518581 A5 JP 2019518581A5
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neural networks
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JP7058264B2 (ja
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JP2019516914A 2016-06-08 2017-06-08 血管内光干渉断層撮影法を用いた冠動脈プラークの自動特徴分析およびリスク評価のためのシステムおよび方法 Expired - Fee Related JP7058264B2 (ja)

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JP2022064872A JP7383070B2 (ja) 2016-06-08 2022-04-11 血管内光干渉断層撮影法を用いた冠動脈プラークの自動特徴分析およびリスク評価のためのシステムおよび方法

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US201662347379P 2016-06-08 2016-06-08
US62/347,379 2016-06-08
PCT/US2017/036587 WO2017214421A1 (en) 2016-06-08 2017-06-08 Systems and methods for automated coronary plaque characterization and risk assessment using intravascular optical coherence tomography

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JP2022064872A Division JP7383070B2 (ja) 2016-06-08 2022-04-11 血管内光干渉断層撮影法を用いた冠動脈プラークの自動特徴分析およびリスク評価のためのシステムおよび方法

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JP2019518581A JP2019518581A (ja) 2019-07-04
JP2019518581A5 true JP2019518581A5 (https=) 2020-07-16
JP7058264B2 JP7058264B2 (ja) 2022-04-21

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JP2022064872A Active JP7383070B2 (ja) 2016-06-08 2022-04-11 血管内光干渉断層撮影法を用いた冠動脈プラークの自動特徴分析およびリスク評価のためのシステムおよび方法

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US (1) US12213810B2 (https=)
EP (1) EP3468453B1 (https=)
JP (2) JP7058264B2 (https=)
KR (1) KR102531819B1 (https=)
AU (1) AU2017277784B2 (https=)
CA (1) CA3026650A1 (https=)
WO (1) WO2017214421A1 (https=)

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CN108416769B (zh) * 2018-03-02 2021-06-04 成都斯斐德科技有限公司 基于预处理的ivoct图像易损斑块自动检测方法
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GB201906103D0 (en) 2019-05-01 2019-06-12 Cambridge Entpr Ltd Method and apparatus for analysing intracoronary images
WO2021026224A1 (en) * 2019-08-05 2021-02-11 Lightlab Imaging, Inc. Longitudinal display of coronary artery calcium burden
KR102261111B1 (ko) * 2019-09-10 2021-06-04 인하대학교 산학협력단 Gan을 이용한 의료 영상의 광강도 분포 일반화 기법
WO2021199967A1 (ja) * 2020-03-30 2021-10-07 テルモ株式会社 プログラム、情報処理方法、学習モデルの生成方法、学習モデルの再学習方法、および、情報処理システム
WO2021206170A1 (ja) * 2020-04-10 2021-10-14 公益財団法人がん研究会 画像診断装置、画像診断方法、画像診断プログラムおよび学習済みモデル
JP7667852B2 (ja) * 2020-08-06 2025-04-23 キヤノン ユーエスエイ，インコーポレイテッド カテーテルベースのマルチモーダル画像における潜在的な偽陽性及び盲点の位置を特定するためのシステム及び方法
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