{"id":228855,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00228855","sets":["934:989:11239:11352"]},"path":["11352"],"owner":"44499","recid":"228855","title":["動画像シミュレータを介した強化学習による細胞追跡手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-10-31"},"_buckets":{"deposit":"640f9788-5029-413b-96f0-945296fc2088"},"_deposit":{"id":"228855","pid":{"type":"depid","value":"228855","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"動画像シミュレータを介した強化学習による細胞追跡手法","author_link":["614335","614334","614336","614337","614332","614333","614339","614331","614338","614330"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"動画像シミュレータを介した強化学習による細胞追跡手法"},{"subitem_title":"Reinforcement Learning-based Cell Tracking Method via Video Simulator","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] 細胞画像処理，物体追跡，深層強化学習，バイオイメージング，細胞遊走，畳み込みニューラルネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2023-10-31","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/228855/files/IPSJ-TOM1602003.pdf","label":"IPSJ-TOM1602003.pdf"},"date":[{"dateType":"Available","dateValue":"2025-10-31"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1602003.pdf","filesize":[{"value":"2.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1eb8fef2-df23-4efa-99b9-adb796a0e699","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"長村, 徹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"瀬尾, 茂人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤本, 健二"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"繁田, 浩功"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松田, 秀雄"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Toru, Nagamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shigeto, Seno","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenji, Fujimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironori, Shigeta","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hideo, Matsuda","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年の光学顕微鏡や蛍光タンパクの技術発展により，細胞を生かしたまま経時観察して動画像として記録可能となってきた．これらの動画像中から細胞動態を抽出し解析するためには細胞追跡が必要である．従来の細胞追跡では，主に教師あり深層学習に基づくtracking-by-detectionの手法が広く用いられる．しかし観測対象や撮影技術に依存して様々な性質を示す動画像に対して，個別に追跡アルゴリズムを調整することや，学習に十分な量のデータを用意することが必要である．この問題を解決するため，細胞動画像を模したシミュレータを環境として利用することで，強化学習に基づく細胞追跡モデルを訓練する手法を提案する．シミュレータでは，細胞の特徴と細胞追跡の正解を含む動画像を無数に生成できるため，データ不足でも学習が可能である．また強化学習では，タスクの目的を設定しておけば，その達成方法は計算機が自動で獲得するため，追跡対象ごとにアルゴリズムを調整する必要がなくなる．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Recent advances in optical microscopy and fluorescent protein technology have made it possible to record moving images of cells over time while keeping them alive. Cell tracking is necessary to extract and analyze cell dynamics from these moving images. Tracking-by-detection methods based on supervised deep learning are widely used for cell tracing. However, it is necessary to individually adjust the tracking algorithm for each cell video that shows various characteristics, and, in addition, to prepare a sufficient amount of data for training. To address these issues, we propose a method for training cell tracking models based on reinforcement learning with a simulator that imitates cell videos as an environment. The simulator can generate a lot of cell videos containing cell features and correct answers for cell tracking. This enables training even when there is insufficient data. Additionally, reinforcement learning does not require individual algorithm adjustment, because the computer automatically finds ways to accomplish the task once the objective is given.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"22","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"11","bibliographicIssueDates":{"bibliographicIssueDate":"2023-10-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"16"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T11:41:40.099850+00:00","created":"2025-01-19T01:27:59.379734+00:00","links":{}}