{"created":"2025-01-19T01:46:07.996149+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241496","sets":["1164:2735:11468:11810"]},"path":["11810"],"owner":"44499","recid":"241496","title":["StarGAN v2を用いたMR画像からのCT画像生成"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-12-02"},"_buckets":{"deposit":"e96c66f4-6cba-4d43-aa3b-bff685754aba"},"_deposit":{"id":"241496","pid":{"type":"depid","value":"241496","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"StarGAN v2を用いたMR画像からのCT画像生成","author_link":["664954","664945","664947","664952","664953","664946","664948","664949","664951","664950"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"StarGAN v2を用いたMR画像からのCT画像生成"},{"subitem_title":"CT Image Generation from MR Image Using StarGAN 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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/241496/files/IPSJ-MPS24151016.pdf","label":"IPSJ-MPS24151016.pdf"},"date":[{"dateType":"Available","dateValue":"2026-12-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS24151016.pdf","filesize":[{"value":"905.8 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etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"CT 画像は正確な解剖学的構造情報を得られるため，病気診断において重要な役割を果たしている．しかし，CT 画像の撮影には X 線を使用するため，小児患者への使用を極力避けるべきである．これまで，画像変換技術を用いて，放射線を使用せずに撮影できる MR 画像から CT 画像を生成する技術が提案されている．ところが，小児患者の CT 画像はデータが少なく，画像変換の十分な学習は困難である．一般的な 1 対 1 の画像変換を学習するネットワークに大人と小児の画像を合わせて学習させると，小児特有の特徴を再現できない．そこで，多数対多数の画像変換が可能な StarGAN v2 を用いて MR 画像から CT 画像の生成を目指す．本稿では，一般的な画像変換ネットワークと比較し，StarGAN v2 の画像変換性能を評価する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-12-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"16","bibliographicVolumeNumber":"2024-MPS-151"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":241496,"updated":"2025-01-19T07:38:39.549161+00:00","links":{}}