{"created":"2023-05-15T14:34:43.903337+00:00","id":44891,"links":{},"metadata":{"_buckets":{"deposit":"074446e0-9f6c-41d1-9c2c-b63704fd062a"},"_deposit":{"created_by":1,"id":"44891","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"44891"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00044891","sets":["1"]},"author_link":["445879","445881","445893","445880","445883","445887","445897","445894","445889","445890","445896","445884","445891","445886","445892","445882","445885","445895","445888"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"202","bibliographicPageStart":"193","bibliographicVolumeNumber":"25","bibliographic_titles":[{"bibliographic_title":"Medical Imaging Technology"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"　MRI画像における腫瘍の検出は、周囲軟部組織とのコントラストが必ずしも高くないため、熟練が要求される．本研究では、複数のMRI画像間の相関関係を客観的・自動的に構築し、腫瘍抽出を支援するシステムの開発を試みた．まず、3種類（Gd造影、T1強調、T2強調）のMRI画像の画素値から自己組織化マップの学習を行い、3種類の画像の相関に従って分布したマップを作成する．次に、このマップに対しクラスタリングを行い、事前に決めた数のクラスタに分類し、その結果を原画像に反映させ、MRIクラスタリング画像を作成する．最後に、腫瘍の含まれるクラスタに対して再分類を行い、擬陽性部位を除去し抽出精度を高めた．本手法を10例のがん患者のMRI画像に適用したところ、8例で腫瘍を他の部位とは異なるクラスタとして分類でき、本手法が腫瘍自動抽出に有用であることが示唆された．","subitem_description_type":"Abstract"}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0288-450X","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"細田, 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