{"_buckets": {"deposit": "a14fd17e-ef51-4f92-8041-0507f4f184e1"}, "_deposit": {"id": "21554", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21554"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021554", "sets": ["314"]}, "author_link": ["63096", "63097", "63098", "63099", "63100", "63101", "63102", "63103", "63104", "63105", "63106", "63107", "63108", "63109"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A study on automated anatomical labeling to abdominal arteries in 3D abdominal CT images by using multil-class AdaBoost : Improvement of classifiers of the artery names", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2012-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "389", "bibliographicPageEnd": "400", "bibliographicPageStart": "395", "bibliographicVolumeNumber": "111", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. MI, 医用画像", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "外科手術における医師の血管構造把握を支援するため,我々は腹部血管を対象とした血管名自動命名法の研究を行ってきた.この手法は,抽出した血管領域から血管木構造を作成し,血管木構造の各血管枝ごとに多クラスAdaBoostを用いた識別器により血管名を命名する.しかし,識別器の誤分類により誤った名前を命名されたり名前なしと命名された血管枝が多数存在した.そこで,新しい特徴量を追加し,更にAdaBoostに用いる識別器学習時の重み調整により,識別器の命名対象血管に対する命名性能を向上させた.提案手法を3次元CT像38症例に適用したところ,血管名命名の平均再現率と平均適合率はそれぞれ87.6%と72.5%であった. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "We have developed an automated anatomical labeling method for the abdominal arteries to support understanding of the structure of the arteries for doctors in abdominal surgeries. This method labels artery names by using classifiers constructed with the multi-class AdaBoost. However, miss-labelings of the classifiers were caused in many cases. In this paper, we present a method to improve artery name labeling performance by adjusting weights of the classifiers of the AdaBoost. We also introduce new future values for the classifiers. We applied the proposed method to 38 cases of 3D contrasted abdominal CT images. The average recall and precision rates of the proposed method were 87.6% and 72.5%, respectively.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "IEICE Technical Report;MI2011-148", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://ci.nii.ac.jp/naid/110009481674/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23702"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人電子情報通信学会", "subitem_publisher_language": "ja"}]}, "item_10_relation_40": {"attribute_name": "シリーズ", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "IEICE Technical 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[{"creatorName": "MISAWA, Kazunari", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "63107", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "FUJIWARA, Michitaka", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "63108", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "MORI, Kensaku", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "63109", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-04-01"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "110009481674.pdf", "filesize": [{"value": "871.6 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 871600.0, "url": {"label": "110009481674.pdf", 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