{"_buckets": {"deposit": "d719fe78-50e3-4e21-9369-cd2fb5a708eb"}, "_deposit": {"id": "21570", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21570"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021570", "sets": ["314"]}, "author_link": ["63306", "63307", "63308", "63309", "63310", "63311", "63312", "63313", "63314", "63315", "63316", "63317", "63318", "63319", "63320", "63321", "63322", "63323", "63324", "63325", "63326", "63327"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Mediastinal Lymph Node Detection Method Based on Radial Structure Tensor and Machine Learning", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-10", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "281", "bibliographicPageEnd": "56", "bibliographicPageStart": "51", "bibliographicVolumeNumber": "113", "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": "本稿では,胸部CT像から縦隔リンパ節を自動検出する手法を提案する.縦隔リンパ節はCT像において,周囲より高いCT値をもつ塊状の組織として観察されるが,他の組織に隣接しているリンパ節や,リンパ節と同様に周囲より高いCT値をもつ組織が多数存在する.このため提案手法では,入力CT像に対し,周辺組織の影響を受けにくいRadial Structure Tensorによる塊状構造強調フィルタ(R-BSE)を適用し,各リンパ節候補領域を得る.さらに,サポートベクタマシンにより各候補領域がリンパ節であるか否かを判定する.判定に用いる特徴量として,候補領域の形状,位置およびCT値の情報を用いる.動脈相胸部CT像46例に対する縦隔リンパ節検出実験の結果,短径5.0[mm]以上のリンパ節を85.6%検出するとき,FP(False Positive)領域数は1症例あたり14.9個であった.また,短径10.0[mm]以上のリンパ節を88.5%検出するとき,FP領域数は1症例あたり14.8個であった. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "In this paper, we present a method for detecting mediastinal lymph nodes from 3D chest CT volumes. Meiastinal lymph nodes are observed as blob structures which have higher CT values than the surrounding regions. However, there are lymph nodes adhering other tissue and tissue which have as high CT value as lymph nodes. In the proposed method, a blobness structure enhancement filter based on Radial Structure Tensor (R-BSE) is utilized to obtain candidate regions because the R-BSE is robust for influence of neighboring tissues. The candidate regions are classified into lymph nodes and others by the support vector machine. Shape, location, and CT value of candidate regions are used as feature values. We applied the proposed method to 46 cases of arterial phase 3D CT volumes. The method detected 85.6% of lymph nodes whose minor asix is no less than 5.0[mm] with 14.9 FP (False Positive)s per patient. The method also detected 88.5% of lymph nodes whose minor axis is no less than 10.0[mm] with 14.8 FPs per patient.", "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;MI2013-55", "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/110009886187/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23718"}]}, "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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