{"_buckets": {"deposit": "06496e1e-6b28-4ce9-a759-615721d358bf"}, "_deposit": {"id": "21559", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21559"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021559", "sets": ["314"]}, "author_link": ["63144", "63145", "63146", "63147", "63148", "63149", "63150", "63151", "63152", "63153"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A method for automated anatomical labeling of abdominal veins by using multi-class AdaBoost", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2012-07", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "142", "bibliographicPageEnd": "70", "bibliographicPageStart": "65", "bibliographicVolumeNumber": "112", "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": "腹部外科手術では血管構造の把握が重要であり,そのためにCT像を利用する場合がある.しかし,体内を複雑に走行する血管の構造を多数のスライスを含むCT像から理解することは困難である.そこで,腹部静脈領域に対して計算機により自動的に解剖学的名称を対応付ける手法を提案する.3次元腹部CT像から抽出された静脈領域は細線化され,木構造として表現される.最初に,特徴的な血管の名称を,分岐数や血管枝の末端の位置などに基づきルールベースで対応付ける.次に,それ以外の血管に対して,機械学習を利用して構築された尤度関数により枝が各血管である尤度を算出する.その後,子枝のうち尤度が最大である枝を探索することにより,血管名を対応付ける.本手法を3次元腹部CT像から抽出した静脈領域画像20症例に対して適用したところ,対応付けの再現率は86.3%,適合率は85.7%,F値は86.0%であった. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "In abdominal surgeries, understanding of structures of blood vessel is important. Surgeons utilize CT images for such purpose. In this paper, a method for automated anatomical labeling of the abdominal veins is presented. A thinning process is applied to a abdominal vein region extracted from a 3D CT image. The result of the thinning process is expressed as a tree structure. Firstly, a characteristic blood vessel is labeld with a rule-based method. For the rest of blood vessels, likelihoods are labeled by using a machine learning technique. Then, branches in the tree structure are labeld by searching a child branch whose likelihood is the maximum. In the experiment using 20 cases of abdominal vein images which are manually extracted from 3D CT images, recall rate, precision rate, and F-measure were 86.3%, 85.7%, and 86.0%, 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;MI2012-33", "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/110009626685/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23707"}]}, "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": 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"vein CT images", "subitem_subject_scheme": "Other"}, {"subitem_subject": "recognition of anatomical names", "subitem_subject_scheme": "Other"}, {"subitem_subject": "analysis of blood vessel structures", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "多クラスAdaBoostを用いた腹部静脈領域に対する解剖学的名称自動対応付け手法", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "多クラスAdaBoostを用いた腹部静脈領域に対する解剖学的名称自動対応付け手法", "subitem_title_language": "ja"}]}, "item_type_id": "10", "owner": "1", "path": ["314"], "permalink_uri": "http://hdl.handle.net/2237/23707", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2016-03-03"}, "publish_date": "2016-03-03", "publish_status": "0", "recid": "21559", "relation": {}, 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