{"_buckets": {"deposit": "b0913539-53ea-459f-a056-882db095333e"}, "_deposit": {"id": "21563", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21563"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021563", "sets": ["314"]}, "author_link": ["63198", "63199", "63200", "63201", "63202", "63203", "63204", "63205", "63206", "63207", "63208", "63209", "63210", "63211"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A Study on Bronchus Segmentation based on Machine Learning and Graph Cuts from Chest CT Image", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "411", "bibliographicPageEnd": "196", "bibliographicPageStart": "191", "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": "本稿では、機械学習とグラフカットによる胸部CT像からの気管支抽出に関して検討する。気管支領域抽出手法の多くは、全気管支枝の内腔領域が直接的あるいは間接的に気管の内腔領域へと接続しているという知識に基づき、気管から領域拡張法あるいはレベルセット法を適用することで気管支領域を抽出している。しかしながら、実際に撮影されたCT像においては、部分容積効果や心拍動、異常症例における腫瘍の影響等により内腔領域の接続性が失われており、領域拡張法のみで全ての気管支枝を正確に抽出することは難しい。そこで本稿では、気管支枝が管状の構造をしているという解剖学的知識に基づき、局所的な濃淡構造から気管支枝の芯線上画素とその気管支枝の半径を検出し、半径に応じたコスト関数をグラフカットにより最小化することで画素ごとに気管支領域を得る手法を提案する。本手法をCT像10例へと適用した結果、一致度69.9%で抽出可能なことを確認した。 ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "This paper describes a bronchus segmentation method based on scale estimation and graph cuts of bronchi from chest CT image. A lot of previous methods utilize region growing or level set algorithms based on anatomical knowledge of connectivity of bronchi However, it is difficult to extract bronchus precisely by only using these algorithms. Because connectivity of bronchi is often lost by partial volume effects, heartbeat, image noise or tumor in actual CT images. In this paper, we propose a method of bronchus segmentation based on another anatomical knowledge about bronchus. The proposed method detects voxels of medial lines of bronchi and its radius by using local intensity structure analysis, and extracts bronchi by using graph cuts segmentation that utilizes cost function with radius information. As the result, Jaccard index was 69.9%.", "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;MI2012-98", "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/110009727729/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23711"}]}, "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 Report;MI2012-98"}]}]}, "item_10_relation_43": {"attribute_name": "関連情報", 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"63209", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "NATORI, Hiroshi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "63210", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "MORI, Kensaku", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "63211", "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": "2018-02-21"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "110009727729.pdf", "filesize": [{"value": "779.6 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 779600.0, "url": {"label": "110009727729.pdf", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/21563/files/110009727729.pdf"}, "version_id": "503ef08d-723b-4f10-9c5c-7cbb6c2772c5"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "気管支", "subitem_subject_scheme": "Other"}, {"subitem_subject": "スケール推定", "subitem_subject_scheme": "Other"}, {"subitem_subject": "グラフカット", "subitem_subject_scheme": "Other"}, {"subitem_subject": "計算機診断支援", "subitem_subject_scheme": "Other"}, {"subitem_subject": "bronchus", "subitem_subject_scheme": "Other"}, {"subitem_subject": "scale estimation", "subitem_subject_scheme": "Other"}, {"subitem_subject": "graph cuts", "subitem_subject_scheme": "Other"}, {"subitem_subject": "computer−aided diagnosis", "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": 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