{"_buckets": {"deposit": "c1fec91b-af27-412e-87cb-a01615ead415"}, "_deposit": {"id": "21574", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21574"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021574", "sets": ["314"]}, "author_link": ["63362", "63363", "63364", "63365", "63366", "63367", "63368", "63369"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A Preliminary Study on Organ Segmentation using Conditional Random Fields from Medical Image", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2014-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "410", "bibliographicPageEnd": "160", "bibliographicPageStart": "155", "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,条件付き確率場による医用画像からの臓器領域抽出に関して検討する.これまで医用画像からの臓器領域自動抽出を目的とした多くの研究が報告されている.しかしながら,それらの多くは経験的に決定する調整パラメータを含むといった問題が存在する.本報告では,臓器領域抽出を医用画像全体にわたる各画素の臓器ラベル推定問題ととらえ,構造学習によりパラメータを学習した確率的グラフィカルモデルに基づいて,臓器領域を自動抽出する手法に関して検討する.確率的グラフィカルモデルにはランダムに画素間を接続した条件付き確率場特徴量には画素の濃度値と臓器ラベルの関係を表すバイナリ特徴を使用する.また,条件付き確率場のパラメータ学習には確率的勾配降下法,臓器ラベル推定には最大事後確率推定を使用する.本手法を腹部造影CT画像5例へと適用した結果,臓器ラベル推定誤差10.0%(一致度:肝臓0.65,脾臓0.61,右腎臓0.61,左腎臓0.63,膵臓0.24)で,臓器領域の自動抽出が可能であることが確認された. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "This paper describes an organ region segmentation method using conditional random fields from medical images. A lot of methods have been proposed to enable automated extraction of organ regions from medical images. However, it is necessary to adjust emperical parameters of them to obtain precise organ regions. In this paper, we propose an organ segmentation method using structured output learning which is based on probabilistic graphical model. The proposed method utilizes randomly connected conditional random fields as probabilistic graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weight parameters of conditional random fields using stochastic gradient descent and estimate organ labels using maximum a posteriori estimation. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 10.0%. DICE coefficients of liver, spleen, right kidney, left kidney, and pancreas are 0.65, 0.61, 0.61, 0.63, 0.24, 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;MI2013-84", "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/110009821255/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23722"}]}, "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 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"attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "二村, 幸孝", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "63362", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "林, 雄一郎", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "63363", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "北坂, 孝幸", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "63364", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "森, 健策", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "63365", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "NIMURA, Yukitaka", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "63366", "nameIdentifierScheme": 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