{"_buckets": {"deposit": "f212290f-c628-475f-970d-e2bba61126b0"}, "_deposit": {"id": "21547", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21547"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021547", "sets": ["314"]}, "author_link": ["62994", "62995", "62996", "62997", "62998", "62999", "63000", "63001", "63002", "63003", "63004", "63005", "63006", "63007"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Organ segmentation from 3D abdominal CT images using likelihood atlas of organ existence and graph cut", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2011-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "364", "bibliographicPageEnd": "228", "bibliographicPageStart": "223", "bibliographicVolumeNumber": "110", "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": "本稿では腹部3次元CT像からの臓器領域抽出手法を提案する.本手法ではCT画像間で臓器存在尤度アトラスを用いて抽出を行う.この際,単一の臓器存在尤度アトラスを用いるのではなく,複数の臓器存在尤度アトラスを選択的に用いる.具体的には,まず,画像間類似度に基づき画像のクラスタリングを行う.各クラスタの平均画像を作成し,平均画像上での臓器存在尤度アトラスの構築を行う.入力画像が与えられた際には平均画像との画像間類似度が最大となる臓器存在尤度アトラスを用いて抽出を行う.選択された臓器存在尤度アトラスと入力画像からMAP推定により粗抽出を行い,最後に,粗抽出結果を基にグラフカットを用いて精密抽出を行う.腹部CT像100例に対して実験を行い,平均一致度は肝臓88.6%,肺臓73.9%,膵臓42.0%,腎臓79.8%であった. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "In this paper, we propose a multi organ segmentation method from 3D abdominal CT images. In our method, we extract organs using multiple likelihood atlases of the organ existence, instead of single atlas. In our method, first we apply a clustering method to training image datasets based on image similarity. We generate average images and atlases for each cluster. When an input image is given, we select an atlas that has the maximum image similarity between the average image and the input image. We use the selected atlas to extract organs. Then, we extract multi organs roughly by the MAP estimation from the selected atlas and the input image. Finally, we perform precise segmentation by using a multi label graph cut. We apply this method to 100 cases of abdominal CT images. Jaccard indices were 88.6% for liver, 73.9% for spleen, 42.0% for pancreas, and 79.8% for kidney, 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;MI2010-123", "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/110008675433/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23695"}]}, "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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