{"_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"}, "item_10_alternative_title_19": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u8a00\u8a9e\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "A Preliminary Study on Organ Segmentation using Conditional Random Fields from Medical Image"}]}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2014-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "410", "bibliographicPageEnd": "160", "bibliographicPageStart": "155", "bibliographicVolumeNumber": "113", "bibliographic_titles": [{"bibliographic_title": "\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u6280\u8853\u7814\u7a76\u5831\u544a. MI, \u533b\u7528\u753b\u50cf"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "\u672c\u7a3f\u3067\u306f,\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834\u306b\u3088\u308b\u533b\u7528\u753b\u50cf\u304b\u3089\u306e\u81d3\u5668\u9818\u57df\u62bd\u51fa\u306b\u95a2\u3057\u3066\u691c\u8a0e\u3059\u308b.\u3053\u308c\u307e\u3067\u533b\u7528\u753b\u50cf\u304b\u3089\u306e\u81d3\u5668\u9818\u57df\u81ea\u52d5\u62bd\u51fa\u3092\u76ee\u7684\u3068\u3057\u305f\u591a\u304f\u306e\u7814\u7a76\u304c\u5831\u544a\u3055\u308c\u3066\u3044\u308b.\u3057\u304b\u3057\u306a\u304c\u3089,\u305d\u308c\u3089\u306e\u591a\u304f\u306f\u7d4c\u9a13\u7684\u306b\u6c7a\u5b9a\u3059\u308b\u8abf\u6574\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u542b\u3080\u3068\u3044\u3063\u305f\u554f\u984c\u304c\u5b58\u5728\u3059\u308b.\u672c\u5831\u544a\u3067\u306f,\u81d3\u5668\u9818\u57df\u62bd\u51fa\u3092\u533b\u7528\u753b\u50cf\u5168\u4f53\u306b\u308f\u305f\u308b\u5404\u753b\u7d20\u306e\u81d3\u5668\u30e9\u30d9\u30eb\u63a8\u5b9a\u554f\u984c\u3068\u3068\u3089\u3048,\u69cb\u9020\u5b66\u7fd2\u306b\u3088\u308a\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5b66\u7fd2\u3057\u305f\u78ba\u7387\u7684\u30b0\u30e9\u30d5\u30a3\u30ab\u30eb\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u3044\u3066,\u81d3\u5668\u9818\u57df\u3092\u81ea\u52d5\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u306b\u95a2\u3057\u3066\u691c\u8a0e\u3059\u308b.\u78ba\u7387\u7684\u30b0\u30e9\u30d5\u30a3\u30ab\u30eb\u30e2\u30c7\u30eb\u306b\u306f\u30e9\u30f3\u30c0\u30e0\u306b\u753b\u7d20\u9593\u3092\u63a5\u7d9a\u3057\u305f\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834\u7279\u5fb4\u91cf\u306b\u306f\u753b\u7d20\u306e\u6fc3\u5ea6\u5024\u3068\u81d3\u5668\u30e9\u30d9\u30eb\u306e\u95a2\u4fc2\u3092\u8868\u3059\u30d0\u30a4\u30ca\u30ea\u7279\u5fb4\u3092\u4f7f\u7528\u3059\u308b.\u307e\u305f,\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u5b66\u7fd2\u306b\u306f\u78ba\u7387\u7684\u52fe\u914d\u964d\u4e0b\u6cd5,\u81d3\u5668\u30e9\u30d9\u30eb\u63a8\u5b9a\u306b\u306f\u6700\u5927\u4e8b\u5f8c\u78ba\u7387\u63a8\u5b9a\u3092\u4f7f\u7528\u3059\u308b.\u672c\u624b\u6cd5\u3092\u8179\u90e8\u9020\u5f71CT\u753b\u50cf5\u4f8b\u3078\u3068\u9069\u7528\u3057\u305f\u7d50\u679c,\u81d3\u5668\u30e9\u30d9\u30eb\u63a8\u5b9a\u8aa4\u5dee10.0%(\u4e00\u81f4\u5ea6:\u809d\u81d30.65,\u813e\u81d30.61,\u53f3\u814e\u81d30.61,\u5de6\u814e\u81d30.63,\u81b5\u81d30.24)\u3067,\u81d3\u5668\u9818\u57df\u306e\u81ea\u52d5\u62bd\u51fa\u304c\u53ef\u80fd\u3067\u3042\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3055\u308c\u305f. 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_type": "Abstract"}]}, "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": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "\u4e00\u822c\u793e\u56e3\u6cd5\u4eba\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a"}]}, "item_10_relation_40": {"attribute_name": "\u30b7\u30ea\u30fc\u30ba", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "IEICE Technical Report;MI2013-84"}]}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "(c)\u4e00\u822c\u793e\u56e3\u6cd5\u4eba\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a \u672c\u6587\u30c7\u30fc\u30bf\u306f\u5b66\u5354\u4f1a\u306e\u8a31\u8afe\u306b\u57fa\u3065\u304dCiNii\u304b\u3089\u8907\u88fd\u3057\u305f\u3082\u306e\u3067\u3042\u308b"}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "0913-5685", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "\u4e8c\u6751, \u5e78\u5b5d"}], "nameIdentifiers": [{"nameIdentifier": "63362", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u6797, \u96c4\u4e00\u90ce"}], "nameIdentifiers": [{"nameIdentifier": "63363", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u5317\u5742, \u5b5d\u5e78"}], "nameIdentifiers": [{"nameIdentifier": "63364", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u68ee, \u5065\u7b56"}], "nameIdentifiers": [{"nameIdentifier": "63365", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "NIMURA, Yukitaka"}], "nameIdentifiers": [{"nameIdentifier": "63366", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "HAYASHI, Yuichiro"}], "nameIdentifiers": [{"nameIdentifier": "63367", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "KITASAKA, Takayuki"}], "nameIdentifiers": [{"nameIdentifier": "63368", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "MORI, Kensaku"}], "nameIdentifiers": [{"nameIdentifier": "63369", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "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": "110009821255.pdf", "filesize": [{"value": "1.3 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1300000.0, "url": {"label": "110009821255.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/21574/files/110009821255.pdf"}, "version_id": "c49422e8-0d24-4769-8cc8-08c15fa008b8"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "\u69cb\u9020\u5b66\u7fd2", "subitem_subject_scheme": "Other"}, {"subitem_subject": "\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834", "subitem_subject_scheme": "Other"}, {"subitem_subject": "\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3", "subitem_subject_scheme": "Other"}, {"subitem_subject": "structured output learning", "subitem_subject_scheme": "Other"}, {"subitem_subject": "conditional random fields", "subitem_subject_scheme": "Other"}, {"subitem_subject": "segmentation", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834\u306b\u3088\u308b\u533b\u7528\u753b\u50cf\u304b\u3089\u306e\u81d3\u5668\u62bd\u51fa\u306b\u95a2\u3059\u308b\u4e88\u5099\u7684\u691c\u8a0e", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834\u306b\u3088\u308b\u533b\u7528\u753b\u50cf\u304b\u3089\u306e\u81d3\u5668\u62bd\u51fa\u306b\u95a2\u3059\u308b\u4e88\u5099\u7684\u691c\u8a0e"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/23722", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2016-03-03"}, "publish_date": "2016-03-03", "publish_status": "0", "recid": "21574", "relation": {}, "relation_version_is_last": true, "title": ["\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u5834\u306b\u3088\u308b\u533b\u7528\u753b\u50cf\u304b\u3089\u306e\u81d3\u5668\u62bd\u51fa\u306b\u95a2\u3059\u308b\u4e88\u5099\u7684\u691c\u8a0e"], "weko_shared_id": null}