{"updated":"2025-01-20T06:55:19.705255+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00158122","sets":["581:8417:8420"]},"path":["8420"],"owner":"11","recid":"158122","title":["Simultaneous Segmentation of Multiple Organs Using Random Walks"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-03-15"},"_buckets":{"deposit":"1be4d1d0-2683-4d3c-a45b-10eb6024c4e2"},"_deposit":{"id":"158122","pid":{"type":"depid","value":"158122","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Simultaneous Segmentation of Multiple Organs Using Random Walks","author_link":["301184","301188","301186","301192","301199","301194","301190","301196","301197","301185","301193","301189","301191","301198","301187","301195"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Simultaneous Segmentation of Multiple Organs Using Random Walks"},{"subitem_title":"Simultaneous Segmentation of Multiple Organs Using Random Walks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：学生・若手研究者論文] multiple organs, medical image segmentation, random walks, knowledge-based algorithm","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2016-03-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University／College of Computer Science and Technology, Zhejiang University"},{"subitem_text_value":"College of Computer Science and Technology, Zhejiang University"},{"subitem_text_value":"Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University"},{"subitem_text_value":"Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University"},{"subitem_text_value":"Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University / College of Computer Science and Technology, Zhejiang University","subitem_text_language":"en"},{"subitem_text_value":"College of Computer Science and Technology, Zhejiang University","subitem_text_language":"en"},{"subitem_text_value":"Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University","subitem_text_language":"en"},{"subitem_text_value":"Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University","subitem_text_language":"en"},{"subitem_text_value":"Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":11,"item_file_price":{"attribute_name":"Billing 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Dong"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yen-Wei, Chen"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Lanfen, Lin"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hongjie, Hu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Chongwu, Jin"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Huajun, Yu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Xian-Hua, Han"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoko, Tateyama"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Chunhua, Dong","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yen-Wei, Chen","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Lanfen, Lin","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hongjie, Hu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Chongwu, Jin","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Huajun, Yu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Xian-Hua, Han","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoko, Tateyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p<0.001).\n\\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.24.320\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p<0.001).\n\\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.24.320\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2016-03-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"57"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":158122,"created":"2025-01-19T00:31:53.719479+00:00"}