{"id":"https://openalex.org/W2921437640","doi":"https://doi.org/10.1117/12.2512651","title":"Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting","display_name":"Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2921437640","doi":"https://doi.org/10.1117/12.2512651","mag":"2921437640"},"language":"en","primary_location":{"id":"doi:10.1117/12.2512651","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512651","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101639829","display_name":"Xiangrong Zhou","orcid":"https://orcid.org/0000-0002-5838-5885"},"institutions":[{"id":"https://openalex.org/I193621644","display_name":"Nagoya Bunri University","ror":"https://ror.org/00z249n13","country_code":"JP","type":"education","lineage":["https://openalex.org/I193621644"]},{"id":"https://openalex.org/I42405503","display_name":"Gifu University","ror":"https://ror.org/024exxj48","country_code":"JP","type":"education","lineage":["https://openalex.org/I42405503"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiangrong Zhou","raw_affiliation_strings":["Gifu Univ. (Japan)","Nagoya Bunri Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gifu Univ. (Japan)","institution_ids":["https://openalex.org/I42405503"]},{"raw_affiliation_string":"Nagoya Bunri Univ. (Japan)","institution_ids":["https://openalex.org/I193621644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034980117","display_name":"Takuya Kojima","orcid":"https://orcid.org/0000-0002-5943-444X"},"institutions":[{"id":"https://openalex.org/I42405503","display_name":"Gifu University","ror":"https://ror.org/024exxj48","country_code":"JP","type":"education","lineage":["https://openalex.org/I42405503"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuya Kojima","raw_affiliation_strings":["Gifu Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gifu Univ. (Japan)","institution_ids":["https://openalex.org/I42405503"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082259804","display_name":"Song Wang","orcid":"https://orcid.org/0000-0003-4152-5295"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["Univ. of South Carolina (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of South Carolina (United States)","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112392653","display_name":"Xinxin Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I193621644","display_name":"Nagoya Bunri University","ror":"https://ror.org/00z249n13","country_code":"JP","type":"education","lineage":["https://openalex.org/I193621644"]},{"id":"https://openalex.org/I42405503","display_name":"Gifu University","ror":"https://ror.org/024exxj48","country_code":"JP","type":"education","lineage":["https://openalex.org/I42405503"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xinxin Zhou","raw_affiliation_strings":["Gifu Univ. (Japan)","Nagoya Bunri Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gifu Univ. (Japan)","institution_ids":["https://openalex.org/I42405503"]},{"raw_affiliation_string":"Nagoya Bunri Univ. (Japan)","institution_ids":["https://openalex.org/I193621644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090297441","display_name":"Takeshi Hara","orcid":"https://orcid.org/0000-0002-0235-238X"},"institutions":[{"id":"https://openalex.org/I42405503","display_name":"Gifu University","ror":"https://ror.org/024exxj48","country_code":"JP","type":"education","lineage":["https://openalex.org/I42405503"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Hara","raw_affiliation_strings":["Gifu Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gifu Univ. (Japan)","institution_ids":["https://openalex.org/I42405503"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076401300","display_name":"Taiki Nozaki","orcid":"https://orcid.org/0000-0002-1922-4085"},"institutions":[{"id":"https://openalex.org/I4210087594","display_name":"St. Luke's International Hospital","ror":"https://ror.org/002wydw38","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210087594"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Taiki Nozaki","raw_affiliation_strings":["St. Luke\u2019s International Hospital (Japan)","St. Luke's International Hospital (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"St. Luke\u2019s International Hospital (Japan)","institution_ids":["https://openalex.org/I4210087594"]},{"raw_affiliation_string":"St. Luke's International Hospital (Japan)","institution_ids":["https://openalex.org/I4210087594"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084751660","display_name":"Masaki Matsusako","orcid":"https://orcid.org/0000-0002-4939-216X"},"institutions":[{"id":"https://openalex.org/I4210087594","display_name":"St. Luke's International Hospital","ror":"https://ror.org/002wydw38","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210087594"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaki Matsusako","raw_affiliation_strings":["St. Luke's International Hospital (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"St. Luke's International Hospital (Japan)","institution_ids":["https://openalex.org/I4210087594"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027406783","display_name":"Hiroshi Fujita","orcid":"https://orcid.org/0000-0002-2936-9296"},"institutions":[{"id":"https://openalex.org/I42405503","display_name":"Gifu University","ror":"https://ror.org/024exxj48","country_code":"JP","type":"education","lineage":["https://openalex.org/I42405503"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Fujita","raw_affiliation_strings":["Gifu Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gifu Univ. (Japan)","institution_ids":["https://openalex.org/I42405503"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"8670","issue":null,"first_page":"34","last_page":"34"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/torso","display_name":"Torso","score":0.8944571018218994},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.8616562485694885},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8127689361572266},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7193344831466675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6958715319633484},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6085944771766663},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.5183849930763245},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49090105295181274},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4704776406288147},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4408397078514099},{"id":"https://openalex.org/keywords/fully-automatic","display_name":"Fully automatic","score":0.4104403257369995},{"id":"https://openalex.org/keywords/anatomy","display_name":"Anatomy","score":0.3044963479042053},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.13576167821884155},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1264374554157257}],"concepts":[{"id":"https://openalex.org/C523889960","wikidata":"https://www.wikidata.org/wiki/Q160695","display_name":"Torso","level":2,"score":0.8944571018218994},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.8616562485694885},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8127689361572266},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7193344831466675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6958715319633484},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6085944771766663},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.5183849930763245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49090105295181274},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4704776406288147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4408397078514099},{"id":"https://openalex.org/C3019831412","wikidata":"https://www.wikidata.org/wiki/Q5778278","display_name":"Fully automatic","level":2,"score":0.4104403257369995},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.3044963479042053},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.13576167821884155},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1264374554157257},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2512651","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512651","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G2295296789","display_name":null,"funder_award_id":"26108005","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W200617456","https://openalex.org/W2000585255","https://openalex.org/W2065698093","https://openalex.org/W2097433388","https://openalex.org/W2110161289","https://openalex.org/W2117539524","https://openalex.org/W2141619730","https://openalex.org/W2164598857","https://openalex.org/W2282928421","https://openalex.org/W2318872361","https://openalex.org/W2590651460","https://openalex.org/W3041368294","https://openalex.org/W3102109558","https://openalex.org/W4231200252","https://openalex.org/W6637373629","https://openalex.org/W6640054144","https://openalex.org/W6651233102","https://openalex.org/W6674903773","https://openalex.org/W6780608271","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4381953457","https://openalex.org/W4254879869","https://openalex.org/W2037557144","https://openalex.org/W3022576529","https://openalex.org/W2285739514","https://openalex.org/W2628526247","https://openalex.org/W2058088690","https://openalex.org/W4288366250","https://openalex.org/W2936519215","https://openalex.org/W2178431931"],"abstract_inverted_index":{"We":[0,82],"propose":[1],"an":[2],"automatic":[3],"approach":[4,85,107],"to":[5,27,42,86,163,203],"anatomy":[6,191],"partitioning":[7,192],"on":[8,50,69,193],"three-dimensional":[9],"(3D)":[10],"computed":[11],"tomography":[12],"(CT)":[13],"images":[14,150,196],"that":[15,66,135,164,174,185],"divides":[16],"the":[17,31,45,88,97,103,106,123,138,142,146,166,176,204],"human":[18,98,167],"torso":[19,99],"into":[20],"several":[21],"volumes":[22],"of":[23,48,54,59,91,94,105,117,137,141,157,165,175],"interest":[24],"(VOIs)":[25],"according":[26],"anatomical":[28],"definition.":[29],"In":[30],"proposed":[32],"approach,":[33],"a":[34,67,70,110,114,187],"deep":[35,188],"convolutional":[36],"neural":[37],"network":[38],"(CNN)":[39],"is":[40],"trained":[41],"automatically":[43],"detect":[44],"bounding":[46],"boxes":[47,61],"organs":[49,95,144],"two-dimensional":[51],"(2D)":[52],"sections":[53],"CT":[55,120,149,195],"images.":[56],"The":[57,131,181],"coordinates":[58],"those":[60],"are":[62],"then":[63,101],"grouped":[64],"so":[65],"vote":[68],"3D":[71,89,119,139,194],"VOI":[72],"(called":[73],"localization)":[74],"for":[75,127,190],"each":[76,128],"organ":[77,129],"can":[78],"be":[79],"obtained":[80],"separately.":[81],"applied":[83],"this":[84],"localize":[87],"VOIs":[90,140],"17":[92],"types":[93],"in":[96,145,207],"and":[100,200],"evaluated":[102],"performance":[104,170],"by":[108],"conducting":[109],"four-fold":[111],"crossvalidation":[112],"using":[113,186],"dataset":[115],"consisting":[116],"240":[118,147],"scans":[121],"with":[122,153],"human-annotated":[124],"ground":[125],"truth":[126],"region.":[130],"preliminary":[132],"results":[133,183],"showed":[134],"86.7%":[136],"3177":[143],"test":[148],"were":[151],"localized":[152],"acceptable":[154],"accuracy":[155],"(mean":[156],"Jaccard":[158],"indexes":[159],"was":[160,171,197],"72.8%)":[161],"compared":[162,202],"annotations.":[168],"This":[169],"better":[172],"than":[173],"state-of-the-art":[177],"method":[178,205],"reported":[179],"recently.":[180],"experimental":[182],"demonstrated":[184],"CNN":[189],"more":[198],"efficient":[199],"useful":[201],"used":[206],"our":[208],"previous":[209],"work.":[210]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}