{"id":"https://openalex.org/W2790662084","doi":"https://doi.org/10.1117/12.2295178","title":"Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images","display_name":"Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images","publication_year":2018,"publication_date":"2018-02-27","ids":{"openalex":"https://openalex.org/W2790662084","doi":"https://doi.org/10.1117/12.2295178","mag":"2790662084"},"language":"en","primary_location":{"id":"doi:10.1117/12.2295178","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2295178","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: 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 2018: 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/A5035435894","display_name":"Xiangrong Zhou","orcid":"https://orcid.org/0000-0001-8737-4977"},"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/A5108653882","display_name":"Kuzuma Yamada","orcid":null},"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":"Kuzuma Yamada","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/A5075027380","display_name":"Ryosuke Takayama","orcid":null},"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":"Ryosuke Takayama","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/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/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"]}]},{"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":"last","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"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"83","last_page":"83"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.998199999332428,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8701267242431641},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8565143942832947},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8116363286972046},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7463496923446655},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7065179944038391},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6064779758453369},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5763845443725586},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4620354473590851},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.43416959047317505},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.4171755015850067},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4121001064777374}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8701267242431641},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8565143942832947},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8116363286972046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7463496923446655},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7065179944038391},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6064779758453369},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5763845443725586},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4620354473590851},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.43416959047317505},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.4171755015850067},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4121001064777374},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2295178","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2295178","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: 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 2018: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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"},{"id":"https://openalex.org/G297644444","display_name":null,"funder_award_id":"26108001","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6954336209","display_name":"Development of an Immersive, VR-Based, Learning-Support System Using Augmented Reality and Kinect","funder_award_id":"26330410","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W56444682","https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1901606657","https://openalex.org/W1903029394","https://openalex.org/W2000585255","https://openalex.org/W2003577231","https://openalex.org/W2067794541","https://openalex.org/W2104276184","https://openalex.org/W2118386984","https://openalex.org/W2141619730","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2282928421","https://openalex.org/W2307535535","https://openalex.org/W2332506823","https://openalex.org/W2523005792","https://openalex.org/W2538001104","https://openalex.org/W2594405495","https://openalex.org/W2738088905","https://openalex.org/W2962914239","https://openalex.org/W2964121744","https://openalex.org/W3041368294","https://openalex.org/W6602315363","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6651233102","https://openalex.org/W6667585554","https://openalex.org/W6673021544","https://openalex.org/W6677794201","https://openalex.org/W6687483927","https://openalex.org/W6727200977","https://openalex.org/W6729068420","https://openalex.org/W6734725725","https://openalex.org/W6780608271"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W4313052709","https://openalex.org/W2945274617","https://openalex.org/W2022929107","https://openalex.org/W80586315","https://openalex.org/W1999008862","https://openalex.org/W4205800335","https://openalex.org/W2386644571"],"abstract_inverted_index":{"The":[0,182,215,275],"purpose":[1],"of":[2,12,34,54,71,80,94,97,108,121,151,174,185,188,207,221,229,237,252,280],"this":[3,112],"study":[4],"is":[5,32,43,90],"to":[6,67,200],"evaluate":[7],"and":[8,20,42,57,63,116,130,138,198,231,244,261,272,277],"compare":[9],"the":[10,35,52,98,118,148,201,213,219,222,250,253,264,278],"performance":[11,96,120,150,180],"modern":[13],"deep":[14,86,99,124,132,156,246,254,281],"learning":[15,48,87,100,125,157,255,282],"techniques":[16],"for":[17,161,179,286],"automatically":[18,197],"recognizing":[19],"segmenting":[21,102],"multiple":[22,103,288],"organ":[23,189],"regions":[24,190],"on":[25,60,101,105,171,292],"3D":[26,131,293],"CT":[27,29,81,109,152,168,193,294],"images.":[28,73,295],"image":[30,40,82,153],"segmentation":[31,59,69,83,95,119,154,223,266,290],"one":[33],"important":[36],"task":[37],"in":[38,191],"medical":[39,72],"analysis":[41],"still":[44],"very":[45],"challenging.":[46],"Deep":[47],"approaches":[49,126,256,283],"have":[50,64],"demonstrated":[51,218,285],"capability":[53],"scene":[55],"recognition":[56],"semantic":[58],"nature":[61],"images":[62,169],"been":[65],"used":[66,128,160,178,269],"address":[68],"problems":[70],"Although":[74],"several":[75],"works":[76],"showed":[77,257],"promising":[78],"results":[79,217,224,251],"by":[84,204,233,241],"using":[85,205],"approaches,":[88],"there":[89],"no":[91],"comprehensive":[92],"evaluation":[93],"organs":[104,238,289],"different":[106,123,172],"portions":[107,173],"scans.":[110],"In":[111],"paper,":[113],"we":[114],"evaluated":[115],"compared":[117,199],"two":[122],"that":[127,146,165,239,268],"2D-":[129],"convolutional":[133],"neural":[134],"networks":[135],"(CNN)":[136],"without-":[137],"with":[139],"a":[140,226,242,258],"pre-processing":[141],"step.":[142],"A":[143,163],"conventional":[144,265],"approach":[145],"presents":[147],"state-of-the-art":[149],"without":[155],"was":[158,177],"also":[159],"comparison.":[162],"dataset":[164],"includes":[166],"240":[167],"scanned":[170],"human":[175,202],"bodies":[176],"evaluation.":[181],"maximum":[183],"number":[184],"17":[186,235],"types":[187,236],"each":[192],"scan":[194],"were":[195,284],"segmented":[196,240],"annotations":[203],"ratio":[206],"intersection":[208],"over":[209],"union":[210],"(IU)":[211],"as":[212],"criterion.":[214],"experimental":[216],"IUs":[220],"had":[225],"mean":[227],"value":[228],"79%":[230],"67%":[232],"averaging":[234],"3D-":[243],"2D":[245],"CNN,":[247],"respectively.":[248],"All":[249],"better":[259],"accuracy":[260],"robustness":[262],"than":[263],"method":[267],"probabilistic":[270],"atlas":[271],"graph-cut":[273],"methods.":[274],"effectiveness":[276],"usefulness":[279],"solving":[287],"problem":[291]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}