{"id":"https://openalex.org/W4200619704","doi":"https://doi.org/10.1109/embc46164.2021.9629671","title":"An Efficient and Accurate 3D Multiple-Contextual Semantic Segmentation Network for Medical Volumetric Images","display_name":"An Efficient and Accurate 3D Multiple-Contextual Semantic Segmentation Network for Medical Volumetric Images","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200619704","doi":"https://doi.org/10.1109/embc46164.2021.9629671","pmid":"https://pubmed.ncbi.nlm.nih.gov/34891948"},"language":"en","primary_location":{"id":"doi:10.1109/embc46164.2021.9629671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9629671","pdf_url":null,"source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"He Li","orcid":null},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"He Li","raw_affiliation_strings":["Graduate Scool of Information Science and Engineering, Ritsumeikan University, Shiga, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate Scool of Information Science and Engineering, Ritsumeikan University, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yutaro Iwamoto","orcid":null},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yutaro Iwamoto","raw_affiliation_strings":["Graduate Scool of Information Science and Engineering, Ritsumeikan University, Shiga, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate Scool of Information Science and Engineering, Ritsumeikan University, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xianhua Han","orcid":null},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xianhua Han","raw_affiliation_strings":["Faculty of Science, Yamaguchi University, Yamaguchi, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Science, Yamaguchi University, Yamaguchi, Japan","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Akira Furukawa","orcid":null},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Furukawa","raw_affiliation_strings":["Tokyo Metropolitan University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuzo Kanasaki","orcid":null},"institutions":[{"id":"https://openalex.org/I4210095413","display_name":"Ijinkai Takeda General Hospital","ror":"https://ror.org/00t61fh47","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210095413"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuzo Kanasaki","raw_affiliation_strings":["Koseikai Takeda Hospital, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Koseikai Takeda Hospital, Kyoto, Japan","institution_ids":["https://openalex.org/I4210095413"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yen-Wei Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yen-Wei Chen","raw_affiliation_strings":["Graduate Scool of Information Science and Engineering, Ritsumeikan University, Shiga, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate Scool of Information Science and Engineering, Ritsumeikan University, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":0.2628,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.65306415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2021","issue":null,"first_page":"3309","last_page":"3312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7324000000953674,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7324000000953674,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.05249999836087227,"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/T10862","display_name":"AI in cancer detection","score":0.03759999945759773,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/segmentation","display_name":"Segmentation","score":0.6736999750137329},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6092000007629395},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5662000179290771},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.557200014591217},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5406000018119812},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5008999705314636},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4966000020503998},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48829999566078186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7946000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7742000222206116},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6736999750137329},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6092000007629395},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5662000179290771},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5406000018119812},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4966000020503998},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48829999566078186},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.444599986076355},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4268999993801117},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4016999900341034},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.3237000107765198},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.28619998693466187},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.26660001277923584}],"mesh":[{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc46164.2021.9629671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9629671","pdf_url":null,"source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:34891948","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34891948","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1987869189","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2464708700","https://openalex.org/W2518297742","https://openalex.org/W2560023338","https://openalex.org/W2608353599","https://openalex.org/W2914806156","https://openalex.org/W2928133111","https://openalex.org/W2962914239","https://openalex.org/W3015788359","https://openalex.org/W3017153481","https://openalex.org/W3200323320","https://openalex.org/W6639824700","https://openalex.org/W6748481559"],"related_works":[],"abstract_inverted_index":{"Convolutional":[0],"neural":[1,40],"networks":[2,30,47,63],"have":[3],"become":[4],"popular":[5],"in":[6,50,180],"medical":[7,157],"image":[8,129,158],"segmentation,":[9],"and":[10,76,86,106,135,163,191,203],"one":[11],"of":[12,59,171,183],"their":[13,18],"most":[14,58],"notable":[15],"achievements":[16],"is":[17],"ability":[19],"to":[20,33,67,101,109,149],"learn":[21],"discriminative":[22],"features":[23,36,53,148],"using":[24],"large":[25],"labeled":[26],"datasets.":[27],"Two-dimensional":[28],"(2D)":[29],"are":[31,48,74],"accustomed":[32],"extracting":[34,51],"multiscale":[35,103],"with":[37,160],"deep":[38],"convolutional":[39],"network":[41,88,179],"extractors,":[42],"i.e.,":[43],"ResNet-101.":[44],"However,":[45],"2D":[46,61],"inefficient":[49],"spatial":[52],"from":[54,115],"volumetric":[55,128],"images.":[56],"Although":[57],"the":[60,146,150,167,177,181,204],"segmentation":[62,142,159,169,185],"can":[64],"be":[65],"extended":[66,71],"three-dimensional":[68],"(3D)":[69],"networks,":[70],"3D":[72,92,97,124,137,152,156,168],"methods":[73],"resource":[75],"time":[77],"intensive.":[78],"In":[79],"this":[80],"paper,":[81],"we":[82,154,175],"propose":[83],"an":[84],"efficient":[85,127],"accurate":[87],"for":[89,126],"fully":[90],"automatic":[91],"segmentation.":[93,130],"We":[94,119],"designed":[95,121],"a":[96,122,140,187],"multiple-contextual":[98,133,147],"extractor":[99,134],"(MCE)":[100],"simulate":[102],"feature":[104,107,117],"extraction":[105],"fusion":[108],"capture":[110],"rich":[111],"global":[112],"contextual":[113],"dependencies":[114],"different":[116],"levels.":[118],"also":[120],"light":[123,136,151],"ResU-Net":[125,138],"The":[131,195],"proposed":[132,173,178],"constituted":[139],"complete":[141],"network.":[143],"By":[144],"feeding":[145],"ResU-Net,":[153],"realized":[155],"high":[161],"efficiency":[162],"accuracy.":[164],"To":[165],"validate":[166],"performance":[170],"our":[172],"method,":[174],"evaluated":[176],"context":[182],"semantic":[184],"on":[186],"private":[188],"spleen":[189,196],"dataset":[190,197,206],"public":[192],"liver":[193,205],"dataset.":[194],"contains":[198,207],"50":[199],"patients'":[200,209],"CT":[201,210],"scans,":[202],"131":[208],"scans.":[211]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2021-12-31T00:00:00"}