{"id":"https://openalex.org/W2922462327","doi":"https://doi.org/10.1117/12.2512790","title":"Improving V-Nets for multi-class abdominal organ segmentation","display_name":"Improving V-Nets for multi-class abdominal organ segmentation","publication_year":2019,"publication_date":"2019-03-14","ids":{"openalex":"https://openalex.org/W2922462327","doi":"https://doi.org/10.1117/12.2512790","mag":"2922462327"},"language":"en","primary_location":{"id":"doi:10.1117/12.2512790","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","raw_type":"proceedings-article"},"type":"article","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/A5101554023","display_name":"Chen Shen","orcid":"https://orcid.org/0000-0001-8284-9048"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chen Shen","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054234902","display_name":"Fausto Milletar\u00ec","orcid":"https://orcid.org/0000-0002-8319-4773"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fausto Milletari","raw_affiliation_strings":["NVIDIA Corp. (United States)"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corp. (United States)","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043710204","display_name":"Holger R. Roth","orcid":"https://orcid.org/0000-0002-3662-8743"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Holger R. Roth","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103062935","display_name":"Hirohisa Oda","orcid":"https://orcid.org/0000-0003-0896-4333"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hirohisa Oda","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074920808","display_name":"Masahiro Oda","orcid":"https://orcid.org/0000-0001-7714-422X"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Oda","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054846833","display_name":"Yuichiro Hayashi","orcid":"https://orcid.org/0000-0001-5241-8669"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichiro Hayashi","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005597709","display_name":"Kazunari Misawa","orcid":"https://orcid.org/0000-0002-2047-3919"},"institutions":[{"id":"https://openalex.org/I4210133851","display_name":"Aichi Cancer Center","ror":"https://ror.org/03kfmm080","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210133851"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunari Misawa","raw_affiliation_strings":["Aichi Cancer Ctr. Research Institute (Japan)"],"affiliations":[{"raw_affiliation_string":"Aichi Cancer Ctr. Research Institute (Japan)","institution_ids":["https://openalex.org/I4210133851"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032527419","display_name":"Kensaku Mori","orcid":"https://orcid.org/0000-0002-0100-4797"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]},{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kensaku Mori","raw_affiliation_strings":["Nagoya Univ. (Japan)","National Institute of Informatics (Japan)"],"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"National Institute of Informatics (Japan)","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5101554023"],"corresponding_institution_ids":["https://openalex.org/I60134161"],"apc_list":null,"apc_paid":null,"fwci":0.5061,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.6794454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"10","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9236999750137329,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9236999750137329,"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/T10862","display_name":"AI in cancer detection","score":0.9093000292778015,"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/computer-science","display_name":"Computer science","score":0.6685677766799927},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5280978083610535},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4930383861064911},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4758167862892151},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4134073257446289}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6685677766799927},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5280978083610535},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4930383861064911},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4758167862892151},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4134073257446289}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2512790","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W2194775991","https://openalex.org/W2555096873","https://openalex.org/W2604237878","https://openalex.org/W2775865496","https://openalex.org/W3177525997","https://openalex.org/W4231200252","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6687483927","https://openalex.org/W6718240422","https://openalex.org/W6730265324","https://openalex.org/W6738365919","https://openalex.org/W6740984670","https://openalex.org/W6744003613","https://openalex.org/W6947887075"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Segmentation":[0],"is":[1,59],"one":[2],"of":[3,15,97,110,129,148],"the":[4,13,24,71,86,95,98,107,126,137,146,149,168],"most":[5],"important":[6],"tasks":[7,113],"in":[8,42],"medical":[9],"image":[10],"analysis.":[11],"With":[12],"development":[14],"deep":[16],"leaning,":[17],"fully":[18],"convolutional":[19],"networks":[20],"(FCNs)":[21],"have":[22],"become":[23],"dominant":[25],"approach":[26],"for":[27,38,56,63],"this":[28,91,140],"task":[29],"and":[30,76,154,164,177],"their":[31],"extension":[32],"to":[33,101,105,180],"3D":[34,57],"achieved":[35],"considerable":[36],"improvements":[37],"automated":[39],"organ":[40],"segmentation":[41,112,169],"volumetric":[43],"imaging":[44],"data,":[45],"such":[46],"as":[47],"computed":[48],"tomography":[49],"(CT).":[50],"One":[51],"popular":[52],"FCN":[53],"network":[54,68],"architecture":[55],"volumes":[58],"V-Net,":[60],"originally":[61],"proposed":[62],"single":[64],"region":[65],"segmentation.":[66],"This":[67],"effectively":[69],"solved":[70],"imbalance":[72],"problem":[73],"between":[74],"foreground":[75],"background":[77],"voxels":[78],"by":[79,131],"proposing":[80],"a":[81,172],"loss":[82],"function":[83],"based":[84],"on":[85,171],"Dice":[87,183],"similarity":[88],"metric.":[89],"In":[90,139],"work,":[92],"we":[93,123,142],"extend":[94],"depth":[96],"original":[99],"V-Net":[100,130],"obtain":[102,178],"better":[103],"features":[104],"model":[106],"increased":[108],"complexity":[109],"multi-class":[111],"at":[114],"higher":[115],"input/output":[116],"resolutions":[117],"using":[118],"modern":[119],"large-memory":[120],"GPUs.":[121],"Furthermore,":[122],"markedly":[124],"improved":[125],"training":[127,150],"behaviour":[128],"employing":[132],"batch":[133],"normalization":[134],"layers":[135],"throughout":[136],"network.":[138],"way,":[141],"can":[143],"efficiently":[144],"improve":[145,167],"stability":[147],"optimization,":[151],"achieving":[152],"faster":[153],"more":[155],"stable":[156],"convergence.":[157],"We":[158],"show":[159],"that":[160],"our":[161],"architectural":[162],"changes":[163],"refinements":[165],"dramatically":[166],"performance":[170],"large":[173],"abdominal":[174],"CT":[175],"dataset":[176],"close":[179],"90%":[181],"average":[182],"score.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
