{"id":"https://openalex.org/W2592605422","doi":"https://doi.org/10.1117/12.2254349","title":"Organ detection in thorax abdomen CT using multi-label convolutional neural networks","display_name":"Organ detection in thorax abdomen CT using multi-label convolutional neural networks","publication_year":2017,"publication_date":"2017-03-03","ids":{"openalex":"https://openalex.org/W2592605422","doi":"https://doi.org/10.1117/12.2254349","mag":"2592605422"},"language":"en","primary_location":{"id":"doi:10.1117/12.2254349","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2254349","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5051871464","display_name":"Gabriel Efrain Humpire Mamani","orcid":"https://orcid.org/0000-0002-3199-8163"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Gabriel Efrain Humpire Mamani","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083631804","display_name":"Arnaud A. A. Setio","orcid":"https://orcid.org/0000-0002-5447-4434"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Arnaud Arindra Adiyoso Setio","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042982766","display_name":"Bram van Ginneken","orcid":"https://orcid.org/0000-0003-2028-8972"},"institutions":[{"id":"https://openalex.org/I4210137873","display_name":"Fraunhofer Institute for Digital Medicine","ror":"https://ror.org/04farme71","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210137873","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["DE","NL"],"is_corresponding":false,"raw_author_name":"Bram van Ginneken","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)","Fraunhofer MEVIS (Germany)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]},{"raw_affiliation_string":"Fraunhofer MEVIS (Germany)","institution_ids":["https://openalex.org/I4210137873"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020018643","display_name":"Colin Jacobs","orcid":"https://orcid.org/0000-0003-1180-3805"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Colin Jacobs","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5051871464"],"corresponding_institution_ids":["https://openalex.org/I145872427"],"apc_list":null,"apc_paid":null,"fwci":1.5041,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.81439092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"10134","issue":null,"first_page":"1013416","last_page":"1013416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.998199999332428,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.998199999332428,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9925000071525574,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/computer-science","display_name":"Computer science","score":0.8120105266571045},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7428358793258667},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6356861591339111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5885716080665588},{"id":"https://openalex.org/keywords/thorax","display_name":"Thorax (insect anatomy)","score":0.5466032028198242},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5194844007492065},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4883539378643036},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4761224389076233},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3228027820587158},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10424646735191345},{"id":"https://openalex.org/keywords/anatomy","display_name":"Anatomy","score":0.10328513383865356},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07482680678367615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8120105266571045},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7428358793258667},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6356861591339111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885716080665588},{"id":"https://openalex.org/C97834683","wikidata":"https://www.wikidata.org/wiki/Q942508","display_name":"Thorax (insect anatomy)","level":2,"score":0.5466032028198242},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5194844007492065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4883539378643036},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4761224389076233},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3228027820587158},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10424646735191345},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.10328513383865356},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07482680678367615}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1117/12.2254349","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2254349","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:fraunhofer.de:N-455979","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-455979.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer MEVIS","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/397664","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/397664","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W954267072","https://openalex.org/W1504543387","https://openalex.org/W1533861849","https://openalex.org/W2110161289","https://openalex.org/W2306541384","https://openalex.org/W2500554657","https://openalex.org/W6625022109","https://openalex.org/W6630203139","https://openalex.org/W6631943919"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W2810679507","https://openalex.org/W2964954556","https://openalex.org/W3019910406"],"abstract_inverted_index":{"A":[0,17],"convolutional":[1],"network":[2,19],"architecture":[3,37,55,149],"is":[4,146],"presented":[5],"to":[6,154],"determine":[7],"bounding":[8,65],"boxes":[9],"around":[10,67],"six":[11],"organs":[12,140,157],"in":[13],"thoraxabdomen":[14],"CT":[15],"scans.":[16,102],"single":[18,58],"for":[20,91,95,115,121,125,130],"each":[21],"orthogonal":[22],"view":[23],"determines":[24],"the":[25,45,61,68,108,116,122,126,131],"presence":[26],"of":[27,47,70,107,139,158],"lungs,":[28,117],"kidneys,":[29,123],"spleen":[30],"and":[31,43,82,87,93,113,119,128],"liver.":[32],"We":[33],"show":[34],"that":[35,38,56,136],"an":[36,50,54],"takes":[39],"additional":[40,51],"slices":[41],"before":[42],"after":[44],"slice":[46],"interest":[48,71,159],"as":[49,160],"input":[52],"outperforms":[53],"processes":[57],"slices.":[59],"From":[60],"slice-based":[62],"analysis,":[63],"a":[64],"box":[66],"structures":[69],"can":[72,150],"be":[73,152],"computed.":[74],"The":[75,97,103],"system":[76],"uses":[77,88],"6":[78],"convolutional,":[79],"4":[80],"pooling":[81],"one":[83],"fully":[84],"connected":[85],"layer":[86],"333":[89],"scans":[90],"training":[92],"110":[94,101],"validation.":[96],"test":[98],"set":[99],"contains":[100],"average":[104],"Dice":[105],"score":[106],"proposed":[109],"method":[110],"was":[111],"0.95":[112,114],"0.59":[118],"0.58":[120],"0.83":[124],"liver":[127],"0.63":[129],"spleen.":[132],"This":[133,148],"paper":[134],"shows":[135],"automatic":[137],"localization":[138],"using":[141],"multi-label":[142],"convolution":[143],"neural":[144],"networks":[145],"possible.":[147],"likely":[151],"used":[153],"identify":[155],"other":[156],"well.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2017-03-16T00:00:00"}