{"id":"https://openalex.org/W2921563631","doi":"https://doi.org/10.1117/12.2512711","title":"Lung segmentation based on a deep learning approach for dynamic chest radiography","display_name":"Lung segmentation based on a deep learning approach for dynamic chest radiography","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2921563631","doi":"https://doi.org/10.1117/12.2512711","mag":"2921563631"},"language":"en","primary_location":{"id":"doi:10.1117/12.2512711","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512711","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":"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/A5112316425","display_name":"Yuki Kitahara","orcid":null},"institutions":[{"id":"https://openalex.org/I10091056","display_name":"Kanazawa University","ror":"https://ror.org/02hwp6a56","country_code":"JP","type":"education","lineage":["https://openalex.org/I10091056"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yuki Kitahara","raw_affiliation_strings":["Kanazawa Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Kanazawa Univ. (Japan)","institution_ids":["https://openalex.org/I10091056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005684560","display_name":"Rie Tanaka","orcid":"https://orcid.org/0000-0002-9476-5287"},"institutions":[{"id":"https://openalex.org/I10091056","display_name":"Kanazawa University","ror":"https://ror.org/02hwp6a56","country_code":"JP","type":"education","lineage":["https://openalex.org/I10091056"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rie Tanaka","raw_affiliation_strings":["Kanazawa Univ. (Japan)"],"affiliations":[{"raw_affiliation_string":"Kanazawa Univ. (Japan)","institution_ids":["https://openalex.org/I10091056"]}]},{"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 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/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"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kensaku Mori","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/A5018690593","display_name":"Kazuo Kasahara","orcid":"https://orcid.org/0000-0001-5551-1543"},"institutions":[{"id":"https://openalex.org/I4210104330","display_name":"Kanazawa University Hospital","ror":"https://ror.org/00xsdn005","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210104330"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuo Kasahara","raw_affiliation_strings":["Kanazawa Univ. Hospital (Japan)"],"affiliations":[{"raw_affiliation_string":"Kanazawa Univ. Hospital (Japan)","institution_ids":["https://openalex.org/I4210104330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101951729","display_name":"Isao Matsumoto","orcid":"https://orcid.org/0000-0001-9069-3530"},"institutions":[{"id":"https://openalex.org/I4210104330","display_name":"Kanazawa University Hospital","ror":"https://ror.org/00xsdn005","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210104330"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Isao Matsumoto","raw_affiliation_strings":["Kanazawa Univ. Hospital (Japan)"],"affiliations":[{"raw_affiliation_string":"Kanazawa Univ. Hospital (Japan)","institution_ids":["https://openalex.org/I4210104330"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5112316425"],"corresponding_institution_ids":["https://openalex.org/I10091056"],"apc_list":null,"apc_paid":null,"fwci":1.524,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.82309834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"130","last_page":"130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9976000189781189,"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":0.9976000189781189,"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":0.9962999820709229,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9941999912261963,"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/segmentation","display_name":"Segmentation","score":0.7266716957092285},{"id":"https://openalex.org/keywords/lung","display_name":"Lung","score":0.6744670867919922},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.63158118724823},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.6244570016860962},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4978063106536865},{"id":"https://openalex.org/keywords/pulmonary-function-testing","display_name":"Pulmonary function testing","score":0.43609803915023804},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.41498103737831116},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4082813858985901},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34948551654815674},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1685854196548462}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7266716957092285},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.6744670867919922},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.63158118724823},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6244570016860962},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4978063106536865},{"id":"https://openalex.org/C75603125","wikidata":"https://www.wikidata.org/wiki/Q1877462","display_name":"Pulmonary function testing","level":2,"score":0.43609803915023804},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.41498103737831116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4082813858985901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34948551654815674},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1685854196548462}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2512711","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512711","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":[{"score":0.5799999833106995,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1999028079","https://openalex.org/W2030928387","https://openalex.org/W2410738162","https://openalex.org/W2413416514","https://openalex.org/W2772978616","https://openalex.org/W6715438446","https://openalex.org/W6785845253"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2187699143","https://openalex.org/W2363602550","https://openalex.org/W609884419","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W2052024821","https://openalex.org/W4294635752","https://openalex.org/W4304166257"],"abstract_inverted_index":{"The":[0,116,139,174],"purpose":[1],"of":[2,42,98,107,119,176,189,206,225,239],"this":[3,65],"study":[4],"was":[5,123,132],"to":[6,22,55,79,112],"develop":[7],"a":[8,13,73,81,202],"lung":[9,86,121,178,190,208,223],"segmentation":[10,144,215,224],"based":[11],"on":[12,104],"deep":[14,82,216],"learning":[15,83,217],"approach":[16,218],"for":[17,27,64,85,93,186,220,236],"dynamic":[18,39,226],"chest":[19,40,227],"radiography,":[20],"and":[21,33,88,128,146,154,180,233],"assess":[23],"the":[24,99,105,113,120,143,147,177,187,207,211,221,237],"clinical":[25,130],"utility":[26,131],"pulmonary":[28,137,196,240],"function":[29],"assessment.":[30],"Maximum":[31],"inhale":[32,153],"exhale":[34,157,212],"images":[35,47,53,57,69,90],"were":[36,62,70,91,110,150,161,168,184],"selected":[37],"in":[38,134,152,156,194,210],"radiographs":[41,228],"214":[43],"cases,":[44],"comprising":[45],"150":[46],"during":[48],"respiration.":[49],"In":[50],"total,":[51],"534":[52],"(2":[54],"4":[56],"per":[58],"case)":[59],"with":[60,136,229],"annotations":[61],"prepared":[63],"study.":[66],"Three":[67],"hundred":[68],"fed":[71],"into":[72],"fullyconvolutional":[74],"neural":[75],"network":[76],"(FCNN)":[77],"architecture":[78],"train":[80],"model":[84],"segmentation,":[87],"234":[89],"used":[92],"testing.":[94],"To":[95],"reduce":[96],"misrecognition":[97],"lung,":[100],"post":[101,172],"processing":[102],"methods":[103],"basis":[106],"time-series":[108],"information":[109],"applied":[111],"resulting":[114],"images.":[115],"change":[117,183,204],"rate":[118,205],"area":[122,179,209],"calculated":[124],"throughout":[125],"all":[126],"frames":[127],"its":[129,181],"assessed":[133],"patients":[135],"diseases.":[138],"Sorenson-Dice":[140],"coefficients":[141],"between":[142],"results":[145],"gold":[148],"standard":[149],"0.94":[151],"0.95":[155],"phases,":[158],"respectively.":[159],"There":[160],"some":[162],"false":[163],"recognitions":[164],"(214/234),":[165],"but":[166],"163":[167],"eliminated":[169],"by":[170],"our":[171],"processing.":[173],"measurement":[175],"respiratory":[182],"useful":[185,235],"evaluation":[188,238],"conditions;":[191],"prolonged":[192],"expiration":[193],"obstructive":[195],"diseases":[197],"could":[198],"be":[199],"detected":[200],"as":[201],"reduced":[203],"phase.":[213],"Semantic":[214],"allows":[219],"sequential":[222],"high":[230],"accuracy":[231],"(94%)":[232],"is":[234],"function.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}