{"id":"https://openalex.org/W3048209253","doi":"https://doi.org/10.1109/access.2020.3015108","title":"M-GAN: Retinal Blood Vessel Segmentation by Balancing Losses Through Stacked Deep Fully Convolutional Networks","display_name":"M-GAN: Retinal Blood Vessel Segmentation by Balancing Losses Through Stacked Deep Fully Convolutional Networks","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3048209253","doi":"https://doi.org/10.1109/access.2020.3015108","mag":"3048209253"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3015108","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3015108","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09162010.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09162010.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042382229","display_name":"Kyeong-Beom Park","orcid":null},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kyeong-Beom Park","raw_affiliation_strings":["Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4737-730X","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100332529","display_name":"Sung Ho Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung Ho Choi","raw_affiliation_strings":["Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0887-0150","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101767652","display_name":"Jae Yeol Lee","orcid":"https://orcid.org/0000-0002-2653-0742"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae Yeol Lee","raw_affiliation_strings":["Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2653-0742","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042382229"],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":9.7464,"has_fulltext":true,"cited_by_count":131,"citation_normalized_percentile":{"value":0.98619042,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"8","issue":null,"first_page":"146308","last_page":"146322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":1.0,"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/T11438","display_name":"Retinal Imaging and Analysis","score":1.0,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.994700014591217,"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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":0.7939894795417786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7601743936538696},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6631765961647034},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6464126706123352},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5682295560836792},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5544697642326355},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5304878354072571},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4990565776824951},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4142327308654785},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.4100538492202759},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09688988327980042}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7939894795417786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7601743936538696},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6631765961647034},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6464126706123352},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5682295560836792},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5544697642326355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5304878354072571},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4990565776824951},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4142327308654785},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.4100538492202759},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09688988327980042},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3015108","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3015108","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09162010.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:da11e881bb214407af2890e689410427","is_oa":true,"landing_page_url":"https://doaj.org/article/da11e881bb214407af2890e689410427","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 146308-146322 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3015108","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3015108","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09162010.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1156133555","display_name":null,"funder_award_id":"2019R1I1A3A01059082","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G5256445905","display_name":null,"funder_award_id":"HI19C0642","funder_id":"https://openalex.org/F4320322034","funder_display_name":"Ministry of Health and Welfare"},{"id":"https://openalex.org/G5823036040","display_name":null,"funder_award_id":"2019R1I1A3A01059082","funder_id":"https://openalex.org/F4320321408","funder_display_name":"Ministry of Education"},{"id":"https://openalex.org/G7855437934","display_name":null,"funder_award_id":"HI19C0642","funder_id":"https://openalex.org/F4320322107","funder_display_name":"Korea Health Industry Development Institute"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"},{"id":"https://openalex.org/F4320322034","display_name":"Ministry of Health and Welfare","ror":"https://ror.org/00vxgjw72"},{"id":"https://openalex.org/F4320322105","display_name":"Korea Institute of Science and Technology Information","ror":"https://ror.org/01k4yrm29"},{"id":"https://openalex.org/F4320322107","display_name":"Korea Health Industry Development Institute","ror":"https://ror.org/00fdzyk40"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3048209253.pdf","grobid_xml":"https://content.openalex.org/works/W3048209253.grobid-xml"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W595827679","https://openalex.org/W845365781","https://openalex.org/W1842610785","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1924902684","https://openalex.org/W2033723371","https://openalex.org/W2035677848","https://openalex.org/W2045199522","https://openalex.org/W2051578148","https://openalex.org/W2055635377","https://openalex.org/W2072130234","https://openalex.org/W2093545979","https://openalex.org/W2100756624","https://openalex.org/W2105923875","https://openalex.org/W2121579674","https://openalex.org/W2125389028","https://openalex.org/W2129071778","https://openalex.org/W2145305441","https://openalex.org/W2150769593","https://openalex.org/W2163344010","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2320230300","https://openalex.org/W2327793514","https://openalex.org/W2488605601","https://openalex.org/W2513326255","https://openalex.org/W2527341761","https://openalex.org/W2556022279","https://openalex.org/W2582996697","https://openalex.org/W2587662788","https://openalex.org/W2593414223","https://openalex.org/W2620915497","https://openalex.org/W2622064152","https://openalex.org/W2726555724","https://openalex.org/W2761299546","https://openalex.org/W2792951596","https://openalex.org/W2793555395","https://openalex.org/W2794329395","https://openalex.org/W2797691066","https://openalex.org/W2802388893","https://openalex.org/W2805367842","https://openalex.org/W2898910301","https://openalex.org/W2919115771","https://openalex.org/W2921406441","https://openalex.org/W2923997689","https://openalex.org/W2946570412","https://openalex.org/W2948525168","https://openalex.org/W2958483065","https://openalex.org/W2963073614","https://openalex.org/W2966989968","https://openalex.org/W2980190282","https://openalex.org/W2982297179","https://openalex.org/W3098547059","https://openalex.org/W3101639073","https://openalex.org/W3157685993","https://openalex.org/W4320013936","https://openalex.org/W6638650905","https://openalex.org/W6639824700","https://openalex.org/W6678815747","https://openalex.org/W6684191040","https://openalex.org/W6727596319","https://openalex.org/W6729665208","https://openalex.org/W6732781371","https://openalex.org/W6740861035","https://openalex.org/W6751010837"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W4380714744","https://openalex.org/W2387995142","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2964074194","https://openalex.org/W2967742050","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Until":[0],"now,":[1],"the":[2,64,129,141,146,173,185,188,191,208,218,222,233,247,259,268,288],"human":[3,15],"expert":[4],"segments":[5],"retinal":[6,32,90,168,219],"blood":[7,169],"vessels":[8,220],"manually":[9],"in":[10,52],"fundus":[11,36,224],"images":[12,37,171],"to":[13,43,85,144,159,165,184,216],"inspect":[14],"retinal-related":[16],"diseases,":[17],"such":[18],"as":[19,196],"diabetic":[20],"retinopathy":[21],"and":[22,40,55,88,117,162,228,237,255,257,275],"vascular":[23],"occlusion.":[24],"Recently,":[25],"many":[26],"studies":[27],"were":[28],"conducted":[29],"for":[30,113,124,201,280],"automatic":[31,212],"vessel":[33,91,149,170,234],"segmentation":[34,53,65,92,116],"from":[35,172],"through":[38,96],"supervised":[39],"unsupervised":[41],"methods":[42],"minimize":[44],"user":[45],"intervention.":[46],"However,":[47],"most":[48],"of":[49,63,69,104,128,148,151,187,207,221,270,284],"them":[50],"lack":[51],"robustness":[54],"cannot":[56],"optimize":[57],"loss":[58],"functions":[59],"so":[60],"that":[61,231,287],"results":[62],"have":[66],"made":[67],"lots":[68],"fake":[70],"or":[71],"thin":[72],"branches.":[73],"This":[74],"article":[75],"proposes":[76],"a":[77,105,121,134,180,197,241],"new":[78],"conditional":[79],"generative":[80],"adversarial":[81,130],"network":[82,123,182],"called":[83],"M-GAN":[84,261,290],"conduct":[86,205],"accurate":[87],"precise":[89],"by":[93],"balancing":[94],"losses":[95],"stacked":[97,142],"deep":[98,110],"fully":[99,198],"convolutional":[100],"networks.":[101],"It":[102],"consists":[103],"newly":[106],"designed":[107],"M-generator":[108,155],"with":[109,120,262],"residual":[111],"blocks":[112],"more":[114,125,226],"robust":[115],"an":[118],"M-discriminator":[119,177],"deeper":[122,181],"efficient":[126],"training":[127],"model.":[131],"In":[132],"particular,":[133],"multi-kernel":[135],"pooling":[136],"block":[137],"is":[138,194],"added":[139],"between":[140],"layers":[143,158,164],"support":[145],"scale-invariance":[147],"segmentations":[150],"different":[152],"sizes.":[153],"The":[154,176],"has":[156,179],"down-sampling":[157,186],"extract":[160],"features":[161],"up-sampling":[163],"make":[166,217],"segmented":[167],"extracted":[174],"features.":[175],"also":[178],"similar":[183],"M-generator,":[189],"but":[190],"final":[192],"layer":[193,200],"constructed":[195],"connected":[199],"decision":[202],"making.":[203],"We":[204,265],"pre-processing":[206],"input":[209,223],"image":[210,225],"using":[211,240],"color":[213],"equalization":[214],"(ACE)":[215],"clear":[227],"perform":[229],"post-processing":[230],"makes":[232],"branches":[235],"smooth":[236],"reduces":[238],"false-negatives":[239],"Lanczos":[242],"resampling":[243],"method.":[244],"To":[245],"verify":[246],"proposed":[248,260,289],"method,":[249],"we":[250],"used":[251],"DRIVE,":[252],"STARE,":[253],"HRF,":[254],"CHASE-DB1datasets":[256],"compared":[258],"other":[263,295],"studies.":[264,296],"measured":[266],"accuracy,":[267],"intersection":[269],"union":[271],"(IoU),":[272],"F1":[273],"score,":[274],"Matthews":[276],"correlation":[277],"coefficient":[278],"(MCC)":[279],"comparative":[281],"analysis.":[282],"Results":[283],"comparison":[285],"proved":[286],"derived":[291],"superior":[292],"performance":[293],"than":[294]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":29},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-09T13:55:54.758798","created_date":"2025-10-10T00:00:00"}