{"id":"https://openalex.org/W7117331396","doi":"https://doi.org/10.1109/access.2025.3648822","title":"Optimized KiU-Net: A Convolutional Autoencoder for Retinal Vessel Segmentation in Medical Images","display_name":"Optimized KiU-Net: A Convolutional Autoencoder for Retinal Vessel Segmentation in Medical Images","publication_year":2025,"publication_date":"2025-12-26","ids":{"openalex":"https://openalex.org/W7117331396","doi":"https://doi.org/10.1109/access.2025.3648822"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3648822","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3648822","pdf_url":null,"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://doi.org/10.1109/access.2025.3648822","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121324970","display_name":"Hazrat Bilal","orcid":null},"institutions":[{"id":"https://openalex.org/I188760350","display_name":"Ollscoil na Gaillimhe \u2013 University of Galway","ror":"https://ror.org/03bea9k73","country_code":"IE","type":"education","lineage":["https://openalex.org/I188760350"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"Hazrat Bilal","raw_affiliation_strings":["CRT-AI, School of Computer Science, University of Galway, Galway, Ireland"],"affiliations":[{"raw_affiliation_string":"CRT-AI, School of Computer Science, University of Galway, Galway, Ireland","institution_ids":["https://openalex.org/I188760350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072731605","display_name":"Malika Bendechache","orcid":"https://orcid.org/0000-0003-0069-1860"},"institutions":[{"id":"https://openalex.org/I188760350","display_name":"Ollscoil na Gaillimhe \u2013 University of Galway","ror":"https://ror.org/03bea9k73","country_code":"IE","type":"education","lineage":["https://openalex.org/I188760350"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Malika Bendechache","raw_affiliation_strings":["ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland"],"affiliations":[{"raw_affiliation_string":"ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland","institution_ids":["https://openalex.org/I188760350"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049336469","display_name":"Cem Direko\u011flu","orcid":null},"institutions":[{"id":"https://openalex.org/I34771391","display_name":"University of Cyprus","ror":"https://ror.org/02qjrjx09","country_code":"CY","type":"education","lineage":["https://openalex.org/I34771391"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"Cem Direkoglu","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Middle East Technical University, Northern Cyprus Campus, Kalkanli, Güzelyurt, Mersin, Türkiye"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Middle East Technical University, Northern Cyprus Campus, Kalkanli, Güzelyurt, Mersin, Türkiye","institution_ids":["https://openalex.org/I34771391"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121324970"],"corresponding_institution_ids":["https://openalex.org/I188760350"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68335217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"2784","last_page":"2799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9940999746322632,"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":0.9940999746322632,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0012000000569969416,"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":0.00039999998989515007,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6337000131607056},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6305999755859375},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5382999777793884},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.5113999843597412},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49079999327659607},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48100000619888306},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4740999937057495},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45739999413490295},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.4546000063419342}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7817000150680542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664999961853027},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6337000131607056},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6305999755859375},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5382999777793884},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.5113999843597412},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49079999327659607},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48100000619888306},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4740999937057495},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45249998569488525},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C2778818243","wikidata":"https://www.wikidata.org/wiki/Q899552","display_name":"Optical coherence tomography","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.260699987411499},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3648822","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3648822","pdf_url":null,"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"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3648822","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3648822","pdf_url":null,"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":[],"awards":[{"id":"https://openalex.org/G1781938974","display_name":null,"funder_award_id":"18/CRT/6223 (Research Ireland Centre for Research Training in Artificial Intelligence)","funder_id":"https://openalex.org/F8766430056","funder_display_name":"Research Ireland"},{"id":"https://openalex.org/G2190095137","display_name":null,"funder_award_id":"13/RC/2094/P_2 (Lero Centre)","funder_id":"https://openalex.org/F8766430056","funder_display_name":"Research Ireland"},{"id":"https://openalex.org/G4634463835","display_name":null,"funder_award_id":"13/RC/2106/P _2","funder_id":"https://openalex.org/F4320336201","funder_display_name":"ADAPT - Centre for Digital Content Technology"},{"id":"https://openalex.org/G5342796136","display_name":null,"funder_award_id":"13/RC/2106/P_2 (ADAPT Centre)","funder_id":"https://openalex.org/F8766430056","funder_display_name":"Research Ireland"}],"funders":[{"id":"https://openalex.org/F4320336201","display_name":"ADAPT - Centre for Digital Content Technology","ror":null},{"id":"https://openalex.org/F8766430056","display_name":"Research Ireland","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1966807770","https://openalex.org/W2270168057","https://openalex.org/W2441649867","https://openalex.org/W2540631188","https://openalex.org/W2689259083","https://openalex.org/W2750120005","https://openalex.org/W2778489784","https://openalex.org/W2787091153","https://openalex.org/W2805544462","https://openalex.org/W2884436604","https://openalex.org/W2891046875","https://openalex.org/W2900936384","https://openalex.org/W2902878689","https://openalex.org/W2905338897","https://openalex.org/W2907750714","https://openalex.org/W2911228988","https://openalex.org/W2914272101","https://openalex.org/W2914493539","https://openalex.org/W2948525168","https://openalex.org/W2962914239","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2965655488","https://openalex.org/W2970108562","https://openalex.org/W2977720001","https://openalex.org/W2978486977","https://openalex.org/W2979893297","https://openalex.org/W2993649495","https://openalex.org/W2996290406","https://openalex.org/W3004820526","https://openalex.org/W3006169930","https://openalex.org/W3010066814","https://openalex.org/W3015788359","https://openalex.org/W3097065222","https://openalex.org/W3149079890","https://openalex.org/W3204177259","https://openalex.org/W4206489121","https://openalex.org/W4221088276","https://openalex.org/W4283828743","https://openalex.org/W4285018307","https://openalex.org/W4285413153","https://openalex.org/W4292387600","https://openalex.org/W4295934721","https://openalex.org/W4313472442","https://openalex.org/W4315646432","https://openalex.org/W4385892186","https://openalex.org/W4390110819","https://openalex.org/W4401069729","https://openalex.org/W4405867999","https://openalex.org/W4406113259","https://openalex.org/W4408182749","https://openalex.org/W4408747274","https://openalex.org/W4411041865","https://openalex.org/W4411078784","https://openalex.org/W4416965033"],"related_works":[],"abstract_inverted_index":{"Medical":[0],"image":[1],"segmentation":[2,113],"plays":[3],"a":[4,141],"central":[5],"role":[6],"in":[7,16,188,193],"enhancing":[8],"diagnosis,":[9],"surgical":[10],"planning,":[11],"and":[12,15,49,58,85,99,116,131,135,164,173,190,206,211],"treatment":[13],"strategies,":[14],"this":[17],"work,":[18],"the":[19,112,122,136,153,156,182,201,215],"focus":[20],"is":[21,33],"on":[22,200],"segmenting":[23],"retinal":[24,53,118],"vessels":[25,119],"from":[26,96],"color":[27],"fundus":[28],"images.":[29],"Precise":[30],"vessel":[31,36,54,60],"extraction":[32,92],"essential":[34],"because":[35],"morphology":[37],"reflects":[38],"several":[39],"ophthalmic":[40],"conditions.":[41],"Wide":[42],"neural":[43],"networks":[44],"such":[45],"as":[46],"U":[47,66,170],"Net":[48,67,82,108],"KiU-Net":[50],"have":[51],"improved":[52],"segmentation;":[55],"however,":[56],"thin":[57,115],"low-contrast":[59,117],"regions":[61],"remain":[62],"difficult":[63],"to":[64,76,88,179],"capture.":[65],"follows":[68],"an":[69,105,159,165],"undercomplete":[70,84],"design":[71,126],"that":[72,110,214],"limits":[73],"its":[74],"ability":[75],"retain":[77],"fine":[78],"structures,":[79],"whereas":[80],"KiU":[81,107,180],"combines":[83],"overcomplete":[86],"paths":[87],"provide":[89],"better":[90],"detail":[91],"but":[93],"still":[94],"suffers":[95],"accuracy":[97],"limitations":[98],"increased":[100],"computational":[101],"cost.":[102],"We":[103],"present":[104],"Optimized":[106],"model":[109,123,157],"improves":[111],"of":[114,162,167,209,224],"while":[120],"keeping":[121],"lightweight.":[124],"The":[125],"refines":[127],"convolution":[128],"channel":[129],"selection":[130],"increases":[132],"encoder":[133],"depth,":[134],"final":[137],"feature":[138],"fusion":[139],"uses":[140],"single":[142],"concatenation":[143],"step,":[144],"which":[145],"supports":[146],"faster":[147],"convergence":[148],"with":[149,195,221],"fewer":[150,196],"parameters.":[151,197],"On":[152],"RITE":[154],"dataset,":[155],"achieves":[158],"F1":[160,189,205],"score":[161],"79.80":[163],"IoU":[166,194,207],"66.30,":[168],"outperforming":[169],"Net,":[171,181],"KiU-Net,":[172],"other":[174],"similar":[175],"sized":[176],"architectures.":[177],"Compared":[178],"gains":[183],"are":[184],"about":[185],"four":[186],"points":[187,192],"six":[191],"Additional":[198],"evaluation":[199],"GlaS":[202],"dataset":[203],"shows":[204],"scores":[208],"82.21":[210],"71.03,":[212],"demonstrating":[213],"method":[216],"remains":[217],"effective":[218],"when":[219],"compared":[220],"existing":[222],"approaches":[223],"comparable":[225],"scale.":[226]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-26T00:00:00"}