{"id":"https://openalex.org/W4393970215","doi":"https://doi.org/10.1145/3644116.3644130","title":"Enhanced U-Net Model for High Precision Retinal Vessel Segmentation","display_name":"Enhanced U-Net Model for High Precision Retinal Vessel Segmentation","publication_year":2023,"publication_date":"2023-10-20","ids":{"openalex":"https://openalex.org/W4393970215","doi":"https://doi.org/10.1145/3644116.3644130"},"language":"en","primary_location":{"id":"doi:10.1145/3644116.3644130","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3644116.3644130","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3644116.3644130","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3644116.3644130","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050250130","display_name":"Yun Zong","orcid":"https://orcid.org/0009-0004-5503-239X"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Zong","raw_affiliation_strings":["People?s Republic of China Guilin Province GuiLin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0009-0004-5503-239X","affiliations":[{"raw_affiliation_string":"People?s Republic of China Guilin Province GuiLin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083867853","display_name":"Jiahao Shao","orcid":"https://orcid.org/0009-0007-0409-4947"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahao Shao","raw_affiliation_strings":["People?s Republic of China Guilin Province GuiLin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0009-0007-0409-4947","affiliations":[{"raw_affiliation_string":"People?s Republic of China Guilin Province GuiLin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024838734","display_name":"Zhao Lin Liu","orcid":"https://orcid.org/0009-0005-7551-2873"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Liu","raw_affiliation_strings":["People?s Republic of China Guilin Province GuiLin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0009-0005-7551-2873","affiliations":[{"raw_affiliation_string":"People?s Republic of China Guilin Province GuiLin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I5343935"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.49647331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"69","last_page":"73"},"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9882000088691711,"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"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9868000149726868,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6502664089202881},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.5544643402099609},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5181978344917297},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5128850936889648},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.48938459157943726},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.469497948884964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46753713488578796},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10102799534797668},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.08012562990188599}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6502664089202881},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.5544643402099609},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5181978344917297},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5128850936889648},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.48938459157943726},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.469497948884964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46753713488578796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10102799534797668},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.08012562990188599},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3644116.3644130","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3644116.3644130","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3644116.3644130","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3644116.3644130","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3644116.3644130","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3644116.3644130","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393970215.pdf","grobid_xml":"https://content.openalex.org/works/W4393970215.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W2033723371","https://openalex.org/W2116628223","https://openalex.org/W2150769593","https://openalex.org/W2973289689","https://openalex.org/W3013766724"],"related_works":["https://openalex.org/W2912321008","https://openalex.org/W1998607122","https://openalex.org/W2324368075","https://openalex.org/W2972124131","https://openalex.org/W338149487","https://openalex.org/W4387265590","https://openalex.org/W2972032537","https://openalex.org/W150363521","https://openalex.org/W3154107650","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,314,336],"research":[1,394,451],"presented":[2],"in":[3,52,65,73,112,173,220,233,292,297,329,355,383,422,469],"this":[4,346,393],"study":[5,389],"introduces":[6],"a":[7,35,146,227,271,293,326,348,412,425,435],"significantly":[8,481],"enhanced":[9,152],"version":[10],"of":[11,20,29,37,48,58,70,81,86,133,142,161,185,253,274,316,332,343,351,369,398,418,427,438,446,472,485],"the":[12,18,25,49,56,67,79,87,89,105,123,130,134,140,148,158,162,183,194,199,204,212,217,224,243,251,254,266,323,330,367,370,373,384,388,419,432,450,458,470,483],"U-Net":[13],"model,":[14],"specifically":[15],"tailored":[16],"for":[17,168,257,411,491],"segmentation":[19,43],"retinal":[21,30,113,163,205,298,333,357,399,428,439,495],"vessel":[22,170,400],"images.":[23,114,206,287,402],"Recognizing":[24],"complexity":[26,368],"and":[27,45,96,102,156,261,280,310,372,379,416,448,456,467,488,493],"delicacy":[28],"imagery,":[31],"our":[32,53],"model":[33,54,149,267,337,433],"incorporates":[34],"series":[36],"sophisticated":[38],"modifications":[39],"designed":[40,191],"to":[41,108,128,154,192,249,268,303,391,395,434,453,463,479,482],"optimize":[42],"accuracy":[44,219,341,361],"efficiency.":[46],"One":[47],"key":[50,209],"innovations":[51],"is":[55,166,182,230,290,300,363,405,477],"integration":[57],"residual":[59,90],"blocks.":[60],"These":[61,188],"blocks":[62,91],"are":[63,99,190],"instrumental":[64],"overcoming":[66],"common":[68],"issue":[69],"feature":[71],"loss":[72],"deep":[74],"neural":[75],"networks.":[76],"By":[77,207,430],"facilitating":[78],"flow":[80],"information":[82],"across":[83],"different":[84,444],"layers":[85,137],"network,":[88],"ensure":[92],"that":[93,229],"both":[94],"high-level":[95],"low-level":[97],"features":[98,202],"effectively":[100],"captured":[101],"utilized,":[103],"enhancing":[104],"model's":[106,195,218,244,420,459],"ability":[107,153],"discern":[109],"fine":[110,378],"details":[111],"Additionally,":[115],"we":[116],"have":[117],"employed":[118],"cascaded":[119],"dilated":[120],"convolutions":[121],"within":[122,203],"model.":[124],"This":[125,288,359,403,474],"technique":[126],"serves":[127],"expand":[129],"receptive":[131],"field":[132,294,331,471],"network's":[135],"convolutional":[136],"without":[138],"increasing":[139],"number":[141],"parameters":[143],"significantly.":[144],"As":[145],"result,":[147],"gains":[150],"an":[151,339],"understand":[155],"interpret":[157],"broader":[159,436],"context":[160],"images,":[164,440],"which":[165],"crucial":[167,291,406],"accurate":[169,487],"segmentation,":[171],"especially":[172],"areas":[174,234],"with":[175,235,284,376,424,443],"complex":[176],"vascular":[177,381],"structures.":[178],"Another":[179],"significant":[180],"enhancement":[181],"incorporation":[184],"attention":[186,213],"mechanisms.":[187],"mechanisms":[189,214],"focus":[193],"computational":[196],"resources":[197],"on":[198,345],"most":[200],"relevant":[201],"prioritizing":[208],"semantic":[210],"features,":[211],"greatly":[215],"improve":[216],"distinguishing":[221],"vessels":[222],"from":[223,270],"surrounding":[225],"tissue,":[226],"task":[228,371],"particularly":[231,364],"challenging":[232],"lesions":[236,447],"or":[237],"other":[238,396],"abnormalities.":[239],"To":[240],"further":[241,454],"augment":[242],"performance,":[245,460],"techniques":[246],"were":[247],"implemented":[248],"increase":[250],"size":[252],"dataset":[255,264],"used":[256],"training.":[258],"A":[259],"larger":[260],"more":[262,413,486],"diverse":[263],"enables":[265],"learn":[269],"wider":[272,464],"range":[273,437],"examples,":[275],"thereby":[276],"improving":[277],"its":[278,352],"generalizability":[279],"robustness":[281],"when":[282],"faced":[283],"new,":[285],"unseen":[286],"approach":[289],"where":[295],"variability":[296],"images":[299],"high":[301,360],"due":[302],"factors":[304],"such":[305],"as":[306,407],"age,":[307],"health":[308],"conditions,":[309],"image":[311,334],"capture":[312],"techniques.":[313],"effectiveness":[315],"these":[317],"enhancements":[318],"was":[319],"rigorously":[320],"tested":[321],"using":[322],"CHASE":[324],"dataset,":[325,347],"standard":[327],"benchmark":[328],"analysis.":[335],"achieved":[338],"impressive":[340],"rate":[342,362],"98.2%":[344],"clear":[349],"indication":[350],"superior":[353],"performance":[354],"segmenting":[356,377],"vessels.":[358],"noteworthy":[365],"given":[366],"challenges":[374],"associated":[375],"intricate":[380],"structures":[382],"retina.":[385],"Looking":[386],"ahead,":[387],"plans":[390],"extend":[392],"types":[397,445],"lesion":[401],"expansion":[404],"it":[408],"will":[409],"allow":[410],"comprehensive":[414],"understanding":[415],"improvement":[417],"capabilities":[421],"dealing":[423],"variety":[426],"conditions.":[429],"applying":[431],"including":[441],"those":[442],"abnormalities,":[449],"aims":[452],"validate":[455],"refine":[457],"potentially":[461],"leading":[462],"clinical":[465],"applications":[466],"advancements":[468],"ophthalmology.":[473],"future":[475],"work":[476],"expected":[478],"contribute":[480],"development":[484],"reliable":[489],"tools":[490],"diagnosing":[492],"monitoring":[494],"diseases.":[496]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}