{"id":"https://openalex.org/W3203442146","doi":"https://doi.org/10.1145/3471287.3471377","title":"SF-U-Net: Using Accurate Shape Estimation and Feature Restoration to Improve Retinal Vessel Segmentation","display_name":"SF-U-Net: Using Accurate Shape Estimation and Feature Restoration to Improve Retinal Vessel Segmentation","publication_year":2021,"publication_date":"2021-05-27","ids":{"openalex":"https://openalex.org/W3203442146","doi":"https://doi.org/10.1145/3471287.3471377","mag":"3203442146"},"language":"en","primary_location":{"id":"doi:10.1145/3471287.3471377","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3471287.3471377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 the 5th International Conference on Information System and Data Mining","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/A5101853607","display_name":"Wenchao Yang","orcid":"https://orcid.org/0009-0004-6805-2728"},"institutions":[{"id":"https://openalex.org/I33849332","display_name":"University of Malaya","ror":"https://ror.org/00rzspn62","country_code":"MY","type":"education","lineage":["https://openalex.org/I33849332"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Wenchao Yang","raw_affiliation_strings":["Faculty of Computer Science and Information Technology,University of Malaya, Malaysia"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Information Technology,University of Malaya, Malaysia","institution_ids":["https://openalex.org/I33849332"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101853607"],"corresponding_institution_ids":["https://openalex.org/I33849332"],"apc_list":null,"apc_paid":null,"fwci":0.2749,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57404636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"120"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9894000291824341,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9890000224113464,"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/segmentation","display_name":"Segmentation","score":0.6195135116577148},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6146603226661682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6073927879333496},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5745246410369873},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5662410259246826},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5121846199035645},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.45605820417404175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39922502636909485},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.07856619358062744}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6195135116577148},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6146603226661682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6073927879333496},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5745246410369873},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5662410259246826},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5121846199035645},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.45605820417404175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39922502636909485},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.07856619358062744},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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:10.1145/3471287.3471377","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3471287.3471377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 the 5th International Conference on Information System and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2206167351","https://openalex.org/W2320230300","https://openalex.org/W2601564443","https://openalex.org/W2896194744","https://openalex.org/W6768774775"],"related_works":["https://openalex.org/W1669643531","https://openalex.org/W2122581818","https://openalex.org/W1631910785","https://openalex.org/W2159066190","https://openalex.org/W2739874619","https://openalex.org/W2110230079","https://openalex.org/W2117933325","https://openalex.org/W2117664411","https://openalex.org/W1721780360","https://openalex.org/W1507266234"],"abstract_inverted_index":{"The":[0,250,275],"features":[1,28,175],"of":[2,17,37,66,69,88,91,94,113,118,144,151,159,170,184,213,218,240,253,260,269],"retinal":[3,45,70,96,219],"blood":[4,46,71,97,171,186,220],"vessels":[5,47,172,221],"are":[6,48],"very":[7,50,83],"essential":[8],"indicators":[9,33,242,261],"playing":[10],"an":[11],"important":[12],"part":[13],"in":[14,54,85,99],"the":[15,21,32,35,60,64,67,74,86,89,92,95,102,111,115,119,123,142,145,149,168,177,182,190,215,224,237,254,266,270,280,283,289],"process":[16],"judging":[18],"and":[19,42,52,121,137,156,161,234,236,288],"diagnosing":[20],"eye":[22],"diseases":[23],"for":[24,34],"doctors.":[25],"Sometimes,":[26],"these":[27],"can":[29],"also":[30],"be":[31,107],"examination":[36],"hypertension,":[38],"coronary":[39],"heart":[40],"disease":[41],"diabetes.":[43],"However,":[44],"often":[49],"small":[51],"complex":[53,185],"distribution,":[55],"which":[56,132],"brings":[57],"toughness":[58],"to":[59,140,165,180,200],"doctors":[61],"when":[62,173],"doing":[63],"operations":[65],"segmentation":[68,90,120,216],"vessels.":[72],"Although":[73],"deep":[75],"learning":[76],"manners":[77],"represented":[78],"by":[79],"U-Net":[80,160],"has":[81],"performed":[82],"well":[84],"field":[87],"images":[93],"vessel":[98,187],"recent":[100],"years,":[101],"above-mentioned":[103,124],"inconvenience":[104,125],"still":[105],"cannot":[106],"effectively":[108],"settled.":[109],"For":[110],"purpose":[112],"improving":[114,214],"correct":[116],"rate":[117],"settling":[122],"we":[126,193,226],"propose":[127],"a":[128,195,258],"network":[129],"called":[130,232],"SF-U-Net,":[131],"uses":[133],"accurate":[134,202],"shape":[135,169],"estimation":[136],"feature":[138,203],"restoration":[139],"achieve":[141,201],"improvement":[143],"accuracy.":[146],"We":[147],"follow":[148],"structure":[150],"Fully":[152],"Convolutional":[153],"Networks":[154],"(FCN)":[155],"Skip":[157],"Connection":[158],"use":[162,227],"deformable":[163],"convolution":[164],"accurately":[166],"capture":[167],"extracting":[174],"at":[176],"coding":[178],"layer":[179],"overcome":[181],"problem":[183],"distribution.":[188],"At":[189],"decoding":[191],"layer,":[192],"adopt":[194],"novel":[196],"dual-stream":[197],"up-sampling":[198],"method":[199],"restoration.":[204],"Experimental":[205],"results":[206,217,239,252],"show":[207],"that":[208],"our":[209],"SF-U-Net":[210,255],"is":[211,278,286,291],"capable":[212],"conspicuously.":[222],"In":[223],"experiment,":[225],"both":[228],"fundus":[229],"image":[230],"datasets":[231],"DRIVE":[233,263],"CHASE-DB1":[235],"experimental":[238,251,267],"multiple":[241],"on":[243,257,262],"them":[244],"surpass":[245],"other":[246],"deep-learning":[247],"methods":[248],"significantly.":[249],"model":[256],"variety":[259],"dataset":[264],"exceed":[265],"performances":[268],"currently":[271],"most":[272],"advanced":[273],"methods.":[274],"mean":[276],"accuracy":[277],"0.9602":[279],"area":[281],"under":[282],"curve":[284],"(AUC)":[285],"0.9848":[287],"sensitivity":[290],"0.8567.":[292]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}