{"id":"https://openalex.org/W3126689504","doi":"https://doi.org/10.1186/s13640-021-00546-6","title":"Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified attention U-Net","display_name":"Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified attention U-Net","publication_year":2021,"publication_date":"2021-01-28","ids":{"openalex":"https://openalex.org/W3126689504","doi":"https://doi.org/10.1186/s13640-021-00546-6","mag":"3126689504"},"language":"en","primary_location":{"id":"doi:10.1186/s13640-021-00546-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13640-021-00546-6","pdf_url":"https://jivp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13640-021-00546-6","source":{"id":"https://openalex.org/S153767265","display_name":"EURASIP Journal on Image and Video Processing","issn_l":"1687-5176","issn":["1687-5176","1687-5281"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Image and Video Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://jivp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13640-021-00546-6","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101564066","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0002-9480-1946"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["School of Information Science & Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science & Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China","institution_ids":["https://openalex.org/I143593769"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005274186","display_name":"Hongqing Zhu","orcid":"https://orcid.org/0000-0002-2122-7066"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongqing Zhu","raw_affiliation_strings":["School of Information Science & Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science & Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China","institution_ids":["https://openalex.org/I143593769"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5005274186"],"corresponding_institution_ids":["https://openalex.org/I143593769"],"apc_list":{"value":1665,"currency":"USD","value_usd":1665},"apc_paid":{"value":1665,"currency":"USD","value_usd":1665},"fwci":0.6335,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.65396864,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2021","issue":"1","first_page":null,"last_page":null},"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":0.9923999905586243,"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":0.9865000247955322,"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/upsampling","display_name":"Upsampling","score":0.7210725545883179},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7135928273200989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6990132331848145},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.639829695224762},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6187739372253418},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6151831150054932},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5211672782897949},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4482668340206146},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4459029734134674},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42435306310653687},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.42276108264923096},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.41805756092071533},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32728394865989685},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2167561948299408}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7210725545883179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7135928273200989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6990132331848145},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.639829695224762},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6187739372253418},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6151831150054932},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5211672782897949},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4482668340206146},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4459029734134674},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42435306310653687},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.42276108264923096},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.41805756092071533},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32728394865989685},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2167561948299408},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13640-021-00546-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13640-021-00546-6","pdf_url":"https://jivp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13640-021-00546-6","source":{"id":"https://openalex.org/S153767265","display_name":"EURASIP Journal on Image and Video Processing","issn_l":"1687-5176","issn":["1687-5176","1687-5281"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Image and Video Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:45da22877d35441caa7f6d60f72bd2d6","is_oa":true,"landing_page_url":"https://doaj.org/article/45da22877d35441caa7f6d60f72bd2d6","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":"EURASIP Journal on Image and Video Processing, Vol 2021, Iss 1, Pp 1-21 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13640-021-00546-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13640-021-00546-6","pdf_url":"https://jivp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13640-021-00546-6","source":{"id":"https://openalex.org/S153767265","display_name":"EURASIP Journal on Image and Video Processing","issn_l":"1687-5176","issn":["1687-5176","1687-5281"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Image and Video Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.699999988079071}],"awards":[{"id":"https://openalex.org/G4646024371","display_name":null,"funder_award_id":"61872143","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3126689504.pdf","grobid_xml":"https://content.openalex.org/works/W3126689504.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1494799991","https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1902027874","https://openalex.org/W1966918398","https://openalex.org/W2038679286","https://openalex.org/W2040806095","https://openalex.org/W2049822456","https://openalex.org/W2118718620","https://openalex.org/W2123434300","https://openalex.org/W2129098176","https://openalex.org/W2132831991","https://openalex.org/W2145305441","https://openalex.org/W2150769593","https://openalex.org/W2163344010","https://openalex.org/W2295526923","https://openalex.org/W2328176404","https://openalex.org/W2338937744","https://openalex.org/W2411244326","https://openalex.org/W2726555724","https://openalex.org/W2742946691","https://openalex.org/W2784956235","https://openalex.org/W2785991051","https://openalex.org/W2792110043","https://openalex.org/W2800821945","https://openalex.org/W2888358068","https://openalex.org/W2898639891","https://openalex.org/W2903033372","https://openalex.org/W2946424305","https://openalex.org/W2946570412","https://openalex.org/W2963446712","https://openalex.org/W2966885495","https://openalex.org/W2979605896","https://openalex.org/W2979865275","https://openalex.org/W2985305563","https://openalex.org/W3005044179","https://openalex.org/W3007838476","https://openalex.org/W3013766724","https://openalex.org/W3048209253","https://openalex.org/W6600388300","https://openalex.org/W6600651459","https://openalex.org/W6602415781","https://openalex.org/W6631190155","https://openalex.org/W6632699545"],"related_works":["https://openalex.org/W2559156603","https://openalex.org/W4287394948","https://openalex.org/W4300832495","https://openalex.org/W2987852271","https://openalex.org/W2967990525","https://openalex.org/W2949066288","https://openalex.org/W3119356360","https://openalex.org/W3202075396","https://openalex.org/W4214604401","https://openalex.org/W2127243424"],"abstract_inverted_index":{"Abstract":[0],"Due":[1],"to":[2,20,73,95,124,167,176,197],"the":[3,17,51,67,79,84,112,188,208],"complex":[4],"morphology":[5],"and":[6,39,63,77,90,103,120,142,173,218],"characteristic":[7],"of":[8,16,70,81,100],"retinal":[9,29,52,71],"vessels,":[10],"it":[11,144],"remains":[12],"challenging":[13],"for":[14,129,157],"most":[15,224],"existing":[18,225],"algorithms":[19],"accurately":[21],"detect":[22],"them.":[23],"This":[24,161],"paper":[25],"proposes":[26],"a":[27,87,139,146,154,181],"supervised":[28],"vessels":[30,53],"extraction":[31],"scheme":[32],"using":[33,109],"constrained-based":[34],"nonnegative":[35],"matrix":[36],"factorization":[37],"(NMF)":[38],"three":[40,55,184,212],"dimensional":[41],"(3D)":[42],"modified":[43,150],"attention":[44,151,189],"U-Net":[45,152],"architecture.":[46],"The":[47],"proposed":[48,162],"method":[49,113],"detects":[50],"by":[54,201],"major":[56],"steps.":[57],"First,":[58],"we":[59],"perform":[60],"Gaussian":[61],"filter":[62],"gamma":[64],"correction":[65],"on":[66,211],"green":[68],"channel":[69],"images":[72],"suppress":[74],"background":[75],"noise":[76],"adjust":[78],"contrast":[80],"images.":[82],"Then,":[83],"study":[85,134],"develops":[86],"new":[88],"within-class":[89,119],"between-class":[91,123],"constrained":[92],"NMF":[93],"algorithm":[94],"extract":[96],"neighborhood":[97],"feature":[98,105,126],"information":[99],"every":[101],"pixel":[102],"reduce":[104],"data":[106],"dimension.":[107],"By":[108],"these":[110,194],"constraints,":[111],"can":[114],"effectively":[115],"gather":[116],"similar":[117],"features":[118,122],"discriminate":[121],"improve":[125],"description":[127],"ability":[128],"each":[130],"pixel.":[131],"Next,":[132],"this":[133],"formulates":[135],"segmentation":[136,200],"task":[137],"as":[138,153],"classification":[140],"problem":[141],"solves":[143],"with":[145,180],"more":[147,198],"contributing":[148],"3D":[149],"two-label":[155],"classifier":[156],"reducing":[158],"computational":[159],"cost.":[160],"network":[163],"contains":[164],"an":[165],"upsampling":[166],"raise":[168],"image":[169,175],"resolution":[170],"before":[171],"encoding":[172],"revert":[174],"its":[177],"original":[178],"size":[179],"downsampling":[182],"after":[183],"max-pooling":[185],"layers.":[186],"Besides,":[187],"gate":[190],"(AG)":[191],"set":[192],"in":[193],"layers":[195],"contributes":[196],"accurate":[199],"maintaining":[202],"details":[203],"while":[204],"suppressing":[205],"noises.":[206],"Finally,":[207],"experimental":[209],"results":[210],"publicly":[213],"available":[214],"datasets":[215],"DRIVE,":[216],"STARE,":[217],"HRF":[219],"demonstrate":[220],"better":[221],"performance":[222],"than":[223],"methods.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
