{"id":"https://openalex.org/W3213390759","doi":"https://doi.org/10.23919/mipro52101.2021.9596705","title":"Evaluation of Deep Approaches to Sclera Segmentation","display_name":"Evaluation of Deep Approaches to Sclera Segmentation","publication_year":2021,"publication_date":"2021-09-27","ids":{"openalex":"https://openalex.org/W3213390759","doi":"https://doi.org/10.23919/mipro52101.2021.9596705","mag":"3213390759"},"language":"en","primary_location":{"id":"doi:10.23919/mipro52101.2021.9596705","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mipro52101.2021.9596705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)","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/A5103266823","display_name":"Matej Vitek","orcid":"https://orcid.org/0000-0001-9824-2136"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Matej Vitek","raw_affiliation_strings":["University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000","institution_ids":["https://openalex.org/I153976015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054344416","display_name":"Andrej Hafner","orcid":null},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Andrej Hafner","raw_affiliation_strings":["University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000","institution_ids":["https://openalex.org/I153976015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027730478","display_name":"Peter Peer","orcid":"https://orcid.org/0000-0001-9744-4035"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Peter Peer","raw_affiliation_strings":["University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000","institution_ids":["https://openalex.org/I153976015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008429878","display_name":"Ale\u0161 Jakli\u010d","orcid":"https://orcid.org/0000-0001-8765-897X"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Ales Jaklic","raw_affiliation_strings":["University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia,1000","institution_ids":["https://openalex.org/I153976015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4303,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64558877,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1097","last_page":"1102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9990000128746033,"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":0.9990000128746033,"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/T10828","display_name":"Biometric Identification and Security","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9966999888420105,"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/sclera","display_name":"Sclera","score":0.9053044319152832},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7528388500213623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7516363859176636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7155404686927795},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6234042048454285},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5734731554985046},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5346711277961731},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.514097273349762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42658916115760803},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3736861050128937},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12026441097259521},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.06914913654327393}],"concepts":[{"id":"https://openalex.org/C2777100477","wikidata":"https://www.wikidata.org/wiki/Q193107","display_name":"Sclera","level":2,"score":0.9053044319152832},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7528388500213623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516363859176636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7155404686927795},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6234042048454285},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5734731554985046},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5346711277961731},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.514097273349762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42658916115760803},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3736861050128937},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12026441097259521},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.06914913654327393},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mipro52101.2021.9596705","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mipro52101.2021.9596705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G3488210829","display_name":null,"funder_award_id":"P2-0214","funder_id":"https://openalex.org/F4320308445","funder_display_name":"American Roentgen Ray Society"}],"funders":[{"id":"https://openalex.org/F4320308445","display_name":"American Roentgen Ray Society","ror":"https://ror.org/05w7kkt66"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1482253155","https://openalex.org/W1539013634","https://openalex.org/W1583736849","https://openalex.org/W1901129140","https://openalex.org/W1992358283","https://openalex.org/W2006321918","https://openalex.org/W2011833660","https://openalex.org/W2013988101","https://openalex.org/W2014477740","https://openalex.org/W2078875463","https://openalex.org/W2100143326","https://openalex.org/W2100588357","https://openalex.org/W2102796633","https://openalex.org/W2104311988","https://openalex.org/W2114415923","https://openalex.org/W2117539524","https://openalex.org/W2129259959","https://openalex.org/W2136975665","https://openalex.org/W2149999708","https://openalex.org/W2152690956","https://openalex.org/W2159577413","https://openalex.org/W2163155631","https://openalex.org/W2194775991","https://openalex.org/W2206453907","https://openalex.org/W2208917183","https://openalex.org/W2412782625","https://openalex.org/W2512927792","https://openalex.org/W2517933622","https://openalex.org/W2542276916","https://openalex.org/W2545985378","https://openalex.org/W2563705555","https://openalex.org/W2737258237","https://openalex.org/W2776621604","https://openalex.org/W2786059825","https://openalex.org/W2819500789","https://openalex.org/W2884822772","https://openalex.org/W2890036715","https://openalex.org/W2940262938","https://openalex.org/W2944289193","https://openalex.org/W2949341804","https://openalex.org/W2963209464","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W2967918761","https://openalex.org/W2978796584","https://openalex.org/W2987900835","https://openalex.org/W3006095928","https://openalex.org/W3006576515","https://openalex.org/W3008850441","https://openalex.org/W3011456725","https://openalex.org/W3034569439","https://openalex.org/W3090032309","https://openalex.org/W4287704907","https://openalex.org/W6632459331","https://openalex.org/W6639824700","https://openalex.org/W6679262086","https://openalex.org/W6724670942","https://openalex.org/W6748481559","https://openalex.org/W6762207591","https://openalex.org/W6779428647","https://openalex.org/W6781276182"],"related_works":["https://openalex.org/W144671092","https://openalex.org/W2353746516","https://openalex.org/W2081283125","https://openalex.org/W198092683","https://openalex.org/W2394638302","https://openalex.org/W2351695492","https://openalex.org/W4290368984","https://openalex.org/W2389049781","https://openalex.org/W2058803809","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Sclera":[0,115],"segmentation":[1,67],"is":[2,75],"an":[3],"important":[4],"processing":[5],"step":[6],"in":[7,78,102],"a":[8],"biometric":[9],"system":[10],"based":[11],"on":[12,38,57,98],"this":[13],"modality.":[14],"We":[15],"propose":[16],"adaptations":[17],"of":[18,68,80,105],"five":[19,70],"deep":[20,71,128],"neural":[21,72],"network":[22,73],"architectures:":[23],"SegNet,":[24,91],"DeepLabv3+,":[25],"HRNetV2,":[26],"UPerNet,":[27,94],"and":[28,41,52,84,93,95],"U-Net":[29,96,111,124],"for":[30,65,86],"sclera":[31,66],"segmentation.":[32],"The":[33],"architectures":[34,74],"are":[35],"experimentally":[36],"evaluated":[37],"recent":[39],"MASD":[40,99],"SBVPI":[42,59],"eye":[43],"image":[44],"datasets":[45],"using":[46],"standard":[47],"metrics:":[48],"precision,":[49],"recall,":[50],"F_1-score":[51],"intersection":[53],"over":[54],"union.":[55],"Experiments":[56],"the":[58,63,69,81,90,103,109,114,126],"dataset":[60],"show":[61],"that":[62],"performance":[64],"very":[76],"similar":[77],"terms":[79,104],"used":[82],"metrics":[83,107],"acceptable":[85],"practical":[87],"applications.":[88],"Additionally,":[89],"HRNetV2":[92],"tested":[97],"perform":[100],"better":[101],"precision":[106],"than":[108],"winning":[110],"adaptation":[112],"from":[113],"Segmentation":[116],"Benchmark":[117],"Competition":[118],"2019":[119],"(SSBC":[120],"2019),":[121],"with":[122],"our":[123],"being":[125],"best":[127],"model.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}