{"id":"https://openalex.org/W4285104185","doi":"https://doi.org/10.1145/3532213.3532288","title":"Region Segmentation of Retina OCT Image Layer Based on MS-UNet","display_name":"Region Segmentation of Retina OCT Image Layer Based on MS-UNet","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W4285104185","doi":"https://doi.org/10.1145/3532213.3532288"},"language":"en","primary_location":{"id":"doi:10.1145/3532213.3532288","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3532213.3532288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Computing and Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100779080","display_name":"Jun Wu","orcid":"https://orcid.org/0000-0002-6325-8418"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wu","raw_affiliation_strings":["TianGong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TianGong University, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100319126","display_name":"Shuang Liu","orcid":"https://orcid.org/0000-0003-4046-5905"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Liu","raw_affiliation_strings":["TianGong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TianGong University, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008957721","display_name":"Zhitao Xiao","orcid":"https://orcid.org/0000-0003-2444-9198"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhitao Xiao","raw_affiliation_strings":["TianGong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TianGong University, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100367225","display_name":"Fang Zhang","orcid":"https://orcid.org/0000-0002-0897-4243"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Zhang","raw_affiliation_strings":["TianGong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TianGong University, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101754023","display_name":"Lei Geng","orcid":"https://orcid.org/0000-0002-5010-2596"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Geng","raw_affiliation_strings":["TianGong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TianGong University, China","institution_ids":["https://openalex.org/I198091727"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I198091727"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09662304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"57","issue":null,"first_page":"498","last_page":"504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9861000180244446,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9853000044822693,"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":0.7521535754203796},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7437804341316223},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7118424773216248},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.620355486869812},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5940065383911133},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5594538450241089},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5256032943725586},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4900740385055542},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.48689571022987366},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48131048679351807},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47439077496528625},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4494572877883911},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.44635096192359924},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2753879129886627},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18736708164215088},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1150522530078888}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7521535754203796},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7437804341316223},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7118424773216248},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.620355486869812},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5940065383911133},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5594538450241089},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5256032943725586},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4900740385055542},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.48689571022987366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48131048679351807},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47439077496528625},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4494572877883911},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.44635096192359924},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2753879129886627},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18736708164215088},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1150522530078888},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3532213.3532288","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3532213.3532288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1895539857","https://openalex.org/W1901129140","https://openalex.org/W2011237852","https://openalex.org/W2074598933","https://openalex.org/W2079413294","https://openalex.org/W2319638030","https://openalex.org/W2395611524","https://openalex.org/W2560023338","https://openalex.org/W2592939477","https://openalex.org/W2606534623","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W3108739992"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4205800335","https://openalex.org/W2022929107","https://openalex.org/W2055202857","https://openalex.org/W80586315","https://openalex.org/W2758994127"],"abstract_inverted_index":{"The":[0,149],"accurate":[1,41],"segmentation":[2,68,84,142,178,197],"of":[3,14,23,50,53,56,65,85,104,137,143,186,192],"the":[4,12,19,24,31,47,51,57,62,66,82,101,114,130,140,154,166,170,184],"retinal":[5,15,58,87],"layer":[6,55,145,187],"is":[7,147],"a":[8,39,135],"key":[9],"link":[10],"in":[11,157],"analysis":[13],"OCT":[16,32,59],"images.":[17],"If":[18],"various":[20],"tissue":[21],"layers":[22],"retina":[25],"can":[26],"be":[27],"accurately":[28],"segmented":[29],"from":[30],"image,":[33],"it":[34],"will":[35],"help":[36],"doctors":[37],"make":[38],"more":[40],"and":[42,61,95,123,190,194],"rapid":[43],"diagnosis.":[44],"Aiming":[45],"at":[46],"high":[48],"complexity":[49],"features":[52],"each":[54,144],"image":[60],"low":[63],"accuracy":[64],"existing":[67],"methods,":[69,179],"this":[70,98,158,180],"paper":[71,159],"designs":[72],"an":[73],"improved":[74],"U-Net":[75,172],"model,":[76],"MS-UNet":[77],"(Multiscale":[78],"Skip-UNet),":[79],"which":[80],"realizes":[81],"precise":[83],"seven":[86],"layers.":[88],"To":[89],"prevent":[90],"problems":[91,185],"such":[92],"as":[93],"over-fitting":[94],"gradient":[96],"dispersion,":[97],"method":[99,155,167,181],"uses":[100,113],"residual":[102],"structure":[103,117],"deep":[105],"over-parameterized":[106],"convolution":[107],"for":[108],"feature":[109,131],"extraction":[110],"firstly.":[111],"Then":[112],"multiscale":[115],"skip":[116],"to":[118],"introduce":[119],"global":[120],"context":[121],"information,":[122],"performs":[124],"edge":[125,188],"contour":[126],"maintenance":[127],"operations":[128],"on":[129,169],"tensor.":[132],"Finally,":[133],"through":[134],"series":[136],"upsampling":[138],"operations,":[139],"effective":[141],"region":[146],"achieved.":[148],"experimental":[150],"results":[151],"proved":[152],"that":[153],"proposed":[156],"improves":[160],"MIoU":[161],"by":[162],"1.39%":[163],"compared":[164],"with":[165,175],"based":[168],"original":[171],"model.":[173],"Compared":[174],"other":[176],"semantic":[177],"effectively":[182],"avoids":[183],"blur":[189],"loss":[191],"details":[193],"has":[195],"better":[196],"performance.":[198]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
