{"id":"https://openalex.org/W4285044580","doi":"https://doi.org/10.3390/app12147012","title":"A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis","display_name":"A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis","publication_year":2022,"publication_date":"2022-07-11","ids":{"openalex":"https://openalex.org/W4285044580","doi":"https://doi.org/10.3390/app12147012"},"language":"en","primary_location":{"id":"doi:10.3390/app12147012","doi":"https://doi.org/10.3390/app12147012","is_oa":true,"landing_page_url":"https://doi.org/10.3390/app12147012","pdf_url":"https://www.mdpi.com/2076-3417/12/14/7012/pdf?version=1657597603","source":{"id":"https://openalex.org/S4210205812","display_name":"Applied Sciences","issn_l":"2076-3417","issn":["2076-3417"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","oarl","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2076-3417/12/14/7012/pdf?version=1657597603","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109463533","display_name":"Wenjing Xu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjing Xu","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103252053","display_name":"Qing Zhu","orcid":"https://orcid.org/0000-0002-5441-3121","observed_orcids":["https://orcid.org/0000-0002-5441-3121"]},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qing Zhu","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103252053"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":{"value":2300,"currency":"CHF","value_usd":2408},"apc_paid":{"value":2300,"currency":"CHF","value_usd":2408},"fwci":0.2774,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.42400865,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"12","issue":"14","first_page":"7012","last_page":"7012"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965999722480774,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.0013000000035390258,"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":0.0007999999797903001,"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/vessel-wall-segmentation","display_name":"vessel wall segmentation","score":0.8169999718666077},{"id":"https://openalex.org/keywords/atherosclerotic-plaque","display_name":"atherosclerotic plaque","score":0.8090000152587891},{"id":"https://openalex.org/keywords/ischemic-stroke","display_name":"ischemic stroke","score":0.9070000052452087},{"id":"https://openalex.org/keywords/edge-aware-segmentation","display_name":"edge-aware segmentation","score":0.7549999952316284},{"id":"https://openalex.org/keywords/hybrid-dilated-convolution","display_name":"hybrid dilated convolution","score":0.9950000047683716},{"id":"https://openalex.org/keywords/multiscale-segmentation","display_name":"multi-scale segmentation","score":0.8920000195503235},{"id":"https://openalex.org/keywords/atherosclerosis","display_name":"atherosclerosis","score":0.8479999899864197}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8253463506698608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.709712564945221},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.6430244445800781},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6412277817726135},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6062407493591309},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5118398070335388},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.45897147059440613},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4380331337451935},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4295046329498291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39949771761894226},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16955113410949707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08833682537078857},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/app12147012","doi":"https://doi.org/10.3390/app12147012","is_oa":true,"landing_page_url":"https://doi.org/10.3390/app12147012","pdf_url":"https://www.mdpi.com/2076-3417/12/14/7012/pdf?version=1657597603","source":{"id":"https://openalex.org/S4210205812","display_name":"Applied Sciences","issn_l":"2076-3417","issn":["2076-3417"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","oarl","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Sciences","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0fbe139ef8734de288d145cbf4e51251","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/0fbe139ef8734de288d145cbf4e51251","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,"listed_in":[],"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":"Applied Sciences, Vol 12, Iss 14, p 7012 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/app12147012","doi":"https://doi.org/10.3390/app12147012","is_oa":true,"landing_page_url":"https://doi.org/10.3390/app12147012","pdf_url":"https://www.mdpi.com/2076-3417/12/14/7012/pdf?version=1657597603","source":{"id":"https://openalex.org/S4210205812","display_name":"Applied Sciences","issn_l":"2076-3417","issn":["2076-3417"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","oarl","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.46810001134872437}],"study_designs":[],"awards":[{"id":"https://openalex.org/G7302166570","display_name":"\u8111\u5352\u4e2d\u76f8\u5173\u8840\u7ba1\u5e8a\u7ca5\u6837\u786c\u5316\u6591\u5757\u7684\u5feb\u901f\u78c1\u5171\u632f\u6210\u50cf\u53ca\u667a\u80fd\u8bca\u65ad\u7814\u7a76","funder_award_id":"81830056","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285044580.pdf","grobid_xml":"https://content.openalex.org/works/W4285044580.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W2032227103","https://openalex.org/W2034982700","https://openalex.org/W2132083787","https://openalex.org/W2151617487","https://openalex.org/W2274501991","https://openalex.org/W2592939477","https://openalex.org/W2738588019","https://openalex.org/W2808475909","https://openalex.org/W2884561390","https://openalex.org/W2913026733","https://openalex.org/W2939010275","https://openalex.org/W2963917315","https://openalex.org/W2966272446","https://openalex.org/W2967968231","https://openalex.org/W2980794690","https://openalex.org/W2991471181","https://openalex.org/W3004401353","https://openalex.org/W3011362322","https://openalex.org/W3109359544","https://openalex.org/W3127006643","https://openalex.org/W3215538359","https://openalex.org/W4224998640"],"related_works":["https://openalex.org/W4385545903","https://openalex.org/W4410974578","https://openalex.org/W3204447960","https://openalex.org/W4401575164","https://openalex.org/W4281865656","https://openalex.org/W2913026733","https://openalex.org/W4385555014","https://openalex.org/W3165081330","https://openalex.org/W4293147835","https://openalex.org/W2972174153"],"abstract_inverted_index":{"To":[0],"develop":[1],"a":[2,156,168],"precise":[3],"semantic":[4],"segmentation":[5,15,84,107,147,187,196],"method":[6,188],"with":[7,41,119,133,155,162,189],"an":[8,190],"emphasis":[9,191],"on":[10,24,177,192],"the":[11,17,25,34,60,72,76,86,125,139,149,163,178,185,193,202],"edges":[12,194],"for":[13,82],"automated":[14,83],"of":[16,37,46,85,96,148,159,171,205],"arterial":[18,87],"vessel":[19,50,88,140,150],"wall":[20,51,151],"and":[21,58,105,114,124,130,152,173,199],"plaque":[22,38],"based":[23],"convolutional":[26],"neural":[27],"network":[28,68,94,144],"(CNN)":[29],"in":[30,39],"order":[31],"to":[32,55,102,136],"facilitate":[33,201],"quantitative":[35,203],"assessment":[36,204],"patients":[40],"ischemic":[42],"stroke.":[43],"A":[44],"total":[45],"124":[47],"subjects\u2019":[48],"MR":[49],"images":[52],"were":[53],"used":[54],"train,":[56],"validate,":[57],"test":[59,179],"model":[61],"using":[62],"deep":[63],"learning.":[64],"An":[65],"end-to-end":[66],"architecture":[67],"that":[69,166,184],"can":[70],"emphasize":[71],"edge":[73,128],"information,":[74],"namely":[75],"Edge":[77],"Vessel":[78],"Segmentation":[79],"Network":[80],"(EVSegNet)":[81],"wall,":[89],"is":[90,100,131],"proposed.":[91],"The":[92,142,181],"EVSegNet":[93],"consists":[95],"two":[97],"workflows:":[98],"one":[99],"implemented":[101],"achieve":[103],"finely":[104],"multiscale":[106],"by":[108],"combining":[109],"Dense":[110],"Upsampling":[111],"Convolution":[112,117],"(DUC)":[113],"Hybrid":[115],"Dilated":[116],"(HDC)":[118],"different":[120],"dilation":[121],"rates":[122],"modules,":[123],"other":[126,174],"utilizes":[127],"information":[129],"fused":[132],"another":[134],"workflow":[135],"finally":[137],"segment":[138],"wall.":[141],"proposed":[143,186],"demonstrates":[145],"robust":[146],"better":[153],"performance":[154],"Dice":[157,169],"(%)":[158,170],"87.5,":[160],"compared":[161],"traditional":[164],"U-net":[165],"has":[167],"81.0":[172],"U-net-based":[175],"models":[176],"dataset.":[180],"results":[182],"suggest":[183],"improves":[195],"accuracy":[197],"effectively":[198],"will":[200],"atherosclerosis.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2022-07-13T00:00:00"}
