{"id":"https://openalex.org/W4416250580","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229415","title":"A Real-time Vein Puncture Segmentation Framework Based on YOLO and DEVA","display_name":"A Real-time Vein Puncture Segmentation Framework Based on YOLO and DEVA","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250580","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229415"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5045132819","display_name":"Qiubo Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiubo Huang","raw_affiliation_strings":["School of Computer Science and Technology Donghua University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Donghua University,Shanghai,China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011623954","display_name":"Weijie Zhao","orcid":"https://orcid.org/0000-0002-8370-8308"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wupeng Zhao","raw_affiliation_strings":["School of Computer Science and Technology Donghua University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Donghua University,Shanghai,China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033854373","display_name":"Shiwen Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiwen Tang","raw_affiliation_strings":["School of Computer Science and Technology Donghua University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Donghua University,Shanghai,China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065168194","display_name":"Dehua Chen","orcid":"https://orcid.org/0000-0003-3978-4394"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dehua Chen","raw_affiliation_strings":["School of Computer Science and Technology Donghua University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Donghua University,Shanghai,China","institution_ids":["https://openalex.org/I181326427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5045132819"],"corresponding_institution_ids":["https://openalex.org/I181326427"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53051702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10641","display_name":"Central Venous Catheters and Hemodialysis","score":0.4860000014305115,"subfield":{"id":"https://openalex.org/subfields/3604","display_name":"Emergency Medical Services"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10641","display_name":"Central Venous Catheters and Hemodialysis","score":0.4860000014305115,"subfield":{"id":"https://openalex.org/subfields/3604","display_name":"Emergency Medical Services"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11670","display_name":"Pressure Ulcer Prevention and Management","score":0.07010000199079514,"subfield":{"id":"https://openalex.org/subfields/3609","display_name":"Occupational Therapy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10868","display_name":"Soft Robotics and Applications","score":0.054099999368190765,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8125},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5720999836921692},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5526000261306763},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5392000079154968},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5013999938964844},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44920000433921814},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.40849998593330383}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.798799991607666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6617000102996826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6309999823570251},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5720999836921692},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5526000261306763},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5013999938964844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.40849998593330383},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.29919999837875366},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.27720001339912415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2237765446","https://openalex.org/W2412782625","https://openalex.org/W2739491435","https://openalex.org/W2920942303","https://openalex.org/W2963150697","https://openalex.org/W2999219213","https://openalex.org/W3035549667","https://openalex.org/W3119780697","https://openalex.org/W3171516518","https://openalex.org/W4289752563","https://openalex.org/W4312396403","https://openalex.org/W4312598093","https://openalex.org/W4312815172","https://openalex.org/W4312868348","https://openalex.org/W4390874575","https://openalex.org/W4390874670","https://openalex.org/W4391450659","https://openalex.org/W4392281787"],"related_works":[],"abstract_inverted_index":{"Vein":[0],"puncture":[1,21,29,55,65],"is":[2],"a":[3,28,79],"common":[4],"procedure":[5],"in":[6,93],"clinical":[7],"practice,":[8],"and":[9,13,23,43,62,67,106,125],"its":[10],"success":[11],"rate":[12],"patient":[14],"experience":[15],"are":[16],"significantly":[17],"influenced":[18],"by":[19,57,155],"the":[20,35,54,59,64,71,90,94,110],"angle":[22,66],"speed.":[24,68],"This":[25],"study":[26],"proposes":[27],"operation":[30],"detection":[31],"method":[32],"based":[33],"on":[34],"combination":[36],"of":[37,53,73],"You":[38],"Only":[39],"Look":[40],"Once":[41],"(YOLO)":[42],"DEcoupled":[44],"Video":[45],"segmentAtion":[46],"(DEVA),":[47],"which":[48],"enables":[49],"real-time":[50],"quantitative":[51],"evaluation":[52],"process":[56],"detecting":[58],"needle":[60],"shaft":[61],"calculating":[63],"To":[69,97],"enhance":[70,116],"accuracy":[72,124],"context":[74],"information":[75],"transfer,":[76],"we":[77,108,130],"introduce":[78,131],"feature":[80,91],"enhancement":[81],"mechanism":[82,113],"that":[83,142],"leverages":[84],"YOLO\u2019s":[85],"multi-scale":[86],"features":[87],"to":[88,114,120],"augment":[89],"representation":[92],"memory":[95],"module.":[96,135],"tackle":[98],"challenges":[99],"such":[100],"as":[101],"speckle":[102],"noise,":[103],"blurred":[104],"segmentation,":[105],"omissions,":[107],"improve":[109,122],"in-clip":[111],"consensus":[112],"further":[115,121],"temporal":[117],"consistency.":[118],"Additionally,":[119],"segmentation":[123,148],"better":[126],"capture":[127],"critical":[128],"features,":[129],"an":[132],"enhanced":[133],"attention":[134],"Experimental":[136],"results":[137],"across":[138],"multiple":[139],"datasets":[140],"demonstrate":[141],"our":[143],"approach":[144],"not":[145],"only":[146],"maintains":[147],"performance":[149],"but":[150],"also":[151],"increases":[152],"processing":[153],"speed":[154],"nearly":[156],"threefold.":[157]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}