{"id":"https://openalex.org/W2791246534","doi":"https://doi.org/10.1117/12.2292974","title":"Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network","display_name":"Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network","publication_year":2018,"publication_date":"2018-02-27","ids":{"openalex":"https://openalex.org/W2791246534","doi":"https://doi.org/10.1117/12.2292974","mag":"2791246534"},"language":"en","primary_location":{"id":"doi:10.1117/12.2292974","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2292974","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Computer-Aided Diagnosis","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/A5021150871","display_name":"Midas Meijs","orcid":"https://orcid.org/0000-0002-3014-8749"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Midas Meijs","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030592531","display_name":"Rashindra Manniesing","orcid":"https://orcid.org/0000-0002-4616-6298"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Rashindra Manniesing","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145872427"],"apc_list":null,"apc_paid":null,"fwci":12.4595,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.98106061,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"7","issue":null,"first_page":"61","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10227","display_name":"Acute Ischemic Stroke Management","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T10227","display_name":"Acute Ischemic Stroke Management","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T11402","display_name":"Vascular Malformations Diagnosis and Treatment","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9944999814033508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8444302082061768},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7524540424346924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6234742403030396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5850409269332886},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5238288044929504},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.523209810256958},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.46404048800468445},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44171959161758423},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3684496283531189},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3411262035369873},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.3307225704193115},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.32128551602363586}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8444302082061768},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7524540424346924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6234742403030396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5850409269332886},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5238288044929504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.523209810256958},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.46404048800468445},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44171959161758423},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3684496283531189},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3411262035369873},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.3307225704193115},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.32128551602363586}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2292974","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2292974","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7400000095367432,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1165382972","https://openalex.org/W1522301498","https://openalex.org/W1915761309","https://openalex.org/W1933266403","https://openalex.org/W1987869189","https://openalex.org/W2014277578","https://openalex.org/W2384495648","https://openalex.org/W2464708700","https://openalex.org/W2532750509","https://openalex.org/W2562403606","https://openalex.org/W2593626777","https://openalex.org/W2767270216","https://openalex.org/W6627641123","https://openalex.org/W6631190155","https://openalex.org/W6734709499"],"related_works":["https://openalex.org/W3197954266","https://openalex.org/W4389009345","https://openalex.org/W3047746737","https://openalex.org/W3021454079","https://openalex.org/W1522196789","https://openalex.org/W4287691568","https://openalex.org/W4220718606","https://openalex.org/W2085143385","https://openalex.org/W2005489729","https://openalex.org/W3090294949"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,6,19,47,49,60,100,113,132,161],"the":[2,7,17,42,45,50,57,61,143],"arteries":[3,162],"and":[4,15,41,67,71,104,118,125,128,155,158,163],"veins":[5,164],"cerebral":[8],"vasculature":[9],"is":[10],"important":[11],"for":[12,16,123],"improved":[13],"visualization":[14],"detection":[18],"vascular":[20],"related":[21],"pathologies":[22],"including":[23],"arteriovenous":[24],"malformations.":[25],"We":[26],"propose":[27],"a":[28,36,95,108],"3D":[29],"fully":[30],"convolutional":[31],"neural":[32],"network":[33],"(CNN)":[34],"using":[35],"time-to-signal":[37],"image":[38],"as":[39,52],"input":[40],"distance":[43],"to":[44],"center":[46],"gravity":[48],"brain":[51],"spatial":[53],"feature":[54],"integrated":[55],"in":[56,165],"final":[58],"layers":[59],"CNN.":[62],"The":[63,80,136],"method":[64,151],"was":[65,83,146],"trained":[66],"validated":[68],"on":[69,73,142],"6":[70],"tested":[72],"4":[74],"4D":[75,167],"CT":[76,168],"patient":[77],"imaging":[78,169],"data.":[79,170],"reference":[81],"standard":[82],"acquired":[84],"by":[85,88],"manual":[86],"annotations":[87],"an":[89,129],"experienced":[90],"observer.":[91],"Quantitative":[92],"evaluation":[93],"showed":[94],"mean":[96,109],"Dice":[97],"similarity":[98],"coefficient":[99],"0.94":[101],"±":[102,106,115,120,134],"0.03":[103],"0.97":[105],"0.01,":[107],"absolute":[110],"volume":[111,141],"difference":[112],"4.36":[114],"5.47":[116],"%":[117,122],"1.79":[119],"2.26":[121],"artery":[124],"vein":[126],"respectively":[127],"overall":[130],"accuracy":[131],"0.96":[133],"0.02.":[135],"average":[137],"calculation":[138],"time":[139],"per":[140],"test":[144],"set":[145],"approximately":[147],"one":[148],"minute.":[149],"Our":[150],"shows":[152],"promising":[153],"results":[154],"enables":[156],"fast":[157],"accurate":[159],"segmentation":[160],"full":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}