{"id":"https://openalex.org/W2791616837","doi":"https://doi.org/10.1117/12.2293561","title":"Vessel layer separation in x-ray angiograms with fully convolutional network","display_name":"Vessel layer separation in x-ray angiograms with fully convolutional network","publication_year":2018,"publication_date":"2018-03-13","ids":{"openalex":"https://openalex.org/W2791616837","doi":"https://doi.org/10.1117/12.2293561","mag":"2791616837"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293561","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling","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/A5031942141","display_name":"Hua Ma","orcid":"https://orcid.org/0000-0002-8717-6847"},"institutions":[{"id":"https://openalex.org/I2801952686","display_name":"Erasmus MC","ror":"https://ror.org/018906e22","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I2801952686"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Hua Ma","raw_affiliation_strings":["Erasmus MC (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Erasmus MC (Netherlands)","institution_ids":["https://openalex.org/I2801952686"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089366425","display_name":"Haidong Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Haidong Hao","raw_affiliation_strings":["Technische Univ. Delft (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Univ. Delft (Netherlands)","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012856150","display_name":"Theo van Walsum","orcid":"https://orcid.org/0000-0001-8257-7759"},"institutions":[{"id":"https://openalex.org/I2801952686","display_name":"Erasmus MC","ror":"https://ror.org/018906e22","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I2801952686"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Theo van Walsum","raw_affiliation_strings":["Erasmus MC (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Erasmus MC (Netherlands)","institution_ids":["https://openalex.org/I2801952686"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3803,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.70040994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9955999851226807,"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":0.9955999851226807,"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/T10193","display_name":"Coronary Interventions and Diagnostics","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9914000034332275,"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":0.6767886877059937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.667023777961731},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5888381004333496},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.48960310220718384},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4788258373737335},{"id":"https://openalex.org/keywords/angiography","display_name":"Angiography","score":0.4720771312713623},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.46718695759773254},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4592966139316559},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.44519221782684326},{"id":"https://openalex.org/keywords/biomedical-engineering","display_name":"Biomedical engineering","score":0.33151060342788696},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3308221995830536},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.31295329332351685},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.30455851554870605},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21240395307540894},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15869176387786865}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6767886877059937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.667023777961731},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5888381004333496},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.48960310220718384},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4788258373737335},{"id":"https://openalex.org/C2780643987","wikidata":"https://www.wikidata.org/wiki/Q468414","display_name":"Angiography","level":2,"score":0.4720771312713623},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.46718695759773254},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4592966139316559},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.44519221782684326},{"id":"https://openalex.org/C136229726","wikidata":"https://www.wikidata.org/wiki/Q327092","display_name":"Biomedical engineering","level":1,"score":0.33151060342788696},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3308221995830536},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.31295329332351685},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.30455851554870605},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21240395307540894},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15869176387786865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293561","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W240701929","https://openalex.org/W276879312","https://openalex.org/W1496308191","https://openalex.org/W1518898045","https://openalex.org/W1522301498","https://openalex.org/W1745334888","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2002664886","https://openalex.org/W2102176695","https://openalex.org/W2133515615","https://openalex.org/W2133665775","https://openalex.org/W2154589756","https://openalex.org/W2154992274","https://openalex.org/W2395611524","https://openalex.org/W2610848865","https://openalex.org/W2963736634","https://openalex.org/W3144372880","https://openalex.org/W4231368895","https://openalex.org/W6629585294","https://openalex.org/W6630858912","https://openalex.org/W6631190155","https://openalex.org/W6637573526","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6680009749","https://openalex.org/W6682766550","https://openalex.org/W6741846745"],"related_works":["https://openalex.org/W3113091479","https://openalex.org/W2162899405","https://openalex.org/W941090075","https://openalex.org/W2044987316","https://openalex.org/W3134374554","https://openalex.org/W2237480245","https://openalex.org/W2075065631","https://openalex.org/W2519167559","https://openalex.org/W4311248832","https://openalex.org/W4386113923"],"abstract_inverted_index":{"Percutaneous":[0],"coronary":[1,9,37],"intervention":[2],"is":[3,21,144,155],"a":[4,17,55,60,158,168,181,228],"minimally-invasive":[5],"procedure":[6],"to":[7,27,40,94,145,173,188,211],"treat":[8],"artery":[10],"disease.":[11],"In":[12],"such":[13],"procedures,":[14],"X-ray":[15,45,79,90,119,152],"angiography,":[16],"real-time":[18,230],"imaging":[19],"technique,":[20],"commonly":[22],"used":[23],"for":[24,89,115],"image":[25,58,100],"guidance":[26],"identify":[28],"lesion":[29],"sites":[30],"and":[31,34,166],"navigate":[32],"catheters":[33],"guide-wires":[35],"within":[36],"arteries.":[38],"Due":[39],"the":[41,175,190,194,212],"physical":[42],"nature":[43],"of":[44,59,78,141,198],"imaging,":[46],"photon":[47],"energy":[48],"undergoes":[49],"absorption":[50],"when":[51],"penetrating":[52],"tissues,":[53],"rendering":[54],"2D":[56],"projection":[57],"3D":[61],"scene,":[62],"in":[63,118,138],"which":[64,134,225],"semi-transparent":[65],"structures":[66,73,97,130],"overlap":[67],"with":[68],"each":[69],"other.":[70],"The":[71,196],"overlapping":[72],"make":[74],"robust":[75],"information":[76],"processing":[77],"images":[80,91,154],"challenging.":[81],"To":[82,161],"tackle":[83],"this":[84,142,162],"issue,":[85],"layer":[86,122,149,192,204],"separation":[87,123,150,205],"techniques":[88,110],"were":[92],"proposed":[93,216],"separate":[95,189],"those":[96],"into":[98],"different":[99],"layers":[101],"based":[102,171],"on":[103,206],"structure":[104],"appearance":[105],"or":[106,131],"motion":[107],"pattern.":[108],"These":[109],"have":[111],"been":[112],"proven":[113],"effective":[114],"vessel":[116,148,176,191,203],"enhancement":[117],"angiograms.":[120],"However,":[121],"approaches":[124],"still":[125],"suffer":[126],"either":[127],"from":[128,151,193],"spurious":[129],"non-real-time":[132],"processing,":[133],"prevent":[135],"their":[136],"application":[137],"clinics.":[139],"Purpose":[140],"work":[143],"investigate":[146],"whether":[147],"angiography":[153],"possible":[156],"via":[157],"data-driven":[159],"strategy.":[160],"end,":[163],"we":[164],"develop":[165],"evaluate":[167],"deep":[169],"learning":[170],"method":[172,217],"extract":[174],"layer.":[177],"More":[178],"specifically,":[179],"U-Net,":[180],"fully":[182],"convolutional":[183],"network":[184],"architecture,":[185],"was":[186],"trained":[187],"background.":[195],"results":[197],"our":[199,215],"experiments":[200],"show":[201],"good":[202],"42":[207],"clinical":[208,231],"sequences.":[209],"Compared":[210],"previous":[213],"state-of-the-art,":[214],"has":[218],"similar":[219],"performance":[220],"but":[221],"runs":[222],"much":[223],"faster,":[224],"makes":[226],"it":[227],"potential":[229],"application.":[232]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}