{"id":"https://openalex.org/W2006337443","doi":"https://doi.org/10.1117/12.877233","title":"Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes","display_name":"Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes","publication_year":2011,"publication_date":"2011-03-03","ids":{"openalex":"https://openalex.org/W2006337443","doi":"https://doi.org/10.1117/12.877233","mag":"2006337443"},"language":"en","primary_location":{"id":"doi:10.1117/12.877233","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.877233","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5051649145","display_name":"Yefeng Zheng","orcid":"https://orcid.org/0000-0003-2195-2847"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yefeng Zheng","raw_affiliation_strings":["Siemens Corp. Research (United States)"],"affiliations":[{"raw_affiliation_string":"Siemens Corp. Research (United States)","institution_ids":["https://openalex.org/I4210137693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029164303","display_name":"Maciej Loziczonek","orcid":null},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maciej Loziczonek","raw_affiliation_strings":["Siemens Corp. Research (United States)"],"affiliations":[{"raw_affiliation_string":"Siemens Corp. Research (United States)","institution_ids":["https://openalex.org/I4210137693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011888340","display_name":"Bogdan Georgescu","orcid":"https://orcid.org/0000-0001-5388-5699"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bogdan Georgescu","raw_affiliation_strings":["Siemens Corp. Research (United States)"],"affiliations":[{"raw_affiliation_string":"Siemens Corp. Research (United States)","institution_ids":["https://openalex.org/I4210137693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028465673","display_name":"S. Kevin Zhou","orcid":"https://orcid.org/0000-0002-6881-4444"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. Kevin Zhou","raw_affiliation_strings":["Siemens Corp. Research (United States)"],"affiliations":[{"raw_affiliation_string":"Siemens Corp. Research (United States)","institution_ids":["https://openalex.org/I4210137693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031536207","display_name":"Fernando Vega-Higuera","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fernando Vega-Higuera","raw_affiliation_strings":["Siemens Medical Solutions GmbH (Germany)"],"affiliations":[{"raw_affiliation_string":"Siemens Medical Solutions GmbH (Germany)","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012751147","display_name":"Dorin Comaniciu","orcid":"https://orcid.org/0000-0002-5238-8647"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dorin Comaniciu","raw_affiliation_strings":["Siemens Corp. Research (United States)"],"affiliations":[{"raw_affiliation_string":"Siemens Corp. Research (United States)","institution_ids":["https://openalex.org/I4210137693"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5051649145"],"corresponding_institution_ids":["https://openalex.org/I4210137693"],"apc_list":null,"apc_paid":null,"fwci":3.05,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.90149888,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"7962","issue":null,"first_page":"79621K","last_page":"79621K"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9997000098228455,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9997000098228455,"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/T10193","display_name":"Coronary Interventions and Diagnostics","score":0.9979000091552734,"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/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7896363735198975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7807402014732361},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6818258762359619},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6194092035293579},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5261597037315369},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4810364544391632},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4667746126651764},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4117133915424347}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7896363735198975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7807402014732361},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6818258762359619},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6194092035293579},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5261597037315369},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4810364544391632},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4667746126651764},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4117133915424347}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.877233","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.877233","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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":10,"referenced_works":["https://openalex.org/W1568747707","https://openalex.org/W1767951414","https://openalex.org/W2005476680","https://openalex.org/W2035509428","https://openalex.org/W2096320880","https://openalex.org/W2101689475","https://openalex.org/W2129534965","https://openalex.org/W2146514558","https://openalex.org/W2166302161","https://openalex.org/W2470525848"],"related_works":["https://openalex.org/W2517104666","https://openalex.org/W2005437358","https://openalex.org/W1669643531","https://openalex.org/W2008656436","https://openalex.org/W2134924024","https://openalex.org/W2023558673","https://openalex.org/W2110230079","https://openalex.org/W1982826852","https://openalex.org/W2613186388","https://openalex.org/W1967061043"],"abstract_inverted_index":{"Automatic":[0],"coronary":[1,11,26,197],"centerline":[2,27],"extraction":[3,28],"and":[4,33,125,150,260],"lumen":[5,198],"segmentation":[6],"facilitate":[7],"the":[8,50,56,63,69,77,88,106,148,168,171,175,179,187,196,203,212,221,225,242,247,253],"diagnosis":[9],"of":[10,20,35,55,90,123,170,181,279],"artery":[12,149],"disease":[13],"(CAD),":[14],"which":[15,140,215],"is":[16,61,82,102,133,216,237],"a":[17,97,121,138,142,151,164,182,229,272,276],"leading":[18],"cause":[19],"death":[21],"in":[22,62,87,112,167,228,257],"developed":[23],"countries.":[24],"Various":[25],"methods":[29],"have":[30],"been":[31],"proposed":[32,103,248],"most":[34],"them":[36],"are":[37],"based":[38,59,100,250],"on":[39,49],"shortest":[40,57,172],"path":[41,58,70],"computation":[42,169],"given":[43],"one":[44],"or":[45],"two":[46],"end":[47],"points":[48],"artery.":[51],"The":[52,128,157],"major":[53],"variation":[54],"approaches":[60],"different":[64],"vesselness":[65,101,165,251,256],"measurements":[66],"used":[67,79,135,194,238],"for":[68,195,209],"cost.":[71],"An":[72,232],"empirically":[73],"designed":[74],"measurement":[75,166],"(e.g.,":[76],"widely":[78],"Hessian":[80,255],"vesselness)":[81],"by":[83,104,218],"no":[84],"means":[85],"optimal":[86],"use":[89],"image":[91,126],"context":[92],"information.":[93],"In":[94],"this":[95],"paper,":[96],"machine":[98],"learning":[99,249],"exploiting":[105],"rich":[107],"domain":[108],"specific":[109],"knowledge":[110],"embedded":[111],"an":[113],"expert-annotated":[114],"dataset.":[115],"For":[116],"each":[117],"voxel,":[118],"we":[119,205],"extract":[120],"set":[122],"geometric":[124],"features.":[127],"probabilistic":[129],"boosting":[130],"tree":[131],"(PBT)":[132],"then":[134],"to":[136,145,154,184,239,270],"train":[137],"classifier,":[139],"assigns":[141],"high":[143],"score":[144,153,159,177],"voxels":[146,210],"inside":[147,186],"low":[152],"those":[155],"outside.":[156],"detection":[158,176],"can":[160,191],"be":[161,185,193],"treated":[162],"as":[163],"path.":[173],"Since":[174],"measures":[178],"probability":[180],"voxel":[183],"vessel":[188],"lumen,":[189],"it":[190,264],"also":[192],"segmentation.":[199],"To":[200],"speed":[201,259],"up":[202],"computation,":[204],"perform":[206],"classification":[207,235],"only":[208,265],"around":[211],"heart":[213,223],"surface,":[214],"achieved":[217],"automatically":[219],"segmenting":[220],"whole":[222],"from":[224],"3D":[226],"volume":[227,274],"preprocessing":[230],"step.":[231],"efficient":[233],"voxel-wise":[234],"strategy":[236],"further":[240],"improve":[241],"speed.":[243],"Experiments":[244],"demonstrate":[245],"that":[246],"outperforms":[252],"conventional":[254],"both":[258],"accuracy.":[261],"On":[262],"average,":[263],"takes":[266],"approximately":[267],"2.3":[268],"seconds":[269],"process":[271],"large":[273],"with":[275],"typical":[277],"size":[278],"512x512x200":[280],"voxels.":[281]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
