{"id":"https://openalex.org/W2792758510","doi":"https://doi.org/10.1117/12.2293748","title":"Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT","display_name":"Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT","publication_year":2018,"publication_date":"2018-03-02","ids":{"openalex":"https://openalex.org/W2792758510","doi":"https://doi.org/10.1117/12.2293748","mag":"2792758510"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293748","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293748","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 Processing","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/A5006025483","display_name":"Zahra Sedghi Gamechi","orcid":"https://orcid.org/0000-0001-9185-0091"},"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":"Zahra Sedghi Gamechi","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/A5023677285","display_name":"Marleen de Bruijne","orcid":"https://orcid.org/0000-0002-6328-902X"},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Marleen de Bruijne","raw_affiliation_strings":["Erasmus MC (Denmark)","Univ. of Copenhagen (Denmark)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Erasmus MC (Denmark)","institution_ids":[]},{"raw_affiliation_string":"Univ. of Copenhagen (Denmark)","institution_ids":["https://openalex.org/I124055696"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062809184","display_name":"Andres Arias","orcid":"https://orcid.org/0000-0003-0239-1060"},"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":"Andres M. Arias-Lorza","raw_affiliation_strings":["Erasmus MC (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Erasmus MC (Netherlands)","institution_ids":["https://openalex.org/I2801952686"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087466692","display_name":"Jesper Holst Pedersen","orcid":"https://orcid.org/0000-0002-0862-7209"},"institutions":[{"id":"https://openalex.org/I2802567020","display_name":"Copenhagen University Hospital","ror":"https://ror.org/05bpbnx46","country_code":"DK","type":"healthcare","lineage":["https://openalex.org/I2802567020"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Jesper Holst Pedersen","raw_affiliation_strings":["Copenhagen Univ. Hospital (Denmark)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Copenhagen Univ. Hospital (Denmark)","institution_ids":["https://openalex.org/I2802567020"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5575,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.63899066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"84","last_page":"84"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9968000054359436,"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"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9905999898910522,"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"}},{"id":"https://openalex.org/T10983","display_name":"Aortic aneurysm repair treatments","score":0.989300012588501,"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/segmentation","display_name":"Segmentation","score":0.6677964329719543},{"id":"https://openalex.org/keywords/aorta","display_name":"Aorta","score":0.5811198949813843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5085471868515015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4767889976501465},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4421384632587433},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4324125647544861},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3851185441017151},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3762643039226532},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.33140814304351807},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3254016041755676},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.15248042345046997}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6677964329719543},{"id":"https://openalex.org/C2779980429","wikidata":"https://www.wikidata.org/wiki/Q101004","display_name":"Aorta","level":2,"score":0.5811198949813843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5085471868515015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4767889976501465},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4421384632587433},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4324125647544861},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3851185441017151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3762643039226532},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.33140814304351807},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3254016041755676},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.15248042345046997}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1117/12.2293748","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293748","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 Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/c987a263-7842-4f94-94b3-7f2564f3bed1","is_oa":false,"landing_page_url":"https://researchprofiles.ku.dk/da/publications/c987a263-7842-4f94-94b3-7f2564f3bed1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401983","display_name":"Research at the University of Copenhagen (University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I124055696","host_organization_name":"University of Copenhagen","host_organization_lineage":["https://openalex.org/I124055696"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sedghi Gamechi , Z , Arias-Lorza , A M , Pedersen , J H & De Bruijne , M 2018 , Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT . in Medical Imaging 2018 : Image Processing . , 105742D , SPIE - International Society for Optical Engineering , Proceedings of SPIE International Symposium on Medical Imaging , vol. 10574 , SPIE Medical Imaging 2018 , Houston , United States , 10/02/2018 . https://doi.org/10.1117/12.2293748","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.eur.nl:publications/794f9c39-01c8-4d25-b642-0d7237cb2702","is_oa":false,"landing_page_url":"https://pure.eur.nl/en/publications/794f9c39-01c8-4d25-b642-0d7237cb2702","pdf_url":null,"source":{"id":"https://openalex.org/S4306401266","display_name":"EUR Research Repository (Erasmus University Rotterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I913958620","host_organization_name":"Erasmus University Rotterdam","host_organization_lineage":["https://openalex.org/I913958620"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sedghi Gamechi, Z, Arias Lorza, AM, Pedersen, JH & de Bruijne, M 2018, Aorta and Pulmonary Artery Segmentation Using Optimal Surface Graph Cuts in Non-contrast CT. in MEDICAL IMAGING 2018: IMAGE PROCESSING ; Proceedings of SPIE (vol. 10574). vol. 10574, Spie-Int Soc Optical Engineering, Bellingham, Proceedings of SPIE. < http://hdl.handle.net/1765/106603 >","raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1893937985","https://openalex.org/W1899587360","https://openalex.org/W1998378411","https://openalex.org/W2010174532","https://openalex.org/W2013121582","https://openalex.org/W2028522051","https://openalex.org/W2051775772","https://openalex.org/W2052971463","https://openalex.org/W2076232498","https://openalex.org/W2080448710","https://openalex.org/W2084493692","https://openalex.org/W2087723260","https://openalex.org/W2120271798","https://openalex.org/W2154528779","https://openalex.org/W2159740825","https://openalex.org/W2162140684","https://openalex.org/W2292574432","https://openalex.org/W2608654208","https://openalex.org/W6653031141","https://openalex.org/W6669682098"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W4385583601","https://openalex.org/W4395685956","https://openalex.org/W3159516372","https://openalex.org/W4398146871","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Accurate":[0],"measurements":[1],"of":[2,7,30,168,191],"the":[3,8,41,55,60,70,81,110,124,171,195],"size":[4],"and":[5,10,21,43,54,97,119,129,173,197],"shape":[6],"aorta":[9,42,172,196],"pulmonary":[11,44,176,200],"arteries":[12,45],"are":[13,116],"important":[14],"as":[15,132],"risk":[16],"factors":[17],"for":[18,22,39,134,170,175,194,199],"cardiovascular":[19],"diseases,":[20],"Chronicle":[23],"Obstacle":[24],"Pulmonary":[25],"Disease":[26],"(COPD).1":[27],"The":[28,87,103,113,138],"aim":[29],"this":[31],"paper":[32],"is":[33,90,106,140],"to":[34,123],"propose":[35],"an":[36],"automated":[37],"method":[38],"segmenting":[40],"in":[46,63,189],"low-dose":[47,144],"non-ECGgated":[48],"non-contrast":[49,146],"CT":[50,147],"scans.":[51],"Low":[52],"contrast":[53],"high":[56],"noise":[57],"level":[58],"make":[59],"automatic":[61],"segmentation":[62,188],"such":[64],"images":[65],"a":[66,74,93,98,135,150,165],"challenging":[67],"task.":[68],"In":[69],"proposed":[71],"method,":[72],"first,":[73],"minimum":[75],"cost":[76,88],"path":[77],"tracking":[78],"algorithm":[79,139],"traces":[80],"centerline":[82],"between":[83],"user-defined":[84],"seed":[85],"points.":[86],"function":[89],"based":[91],"on":[92,142,183],"multi-directional":[94],"medialness":[95,111],"filter":[96],"lumen":[99],"intensity":[100],"similarity":[101],"metric.":[102],"vessel":[104,127],"radius":[105,128],"also":[107],"estimated":[108],"from":[109,149],"filter.":[112],"extracted":[114,125],"centerlines":[115],"then":[117],"smoothed":[118],"dilated":[120],"non-uniformly":[121],"according":[122],"local":[126],"subsequently":[130],"used":[131],"initialization":[133],"graph-cut":[136],"segmentation.":[137],"evaluated":[141],"225":[143],"non-ECG-gated":[145],"scans":[148,158,185],"lung":[151],"cancer":[152],"screening":[153],"trial.":[154],"Quantitatively":[155],"analyzing":[156],"25":[157],"with":[159],"full":[160],"manual":[161],"annotations,":[162],"we":[163],"obtain":[164],"dice":[166],"overlap":[167],"0.94&plusmn;0.01":[169],"0.92&plusmn;0.01":[174],"arteries.":[177,201],"Qualitative":[178],"validation":[179],"by":[180],"visual":[181],"inspection":[182],"200":[184],"shows":[186],"successful":[187],"93%":[190],"all":[192],"cases":[193],"94%":[198]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}