{"id":"https://openalex.org/W3215538359","doi":"https://doi.org/10.1117/12.2611112","title":"Deep-learning-based carotid artery vessel wall segmentation in black-blood MRI using anatomical priors","display_name":"Deep-learning-based carotid artery vessel wall segmentation in black-blood MRI using anatomical priors","publication_year":2022,"publication_date":"2022-03-31","ids":{"openalex":"https://openalex.org/W3215538359","doi":"https://doi.org/10.1117/12.2611112","mag":"3215538359"},"language":"en","primary_location":{"id":"doi:10.1117/12.2611112","is_oa":true,"landing_page_url":"https://doi.org/10.1117/12.2611112","pdf_url":"https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12032/120320Y/Deep-learning-based-carotid-artery-vessel-wall-segmentation-in-black/10.1117/12.2611112.pdf","source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Processing","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12032/120320Y/Deep-learning-based-carotid-artery-vessel-wall-segmentation-in-black/10.1117/12.2611112.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023345684","display_name":"Dieuwertje Alblas","orcid":"https://orcid.org/0000-0002-7754-7405"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Dieuwertje Alblas","raw_affiliation_strings":["Univ. of Twente (Netherlands)","University of Twente ,"],"affiliations":[{"raw_affiliation_string":"Univ. of Twente (Netherlands)","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"University of Twente ,","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078725649","display_name":"Christoph Br\u00fcne","orcid":"https://orcid.org/0000-0003-0145-5069"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Christoph Brune","raw_affiliation_strings":["Univ. Twente (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Univ. Twente (Netherlands)","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028639654","display_name":"Jelmer M. Wolterink","orcid":"https://orcid.org/0000-0001-5505-475X"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Jelmer Wolterink","raw_affiliation_strings":["Univ. Twente (Netherlands)"],"affiliations":[{"raw_affiliation_string":"Univ. Twente (Netherlands)","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023345684"],"corresponding_institution_ids":["https://openalex.org/I94624287"],"apc_list":null,"apc_paid":null,"fwci":2.6357,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.87830363,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"24","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9997000098228455,"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/T10924","display_name":"Cardiovascular Health and Disease Prevention","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular 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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9941999912261963,"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/segmentation","display_name":"Segmentation","score":0.8369337916374207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6823071241378784},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.6502649188041687},{"id":"https://openalex.org/keywords/lumen","display_name":"Lumen (anatomy)","score":0.6247380971908569},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5458285808563232},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4940679371356964},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.477789968252182},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44456642866134644},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.41874077916145325},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39643368124961853},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3815748691558838},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1687702238559723},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.1470981240272522},{"id":"https://openalex.org/keywords/surgery","display_name":"Surgery","score":0.06379371881484985}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8369337916374207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6823071241378784},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.6502649188041687},{"id":"https://openalex.org/C131631996","wikidata":"https://www.wikidata.org/wiki/Q1141325","display_name":"Lumen (anatomy)","level":2,"score":0.6247380971908569},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5458285808563232},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4940679371356964},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.477789968252182},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44456642866134644},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.41874077916145325},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39643368124961853},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3815748691558838},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1687702238559723},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.1470981240272522},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.06379371881484985}],"mesh":[],"locations_count":8,"locations":[{"id":"doi:10.1117/12.2611112","is_oa":true,"landing_page_url":"https://doi.org/10.1117/12.2611112","pdf_url":"https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12032/120320Y/Deep-learning-based-carotid-artery-vessel-wall-segmentation-in-black/10.1117/12.2611112.pdf","source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2112.01137","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2112.01137","pdf_url":"https://arxiv.org/pdf/2112.01137","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/871350e7-7840-40de-bd17-8dbc651c393c","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/871350e7-7840-40de-bd17-8dbc651c393c","pdf_url":"https://ris.utwente.nl/ws/files/283040086/120320Y_alblas_brune_wolterink.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alblas, D, Brune, C & Wolterink, J M 2022, Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors. in O Colliot, I Isgum, B A Landman & M H Loew (eds), Medical Imaging 2022 : Image Processing. vol. 12032, 120320Y, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12032, SPIE, Medical Imaging 2022: Physics of Medical Imaging, Virtual, Online, 21/03/22. https://doi.org/10.1117/12.2611112","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"mag:3215538359","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2112.01137v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:ris.utwente.nl:openaire/c2c920f2-99bd-47a7-bfb7-790b4c63cd19","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2112.01137","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alblas, D, Brune, C & Wolterink, J M 2021 'Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors' ArXiv.org. < https://arxiv.org/abs/2112.01137 >","raw_type":"info:eu-repo/semantics/preprint"},{"id":"pmh:oai:ris.utwente.nl:publications/c2c920f2-99bd-47a7-bfb7-790b4c63cd19","is_oa":false,"landing_page_url":"https://arxiv-org.ezproxy2.utwente.nl/abs/2112.01137","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alblas , D , Brune , C &amp; Wolterink , J M 2021 ' Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors ' ArXiv.org . &lt; https://arxiv-org.ezproxy2.utwente.nl/abs/2112.01137 &gt;","raw_type":"workingPaper"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/c2c920f2-99bd-47a7-bfb7-790b4c63cd19","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/c2c920f2-99bd-47a7-bfb7-790b4c63cd19","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alblas , D , Brune , C &amp; Wolterink , J M 2021 ' Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors ' ArXiv.org . &lt; https://arxiv-org.ezproxy2.utwente.nl/abs/2112.01137 &gt;","raw_type":"workingPaper"},{"id":"doi:10.48550/arxiv.2112.01137","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2112.01137","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1117/12.2611112","is_oa":true,"landing_page_url":"https://doi.org/10.1117/12.2611112","pdf_url":"https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12032/120320Y/Deep-learning-based-carotid-artery-vessel-wall-segmentation-in-black/10.1117/12.2611112.pdf","source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8530603449","display_name":null,"funder_award_id":"18192","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3215538359.pdf","grobid_xml":"https://content.openalex.org/works/W3215538359.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W200744690","https://openalex.org/W1832101600","https://openalex.org/W1985932775","https://openalex.org/W2061896040","https://openalex.org/W2072999028","https://openalex.org/W2113755685","https://openalex.org/W2464708700","https://openalex.org/W2560585316","https://openalex.org/W2568368793","https://openalex.org/W2963840672","https://openalex.org/W2966272446","https://openalex.org/W2990368613","https://openalex.org/W3109359544","https://openalex.org/W3158629041","https://openalex.org/W3171835350","https://openalex.org/W4243801081","https://openalex.org/W4250685322","https://openalex.org/W6608208705","https://openalex.org/W6677056540","https://openalex.org/W6696085341"],"related_works":[],"abstract_inverted_index":{"Carotid":[0],"artery":[1,87,148],"vessel":[2,23,71,106,120,169,216],"wall":[3,72,170,217],"thickness":[4],"measurement":[5],"is":[6,205],"an":[7,29,114],"essential":[8],"step":[9],"in":[10,35,66,79,88,155],"the":[11,22,26,105,143,168,191,214],"monitoring":[12],"of":[13,21,142,165,175,213],"patients":[14],"with":[15,218],"atherosclerosis.":[16],"This":[17],"requires":[18],"accurate":[19],"segmentation":[20,49,73,134,141,198],"wall,":[24,34,149,185],"i.e.,":[25,159],"region":[27],"between":[28],"artery\u2019s":[30],"lumen":[31,182],"and":[32,145,150,171,178,183],"outer":[33,184],"black-blood":[36],"magnetic":[37],"resonance":[38],"(MR)":[39],"images.":[40],"Commonly":[41],"used":[42],"convolutional":[43],"neural":[44],"networks":[45],"(CNNs)":[46],"for":[47,52,167,181],"semantic":[48,197],"are":[50],"suboptimal":[51],"this":[53,67,111],"task":[54],"as":[55,74],"their":[56],"use":[57],"does":[58],"not":[59],"guarantee":[60],"a":[61,75,80,125,156,160,195],"contiguous":[62],"ring-shaped":[63,119],"segmentation.":[64],"Instead,":[65],"work,":[68],"we":[69,93,123,188],"cast":[70],"multi-task":[76],"regression":[77],"problem":[78,112],"polar":[81],"coordinate":[82],"system.":[83],"For":[84],"each":[85,89],"carotid":[86,147,215],"axial":[90],"image":[91],"slice,":[92],"aim":[94],"to":[95,110,140,207],"simultaneously":[96],"find":[97],"two":[98],"non-intersecting":[99],"nested":[100],"contours":[101],"that":[102,117,131,203],"together":[103],"delineate":[104],"wall.":[107],"CNNs":[108],"applied":[109],"enable":[113],"inductive":[115],"bias":[116],"guarantees":[118],"walls.":[121],"Moreover,":[122,187],"identify":[124],"problem-specific":[126],"training":[127],"data":[128],"augmentation":[129],"technique":[130],"substantially":[132],"affects":[133],"performance.":[135],"We":[136],"apply":[137],"our":[138],"method":[139,192],"internal":[144],"external":[146],"achieve":[151],"top-ranking":[152],"quantitative":[153],"results":[154,201],"public":[157],"challenge,":[158],"median":[161,172],"Dice":[162],"similarity":[163],"coefficient":[164],"0.813":[166],"Hausdorff":[173],"distances":[174],"0.552":[176],"mm":[177,180],"0.776":[179],"respectively.":[186],"show":[189,202],"how":[190],"improves":[193],"over":[194],"conventional":[196],"approach.":[199],"These":[200],"it":[204],"feasible":[206],"automatically":[208],"obtain":[209],"anatomically":[210],"plausible":[211],"segmentations":[212],"high":[219],"accuracy.":[220]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2022-10-07T00:00:00"}