{"id":"https://openalex.org/W7164001659","doi":"https://doi.org/10.48550/arxiv.2606.09400","title":"vesselFM-CT: Segmenting All Blood Vessels in CT Images for System-Level Cardiovascular Analysis","display_name":"vesselFM-CT: Segmenting All Blood Vessels in CT Images for System-Level Cardiovascular Analysis","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164001659","doi":"https://doi.org/10.48550/arxiv.2606.09400"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.09400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085960856","display_name":"Bastian Wittmann","orcid":"https://orcid.org/0000-0002-6308-2926"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wittmann, Bastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138235866","display_name":"Chinmay Prabhakar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prabhakar, Chinmay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018632773","display_name":"Suprosanna Shit","orcid":"https://orcid.org/0000-0003-4435-7207"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shit, Suprosanna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138239295","display_name":"Bjoern Menze","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Menze, Bjoern","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.2653000056743622,"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":0.2653000056743622,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.07119999825954437,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.06880000233650208,"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":0.7099000215530396},{"id":"https://openalex.org/keywords/blood-vessel","display_name":"Blood vessel","score":0.41780000925064087},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/vascular-network","display_name":"Vascular network","score":0.38420000672340393},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.3840999901294708},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36980000138282776}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7099000215530396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5673999786376953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5052000284194946},{"id":"https://openalex.org/C2777663904","wikidata":"https://www.wikidata.org/wiki/Q988343","display_name":"Blood vessel","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4113999903202057},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.40799999237060547},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C3019347889","wikidata":"https://www.wikidata.org/wiki/Q11068","display_name":"Vascular network","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.36899998784065247},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.09400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5718849301338196,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"vascular":[1,58,82,106],"network":[2,83],"in":[3,16,116,149],"the":[4,41,57,70,74,81,110,121,125,139,170,174,190],"human":[5],"body":[6],"is":[7,154],"characterized":[8],"by":[9],"blood":[10,147],"vessels":[11,115,148],"exhibiting":[12],"drastic":[13],"structural":[14],"variations":[15],"radius,":[17],"length,":[18],"topological":[19],"properties,":[20],"and":[21,77,101,161,184,203,210],"branching":[22],"patterns.":[23],"This":[24],"heterogeneity,":[25],"together":[26],"with":[27],"location-specific":[28],"anatomical":[29],"background":[30],"variations,":[31],"poses":[32],"a":[33,46,85,198],"significant":[34],"challenge":[35],"for":[36],"robust,":[37],"large-scale":[38],"analysis":[39],"of":[40,56,80,105,112,124,143,173,189,201],"entire":[42],"cardiovascular":[43,126,175,191],"system.":[44,176],"As":[45],"result,":[47],"most":[48],"research":[49],"has":[50],"focused":[51],"on":[52],"narrow,":[53],"isolated":[54],"segments":[55],"network.":[59],"While":[60],"such":[61],"targeted":[62],"studies":[63],"provide":[64],"valuable":[65],"insights,":[66],"they":[67],"inherently":[68],"limit":[69],"ability":[71],"to":[72,92,96,128],"assess":[73],"systemic":[75],"health":[76],"functional":[78],"integrity":[79],"as":[84],"whole.":[86],"In":[87],"this":[88,94,134],"work,":[89],"we":[90,136],"aim":[91],"bridge":[93],"gap":[95],"advance":[97],"both":[98],"clinical":[99,202],"diagnostics":[100],"our":[102,163],"fundamental":[103],"understanding":[104],"physiology.":[107],"We":[108,177],"propose":[109],"task":[111],"segmenting":[113,145],"all":[114,146,182],"CT":[117,151,194,212],"images,":[118,195],"ranging":[119],"from":[120,193],"largest":[122],"components":[123],"system":[127,192],"even":[129],"minuscule":[130],"mesenteric":[131],"vessels.":[132],"To":[133],"end,":[135],"introduce":[137],"vesselFM-CT,":[138],"first":[140],"model":[141],"capable":[142],"robustly":[144],"3D":[150],"images.":[152],"VesselFM-CT":[153],"trained":[155],"via":[156],"an":[157],"iterative,":[158],"multi-step":[159],"process":[160],"optimizes":[162],"proposed":[164],"TubeLoss":[165],"loss":[166],"function,":[167],"effectively":[168],"addressing":[169],"inherent":[171],"heterogeneity":[172],"demonstrate":[178],"that":[179],"vesselFM-CT":[180],"outperforms":[181],"baselines":[183],"enables":[185],"automated,":[186],"precise":[187],"extraction":[188],"thereby":[196],"unlocking":[197],"wide":[199],"range":[200],"technical":[204],"perspectives,":[205],"including":[206],"automated":[207],"disease":[208],"classification":[209],"synthetic":[211],"image":[213],"generation.":[214]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}