{"id":"https://openalex.org/W7160040761","doi":"https://doi.org/10.48550/arxiv.2605.00538","title":"Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images","display_name":"Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160040761","doi":"https://doi.org/10.48550/arxiv.2605.00538"},"language":null,"primary_location":{"id":"pmh:oai:edoc.mdc-berlin.de:26517","is_oa":false,"landing_page_url":"https://orcid.org/0000-0002-2353-5310","pdf_url":null,"source":{"id":"https://openalex.org/S4306400224","display_name":"MDC Repository (Max-Delbrueck-Center for Molecular Medicine)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"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.2605.00538","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135226382","display_name":"Rajalakshmi Palaniappan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Palaniappan, Rajalakshmi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135210403","display_name":"Christoph Karg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karg, Christoph","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135229195","display_name":"Nemesio Navarro-Arambula","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Navarro-Arambula, Nemesio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135120428","display_name":"Peter Hirsch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hirsch, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045371128","display_name":"Kristin Kraeker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kraeker, Kristin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052086768","display_name":"Lisa Mais","orcid":"https://orcid.org/0000-0002-9281-2668"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mais, Lisa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135152455","display_name":"Dagmar Kainmueller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kainmueller, Dagmar","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":0.3781999945640564,"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":0.3781999945640564,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.1006999984383583,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.09920000284910202,"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":0.7297999858856201},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.567799985408783},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5360000133514404},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46320000290870667},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.453900009393692},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.41449999809265137},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.3813000023365021}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7297999858856201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6141999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6000999808311462},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.567799985408783},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5360000133514404},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46320000290870667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.453900009393692},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C64331007","wikidata":"https://www.wikidata.org/wiki/Q831672","display_name":"Spanning tree","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2847999930381775},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.26969999074935913},{"id":"https://openalex.org/C2777663904","wikidata":"https://www.wikidata.org/wiki/Q988343","display_name":"Blood vessel","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:edoc.mdc-berlin.de:26517","is_oa":false,"landing_page_url":"https://orcid.org/0000-0002-2353-5310","pdf_url":null,"source":{"id":"https://openalex.org/S4306400224","display_name":"MDC Repository (Max-Delbrueck-Center for Molecular Medicine)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.48550/arxiv.2605.00538","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00538","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00538","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00538","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Blood":[0],"vessel":[1,75,81,170],"segmentation":[2,67,82],"and":[3,33,68,114,138,147,172],"-tracing":[4],"are":[5],"essential":[6],"tasks":[7,64],"in":[8],"many":[9],"medical":[10],"imaging":[11],"applications.":[12],"Although":[13],"numerous":[14],"methods":[15],"exist,":[16],"the":[17,29,61,87,99,120,157],"prevailing":[18],"segment-then-fix":[19],"paradigm":[20],"is":[21],"fundamentally":[22],"limited":[23],"regarding":[24],"its":[25],"suitability":[26],"for":[27,150],"modeling":[28],"task":[30],"of":[31,65,98,122,130,141,160],"complete":[32],"topologically":[34,46],"accurate":[35,48],"vascular":[36,49,88,162,175],"network":[37],"reconstruction.":[38],"Here,":[39],"we":[40,93,135],"propose":[41,136],"an":[42],"approach":[43,71,103,124,154],"to":[44,85,125,166],"extract":[45,86],"more":[47],"graphs":[50],"from":[51,60,90],"3D":[52,127],"image":[53],"data,":[54],"building":[55],"upon":[56],"highly":[57],"successful":[58],"ideas":[59],"related":[62],"biomedical":[63],"cell":[66],"-tracking.":[69],"Our":[70,102],"first":[72],"predicts":[73],"voxel-wise":[74],"direction":[76],"vectors":[77],"joint":[78],"with":[79],"standard":[80],"masks.":[83],"Second,":[84],"graph":[89],"these":[91],"predictions,":[92],"introduce":[94],"a":[95,178],"direction-vector-guided":[96],"extension":[97],"TEASAR":[100],"algorithm.":[101],"achieves":[104],"state-of-the-art":[105],"performance":[106],"on":[107],"three":[108],"benchmark":[109],"datasets,":[110],"spanning":[111],"both":[112],"synthetic":[113],"real":[115],"imagery.":[116],"We":[117],"further":[118],"demonstrate":[119],"applicability":[121],"our":[123,153],"challenging":[126],"micro-CT":[128],"scans":[129],"rat":[131],"heart":[132],"vasculature.":[133],"Finally,":[134],"meaningful":[137],"interpretable":[139],"measures":[140],"topological":[142,158],"error,":[143],"namely":[144],"false":[145,148],"splits":[146],"merges":[149],"graphs.":[151],"Overall,":[152],"substantially":[155],"improves":[156],"accuracy":[159],"reconstructed":[161],"graphs,":[163],"being":[164],"able":[165],"separate":[167],"closely":[168],"apposed":[169],"segments":[171],"handle":[173],"multiple":[174],"trees":[176],"within":[177],"single":[179],"volume.":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-05T00:00:00"}
