{"id":"https://openalex.org/W7164334560","doi":"https://doi.org/10.48550/arxiv.2606.12319","title":"Anatomically Conditioned Recurrent Refinement for Topology-Aware Circle of Willis Segmentation","display_name":"Anatomically Conditioned Recurrent Refinement for Topology-Aware Circle of Willis Segmentation","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164334560","doi":"https://doi.org/10.48550/arxiv.2606.12319"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12319","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12319","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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.12319","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127931179","display_name":"Juraj Peri\u0107","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peri\u0107, Juraj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055227189","display_name":"Marija Habijan","orcid":"https://orcid.org/0000-0002-3754-498X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Habijan, Marija","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003190972","display_name":"Dario Mu\u017eevi\u0107","orcid":"https://orcid.org/0000-0002-5139-7728"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mu\u017eevi\u0107, Dario","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083837803","display_name":"Irena Gali\u0107","orcid":"https://orcid.org/0000-0002-0211-4568"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gali\u0107, Irena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068395991","display_name":"Danilo Babin","orcid":"https://orcid.org/0000-0002-2881-6760"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Babin, Danilo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031078128","display_name":"Aleksandra Pi\u017eurica","orcid":"https://orcid.org/0000-0002-9322-4999"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pi\u017eurica, Aleksandra","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":0.2705000042915344,"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":0.2705000042915344,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.24300000071525574,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.13269999623298645,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6392999887466431},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.5960000157356262},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5670999884605408},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5266000032424927},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.4668999910354614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44830000400543213}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6392999887466431},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.5960000157356262},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5670999884605408},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5564000010490417},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5263000130653381},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3797000050544739},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34880000352859497},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3440000116825104},{"id":"https://openalex.org/C191399826","wikidata":"https://www.wikidata.org/wiki/Q326908","display_name":"Hausdorff space","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C2776477805","wikidata":"https://www.wikidata.org/wiki/Q4460773","display_name":"Topological data analysis","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2775999903678894},{"id":"https://openalex.org/C185568154","wikidata":"https://www.wikidata.org/wiki/Q530242","display_name":"Mathematical morphology","level":4,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12319","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12319","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.2606.12319","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12319","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":[{"score":0.8097409009933472,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Segmenting":[0],"the":[1,48,101,125],"Circle":[2],"of":[3],"Willis":[4],"(CoW)":[5],"from":[6,92],"Magnetic":[7],"Resonance":[8],"Angiography":[9],"(MRA)":[10],"is":[11],"challenging":[12],"due":[13],"to":[14,24,33,96],"complex":[15],"topology":[16],"and":[17,70,114],"thin":[18],"vascular":[19],"structures":[20],"that":[21,65,75,90],"are":[22],"prone":[23],"fragmentation.":[25],"Standard":[26],"Convolutional":[27],"Neural":[28],"Networks":[29],"(CNNs)":[30],"often":[31],"fail":[32],"capture":[34],"these":[35],"topological":[36,78,122],"constraints,":[37],"resulting":[38],"in":[39],"\"broken":[40],"vessel\"":[41],"artifacts.":[42],"To":[43],"address":[44],"this,":[45],"we":[46],"propose":[47],"Anatomically":[49],"Conditioned":[50],"Recurrent":[51],"Refinement":[52],"U-Net":[53],"(AC2RUNet).":[54],"Our":[55],"architecture":[56],"decouples":[57],"segmentation":[58],"into":[59],"two":[60],"streams:":[61],"a":[62,71,85],"Static":[63],"Stream":[64,74],"extracts":[66],"invariant":[67],"anatomical":[68],"features":[69],"lightweight":[72],"Dynamic":[73],"iteratively":[76],"refines":[77],"errors":[79,117],"over":[80,124],"time.":[81],"We":[82],"further":[83],"introduce":[84],"dynamic":[86],"curriculum":[87],"learning":[88],"strategy":[89],"transitions":[91],"high-recall":[93],"geometric":[94],"supervision":[95],"topology-aware":[97],"constraints.":[98],"Validated":[99],"on":[100],"TopCoW":[102],"dataset,":[103],"AC2RUNet":[104],"substantially":[105],"reduces":[106],"Hausdorff":[107],"Distance":[108],"(4.72":[109],"mm":[110],"vs":[111,119],"9.17":[112],"mm)":[113],"Betti":[115],"number":[116],"(0.19":[118],"0.40),":[120],"improving":[121],"connectivity":[123],"nnU-Net":[126],"baseline":[127],"while":[128],"maintaining":[129],"comparable":[130],"volumetric":[131],"Dice.":[132]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-12T00:00:00"}
