{"id":"https://openalex.org/W7131391844","doi":"https://doi.org/10.48550/arxiv.2602.20215","title":"Vision-Based Reasoning with Topology-Encoded Graphs for Anatomical Path Disambiguation in Robot-Assisted Endovascular Navigation","display_name":"Vision-Based Reasoning with Topology-Encoded Graphs for Anatomical Path Disambiguation in Robot-Assisted Endovascular Navigation","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131391844","doi":"https://doi.org/10.48550/arxiv.2602.20215"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.20215","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20215","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.20215","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126817800","display_name":"Jiyuan Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao, Jiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126788105","display_name":"Zhengyu Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Zhengyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126827403","display_name":"Wentong Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Wentong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044674377","display_name":"Tianliang Yao","orcid":"https://orcid.org/0009-0000-7063-3880"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Tianliang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126832472","display_name":"Dong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Dong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126788392","display_name":"Tao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126779223","display_name":"Yizhe Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yizhe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126809386","display_name":"Peng Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Peng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5126817800"],"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/T10868","display_name":"Soft Robotics and Applications","score":0.7513999938964844,"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/T10868","display_name":"Soft Robotics and Applications","score":0.7513999938964844,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.03660000115633011,"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":0.028200000524520874,"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/robustness","display_name":"Robustness (evolution)","score":0.65420001745224},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.4934999942779541},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.43050000071525574},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.40310001373291016},{"id":"https://openalex.org/keywords/subtraction","display_name":"Subtraction","score":0.36980000138282776},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.3695000112056732},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.36809998750686646},{"id":"https://openalex.org/keywords/digital-subtraction-angiography","display_name":"Digital subtraction angiography","score":0.3587999939918518}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.65420001745224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6398000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237000226974487},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6169000267982483},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.4934999942779541},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C68060419","wikidata":"https://www.wikidata.org/wiki/Q40754","display_name":"Subtraction","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C2778286760","wikidata":"https://www.wikidata.org/wiki/Q1224954","display_name":"Digital subtraction angiography","level":3,"score":0.3587999939918518},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.2962000072002411},{"id":"https://openalex.org/C161759796","wikidata":"https://www.wikidata.org/wiki/Q3982902","display_name":"Teleoperation","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.28439998626708984},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.20215","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20215","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.20215","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20215","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"Robotic-assisted":[0],"percutaneous":[1],"coronary":[2,89],"intervention":[3],"(PCI)":[4],"is":[5,85],"constrained":[6],"by":[7],"the":[8,78,99,112,142,152,186,224,230],"inherent":[9],"limitations":[10],"of":[11,44,94,101,181,194,201,229],"2D":[12,40],"Digital":[13],"Subtraction":[14],"Angiography":[15],"(DSA).":[16],"Unlike":[17],"physicians,":[18],"who":[19],"can":[20],"directly":[21],"manipulate":[22],"guidewires":[23],"and":[24,50,105,118,123,159,196,209,211,215,227],"integrate":[25],"tactile":[26,51],"feedback":[27],"with":[28,133],"their":[29],"prior":[30],"anatomical":[31],"knowledge,":[32],"teleoperated":[33],"robotic":[34,74,220],"systems":[35],"must":[36],"rely":[37],"solely":[38],"on":[39,218],"projections.":[41],"This":[42],"mode":[43],"operation,":[45],"simultaneously":[46],"lacking":[47],"spatial":[48],"context":[49],"sensation,":[52],"may":[53],"give":[54],"rise":[55],"to":[56,137,155],"projection-induced":[57,168],"ambiguities":[58],"at":[59],"vascular":[60],"bifurcations.":[61],"To":[62],"address":[63],"this":[64],"challenge,":[65],"we":[66],"propose":[67],"a":[68,82,145,172,178,191,197,219],"two-stage":[69],"framework":[70],"(SCAR-UNet-GAT)":[71],"for":[72,87],"real-time":[73],"path":[75,184],"planning.":[76],"In":[77,141],"first":[79],"stage,":[80,144],"SCAR-UNet,":[81],"spatial-coordinate-attention-regularized":[83],"U-Net,":[84],"employed":[86],"accurate":[88],"vessel":[90,116,153],"segmentation.":[91],"The":[92],"integration":[93],"multi-level":[95],"attention":[96],"mechanisms":[97],"enhances":[98],"delineation":[100],"thin,":[102],"tortuous":[103],"vessels":[104],"improves":[106],"robustness":[107,228],"against":[108],"imaging":[109],"noise.":[110],"From":[111],"resulting":[113],"binary":[114],"masks,":[115],"centerlines":[117],"bifurcation":[119],"points":[120],"are":[121,131],"extracted,":[122],"geometric":[124],"descriptors":[125],"(e.g.,":[126],"branch":[127],"diameter,":[128],"intersection":[129],"angles)":[130],"fused":[132],"local":[134],"DSA":[135,174],"patches":[136],"construct":[138],"node":[139],"features.":[140],"second":[143],"Graph":[146],"Attention":[147],"Network":[148],"(GAT)":[149],"reasons":[150],"over":[151],"graph":[154],"identify":[156],"anatomically":[157],"consistent":[158],"clinically":[160],"feasible":[161],"trajectories,":[162],"effectively":[163],"distinguishing":[164],"true":[165],"bifurcations":[166],"from":[167],"false":[169],"crossings.":[170],"On":[171],"clinical":[173],"dataset,":[175],"SCAR-UNet":[176],"achieved":[177],"Dice":[179],"coefficient":[180],"93.1%.":[182],"For":[183],"disambiguation,":[185],"proposed":[187,231],"GAT-based":[188],"method":[189],"attained":[190],"success":[192,199],"rate":[193,200],"95.0%":[195],"target-arrival":[198],"90.0%,":[202],"substantially":[203],"outperforming":[204],"conventional":[205],"shortest-path":[206],"planning":[207,213],"(60.0%":[208],"55.0%)":[210],"heuristic-based":[212],"(75.0%":[214],"70.0%).":[216],"Validation":[217],"platform":[221],"further":[222],"confirmed":[223],"practical":[225],"feasibility":[226],"framework.":[232]},"counts_by_year":[],"updated_date":"2026-02-26T06:34:08.959763","created_date":"2026-02-26T00:00:00"}