{"id":"https://openalex.org/W7133312736","doi":"https://doi.org/10.48550/arxiv.2603.00798","title":"Efficient Conformal Volumetry for Template-Based Segmentation","display_name":"Efficient Conformal Volumetry for Template-Based Segmentation","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7133312736","doi":"https://doi.org/10.48550/arxiv.2603.00798"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00798","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.00798","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059261901","display_name":"Matt Y. Cheung","orcid":"https://orcid.org/0000-0002-7846-3297"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheung, Matt Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073971393","display_name":"Ashok Veeraraghavan","orcid":"https://orcid.org/0000-0001-5043-7460"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Veeraraghavan, Ashok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Balakrishnan, Guha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balakrishnan, Guha","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":0.6669999957084656,"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":0.6669999957084656,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.14980000257492065,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.05770000070333481,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/conformal-map","display_name":"Conformal map","score":0.6391000151634216},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6353999972343445},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.487199991941452},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4814999997615814},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4117000102996826},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.40049999952316284},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3846000134944916},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.359499990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523000001907349},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.6391000151634216},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6353999972343445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6061000227928162},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5812000036239624},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.487199991941452},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35499998927116394},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3465999960899353},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2743000090122223},{"id":"https://openalex.org/C2776673561","wikidata":"https://www.wikidata.org/wiki/Q655357","display_name":"Atlas (anatomy)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.272599995136261}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00798","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00798","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7347859144210815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Template-based":[0],"segmentation,":[1],"a":[2,16,20,69,86,109],"widely":[3],"used":[4,26],"paradigm":[5],"in":[6,59,73,80,162],"medical":[7,163],"imaging,":[8],"propagates":[9],"anatomical":[10],"labels":[11],"via":[12],"deformable":[13],"registration":[14,66,135,157],"from":[15,104,114],"labeled":[17],"atlas":[18],"to":[19,27,154],"target":[21,139],"image,":[22],"and":[23,128,134],"is":[24],"often":[25,57],"compute":[28],"volumetric":[29,92,111],"biomarkers":[30],"for":[31,42,159],"downstream":[32],"decision-making.":[33],"While":[34],"conformal":[35,148],"prediction":[36],"(CP)":[37],"provides":[38],"finite-sample":[39],"valid":[40],"intervals":[41,76,145],"scalar":[43],"metrics,":[44],"existing":[45],"segmentation-based":[46],"uncertainty":[47],"quantification":[48],"(UQ)":[49],"approaches":[50],"either":[51],"rely":[52],"on":[53,97,121],"learned":[54,110],"model":[55],"features,":[56],"unavailable":[58],"classic":[60],"template-based":[61,105,122],"pipelines,":[62],"or":[63],"treat":[64],"the":[65,100,156],"process":[67,158],"as":[68],"black":[70],"box,":[71],"resulting":[72],"overly":[74],"conservative":[75],"when":[77],"applied":[78],"directly":[79],"output":[81],"space.":[82],"We":[83,118],"introduce":[84],"ConVOLT,":[85],"CP":[87],"framework":[88],"that":[89],"achieves":[90,138],"efficient":[91,160],"UQ":[93,161],"by":[94],"conditioning":[95],"calibration":[96],"properties":[98],"of":[99],"estimated":[101],"deformation":[102,115],"field":[103],"segmentation.":[106],"ConVOLT":[107,120,137],"calibrates":[108],"scaling":[112],"factor":[113],"space":[116],"features.":[117],"evaluate":[119],"segmentation":[123],"tasks":[124],"involving":[125],"global,":[126],"regional,":[127],"label":[129],"volumetry":[130],"across":[131],"multiple":[132],"datasets":[133],"methods.":[136],"coverage":[140],"while":[141],"producing":[142],"substantially":[143],"tighter":[144],"than":[146],"output-space":[147],"baselines.":[149],"Our":[150],"work":[151],"paves":[152],"way":[153],"exploit":[155],"imaging":[164],"pipelines.":[165]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}
