{"id":"https://openalex.org/W4417473016","doi":"https://doi.org/10.1109/sipaim67325.2025.11283217","title":"Leveraging Uncertainty for Quality Assessment of TotalSegmentator Segmentations: A Preliminary Study","display_name":"Leveraging Uncertainty for Quality Assessment of TotalSegmentator Segmentations: A Preliminary Study","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W4417473016","doi":"https://doi.org/10.1109/sipaim67325.2025.11283217"},"language":null,"primary_location":{"id":"doi:10.1109/sipaim67325.2025.11283217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim67325.2025.11283217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106871917","display_name":"Sergio Carreras-Salinas","orcid":null},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Sergio Carreras-Salinas","raw_affiliation_strings":["Universidad Carlos III de Madrid,Departamento de Bioingenier&#x00ED;a,Madrid,Spain"],"affiliations":[{"raw_affiliation_string":"Universidad Carlos III de Madrid,Departamento de Bioingenier&#x00ED;a,Madrid,Spain","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075187500","display_name":"Luc\u00eda Cubero","orcid":"https://orcid.org/0000-0003-3134-1433"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Luc\u00eda Cubero","raw_affiliation_strings":["Universidad Carlos III de Madrid,Departamento de Bioingenier&#x00ED;a,Madrid,Spain"],"affiliations":[{"raw_affiliation_string":"Universidad Carlos III de Madrid,Departamento de Bioingenier&#x00ED;a,Madrid,Spain","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091671326","display_name":"Eva M\u00e9ndez","orcid":"https://orcid.org/0000-0002-5337-4722"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Eva M\u00e9ndez","raw_affiliation_strings":["Universidad Carlos III de Madrid,Departamento de Biblioteconom&#x00ED;a y Documentaci&#x00F3;n,Madrid,Spain"],"affiliations":[{"raw_affiliation_string":"Universidad Carlos III de Madrid,Departamento de Biblioteconom&#x00ED;a y Documentaci&#x00F3;n,Madrid,Spain","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057280769","display_name":"Javier Pascau","orcid":"https://orcid.org/0000-0003-1484-731X"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Pascau","raw_affiliation_strings":["Universidad Carlos III de Madrid,Departamento de Bioingenier&#x00ED;a,Madrid,Spain"],"affiliations":[{"raw_affiliation_string":"Universidad Carlos III de Madrid,Departamento de Bioingenier&#x00ED;a,Madrid,Spain","institution_ids":["https://openalex.org/I50357001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5106871917"],"corresponding_institution_ids":["https://openalex.org/I50357001"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41530149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.6942999958992004,"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":0.6942999958992004,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.026799999177455902,"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/T10862","display_name":"AI in cancer detection","score":0.025499999523162842,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/quality-assessment","display_name":"Quality assessment","score":0.5242000222206116},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4819999933242798},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4742000102996826},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.39489999413490295},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.36640000343322754},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.32580000162124634},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.32280001044273376},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.32010000944137573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6571000218391418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5424000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5266000032424927},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.5242000222206116},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5142999887466431},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4819999933242798},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4742000102996826},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.39489999413490295},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.30869999527931213},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C106436119","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assurance","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sipaim67325.2025.11283217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim67325.2025.11283217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320313831","display_name":"Comunidad de Madrid","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W130099911","https://openalex.org/W3014974815","https://openalex.org/W3102100346","https://openalex.org/W3183048323","https://openalex.org/W4225740823","https://openalex.org/W4383218413","https://openalex.org/W4396621232"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3],"present":[4],"a":[5,35,61,71],"framework":[6],"for":[7],"leveraging":[8],"Monte":[9],"Carlo":[10],"Dropout-Based":[11],"uncertainty":[12,29],"estimation":[13],"to":[14,27],"assess":[15],"the":[16,41,86],"quality":[17,67],"of":[18,88],"segmentations":[19,39],"produced":[20],"by":[21],"TotalSegmentator.":[22],"Entropy":[23],"maps":[24],"were":[25],"used":[26,73],"quantify":[28],"across":[30],"13":[31],"abdominothoracic":[32],"organs,":[33],"and":[34,78],"use-case":[36],"on":[37,50],"liver":[38],"from":[40],"NLST":[42],"database":[43],"demonstrated":[44],"that":[45],"machine":[46],"learning":[47],"classifiers":[48],"trained":[49],"uncertainty-derived":[51],"features":[52],"can":[53],"effectively":[54],"detect":[55],"poor":[56],"segmentations.":[57],"This":[58],"serves":[59],"as":[60],"starting":[62],"point":[63],"toward":[64],"integrating":[65],"automatic":[66],"control":[68],"in":[69,75,85],"TotalSegmentator,":[70],"widely":[72],"tool":[74],"both":[76],"research":[77],"clinical":[79],"scenarios":[80],"involving":[81],"medical":[82],"imaging":[83],"segmentation,":[84],"absence":[87],"ground":[89],"truth.":[90]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-18T00:00:00"}
