{"id":"https://openalex.org/W4362489491","doi":"https://doi.org/10.1117/12.2653552","title":"Evaluation of multiparametric MRI for deep learning-based segmentation of Wilms tumor","display_name":"Evaluation of multiparametric MRI for deep learning-based segmentation of Wilms tumor","publication_year":2023,"publication_date":"2023-04-03","ids":{"openalex":"https://openalex.org/W4362489491","doi":"https://doi.org/10.1117/12.2653552"},"language":"en","primary_location":{"id":"doi:10.1117/12.2653552","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"},"type":"conference-paper","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/A5086008018","display_name":"Myrthe A. D. Buser","orcid":"https://orcid.org/0000-0003-0640-6434"},"institutions":[{"id":"https://openalex.org/I4210127118","display_name":"Princess M\u00e1xima Center","ror":"https://ror.org/02aj7yc53","country_code":"NL","type":"funder","lineage":["https://openalex.org/I4210127118"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Myrthe A. Buser","raw_affiliation_strings":["Princess M\u00e1xima Ctr. for Pediatric Oncology (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Princess M\u00e1xima Ctr. for Pediatric Oncology (Netherlands)","institution_ids":["https://openalex.org/I4210127118"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044586708","display_name":"Marc H. W. A. Wijnen","orcid":"https://orcid.org/0000-0001-9339-4253"},"institutions":[{"id":"https://openalex.org/I4210127118","display_name":"Princess M\u00e1xima Center","ror":"https://ror.org/02aj7yc53","country_code":"NL","type":"funder","lineage":["https://openalex.org/I4210127118"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Marc H. W. A. Wijnen","raw_affiliation_strings":["Princess M\u00e1xima Ctr. for Pediatric Oncology (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Princess M\u00e1xima Ctr. for Pediatric Oncology (Netherlands)","institution_ids":["https://openalex.org/I4210127118"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014301113","display_name":"Alida F. W. van der Steeg","orcid":"https://orcid.org/0000-0003-0168-7513"},"institutions":[{"id":"https://openalex.org/I4210127118","display_name":"Princess M\u00e1xima Center","ror":"https://ror.org/02aj7yc53","country_code":"NL","type":"funder","lineage":["https://openalex.org/I4210127118"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Alida F. W. van der Steeg","raw_affiliation_strings":["Princess M\u00e1xima Ctr. for Pediatric Oncology (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Princess M\u00e1xima Ctr. for Pediatric Oncology (Netherlands)","institution_ids":["https://openalex.org/I4210127118"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029655930","display_name":"Bas H. M. van der Velden","orcid":"https://orcid.org/0000-0003-3750-2824"},"institutions":[{"id":"https://openalex.org/I193662353","display_name":"Utrecht University","ror":"https://ror.org/04pp8hn57","country_code":"NL","type":"education","lineage":["https://openalex.org/I193662353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Bas H. M. van der Velden","raw_affiliation_strings":["Utrecht Univ. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Utrecht Univ. (Netherlands)","institution_ids":["https://openalex.org/I193662353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"36","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12140","display_name":"Renal and related cancers","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12140","display_name":"Renal and related cancers","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6300916075706482},{"id":"https://openalex.org/keywords/percentile","display_name":"Percentile","score":0.6292051672935486},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.6244031190872192},{"id":"https://openalex.org/keywords/wilms-tumor","display_name":"Wilms' tumor","score":0.5882672071456909},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.5818936824798584},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.5627641677856445},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.499011754989624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4638063907623291},{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.46329841017723083},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42616114020347595},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34390872716903687},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3386550843715668},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23497295379638672},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.1253770887851715},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10315057635307312}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6300916075706482},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.6292051672935486},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6244031190872192},{"id":"https://openalex.org/C2775999222","wikidata":"https://www.wikidata.org/wiki/Q756289","display_name":"Wilms' tumor","level":2,"score":0.5882672071456909},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.5818936824798584},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5627641677856445},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.499011754989624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4638063907623291},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.46329841017723083},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42616114020347595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34390872716903687},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3386550843715668},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23497295379638672},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.1253770887851715},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10315057635307312}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1117/12.2653552","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"},{"id":"pmh:wur:oai:library.wur.nl:wurpubs/614024","is_oa":false,"landing_page_url":"https://research.wur.nl/en/publications/evaluation-of-multiparametric-mri-for-deep-learning-based-segment","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3104750253","https://openalex.org/W3021239166","https://openalex.org/W2475857072","https://openalex.org/W4366341510","https://openalex.org/W2390936256","https://openalex.org/W2483429559","https://openalex.org/W2016385589","https://openalex.org/W2009559548","https://openalex.org/W2906397153","https://openalex.org/W2385445039"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"techniques":[2],"to":[3,21,80,146,155],"segment":[4],"Wilms":[5,28],"tumor":[6,29],"typically":[7],"use":[8],"a":[9],"single":[10],"MRI":[11,25,94],"sequence":[12],"as":[13],"input.":[14],"The":[15,85],"aim":[16],"of":[17,36,51,73],"this":[18],"study":[19],"was":[20,89,102],"assess":[22],"whether":[23],"multiparametric":[24],"input":[26,49,83,117,123],"improves":[27],"segmentation.":[30],"45":[31],"patients":[32],"were":[33,39,62,78],"consecutively":[34],"included,":[35],"which":[37],"36":[38],"used":[40,63,79],"for":[41,45,64],"training":[42],"and":[43,57,69,109],"nine":[44],"testing.":[46],"All":[47,137],"seven":[48],"combinations":[50,124],"postcontrast":[52,106],"<i>T</i><sub>1</sub>-weighted":[53,107,157],"imaging,":[54,56],"<i>T</i><sub>2</sub>-weighted":[55,159],"diffusion":[58],"weighted":[59],"imaging":[60,108,160],"(DWI)":[61],"nnU-Net":[65],"training.":[66],"Dice":[67,87,126],"scores":[68],"the":[70,74,82,99],"95th":[71],"percentile":[72],"Haussdorf":[75],"distance":[76],"(HD95)":[77],"evaluate":[81],"combinations.":[84],"median":[86,100,129],"score":[88],"highest":[90],"when":[91,104],"combining":[92,105],"all":[93],"sequences":[95,154],"(Dice":[96],"=":[97,112,127,131,135],"0.93),":[98],"HD95":[101,130],"lowest":[103],"DWI":[110,116],"(HD95":[111],"5.4":[113],"mm).":[114],"Single-parametric":[115],"performed":[118],"significantly":[119,163],"worse":[120],"than":[121],"other":[122,138],"(median":[125],"0.64,":[128],"29.5":[132],"mm,":[133],"<i>p</i>":[134],"0.004).":[136],"combinations,":[139],"including":[140],"standalone":[141,156],"sequences,":[142],"showed":[143],"similar":[144],"performance":[145],"each":[147],"other.":[148],"Our":[149],"results":[150],"suggest":[151],"that":[152],"adding":[153],"or":[158],"does":[161],"not":[162],"improve":[164],"segmentation":[165],"results.":[166]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}