{"id":"https://openalex.org/W7224817057","doi":"https://doi.org/10.21203/rs.3.rs-11019607/v1","title":"How reliable is deep learning segmentation of the infant brain? Variability and failure analysis of segmentation pipelines under a common protocol in 51 infants aged 1 to 9 months","display_name":"How reliable is deep learning segmentation of the infant brain? Variability and failure analysis of segmentation pipelines under a common protocol in 51 infants aged 1 to 9 months","publication_year":2026,"publication_date":"2026-10-30","ids":{"openalex":"https://openalex.org/W7224817057","doi":"https://doi.org/10.21203/rs.3.rs-11019607/v1"},"language":"en","primary_location":{"id":"doi:10.21203/rs.3.rs-11019607/v1","doi":"https://doi.org/10.21203/rs.3.rs-11019607/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11019607/v1","pdf_url":"https://www.researchsquare.com/article/rs-11019607/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.researchsquare.com/article/rs-11019607/latest.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118780715","display_name":"Tasnia Binte Mamun","orcid":"https://orcid.org/0009-0001-0262-9545","observed_orcids":["https://orcid.org/0009-0001-0262-9545"]},"institutions":[{"id":"https://openalex.org/I183697816","display_name":"Bangladesh University of Engineering and Technology","ror":"https://ror.org/05a1qpv97","country_code":"BD","type":"education","lineage":["https://openalex.org/I183697816"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Tasnia Binte Mamun","raw_affiliation_strings":["Bangladesh University of Engineering and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bangladesh University of Engineering and Technology","institution_ids":["https://openalex.org/I183697816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079610013","display_name":"Samiul Based Shuvo","orcid":"https://orcid.org/0000-0002-5035-2114","observed_orcids":["https://orcid.org/0000-0002-5035-2114"]},"institutions":[{"id":"https://openalex.org/I183697816","display_name":"Bangladesh University of Engineering and Technology","ror":"https://ror.org/05a1qpv97","country_code":"BD","type":"education","lineage":["https://openalex.org/I183697816"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Samiul Based Shuvo","raw_affiliation_strings":["Bangladesh University of Engineering and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bangladesh University of Engineering and Technology","institution_ids":["https://openalex.org/I183697816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183697816"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.9970999956130981,"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":0.9970999956130981,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/segmentation-variability","display_name":"segmentation variability","score":0.7110000252723694},{"id":"https://openalex.org/keywords/nnu-net","display_name":"nnU-Net","score":0.8539999723434448},{"id":"https://openalex.org/keywords/transunet","display_name":"TransUNet","score":0.843999981880188},{"id":"https://openalex.org/keywords/swin-unetr","display_name":"Swin UNETR","score":0.9909999966621399},{"id":"https://openalex.org/keywords/dice-score","display_name":"Dice score","score":0.5770000219345093},{"id":"https://openalex.org/keywords/brain-volume-estimation","display_name":"brain volume estimation","score":0.4090000092983246}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6671000123023987},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.6308000087738037},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.6053000092506409},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5877000093460083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5679000020027161},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.5238000154495239},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4242999851703644},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3352999985218048},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.299699991941452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29760000109672546},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28439998626708984}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21203/rs.3.rs-11019607/v1","doi":"https://doi.org/10.21203/rs.3.rs-11019607/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11019607/v1","pdf_url":"https://www.researchsquare.com/article/rs-11019607/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.21203/rs.3.rs-11019607/v1","doi":"https://doi.org/10.21203/rs.3.rs-11019607/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11019607/v1","pdf_url":"https://www.researchsquare.com/article/rs-11019607/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.766700029373169,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224817057.pdf","grobid_xml":"https://content.openalex.org/works/W7224817057.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1909740415","https://openalex.org/W2791510948","https://openalex.org/W2793631099","https://openalex.org/W2805284222","https://openalex.org/W2902154261","https://openalex.org/W2910094941","https://openalex.org/W2920149494","https://openalex.org/W2953130519","https://openalex.org/W3028368183","https://openalex.org/W3180629942","https://openalex.org/W4212875960","https://openalex.org/W4221163766","https://openalex.org/W4360798582","https://openalex.org/W4389881743","https://openalex.org/W4391756091","https://openalex.org/W4399125735","https://openalex.org/W4401256393","https://openalex.org/W4403067601","https://openalex.org/W4404481676","https://openalex.org/W4408739104","https://openalex.org/W4413140499","https://openalex.org/W7143331289"],"related_works":["https://openalex.org/W4323534686","https://openalex.org/W4281630342","https://openalex.org/W4221134866","https://openalex.org/W4417087279","https://openalex.org/W2775394473","https://openalex.org/W7127907735","https://openalex.org/W4403090061","https://openalex.org/W7084576973","https://openalex.org/W7139032791","https://openalex.org/W3038918411"],"abstract_inverted_index":{"Abstract":[0],"Background":[1],"Deep":[2],"learning":[3],"segmentation":[4,44],"increasingly":[5],"supplies":[6],"the":[7,37,87,130,147,150,190,212,217,228],"brain":[8,42,230],"volumes":[9],"used":[10],"in":[11,180,197],"infant":[12,41,229],"neuroimaging,":[13],"yet":[14],"pipelines":[15,45,70,149,226],"are":[16],"usually":[17],"ranked":[18],"by":[19],"a":[20,59,79,251],"single":[21],"mean":[22,191],"Dice":[23,122,156],"score,":[24],"which":[25],"hides":[26],"variability":[27,248],"and":[28,53,56,67,78,84,95,116,142,169,193,214,236,249],"rare":[29,54],"failures.":[30,256],"Objective":[31],"To":[32],"quantify":[33],"how":[34],"much":[35],"of":[36,155,182],"apparent":[38],"difference":[39],"between":[40],"MRI":[43,97],"reflects":[46],"retraining":[47,205],"variability,":[48],"cross-validation":[49],"partitioning,":[50],"aggregation":[51],"choice":[52],"failures,":[55],"to":[57,104,202],"derive":[58],"reporting":[60,247],"template":[61],"for":[62,129,133,136,140,144,185],"validating":[63],"such":[64],"tools.":[65],"Materials":[66],"methods":[68],"Four":[69],"(nnU-Net,":[71],"3D":[72,148,225],"TransUNet,":[73,134],"Swin":[74,137,178,188],"UNETR,":[75,138],"2.5D":[76],"STSNet)":[77],"cross-fitted":[80],"consensus":[81,218,253],"were":[82,164],"trained":[83],"evaluated":[85],"on":[86],"public":[88],"Baby":[89],"Open":[90],"Brains":[91],"dataset":[92],"(71":[93],"T1":[94],"T2-weighted":[96],"visits":[98],"from":[99],"51":[100,183],"infants":[101],"aged":[102],"1.0":[103],"9.4":[105],"months,":[106],"31":[107],"expert-corrected":[108],"labels)":[109],"with":[110,232],"subject-disjoint":[111],"five-fold":[112],"cross-validation,":[113],"subject-weighted":[114],"summaries":[115],"Holm-corrected":[117],"paired":[118,162],"tests.":[119],"Results":[120],"Mean":[121],"was":[123],"0.8867":[124],"(95%":[125],"confidence":[126,172],"interval":[127,173],"0.8802\u20130.8932)":[128],"consensus,":[131],"0.8805":[132],"0.8772":[135],"0.8722":[139],"nnU-Net":[141,186],"0.7868":[143],"STSNet.":[145],"Among":[146],"pipeline":[151],"term":[152],"explained":[153],"0.134%":[154],"variation":[157],"(\ud835\udc5d":[158],"=":[159],"0.177),":[160],"all":[161,255],"effects":[163],"small":[165,233],"(|\ud835\udc51|":[166],"\u2264":[167],"0.22)":[168],"every":[170],"mean-difference":[171],"included":[174],"zero.":[175],"TransUNet":[176],"exceeded":[177,204],"UNETR":[179,189],"46":[181],"infants;":[184],"versus":[187],"(\u22120.0050)":[192],"median":[194],"(+0.0072)":[195],"disagreed":[196],"sign.":[198],"Fold-level":[199],"differences":[200,235],"(\u22120.0490":[201],"+0.0169)":[203],"standard":[206],"deviations":[207],"(0.0005\u20130.0016).":[208],"Two":[209],"scans":[210],"drove":[211],"reversal":[213],"most":[215],"failures;":[216,250],"produced":[219],"none.":[220],"Conclusion":[221],"Three":[222],"competently":[223],"configured":[224],"segmented":[227],"accurately":[231],"mutual":[234],"no":[237],"detected":[238],"age":[239],"dependence.":[240],"Choosing":[241],"among":[242],"them":[243],"mattered":[244],"less":[245],"than":[246],"simple":[252],"removed":[254]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-31T00:00:00"}
