{"id":"https://openalex.org/W7204850731","doi":"https://doi.org/10.64898/2026.08.26.26361439","title":"Augmenting Deep Learning-Based PSMA PET/CT Metastasis Segmentation With a Population-Level Spatial Atlas","display_name":"Augmenting Deep Learning-Based PSMA PET/CT Metastasis Segmentation With a Population-Level Spatial Atlas","publication_year":2026,"publication_date":"2026-08-31","ids":{"openalex":"https://openalex.org/W7204850731","doi":"https://doi.org/10.64898/2026.08.26.26361439","pmid":"https://pubmed.ncbi.nlm.nih.gov/42922619","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13684697"},"language":"en","primary_location":{"id":"doi:10.64898/2026.08.26.26361439","doi":"https://doi.org/10.64898/2026.08.26.26361439","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.08.26.26361439","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/31/2026.08.26.26361439.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/31/2026.08.26.26361439.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148778359","display_name":"Grant Neakasen Chau","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Grant Chau","raw_affiliation_strings":["Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA","Department of Computer Science Dartmouth College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA","institution_ids":["https://openalex.org/I107672454"]},{"raw_affiliation_string":"Department of Computer Science Dartmouth College","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005470789","display_name":"Bashirul Azam Biswas","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bashirul Azam Biswas","raw_affiliation_strings":["Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023768189","display_name":"Biratal Raj Wagle","orcid":"https://orcid.org/0009-0006-5037-9620","observed_orcids":["https://orcid.org/0009-0006-5037-9620"]},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Biratal Raj Wagle","raw_affiliation_strings":["Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040792793","display_name":"Matthew E. Maeder","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1289422878","display_name":"Dartmouth\u2013Hitchcock Medical Center","ror":"https://ror.org/00d1dhh09","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I107672454","https://openalex.org/I1289422878","https://openalex.org/I4390039337"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew E. Maeder","raw_affiliation_strings":["Radiology, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radiology, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA","institution_ids":["https://openalex.org/I1289422878"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061395570","display_name":"James B. Yu","orcid":"https://orcid.org/0000-0002-3119-3226","observed_orcids":["https://orcid.org/0000-0002-3119-3226"]},"institutions":[{"id":"https://openalex.org/I1289422878","display_name":"Dartmouth\u2013Hitchcock Medical Center","ror":"https://ror.org/00d1dhh09","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I107672454","https://openalex.org/I1289422878","https://openalex.org/I4390039337"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James B. Yu","raw_affiliation_strings":["Radiation Oncology, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radiation Oncology, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA","institution_ids":["https://openalex.org/I1289422878"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067806540","display_name":"Indrani Bhattacharya","orcid":"https://orcid.org/0000-0003-1557-6961","observed_orcids":["https://orcid.org/0000-0003-1557-6961"]},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Indrani Bhattacharya","raw_affiliation_strings":["Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA","Department of Computer Science Dartmouth College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA","institution_ids":["https://openalex.org/I107672454"]},{"raw_affiliation_string":"Department of Computer Science Dartmouth College","institution_ids":["https://openalex.org/I107672454"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5148778359"],"corresponding_institution_ids":["https://openalex.org/I107672454"],"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":0.9657999873161316,"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":0.9657999873161316,"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":0.018799999728798866,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.007300000172108412,"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/prostate-cancer-metastasis","display_name":"prostate cancer metastasis","score":0.8199999928474426},{"id":"https://openalex.org/keywords/spatial-priors","display_name":"spatial priors","score":0.6880000233650208},{"id":"https://openalex.org/keywords/bayesian-fusion","display_name":"Bayesian fusion","score":0.7179999947547913},{"id":"https://openalex.org/keywords/nnu-net","display_name":"nnU-Net","score":0.8130000233650208},{"id":"https://openalex.org/keywords/false-positive-reduction","display_name":"false positive reduction","score":0.628000020980835},{"id":"https://openalex.org/keywords/liver-metastases","display_name":"liver metastasis","score":0.6600000262260437},{"id":"https://openalex.org/keywords/anatomical-prior-knowledge","display_name":"anatomical prior knowledge","score":0.3440000116825104},{"id":"https://openalex.org/keywords/multi-atlas-label-fusion","display_name":"multi-atlas label fusion","score":0.734000027179718},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.6060000061988831},{"id":"https://openalex.org/keywords/false-negative-reduction","display_name":"false negative reduction","score":0.37700000405311584},{"id":"https://openalex.org/keywords/prostate-cancer-staging","display_name":"prostate cancer staging","score":0.38999998569488525},{"id":"https://openalex.org/keywords/treatment-response-assessment","display_name":"treatment response assessment","score":0.5730000138282776}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7688000202178955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7317000031471252},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6664999723434448},{"id":"https://openalex.org/C2776673561","wikidata":"https://www.wikidata.org/wiki/Q655357","display_name":"Atlas (anatomy)","level":2,"score":0.6485999822616577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6144000291824341},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5227000117301941},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4106000065803528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3783999979496002},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.29249998927116394},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.2793999910354614},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2669000029563904},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25699999928474426},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.64898/2026.08.26.26361439","doi":"https://doi.org/10.64898/2026.08.26.26361439","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.08.26.26361439","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/31/2026.08.26.26361439.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},{"id":"pmid:42922619","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42922619","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"medRxiv : the preprint server for health sciences","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13684697","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13684697/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"medRxiv","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.64898/2026.08.26.26361439","doi":"https://doi.org/10.64898/2026.08.26.26361439","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.08.26.26361439","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/31/2026.08.26.26361439.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"sustainable_development_goals":[{"score":0.6240000128746033,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310400","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204850731.pdf","grobid_xml":"https://content.openalex.org/works/W7204850731.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1647857302","https://openalex.org/W1970928383","https://openalex.org/W2022729666","https://openalex.org/W2087204158","https://openalex.org/W2121044470","https://openalex.org/W2158128575","https://openalex.org/W2207385770","https://openalex.org/W2798753173","https://openalex.org/W2981468397","https://openalex.org/W3012305372","https://openalex.org/W3014974815","https://openalex.org/W3088497176","https://openalex.org/W3125281938","https://openalex.org/W3130934287","https://openalex.org/W3176208875","https://openalex.org/W3180641083","https://openalex.org/W3189516440","https://openalex.org/W4220655225","https://openalex.org/W4221060446","https://openalex.org/W4230920194","https://openalex.org/W4300979672","https://openalex.org/W4318486293","https://openalex.org/W4322758615","https://openalex.org/W4366240674","https://openalex.org/W4386736412","https://openalex.org/W4387009447","https://openalex.org/W4391239699","https://openalex.org/W4392578267","https://openalex.org/W4403134216","https://openalex.org/W4403690761","https://openalex.org/W4403889525","https://openalex.org/W4407164754","https://openalex.org/W4412411984","https://openalex.org/W4415344737","https://openalex.org/W4416122151","https://openalex.org/W7153142035","https://openalex.org/W7167941076"],"related_works":["https://openalex.org/W4394567287","https://openalex.org/W7166853558","https://openalex.org/W6987087333","https://openalex.org/W4362531944","https://openalex.org/W4403204441","https://openalex.org/W7167941076","https://openalex.org/W7162654569","https://openalex.org/W4389326972","https://openalex.org/W4292080822","https://openalex.org/W4392293823"],"abstract_inverted_index":{"A":[0],"bstract":[1],"Automated":[2],"lesion":[3,31,154,327],"segmentation":[4,32,104,141,155],"is":[5,182,271,387],"increasingly":[6],"central":[7],"to":[8,51,100,196,207,215,232,280,332,389],"PSMA":[9,131],"PET/CT":[10,132],"interpretation,":[11],"supporting":[12],"staging,":[13],"treatment":[14],"planning,":[15],"and":[16,75,106,134,161,185,286,312,335,360],"response":[17],"assessment":[18],"at":[19],"a":[20,109,115,123,139,144,172,222,234,238,253,258,302],"scale":[21],"that":[22,250,369],"outpaces":[23],"available":[24],"nuclear-medicine":[25],"expertise.":[26],"However,":[27],"automated":[28],"PSMA-PET/CT":[29],"whole-body":[30,130,176,380],"models":[33,67],"are":[34],"trained":[35,140,259],"on":[36,301,313],"images":[37],"alone,":[38],"with":[39,114,138,198,237,257,326],"no":[40,288],"knowledge":[41,57,93],"of":[42,58,94,175,228],"where":[43,390],"in":[44,64,71,78,147,210,249,340,379],"the":[45,83,159,162,166,178,194,211,281,298,346,384,391],"body":[46],"prostate":[47],"metastases":[48,169],"actually":[49,393],"tend":[50],"occur.":[52],"Radiologists":[53],"use":[54],"clinical":[55],"domain":[56],"metastatic":[59,95],"spread,":[60],"but":[61],"its":[62,136],"absence":[63],"machine":[65],"learning":[66,103,240,377],"produces":[68],"false":[69],"positives":[70],"anatomically":[72],"implausible":[73],"locations":[74],"missed":[76],"lesions":[77],"high-risk":[79,348],"sites":[80,349],"such":[81,108],"as":[82,158,165],"liver.":[84],"In":[85],"this":[86,199,269,351],"work,":[87,352],"we":[88],"explore":[89],"whether":[90],"population-level":[91,370],"spatial":[92,371],"spread":[96],"can":[97,373],"be":[98,112],"used":[99],"augment":[101,375],"deep":[102,239,376],"predictions,":[105,242],"how":[107],"prior":[110,256,392],"should":[111],"fused":[113],"network\u2019s":[116,260],"output,":[117],"without":[118],"additional":[119],"training.":[120],"We":[121,219],"build":[122],"data-driven":[124,163],"metastasis":[125],"atlas":[126,164,236,274,291],"from":[127,151,244,330],"375":[128],"expert-annotated":[129],"scans":[133],"investigate":[135],"fusion":[137,205,248,292,385],"network":[142,283],"under":[143],"Bayesian":[145,190,213,355],"framework,":[146],"which":[148],"prediction":[149],"probabilities":[150],"an":[152,314],"nnU-Net-based":[153],"model":[156],"serve":[157],"likelihood":[160],"prior.":[167],"Because":[168],"occupy":[170],"only":[171],"small":[173],"fraction":[174],"voxels,":[177],"atlas\u2019s":[179],"peak":[180],"probability":[181],"too":[183],"low,":[184],"standard":[186,204],"power-scaled":[187,361],"or":[188],"naive":[189,212,354],"pooling":[191,356,362],"references":[192],"lack":[193],"tools":[195],"deal":[197],"shortcoming.":[200],"This":[201,290],"causes":[202],"these":[203],"strategies":[206],"fail":[208],"and,":[209],"case,":[214],"sharply":[216,359],"degrade":[217],"performance.":[218],"instead":[220],"derive":[221],"calibrated,":[223],"background-referenced":[224],"log-odds":[225],"fusion,":[226],"one":[227],"many":[229],"possible":[230],"approaches":[231],"combine":[233],"population":[235,255],"model\u2019s":[241],"distinct":[243],"classical":[245],"multi-atlas":[246],"label":[247],"it":[251,364],"fuses":[252],"single":[254],"softmax":[261],"rather":[262],"than":[263],"combining":[264],"several":[265],"registered":[266],"atlases.":[267],"Furthermore,":[268],"approach":[270],"neutral":[272],"outside":[273],"support":[275],"by":[276],"construction,":[277],"reduces":[278],"exactly":[279],"baseline":[282,299,339],"when":[284],"unweighted,":[285],"requires":[287],"retraining.":[289],"significantly":[293],"improved":[294],"mean":[295],"Dice":[296,336],"over":[297,338],"nnU-Net":[300],"disjoint":[303],"internal":[304],"test":[305],"set":[306],"(+0.011,":[307],"Holm-adjusted":[308,319],"p":[309,320],"=":[310,321],"0.021)":[311],"independent":[315],"external":[316],"cohort":[317],"(+0.0129,":[318],"3.8\u00d710":[322],"\u2212":[323],"16":[324],"),":[325],"sensitivity":[328],"improving":[329,337],"0.849":[331],"0.861":[333],"internally":[334],"every":[341],"stratified":[342],"anatomic":[343],"region,":[344],"including":[345],"rare,":[347],"motivating":[350],"while":[353],"degrades":[357],"performance":[358],"underperforms":[363],"throughout.":[365],"Our":[366],"findings":[367],"suggest":[368],"priors":[372],"meaningfully":[374],"predictions":[378],"oncologic":[381],"segmentation,":[382],"provided":[383],"rule":[386],"calibrated":[388],"carries":[394],"signal.":[395]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-09-01T00:00:00"}