{"id":"W2889829193","doi":"10.1371/journal.pone.0196945","title":"Improving the SIENA performance using BEaST brain extraction","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroRx Research (Canada); McGill University; Montreal Neurological Institute and Hospital","funders":"Genentech; National Institutes of Health; Mitacs; Eisai; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Eli Lilly and Company; U.S. Department of Defense; Compute Canada; Northern California Institute for Research and Education; Acorda Therapeutics; McGill University; Pfizer; Biogen; BioClinica; Sanofi Genzyme; F. Hoffmann-La Roche; University of Southern California; Sanofi; Alzheimer's Disease Neuroimaging Initiative; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Reproducibility; Brain size; Medicine; Segmentation; Nuclear medicine; Magnetic resonance imaging; Mathematics; Computer science; Artificial intelligence; Statistics; Radiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003863888,0.00006204321,0.00006451862,0.00004331189,0.0002104548,0.0001123711,0.0004200786,0.00002779956,0.00004399869],"category_scores_gemma":[0.0001571245,0.00004639624,0.00001494887,0.0002111601,0.0001003799,0.0007287416,0.0001320104,0.0001187257,0.00005576398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004383421,"about_ca_system_score_gemma":0.00003339418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002699461,"about_ca_topic_score_gemma":0.000001956385,"domain_scores_codex":[0.9991202,0.00004624978,0.0001353682,0.0001786158,0.000357747,0.0001617811],"domain_scores_gemma":[0.9993556,0.00006963252,0.00008787876,0.0003356919,0.0001027938,0.00004837292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002577636,0.0001529412,0.0002320871,0.00002646406,0.00001284381,0.00000115394,0.0004945106,5.903897e-7,0.8793629,0.000126625,0.0002636491,0.1193236],"study_design_scores_gemma":[0.0000660719,0.00008043607,0.0005710296,0.00005485849,0.00000724542,0.000004869637,0.00001571488,0.2564803,0.7425612,0.00006196844,0.00003037199,0.00006592038],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2341411,0.00001104058,0.7643435,0.0006873928,0.00005757891,0.000137578,2.762799e-7,0.0001883058,0.0004332257],"genre_scores_gemma":[0.5908774,0.000005003566,0.4071825,0.001349341,0.0002725982,0.00001308012,6.48679e-7,0.000007077528,0.0002924252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.357161,"threshold_uncertainty_score":0.1891984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06385642346107932,"score_gpt":0.2833986253565527,"score_spread":0.2195422018954734,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}