{"id":"W4387831820","doi":"10.1109/access.2023.3326342","title":"PCSS: Skull Stripping With Posture Correction From 3D Brain MRI for Diverse Imaging Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Skull; Neuroimaging; Stripping (fiber); Computer science; Neuroscience; Medicine; Anatomy; Materials science; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001036822,0.0001284149,0.0001497718,0.0001059275,0.00009306136,0.0001027287,0.0001804133,0.00003528819,0.0001034086],"category_scores_gemma":[0.00002566524,0.0001120355,0.00005143552,0.0002258467,0.0000258347,0.0002950445,0.00003047382,0.0001356135,0.00004526987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005527066,"about_ca_system_score_gemma":0.00000674893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001794534,"about_ca_topic_score_gemma":0.00001728409,"domain_scores_codex":[0.9992202,0.00001356044,0.0001283708,0.0002163897,0.0001853462,0.0002361619],"domain_scores_gemma":[0.9995685,0.0001369359,0.0000298984,0.0001697591,0.00001312728,0.00008183433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002677212,0.00004505309,0.03124281,0.0001124543,0.0004652806,0.00009883976,0.001215745,0.4763289,0.01586051,0.000002821001,0.2482609,0.2263398],"study_design_scores_gemma":[0.0006345049,0.00001125585,0.005344577,0.0001232497,0.0001273642,0.000002366416,0.000495517,0.9651089,0.005055493,0.00007444483,0.02272781,0.0002945586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3429362,0.00018528,0.6522217,0.001240982,0.001812183,0.0002474453,0.00007941959,0.0008149522,0.0004618563],"genre_scores_gemma":[0.9976185,0.00009391087,0.0007292628,0.00032191,0.0004513827,0.00004223067,0.0001370723,0.00004139764,0.0005643132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6546823,"threshold_uncertainty_score":0.4568677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330041114209765,"score_gpt":0.2468289955423489,"score_spread":0.2335285844002513,"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."}}