{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001059373,0.002217124,0.001053648,0.001732689,0.0004790718,0.001051607,0.001934372,0.0009519809,0.003078659],"category_scores_gemma":[0.003543615,0.0007724848,0.001570658,0.001331887,0.0006926641,0.0009998393,0.002467727,0.001623417,0.003688528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00037399,"about_ca_system_score_gemma":0.00170793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005156586,"about_ca_topic_score_gemma":0.01177815,"domain_scores_codex":[0.9991354,0.0001294377,0.00004944597,0.0001595985,0.0004618862,0.00006423528],"domain_scores_gemma":[0.9989723,0.0002082797,0.0001514554,0.0003853655,0.0002312001,0.00005139571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003334857,0.0001180098,0.002967755,0.0003825925,0.0004348359,0.0006332379,0.0002034291,0.070903,0.04026441,0.003836344,0.04314319,0.8367798],"study_design_scores_gemma":[0.00008786455,0.0002634759,0.005913456,0.0001239658,0.0001879049,0.004165969,0.0002219822,0.8615542,0.06945343,0.0141757,0.04370103,0.0001509734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01712714,0.001173773,0.9656959,0.0004315132,0.0003465731,0.0002354355,0.001432559,0.0115867,0.001970425],"genre_scores_gemma":[0.1609973,0.002560083,0.8133623,0.0005933777,0.0003277803,0.0003141602,0.01132241,0.001907664,0.008614814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005156586,"threshold_uncertainty_score":0.01029915,"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."}}