{"id":"W3092442943","doi":"10.1002/mrm.28547","title":"BISON: Brain tissue segmentation pipeline using T <sub>1</sub> ‐weighted magnetic resonance images and a random forest classifier","year":2020,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Random forest; Magnetic resonance imaging; Artificial intelligence; Hyperintensity; White matter; Computer science; Image segmentation; Pattern recognition (psychology); Kappa; Nuclear medicine; Medicine; Radiology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003163011,0.001858004,0.000929118,0.002708941,0.0007568438,0.001074688,0.001344942,0.001082785,0.005387063],"category_scores_gemma":[0.003079111,0.000914209,0.00121076,0.00094112,0.0004467395,0.001156102,0.001375036,0.001108495,0.003891056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139118,"about_ca_system_score_gemma":0.001990995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0115479,"about_ca_topic_score_gemma":0.01871834,"domain_scores_codex":[0.9991348,0.0001290852,0.00007034364,0.0002990798,0.0002864675,0.00008012302],"domain_scores_gemma":[0.9991334,0.0002257152,0.0001220535,0.00008950374,0.000375427,0.00005391518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001031809,0.0002843417,0.008669514,0.000363676,0.0002490917,0.0002936781,0.0003909449,0.02605417,0.065399,0.0029354,0.03884845,0.85548],"study_design_scores_gemma":[0.0001528965,0.0004003496,0.02246805,0.0001229305,0.000160511,0.0009322676,0.0001215603,0.8618816,0.07306498,0.00767394,0.03284929,0.0001716465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02363347,0.0002711411,0.9403287,0.0001207696,0.00008401427,0.0007443706,0.001545534,0.03173272,0.001539287],"genre_scores_gemma":[0.06167354,0.0002106779,0.9253,0.0001537887,0.00003965481,0.0009884258,0.005137891,0.001873871,0.004622155],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0115479,"threshold_uncertainty_score":0.02296138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02346908784418475,"score_gpt":0.2960747590620479,"score_spread":0.2726056712178632,"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."}}