{"id":"W2160871494","doi":"10.1109/isbi.2006.1624936","title":"Automatic Mri Brain Tissue Segmentation Using a Hybrid Statistical and Geometric Model","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Artificial intelligence; Voxel; Computer science; Image segmentation; White matter; Computer vision; Magnetic resonance imaging; Pattern recognition (psychology); Brain tissue; Scale-space segmentation; Biomedical engineering; Radiology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001444312,0.0008844686,0.001265353,0.001918265,0.0005167609,0.00115028,0.001755146,0.001437855,0.0007631053],"category_scores_gemma":[0.00241717,0.000928606,0.001787763,0.001090939,0.001359884,0.002373759,0.001199892,0.0008319388,0.0006736843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006394159,"about_ca_system_score_gemma":0.00109729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816198,"about_ca_topic_score_gemma":0.003577498,"domain_scores_codex":[0.9987769,0.0002081516,0.00007046317,0.0002913023,0.0005852695,0.00006786374],"domain_scores_gemma":[0.9989393,0.0004042512,0.0001678015,0.000228066,0.0002233234,0.00003728116],"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":[0.0001331732,0.00008532016,0.001544066,0.0001799491,0.0002431824,0.0002507979,0.0001772717,0.452948,0.1842584,0.02131269,0.001739938,0.3371272],"study_design_scores_gemma":[0.000009516666,0.00008210551,0.0007805087,0.000008480384,0.0000404086,0.000337385,0.0000143933,0.9730737,0.0179272,0.005444751,0.002235607,0.00004592683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003410742,0.00007963854,0.9958504,0.00003720333,0.000008346203,0.00001159497,0.000012047,0.0004593363,0.0001307385],"genre_scores_gemma":[0.1033372,0.0002801401,0.894501,0.0001010564,0.00005648431,0.00009951222,0.0001868101,0.0003602515,0.001077549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001918265,"threshold_uncertainty_score":0.007638335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.017973014726919,"score_gpt":0.3176414022037991,"score_spread":0.2996683874768801,"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."}}