{"id":"W2168567852","doi":"10.1109/isspit.2006.270779","title":"MRI Brain Extraction with Combined Expectation Maximization and Geodesic Active Contours","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Geodesic; Robustness (evolution); Computer science; Artificial intelligence; Maximization; Statistical parametric mapping; Pattern recognition (psychology); Expectation–maximization algorithm; Parametric statistics; Computer vision; Magnetic resonance imaging; Feature extraction; Mathematics; Mathematical optimization; Maximum likelihood; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001088792,0.00008358469,0.00007887206,0.00009057108,0.00007233746,0.0001150817,0.0001030242,0.00003699973,0.00004017933],"category_scores_gemma":[0.00002360582,0.00006915454,0.00001020442,0.0001932975,0.00005003226,0.001031613,0.00002195152,0.0000572857,0.000004426274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000044089,"about_ca_system_score_gemma":0.00002459738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000177411,"about_ca_topic_score_gemma":0.00005346311,"domain_scores_codex":[0.9992376,0.00005913619,0.000136094,0.0002318002,0.0002251104,0.0001102762],"domain_scores_gemma":[0.9995262,0.0001045556,0.00009593648,0.000138632,0.00008874376,0.00004592818],"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":[0.0002986752,0.0007131043,0.005723056,0.0000660358,0.00006886827,0.00006657648,0.003359446,0.000561591,0.1430331,0.1288723,0.04675253,0.6704847],"study_design_scores_gemma":[0.003212429,0.0007989047,0.1074265,0.00005308788,0.00002053318,0.000062224,0.0006998507,0.1259919,0.7473232,0.01360144,0.0002345035,0.0005754824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01235115,0.000007190893,0.9825546,0.001828603,0.00003564982,0.0002524054,4.232245e-7,0.0003613218,0.002608716],"genre_scores_gemma":[0.5676841,0.000006203792,0.4306809,0.0006056835,0.00002821375,0.00004656244,0.00002487517,0.000008073946,0.000915414],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6699092,"threshold_uncertainty_score":0.282004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005276937525245311,"score_gpt":0.2429012268990872,"score_spread":0.2376242893738419,"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."}}