{"id":"W2034344854","doi":"10.1016/j.mri.2010.08.007","title":"Unsupervised MRI segmentation of brain tissues using a local linear model and level set","year":2010,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Level set (data structures); Level set method; White matter; Magnetic resonance imaging; Image segmentation; Computer vision","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":[0.0003871391,0.0001374953,0.0001611604,0.0001177878,0.00007277769,0.00008054025,0.0003750295,0.0000403357,0.00003178907],"category_scores_gemma":[0.00009102464,0.0001387673,0.0000270666,0.0002252393,0.0002721589,0.0004196351,0.0002053286,0.0001703471,0.000002513873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001721359,"about_ca_system_score_gemma":0.00007579965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001279959,"about_ca_topic_score_gemma":0.00001193846,"domain_scores_codex":[0.99869,0.0000561557,0.000315494,0.0003580708,0.0003563911,0.0002238929],"domain_scores_gemma":[0.9992623,0.0000770157,0.00009238376,0.0003640706,0.0001074263,0.00009680224],"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":[0.000004476809,0.00002154872,0.001167762,0.00002989319,0.000001078981,0.000007636594,0.000972362,0.000156206,0.2446717,0.0003687358,0.0004822367,0.7521163],"study_design_scores_gemma":[0.0003953629,0.00003505058,0.00151391,0.00004752603,0.00000455616,0.00002567092,0.00008154642,0.8941002,0.1024571,0.0009425441,0.0002575818,0.0001388939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05032312,0.0007952742,0.947566,0.0008498266,0.00007009962,0.0002125318,0.000008226572,0.00009134465,0.00008353718],"genre_scores_gemma":[0.1684744,0.00003821457,0.8305768,0.0006801291,0.00002888102,0.00001295192,0.000003951822,0.00001344012,0.0001711918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.893944,"threshold_uncertainty_score":0.5658766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0305411140393526,"score_gpt":0.3129307935829534,"score_spread":0.2823896795436008,"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."}}