{"id":"W2036250545","doi":"10.1007/s11548-011-0649-2","title":"3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Image segmentation; Feature (linguistics); Scale-space segmentation; Prior probability; Brain tumor; Level set method; Computer vision; Bayesian probability; Medicine; Pathology","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":[0.0009662668,0.0001582415,0.0003083764,0.0005640061,0.00007419005,0.00008848933,0.0005207471,0.0001014333,0.00004682217],"category_scores_gemma":[0.0001586101,0.0001336569,0.0001304828,0.0001599792,0.00009848185,0.0006013637,0.0001107876,0.0002904879,0.000002741016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058748,"about_ca_system_score_gemma":0.0001718125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007640505,"about_ca_topic_score_gemma":6.58266e-7,"domain_scores_codex":[0.9981034,0.0004190388,0.0005959808,0.0002354688,0.0004785305,0.0001676111],"domain_scores_gemma":[0.9977615,0.00090559,0.0006785694,0.0001380157,0.0003903351,0.0001259754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001766013,0.001559679,0.0946594,0.0000914964,0.003440124,0.006176061,0.01268029,0.001155504,0.01675669,0.007386142,0.1384832,0.7158453],"study_design_scores_gemma":[0.004948159,0.001363042,0.6911795,0.0008994261,0.0001383643,0.03511139,0.0001940742,0.2468991,0.01001332,0.005560432,0.002346748,0.001346534],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.101428,0.00006443701,0.8945569,0.001784392,0.001997464,0.0000771388,0.000005417755,0.00004133239,0.0000449063],"genre_scores_gemma":[0.2709323,0.00003693981,0.7185963,0.009674778,0.0006862485,0.000004705717,0.00002385481,0.00001340819,0.00003134347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7144988,"threshold_uncertainty_score":0.5450369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452413227737394,"score_gpt":0.3019003378658711,"score_spread":0.2573762055884971,"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."}}