{"id":"W2012255096","doi":"10.1109/tmi.2014.2300694","title":"Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Ontario Institute for Cancer Research","keywords":"Image segmentation; Regular polygon; Segmentation; Computer vision; Prostate; Artificial intelligence; Symmetry (geometry); Computer science; Mathematics; Medicine; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007591746,0.0002308535,0.0002323633,0.0002652247,0.0003411994,0.0002908995,0.000360944,0.00007209348,0.00007328155],"category_scores_gemma":[0.00003341727,0.000194183,0.00003814771,0.0004684484,0.0003776047,0.0007871833,0.000008022531,0.0003703207,0.000002883688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007582875,"about_ca_system_score_gemma":0.0001125929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004487549,"about_ca_topic_score_gemma":0.000001002056,"domain_scores_codex":[0.9973887,0.000278419,0.0003621274,0.0006141481,0.001024944,0.0003316204],"domain_scores_gemma":[0.9988017,0.0001411937,0.0001197733,0.0003631193,0.0001224082,0.0004517679],"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":[0.00005982482,0.0007016681,0.00008037767,0.0001207678,0.00004632719,0.00003959118,0.001667009,0.2311682,0.003662915,0.0002368212,0.00004614914,0.7621703],"study_design_scores_gemma":[0.001011366,0.0001197251,0.00001581023,0.00007733533,0.00002926704,0.0001444947,0.000207093,0.9602038,0.03792369,0.00003409088,0.000004512831,0.0002287884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004230037,0.00002317591,0.9939608,0.00051105,0.0002436403,0.0003866037,0.000003802279,0.0004372385,0.0002036692],"genre_scores_gemma":[0.3664071,0.00001842106,0.6324245,0.001009021,0.00005217636,0.00004477632,0.00000637191,0.00002204003,0.00001563753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7619416,"threshold_uncertainty_score":0.7918553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108376066025516,"score_gpt":0.267715117843115,"score_spread":0.2568775112405633,"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."}}