{"id":"W13685733","doi":"10.1007/978-3-642-38868-2_26","title":"Efficient 3D Multi-region Prostate MRI Segmentation Using Dual Optimization","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Consistency (knowledge bases); Segmentation; Relaxation (psychology); Image segmentation; Artificial intelligence; Prostate; Algorithm; Pattern recognition (psychology); Mathematical optimization; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007902507,0.001250036,0.002057882,0.001554314,0.0006049172,0.001977508,0.001692762,0.001813907,0.003383497],"category_scores_gemma":[0.00173449,0.001615239,0.001864146,0.001349997,0.0005451049,0.00125519,0.002594678,0.001612238,0.00139699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007181023,"about_ca_system_score_gemma":0.001495412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003140866,"about_ca_topic_score_gemma":0.004366767,"domain_scores_codex":[0.9993712,0.0001175268,0.00003800499,0.0001190081,0.0002813434,0.00007287156],"domain_scores_gemma":[0.9993345,0.0003031672,0.00008105291,0.00009671462,0.0001343466,0.00005025012],"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":[0.000660049,0.0002271371,0.00117779,0.0003559098,0.000209908,0.0002337856,0.0001778434,0.6071939,0.05232614,0.01081468,0.004453286,0.3221696],"study_design_scores_gemma":[0.00001333112,0.00002002933,0.0001229974,0.000006337787,0.00001182556,0.00006786862,0.000008254672,0.9936174,0.003431218,0.001933488,0.000757577,0.00000966505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006656528,0.000142295,0.9913977,0.00008732064,0.00001788752,0.00002508059,0.00005840411,0.0006835087,0.0009311711],"genre_scores_gemma":[0.1254969,0.000154849,0.8702862,0.0001072174,0.00004102058,0.0001220911,0.0003311835,0.0007104219,0.002750089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003383497,"threshold_uncertainty_score":0.01131892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993840555957049,"score_gpt":0.2860397709251951,"score_spread":0.2661013653656246,"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."}}