{"id":"W4233711253","doi":"10.1109/iembs.2006.4397912","title":"Segmentation of Prostate from 3-D Ultrasound Volumes Using Shape and Intensity Priors in Level Set Framework","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Level set (data structures); Prior probability; Computer science; Segmentation; Image segmentation; Artificial intelligence; Computer vision; Intensity (physics); Set (abstract data type); Level set method; Ultrasound; Pattern recognition (psychology); Radiology; Medicine; Bayesian probability; Physics; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007927683,0.0006742243,0.001001555,0.001258532,0.00032963,0.001044094,0.001092136,0.001019747,0.00107514],"category_scores_gemma":[0.001922399,0.0008948893,0.001392849,0.0007322612,0.0006043884,0.0009821674,0.001067946,0.0008225545,0.0007612464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000620645,"about_ca_system_score_gemma":0.001112865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266552,"about_ca_topic_score_gemma":0.003390657,"domain_scores_codex":[0.9993349,0.0001305858,0.00003997047,0.0001147602,0.0003444449,0.00003533816],"domain_scores_gemma":[0.999427,0.0002481558,0.00008092215,0.0001193848,0.0001023231,0.00002228458],"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":[0.0002158024,0.00008524895,0.001114005,0.0003161465,0.000141168,0.0002132081,0.0002058635,0.3598419,0.1568192,0.01058087,0.00185578,0.4686108],"study_design_scores_gemma":[0.00001385545,0.00005224158,0.001332135,0.00001900178,0.00002866695,0.000262796,0.00001495882,0.9543912,0.03339998,0.008164052,0.002275362,0.00004572951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00461429,0.00008322917,0.9943538,0.00003718677,0.000006448854,0.00002747847,0.00003411746,0.0005939136,0.0002495829],"genre_scores_gemma":[0.08349264,0.000295755,0.9145216,0.00006739824,0.00002580658,0.0001168372,0.0002922215,0.0003116832,0.0008760879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002266552,"threshold_uncertainty_score":0.004506648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543537095601125,"score_gpt":0.2939300701321404,"score_spread":0.2484946991761292,"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."}}