{"id":"W1617148189","doi":"10.1109/iembs.2000.901440","title":"Automated prostate implant seed localization from fluoroscopic images using simulated annealing","year":2002,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôtel-Dieu de Québec; Centre hospitalier universitaire de Québec; Université Laval","funders":"","keywords":"Imaging phantom; Simulated annealing; Fluoroscopy; Computer science; Artificial intelligence; Computer vision; Image matching; Radiography; Prostate; Biomedical engineering; Nuclear medicine; Radiology; Algorithm; Medicine; Image (mathematics)","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":[0.000770704,0.0006646522,0.0006696619,0.0006704495,0.0003659544,0.0006603839,0.0007431417,0.000831291,0.001016492],"category_scores_gemma":[0.001932883,0.0006201238,0.0007742244,0.000488419,0.0005060495,0.0004339098,0.0003859753,0.0004849489,0.0004103305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005918327,"about_ca_system_score_gemma":0.0006492049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003280951,"about_ca_topic_score_gemma":0.004966426,"domain_scores_codex":[0.9996145,0.0001388591,0.00002424235,0.00005979202,0.0001347795,0.00002782333],"domain_scores_gemma":[0.9992183,0.0004664741,0.00008209731,0.0001198043,0.00009911359,0.00001413861],"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":[0.000282709,0.0001300391,0.002117811,0.0001605061,0.000205936,0.0001172894,0.0002703822,0.6612526,0.06079212,0.005723067,0.001057788,0.2678899],"study_design_scores_gemma":[0.00002140324,0.00009873106,0.0007480628,0.000008360314,0.00003405633,0.0001170093,0.00001483217,0.973349,0.02224511,0.001447774,0.001888687,0.000026916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02207866,0.0001311282,0.9758399,0.00005272294,0.00001582123,0.00005674846,0.00001605098,0.0009439078,0.0008650417],"genre_scores_gemma":[0.1662033,0.0001177712,0.8323954,0.00003226478,0.000008417701,0.0001280973,0.00006878132,0.0001555101,0.0008904406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003280951,"threshold_uncertainty_score":0.006523669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525367478059243,"score_gpt":0.2266966764812897,"score_spread":0.2114430017006972,"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."}}