{"id":"W2083745515","doi":"10.1117/12.770331","title":"2D/3D registration with the CMA-ES method","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Kalman filter; Computer vision; Maxima and minima; Transformation (genetics); Simplex; Computation; Image registration; Imaging phantom; Algorithm; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006653997,0.0003130554,0.000397817,0.00008374413,0.000143156,0.0000851891,0.0007996177,0.0001409922,0.00001708527],"category_scores_gemma":[0.0003168228,0.0002015134,0.0004594566,0.0004499467,0.0003086923,0.0004040171,0.000064819,0.0004213994,0.000002240238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009542548,"about_ca_system_score_gemma":0.00002779066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001335001,"about_ca_topic_score_gemma":3.168829e-7,"domain_scores_codex":[0.9979613,2.189342e-8,0.0005332561,0.0003015433,0.000831101,0.0003727776],"domain_scores_gemma":[0.9984913,0.000192815,0.000212267,0.00007979797,0.0008967761,0.0001269831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007600876,0.0001655743,0.001026418,0.001028396,0.002184912,0.000001008132,0.001626957,0.007666813,0.6410743,0.2953583,0.04750605,0.00228522],"study_design_scores_gemma":[0.002251566,0.0004177803,0.002337212,0.0006981664,0.0009463188,0.0002170537,0.003960399,0.738439,0.2143413,0.001044428,0.03415731,0.001189416],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899009,0.0001672024,0.002085692,0.003339261,0.0001296661,0.000282018,0.00001653368,0.000189272,0.003889421],"genre_scores_gemma":[0.7147659,0.0003239997,0.2828673,0.0002312279,0.0007577693,0.000189158,0.00001350824,0.0001122542,0.0007389181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7307723,"threshold_uncertainty_score":0.8217479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284800818361125,"score_gpt":0.2337165615781537,"score_spread":0.2208685533945425,"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."}}