{"id":"W2066993558","doi":"10.1118/1.2123350","title":"2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Offset (computer science); Image registration; Artificial intelligence; Computer vision; Patient registration; Cardiac cycle; Medicine; Computer science; Nuclear medicine; Cardiology","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":[0.000357551,0.00007863843,0.0001467711,0.00001797116,0.00004302861,0.0000218837,0.0002681096,0.00006404765,0.000004837],"category_scores_gemma":[0.0002151483,0.00007069085,0.0000460613,0.0001348671,0.0001342876,0.0002834656,0.00006800659,0.00008863617,0.000001202827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001593204,"about_ca_system_score_gemma":0.00008502521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004577195,"about_ca_topic_score_gemma":5.812565e-7,"domain_scores_codex":[0.9989138,0.00002374217,0.0002295918,0.0002066788,0.0004874552,0.0001387795],"domain_scores_gemma":[0.9993878,0.0001228945,0.0001091929,0.0001854395,0.00007160939,0.0001230632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006679347,0.00007820845,0.001572378,0.0001102691,0.00001695946,0.000002706884,0.0005669534,0.000006806827,0.0009836543,0.002048993,0.01759197,0.9770144],"study_design_scores_gemma":[0.003374779,0.001572736,0.02612634,0.001621341,0.0001175579,0.00004574037,0.0001926753,0.1692116,0.7354887,0.04196518,0.01894739,0.00133594],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002379229,0.0004823398,0.9953686,0.001021716,0.0000712572,0.0002318542,0.000003590675,0.000109818,0.0003316153],"genre_scores_gemma":[0.6880669,0.00003552116,0.309938,0.001268458,0.0004539024,0.00008373489,0.00002403262,0.00001098577,0.0001184768],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9756785,"threshold_uncertainty_score":0.2882689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134332005236337,"score_gpt":0.309286989899083,"score_spread":0.2879436698467196,"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."}}