{"id":"W2014748826","doi":"10.1109/tmi.2012.2195009","title":"Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Fluoroscopy; Image registration; Computer vision; Affine transformation; Artificial intelligence; Computer science; Matching (statistics); Minification; Rigid transformation; Image (mathematics); Radiology; Medicine; Mathematics; Geometry","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":[0.0008095002,0.0001497542,0.0002840618,0.0001575701,0.00008753546,0.00002258601,0.0004152447,0.00006612995,0.00008004138],"category_scores_gemma":[0.00003975233,0.000132577,0.0001945362,0.000235895,0.0002994196,0.001131703,0.000004993141,0.0002150434,0.00000395086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005413408,"about_ca_system_score_gemma":0.000138798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003716308,"about_ca_topic_score_gemma":0.000005733036,"domain_scores_codex":[0.9979874,0.000115572,0.0006114929,0.0002615987,0.0007489999,0.0002748874],"domain_scores_gemma":[0.9985919,0.0002982418,0.0002441489,0.0004196368,0.0002146933,0.0002313866],"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":[0.0001862641,0.00233691,0.000309654,0.0009781967,0.0003284804,0.00001242621,0.003989922,0.001040046,0.03441357,0.002913126,0.003204079,0.9502873],"study_design_scores_gemma":[0.00148436,0.0007417804,0.0003951549,0.002440362,0.0002260621,0.00009171164,0.0005426102,0.2818163,0.7102752,0.001327815,0.0001358129,0.0005228193],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001268343,0.0002781237,0.9967323,0.0003846758,0.0004748345,0.0004296796,0.00003018495,0.0001287381,0.000273107],"genre_scores_gemma":[0.6394687,0.00007361706,0.3599728,0.0002019069,0.00003913734,0.0001477444,0.000005571616,0.00001331236,0.00007721653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9497645,"threshold_uncertainty_score":0.5406333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03001594939189509,"score_gpt":0.3188771557501714,"score_spread":0.2888612063582764,"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."}}