{"id":"W2073252747","doi":"10.1016/j.compmedimag.2008.05.001","title":"Optimal 3D reconstruction of coronary arteries for 3D clinical assessment","year":2008,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RANSAC; Outlier; Frenet–Serret formulas; Computer vision; Constraint (computer-aided design); Artificial intelligence; Coronary arteries; Fluoroscopy; Pixel; Curvature; 3D reconstruction; Mathematics; Computer science; Artery; Image (mathematics); Medicine; Radiology; Geometry; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098112,0.0008139372,0.0008457638,0.001575059,0.0002392004,0.001786726,0.0006078056,0.00139599,0.003628958],"category_scores_gemma":[0.004744515,0.001287977,0.001017442,0.0008893891,0.0004046039,0.000853164,0.001101475,0.0013073,0.001301944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003593348,"about_ca_system_score_gemma":0.001344795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001677677,"about_ca_topic_score_gemma":0.001871562,"domain_scores_codex":[0.9992414,0.0002515201,0.00006574855,0.00007586472,0.0003070819,0.000058418],"domain_scores_gemma":[0.9991056,0.0004496384,0.00006053417,0.0001666521,0.0001712009,0.00004643587],"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":[0.0008033651,0.0002183263,0.005629721,0.000632757,0.0001823276,0.0007128483,0.0004063903,0.2994882,0.1042939,0.01683524,0.0103948,0.560402],"study_design_scores_gemma":[0.00005353475,0.00008377834,0.001788584,0.00005986455,0.00005979995,0.001707723,0.00004958252,0.954444,0.02646421,0.01047535,0.004747908,0.00006571774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01463282,0.0004871819,0.9828461,0.0001709176,0.0000264372,0.00004119384,0.0001653223,0.0008235337,0.000806556],"genre_scores_gemma":[0.1976653,0.001058636,0.799408,0.0001285831,0.00004941833,0.0001329473,0.000328737,0.0004317932,0.0007965559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003628958,"threshold_uncertainty_score":0.0121401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03369036586619951,"score_gpt":0.3499853660381664,"score_spread":0.3162950001719669,"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."}}