{"id":"W2072590435","doi":"10.1118/1.3566015","title":"Technical Note: Unsupervised C‐arm pose tracking with radiographic fiducial","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Fluoroscopy; Artificial intelligence; Computer vision; Ground truth; Segmentation; Computer science; Prostate brachytherapy; Image-guided radiation therapy; Radiography; Robotic arm; Pose; Image registration; Nuclear medicine; Medicine; Medical imaging; Radiology; Brachytherapy; Image (mathematics); Radiation therapy","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":[0.001615014,0.0008277947,0.0004402866,0.0007484893,0.0004953588,0.0008627184,0.001173466,0.001188027,0.002673896],"category_scores_gemma":[0.00523553,0.0004522628,0.0008147967,0.000641182,0.0005315375,0.0007678293,0.0007520234,0.0008050918,0.001407832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004868509,"about_ca_system_score_gemma":0.001381917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004861107,"about_ca_topic_score_gemma":0.007278027,"domain_scores_codex":[0.9986828,0.0001839974,0.00009417118,0.0003679451,0.0006007656,0.0000702976],"domain_scores_gemma":[0.9964895,0.001222227,0.000334461,0.000852738,0.001037062,0.00006394162],"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":[0.0003896939,0.0002328429,0.0156476,0.0003026984,0.0002292002,0.00027655,0.0002167255,0.08874802,0.1199064,0.004136144,0.008821244,0.761093],"study_design_scores_gemma":[0.0001174435,0.0007563507,0.02658439,0.0000619476,0.000192816,0.003265524,0.00006897566,0.7531453,0.1916963,0.002921125,0.02101914,0.0001707148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01823264,0.0001323448,0.978699,0.0001656121,0.00004577376,0.000103168,0.00009703622,0.001283069,0.001241328],"genre_scores_gemma":[0.204379,0.0002305381,0.7891625,0.0002095641,0.0001117705,0.0002378106,0.0007193671,0.0003193215,0.004630133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004861107,"threshold_uncertainty_score":0.009665668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04854341395690814,"score_gpt":0.2944031790398107,"score_spread":0.2458597650829026,"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."}}