{"id":"W1607529660","doi":"10.1007/978-3-642-15745-5_77","title":"3D Ultrasound to Stereoscopic Camera Registration through an Air-Tissue Boundary","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Fiducial marker; Computer vision; Stereoscopy; Artificial intelligence; 3D ultrasound; Ultrasound; Computer science; Tracking (education); Image registration; Medicine; Radiology; Image (mathematics)","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":[0.0009752714,0.0006494667,0.0005782427,0.001155613,0.000510061,0.001958013,0.0007479763,0.001190891,0.006932793],"category_scores_gemma":[0.004040115,0.0008415034,0.0006929256,0.0008977152,0.0006670131,0.001403433,0.002599749,0.0013499,0.002224565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006035304,"about_ca_system_score_gemma":0.002354111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003294399,"about_ca_topic_score_gemma":0.003672507,"domain_scores_codex":[0.9989705,0.0002219667,0.00006182526,0.0001851661,0.0004694682,0.00009112824],"domain_scores_gemma":[0.9991471,0.0002922948,0.00008730885,0.0001825447,0.0002334744,0.00005738679],"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":[0.001077094,0.0001750135,0.002081508,0.0004594454,0.00006786415,0.001102978,0.0009220542,0.02839153,0.4695674,0.03255717,0.007899612,0.4556983],"study_design_scores_gemma":[0.0001742825,0.000561589,0.008278613,0.00020434,0.0001249976,0.005229887,0.000489192,0.3895545,0.5333796,0.01500682,0.04684828,0.0001479815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03146699,0.0002881897,0.9577039,0.0002491813,0.0001438951,0.0001767234,0.0001819476,0.001766556,0.008022556],"genre_scores_gemma":[0.2737711,0.0004384318,0.7166937,0.0001593689,0.00006642918,0.0001520711,0.0002712875,0.0006423833,0.007805252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006932793,"threshold_uncertainty_score":0.02319252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295884090933904,"score_gpt":0.3288610079169619,"score_spread":0.3059021670076229,"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."}}