{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003363528,0.0001195952,0.0001604597,0.0001077445,0.0001694089,0.0001285958,0.0002073196,0.0000767577,0.0001023083],"category_scores_gemma":[0.0002801747,0.00009889418,0.0000197042,0.0007618899,0.0002695611,0.0003872289,0.00003960204,0.0003318301,0.00001986501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004176483,"about_ca_system_score_gemma":0.0001776239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009505994,"about_ca_topic_score_gemma":0.0003795314,"domain_scores_codex":[0.9986819,0.00002451411,0.0001942111,0.0004451763,0.0003752631,0.0002789315],"domain_scores_gemma":[0.9990693,0.0002405141,0.00004092785,0.0003858896,0.00009259291,0.0001707316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005824032,0.0001540283,0.0237785,0.00002124929,0.000005019532,0.00006077299,0.005701876,0.0126764,0.0667531,0.00037247,0.000007465665,0.8904109],"study_design_scores_gemma":[0.006305733,0.003132086,0.5605903,0.0004102213,0.00004117787,0.0007088318,0.00002860276,0.1830831,0.1820131,0.01313353,0.04920326,0.001350099],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4774037,0.00001057648,0.5207989,0.0008510026,0.0005102144,0.0001378792,5.561952e-7,0.00004280849,0.0002443414],"genre_scores_gemma":[0.7719173,6.967182e-7,0.2235616,0.004184301,0.000318147,0.000003049274,0.00000532541,0.000006116782,0.000003393554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8890608,"threshold_uncertainty_score":0.4032788,"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."}}