{"id":"W2066860967","doi":"10.1007/s11548-013-0904-9","title":"Improving N-wire phantom-based freehand ultrasound calibration","year":2013,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Imaging phantom; Calibration; Computer science; Fiducial marker; Computer vision; Artificial intelligence; Intersection (aeronautics); Ultrasound; Segmentation; Image-guided surgery; Image plane; Acoustics; Optics; Image (mathematics); Physics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0015053,0.001373965,0.0008020133,0.001025254,0.0003378556,0.0009599504,0.001513382,0.001961391,0.008468566],"category_scores_gemma":[0.009401827,0.0009979117,0.0005137515,0.0009105422,0.0002917715,0.002007852,0.002293405,0.0008334498,0.003744867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003335401,"about_ca_system_score_gemma":0.0006404067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009893087,"about_ca_topic_score_gemma":0.001787185,"domain_scores_codex":[0.9975066,0.000639467,0.0001173465,0.0003688163,0.001266778,0.0001010055],"domain_scores_gemma":[0.9940681,0.002933882,0.0004005315,0.00112432,0.001363952,0.000109172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000917935,0.0002769317,0.002679525,0.000489676,0.00008855474,0.0003175898,0.0002926667,0.0313095,0.4898048,0.002003607,0.003823218,0.467996],"study_design_scores_gemma":[0.00006821743,0.0003540465,0.004850957,0.00007700492,0.0001225976,0.002238724,0.00009429135,0.5611237,0.414949,0.001103924,0.01490517,0.000112397],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03313082,0.0004853306,0.9585883,0.0001272003,0.00008066472,0.00006102961,0.00007448325,0.004071378,0.00338083],"genre_scores_gemma":[0.2534448,0.0005166531,0.7381337,0.0002684081,0.00004959499,0.00006953141,0.0003655898,0.001301901,0.005849769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008468566,"threshold_uncertainty_score":0.02833021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017493662114638,"score_gpt":0.2381273624220966,"score_spread":0.2279524258009503,"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."}}