{"id":"W4253780412","doi":"10.1109/visual.2002.1183755","title":"Direct surface extraction from 3D freehand ultrasound images","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Ultrasound; Extraction (chemistry); 3D ultrasound; Feature extraction; Radiology; Medicine","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":[0.0003480874,0.000879916,0.001053292,0.001888771,0.0003116802,0.001355644,0.0008274982,0.001212767,0.002304166],"category_scores_gemma":[0.002008078,0.0009358538,0.0009502971,0.0009397223,0.000572721,0.001503425,0.00164667,0.0009669266,0.002160801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002360087,"about_ca_system_score_gemma":0.0006272132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008344952,"about_ca_topic_score_gemma":0.001435179,"domain_scores_codex":[0.9992114,0.00005937965,0.00004672359,0.0001131423,0.0005128178,0.00005655971],"domain_scores_gemma":[0.9989261,0.0004144308,0.00009010234,0.0002642437,0.0002731551,0.00003188134],"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":[0.0001124153,0.00003622427,0.000951807,0.0004734565,0.00005254766,0.0004120243,0.000383257,0.01974965,0.3395596,0.004606924,0.002750238,0.6309119],"study_design_scores_gemma":[0.0000410749,0.0002348997,0.005451835,0.0001154735,0.00006606077,0.003188194,0.0004249558,0.4515734,0.4625925,0.01808677,0.05802477,0.000200103],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009830686,0.0002802273,0.9874738,0.00007357895,0.00003935428,0.00004045988,0.0001220553,0.001234635,0.0009051359],"genre_scores_gemma":[0.100844,0.0008878378,0.8936412,0.00009782095,0.00006046769,0.0001103013,0.0007198245,0.0005019475,0.003136555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002304166,"threshold_uncertainty_score":0.007708192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007644307807916764,"score_gpt":0.2047432131951573,"score_spread":0.1970989053872406,"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."}}