{"id":"W1581322187","doi":"10.1109/icra.2015.7139855","title":"3D shape visualization of curved needles in tissue from 2D ultrasound images using RANSAC","year":2015,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"RANSAC; Artificial intelligence; Imaging phantom; Thresholding; Computer vision; Visualization; Computer science; Ultrasound; 3D ultrasound; Epipolar geometry; Image (mathematics); Acoustics; Nuclear medicine; Medicine; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005678584,0.00006498363,0.0001012809,0.00005214532,0.00000994492,0.00001673249,0.00005974243,0.00003977296,0.0001257949],"category_scores_gemma":[0.00002559808,0.00006667964,0.00001133578,0.0001812116,0.00001430266,0.0000748152,0.000006694861,0.00003009325,0.00001522473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002428057,"about_ca_system_score_gemma":0.00000940895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005098691,"about_ca_topic_score_gemma":0.00004687297,"domain_scores_codex":[0.999604,0.000008294395,0.0001599152,0.00007490888,0.00007282605,0.00008002802],"domain_scores_gemma":[0.999744,0.00006384648,0.000017437,0.0001033586,0.00003622743,0.00003514028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003683393,0.00006547142,0.01342656,0.00002876861,0.00002360073,7.877173e-7,0.001582027,0.4967951,0.4848483,0.0006987697,0.0007140405,0.001812884],"study_design_scores_gemma":[0.0006675147,0.00001401789,0.01186275,0.0000419831,0.00002666498,0.000001459962,0.000681522,0.8130274,0.1714706,0.001282511,0.0006576586,0.0002659037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515036,0.0002490714,0.4456834,0.000008350203,0.00006738682,0.0001068366,0.00001405501,0.0001016338,0.002265614],"genre_scores_gemma":[0.9789366,0.00002350532,0.0209148,0.00000823682,0.00003200549,0.000004318047,0.00003339388,0.00001689685,0.0000302776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.427433,"threshold_uncertainty_score":0.2719117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03508934802090893,"score_gpt":0.2866504973948395,"score_spread":0.2515611493739306,"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."}}