{"id":"W4243554135","doi":"10.1109/iembs.2006.4398012","title":"A Non-Rigid Image Registration Technique for 3D Ultrasound Carotid Images using a \"Twisting and Bending\" Model","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Ivey Foundation","keywords":"Image registration; Computer vision; Artificial intelligence; Mutual information; Similarity (geometry); Computer science; Metric (unit); Ultrasound; Medicine; Image (mathematics); Radiology; Engineering","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":[0.0008777329,0.000593666,0.0006260033,0.0006654917,0.000407383,0.0007422217,0.0008859155,0.0009205188,0.001847867],"category_scores_gemma":[0.001706576,0.0006453395,0.001242446,0.0007712532,0.0005186971,0.0007473312,0.0007772038,0.001122186,0.000890968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005162548,"about_ca_system_score_gemma":0.001107098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00483056,"about_ca_topic_score_gemma":0.004705736,"domain_scores_codex":[0.9992248,0.0001536806,0.00005519653,0.000185443,0.0003395033,0.00004143043],"domain_scores_gemma":[0.9996142,0.0001022898,0.00007205383,0.0001099392,0.00008146094,0.00001993326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00028017,0.0001408117,0.001398834,0.0002327461,0.0001413703,0.000355904,0.000363749,0.4651993,0.1713122,0.01073376,0.002151553,0.3476897],"study_design_scores_gemma":[0.00000738293,0.00009069193,0.0008559641,0.000006492425,0.0000177115,0.000200697,0.00001335824,0.9808393,0.01496912,0.000717252,0.002252574,0.00002951483],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007350453,0.00003588967,0.9914249,0.0000498795,0.00001196089,0.00005540425,0.00002198304,0.0006749761,0.0003745017],"genre_scores_gemma":[0.1505858,0.0001772112,0.8457221,0.00006154372,0.000018229,0.0002057676,0.0001736024,0.0002871693,0.002768609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00483056,"threshold_uncertainty_score":0.009604931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0204381952521933,"score_gpt":0.2728421316531945,"score_spread":0.2524039364010012,"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."}}