{"id":"W1969422115","doi":"10.1016/j.media.2008.07.003","title":"3D estimation of soft biological tissue deformation from radio-frequency ultrasound volume acquisitions","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Hôpital Notre-Dame","funders":"","keywords":"Elastography; Imaging phantom; 3D ultrasound; Ultrasound; Deformation (meteorology); Ultrasonic sensor; Ultrasound elastography; Computer science; Similarity (geometry); Biomedical engineering; Orientation (vector space); Acoustics; Mathematics; Algorithm; Materials science; Artificial intelligence; Physics; Geometry; Optics; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003182789,0.0001782215,0.0005945399,0.0004763767,0.0001852151,0.00001782371,0.0001529771,0.0001846843,0.005474281],"category_scores_gemma":[0.001676527,0.0001386607,0.0003654239,0.001583575,0.0006527175,0.0002535411,0.00002084163,0.000264448,0.0001819207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004752961,"about_ca_system_score_gemma":0.00009353791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007764978,"about_ca_topic_score_gemma":0.0000114989,"domain_scores_codex":[0.9979836,0.0001078846,0.0006058821,0.0003014412,0.0007365987,0.0002645719],"domain_scores_gemma":[0.9986077,0.0003191313,0.0001705576,0.0003880505,0.0001990746,0.0003154706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001605952,0.001978801,0.8400257,0.0001202456,0.005894998,0.0002716937,0.003483826,0.0006847688,0.1050999,0.00006001937,0.006991026,0.03522846],"study_design_scores_gemma":[0.00257326,0.0006171281,0.90344,0.0001777057,0.007531145,0.001013698,0.0004786676,0.07542424,0.004785694,0.00122907,0.002053425,0.0006759467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5729389,0.000308564,0.425204,0.0005658935,0.00004334021,0.00008586133,0.00005083964,0.00008702577,0.0007156597],"genre_scores_gemma":[0.9365237,0.0002363903,0.06084394,0.0003574505,0.00013383,0.00001381312,0.001773616,0.00001109835,0.0001061242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.36436,"threshold_uncertainty_score":0.9954349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020306237214801,"score_gpt":0.2630836163661459,"score_spread":0.2528805539939979,"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."}}