{"id":"W4399416135","doi":"10.1109/tuffc.2024.3410671","title":"Displacement Tracking Techniques in Ultrasound Elastography: From Cross Correlation to Deep Learning","year":2024,"lang":"en","type":"review","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Elastography; Ultrasound elastography; Tracking (education); Displacement (psychology); Correlation; Ultrasound; Artificial intelligence; Computer vision; Match moving; Cross-correlation; Deep learning; Computer science; Radiology; Acoustics; Medicine; Physics; Mathematics; Psychology; Motion (physics); Geometry; Statistics","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":[0.001906235,0.001016871,0.0008462209,0.001468913,0.0002187665,0.00139752,0.001094929,0.001519785,0.00187562],"category_scores_gemma":[0.004835985,0.0005856368,0.0008784684,0.002314598,0.0007523631,0.001878363,0.001187683,0.002354122,0.001026357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006837793,"about_ca_system_score_gemma":0.0006588688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002622169,"about_ca_topic_score_gemma":0.001568254,"domain_scores_codex":[0.9992692,0.0001876764,0.00006272294,0.0001844181,0.0002607687,0.00003518374],"domain_scores_gemma":[0.9987055,0.0007279305,0.0001232895,0.0001154167,0.0002842034,0.00004361968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008511344,0.00007829899,0.001780334,0.001025947,0.000158801,0.0001208412,0.0001363548,0.06393314,0.006827916,0.03735468,0.0104349,0.8780637],"study_design_scores_gemma":[0.00002767418,0.0002447167,0.002178032,0.0005061073,0.0001680419,0.0004236177,0.00005515221,0.870946,0.01859587,0.04219006,0.06455128,0.0001134376],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003763824,0.04826559,0.9427727,0.001386764,0.0003828504,0.00005267148,0.000132125,0.0005067998,0.002736564],"genre_scores_gemma":[0.1627368,0.1605761,0.6590708,0.001856739,0.00193242,0.0003365637,0.0009063571,0.0004653875,0.01211882],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002622169,"threshold_uncertainty_score":0.01008123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534517363458736,"score_gpt":0.3043593989239686,"score_spread":0.2890142252893813,"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."}}