{"id":"W2886933858","doi":"10.1109/tmi.2018.2810778","title":"Real-Time FEM-Based Registration of 3-D to 2.5-D Transrectal Ultrasound Images","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Image registration; Computer science; Computer vision; Artificial intelligence; Ultrasound; Finite element method; Image (mathematics); Medicine; Radiology","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":[0.0003617768,0.0003401753,0.0002991993,0.0004826746,0.0001681287,0.0004480618,0.0004442173,0.0004929451,0.001678672],"category_scores_gemma":[0.001261603,0.0003456782,0.0004161437,0.0003251487,0.000293197,0.0002962259,0.0005046055,0.0003400084,0.0007431195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002649692,"about_ca_system_score_gemma":0.0005660032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00136097,"about_ca_topic_score_gemma":0.00260762,"domain_scores_codex":[0.9996519,0.00006080334,0.00002240275,0.00007086658,0.0001754954,0.00001844559],"domain_scores_gemma":[0.9997525,0.00007168359,0.00004208221,0.00007876116,0.00004187403,0.00001314363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002445199,0.0001093054,0.002174045,0.0002261448,0.0000665089,0.0002755721,0.000301535,0.1613421,0.4314729,0.003287411,0.002893897,0.3976061],"study_design_scores_gemma":[0.00002136609,0.0001667213,0.005434419,0.00002649067,0.0000271052,0.001149,0.00005165411,0.8089604,0.1678792,0.001092507,0.01510409,0.00008714323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02602,0.0001172156,0.9707658,0.00008056915,0.0000337306,0.00004432772,0.0001092521,0.001894374,0.0009346796],"genre_scores_gemma":[0.2456467,0.0002016644,0.7517039,0.0001027671,0.00001576943,0.0001133249,0.0002882191,0.0002872973,0.001640386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001678672,"threshold_uncertainty_score":0.005615711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006658917828114612,"score_gpt":0.2487833842745296,"score_spread":0.242124466446415,"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."}}