{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000443279,0.0002025818,0.0002929704,0.000272623,0.0001148555,0.00003553632,0.0002248793,0.00008163422,0.001816437],"category_scores_gemma":[0.00008463563,0.0001940121,0.0001625157,0.0005344531,0.0003186295,0.0001223517,5.152859e-7,0.00031543,0.0001877183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005621122,"about_ca_system_score_gemma":0.0000767791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001427391,"about_ca_topic_score_gemma":0.00001994061,"domain_scores_codex":[0.998114,0.00006077727,0.0004505772,0.0002746056,0.0007642809,0.0003357518],"domain_scores_gemma":[0.9988562,0.000288188,0.00003532797,0.0002956653,0.00009679563,0.0004278607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004645283,0.000317375,0.0001077734,0.0001754889,0.0001843242,0.00005087218,0.0006365207,0.01651303,0.8045464,0.00001038326,0.012645,0.1647663],"study_design_scores_gemma":[0.0008404929,0.00009029791,0.0001831927,0.0004152536,0.0001760374,0.0000326032,0.0001162131,0.3497517,0.6470834,0.00004452537,0.0008418824,0.0004243667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05215774,0.00001951843,0.939934,0.001489903,0.000436833,0.000105195,0.00002813512,0.0004374469,0.005391284],"genre_scores_gemma":[0.9951805,0.00004321737,0.003921617,0.0003196936,0.0001844297,0.00001820784,0.000007806705,0.0000409254,0.0002836243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9430227,"threshold_uncertainty_score":0.999096,"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."}}