{"id":"W2134012543","doi":"10.1109/tmi.2014.2375207","title":"Three-Dimensional Nonrigid MR-TRUS Registration Using Dual Optimization","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Image registration; Similarity (geometry); Fiducial marker; Artificial intelligence; Magnetic resonance imaging; Ultrasound; Prostate biopsy; Computer science; Feature (linguistics); Prostate; Computer vision; Mathematics; Pattern recognition (psychology); Nuclear medicine; Medicine; Image (mathematics); 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":[],"consensus_categories":[],"category_scores_codex":[0.0008981904,0.0002044254,0.0001972991,0.0002350052,0.0003237562,0.0001681779,0.0004405509,0.0001130776,0.0004906894],"category_scores_gemma":[0.0001221749,0.0001966149,0.00009315093,0.000431123,0.0002058151,0.0008697812,0.000007496692,0.0004587799,0.00004280999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111135,"about_ca_system_score_gemma":0.0001943083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001128249,"about_ca_topic_score_gemma":0.00002262358,"domain_scores_codex":[0.9971139,0.0001539625,0.0004867721,0.0005188331,0.001392075,0.0003344739],"domain_scores_gemma":[0.9986163,0.0002606581,0.0001434422,0.0004688137,0.000145724,0.0003650685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002643013,0.0004570546,0.00002617793,0.00003972715,0.00004124645,0.0000941891,0.000188351,0.1531479,0.009822299,0.001011623,0.002203375,0.8329416],"study_design_scores_gemma":[0.0004631139,0.00004244442,0.00001329199,0.00009978416,0.00001599911,0.0001241927,0.000006934571,0.9731548,0.02534926,0.0004452722,0.00008344591,0.0002014554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005258613,0.00001544569,0.9952124,0.002301972,0.00091265,0.000188876,0.000002108646,0.0005539113,0.0002867496],"genre_scores_gemma":[0.2590249,0.00001101754,0.7369688,0.003683589,0.0001906282,0.00003052079,0.000007811221,0.0000277157,0.00005499869],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8327401,"threshold_uncertainty_score":0.8017724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893177164216941,"score_gpt":0.289298097446787,"score_spread":0.2703663258046176,"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."}}