{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000801112,0.0006691884,0.000836942,0.0009361624,0.0002910663,0.000891089,0.001033712,0.0007103477,0.0009868296],"category_scores_gemma":[0.001611855,0.0005415053,0.001115388,0.0008136817,0.0005073464,0.000851639,0.001398965,0.0007058958,0.0006852421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000425692,"about_ca_system_score_gemma":0.0009427328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00193951,"about_ca_topic_score_gemma":0.00233388,"domain_scores_codex":[0.9990828,0.0001876974,0.00006268194,0.0002139871,0.0004010667,0.00005186629],"domain_scores_gemma":[0.9996129,0.0001078607,0.00007494789,0.00009787072,0.00008342094,0.00002302675],"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":[0.0003333596,0.0001669613,0.001750028,0.0002770005,0.0002059634,0.0003213363,0.0002107264,0.4113041,0.1074787,0.009579029,0.001698276,0.4666745],"study_design_scores_gemma":[0.000008979131,0.0000611174,0.0005330119,0.000006003444,0.00001665473,0.0002281325,0.00001295021,0.9820874,0.01353778,0.001512344,0.001972129,0.00002338386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01097159,0.0001709368,0.987932,0.00005402089,0.00001618875,0.00003382822,0.00002365609,0.0003555854,0.0004420937],"genre_scores_gemma":[0.2493705,0.0003146117,0.746564,0.0001197506,0.00003619773,0.0001543495,0.0002897405,0.0003289039,0.002821838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00193951,"threshold_uncertainty_score":0.004236758,"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."}}