{"id":"W2008437871","doi":"10.1109/embc.2012.6346134","title":"Two solutions for registration of ultrasound to MRI for image-guided prostate interventions","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Image registration; Computer science; Ultrasound; Segmentation; Artificial intelligence; Computer vision; Modalities; Prostate; Image segmentation; Prostate gland; Medicine; Medical physics; Radiology; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009941939,0.00007268376,0.0001041096,0.00009633121,0.0001061774,0.00006444428,0.0002910846,0.00002263284,0.00004365244],"category_scores_gemma":[0.0005867877,0.00006681341,0.0001268116,0.0001842141,0.00004655984,0.0008000115,0.000067176,0.00002941802,0.000008435047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003777114,"about_ca_system_score_gemma":0.00003941552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003605141,"about_ca_topic_score_gemma":0.00001855171,"domain_scores_codex":[0.9989702,0.00002862992,0.0004007106,0.0001652889,0.0001555729,0.0002796318],"domain_scores_gemma":[0.998922,0.0002365864,0.0001332358,0.0002886698,0.0002943403,0.0001251424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001359961,0.0004439757,0.00008520673,0.0002828884,0.00003645979,1.323743e-7,0.001266125,0.00001395273,0.2009235,0.3348304,0.4368057,0.02529808],"study_design_scores_gemma":[0.001302288,0.0005883395,0.0005809297,0.0001382943,0.00004580683,0.00001770872,0.0002828971,0.005564989,0.9542407,0.03057821,0.006327497,0.0003323558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001705598,0.00002111192,0.9953902,0.001507662,0.0001737413,0.001452569,0.00002847889,0.000172988,0.001082674],"genre_scores_gemma":[0.04667185,0.000003709873,0.9503267,0.0003633337,0.00005175971,0.0006439928,0.00003081861,0.000006645081,0.001901208],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7533172,"threshold_uncertainty_score":0.2724572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08301715854857426,"score_gpt":0.4008010885742414,"score_spread":0.3177839300256671,"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."}}