{"id":"W2084979303","doi":"10.1109/iembs.2010.5626494","title":"Targeting error simulator for image-guided prostate needle placement","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Computer science; Ground truth; Image warping; Prostate biopsy; Segmentation; Computer vision; Image registration; Artificial intelligence; Image segmentation; Prostate; Image (mathematics); Medicine","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":[0.0005856826,0.0001196509,0.0001184471,0.00006887298,0.000143219,0.0002579496,0.0005379891,0.00004745845,0.0002996239],"category_scores_gemma":[0.0003663021,0.00009876918,0.00004903237,0.0001451172,0.00006817753,0.0005608875,0.0001873035,0.0001363339,0.00005731867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002042939,"about_ca_system_score_gemma":0.00007926028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001565893,"about_ca_topic_score_gemma":0.000002106632,"domain_scores_codex":[0.9987686,0.00002152031,0.0003107413,0.000325182,0.0002757995,0.0002981039],"domain_scores_gemma":[0.9990796,0.0001328469,0.00009374441,0.0003574176,0.000183954,0.000152417],"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":[0.00001088371,0.0001300397,0.00009905344,0.00006485347,0.0000165149,0.000007087564,0.00108592,0.0000848785,0.7953742,0.006216985,0.1727036,0.02420598],"study_design_scores_gemma":[0.0007268012,0.0001055411,0.00002060352,0.000007322595,0.000004054414,0.000003815572,0.00008670036,0.3282354,0.6612248,0.001542984,0.007819405,0.0002225216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004629483,0.000003977821,0.9913285,0.00123508,0.0003787628,0.000834043,0.000003127261,0.0006359852,0.0009510565],"genre_scores_gemma":[0.05010208,8.947649e-7,0.945918,0.001689094,0.00007978574,0.0001572817,0.00001006552,0.00001284853,0.002029988],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3281506,"threshold_uncertainty_score":0.402769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222052577146428,"score_gpt":0.3295995374427827,"score_spread":0.3073790116713184,"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."}}