{"id":"W2990388635","doi":"10.1007/s11548-019-02091-0","title":"Multi-atlas segmentation of the facial nerve from clinical CT for virtual reality simulators","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Ear Surgery and Otitis Media","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Fiducial marker; Atlas (anatomy); Computer vision; Artificial intelligence; Hausdorff distance; Software; Anatomy; Medicine","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":[0.0008975012,0.00008888777,0.0004893366,0.0001093624,0.00002562319,0.000009697204,0.0001172662,0.00009222981,0.00003704456],"category_scores_gemma":[0.0003007221,0.00005925076,0.0004244388,0.00004408304,0.0001305118,0.00008802245,0.0000339832,0.0002265278,0.000001746665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000256501,"about_ca_system_score_gemma":0.0001592773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000111466,"about_ca_topic_score_gemma":0.000003440889,"domain_scores_codex":[0.9984716,0.0002765325,0.0008121713,0.0001291263,0.0002182248,0.00009238206],"domain_scores_gemma":[0.9960279,0.002910742,0.0005791252,0.00009799202,0.0003118957,0.00007235631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007800118,0.0001599785,0.9618134,0.000009532182,0.000645766,0.00006147331,0.00008761376,0.000193171,0.0008122955,0.00003116419,0.002680749,0.03272477],"study_design_scores_gemma":[0.001849119,0.0001565875,0.9912208,0.0001411039,0.00007290628,0.0004460604,0.00002895862,0.003721499,0.000493673,0.00005893792,0.001750354,0.00006004627],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807445,0.0001415923,0.008585759,0.0008409693,0.009524742,0.0001133545,0.00003506673,0.000004547286,0.000009498353],"genre_scores_gemma":[0.9966828,0.00007397989,0.001563648,0.0005678466,0.001016148,9.283835e-7,0.00004047392,0.000006481925,0.00004771473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03266473,"threshold_uncertainty_score":0.2416176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04941256895559552,"score_gpt":0.3461269640320835,"score_spread":0.296714395076488,"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."}}