{"id":"W2057441779","doi":"10.1016/j.media.2014.10.004","title":"Right ventricle segmentation from cardiac MRI: A collation study","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":242,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; CARE Canada","funders":"Medical Research Council; National Science Council; Ministerio de Ciencia e Innovación; British Heart Foundation; Engineering and Physical Sciences Research Council; Comunidad de Madrid","keywords":"Hausdorff distance; Segmentation; Computer science; Artificial intelligence; Tracing; Metric (unit); Pattern recognition (psychology); Computer vision; Ventricle; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002620025,0.0001251551,0.0004849849,0.0001814805,0.00008210539,0.00003628726,0.00005249178,0.00005611253,0.002769085],"category_scores_gemma":[0.0002090953,0.00009836753,0.001044542,0.0008558116,0.00003507929,0.0000733126,0.00003259191,0.00007823001,0.0002004895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008664466,"about_ca_system_score_gemma":0.00004370267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008333034,"about_ca_topic_score_gemma":0.0000313906,"domain_scores_codex":[0.9981651,0.0001817782,0.0002624448,0.0003222448,0.0009055677,0.0001628893],"domain_scores_gemma":[0.9990149,0.0001172075,0.00007158797,0.0003398297,0.0001073178,0.0003491488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006109179,0.0007691584,0.9791433,0.000004002635,0.009038249,0.00005045549,0.0001428275,0.000006900107,0.0001557773,0.000004110281,0.0007865799,0.009837528],"study_design_scores_gemma":[0.002184983,0.0001394683,0.9649914,0.000009253728,0.02562369,3.33456e-7,0.0004004903,0.005589893,0.0003170173,0.00004776209,0.0005978284,0.00009786756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921397,0.0002635947,0.00543326,0.0003429684,0.00009087827,0.000366754,0.00003542805,0.00005225822,0.001275165],"genre_scores_gemma":[0.9981598,0.00003761934,0.0003266437,0.0002120289,0.0002640944,0.00004471332,0.0007875902,0.00001183888,0.0001557032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01658544,"threshold_uncertainty_score":0.9981425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005184458891641832,"score_gpt":0.3344543014174768,"score_spread":0.329269842525835,"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."}}