{"id":"W3093969787","doi":"10.1186/s12891-020-03679-3","title":"LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images","year":2020,"lang":"en","type":"article","venue":"BMC Musculoskeletal Disorders","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre de réadaptation Lethbridge-Layton-Mackay; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Segmentation; Medicine; Lumbar; Artificial intelligence; Computer science; Ground truth; Image segmentation; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008073232,0.0002531837,0.0002407006,0.00005605075,0.00009022438,0.00006976661,0.0002333491,0.00006676904,0.0008671204],"category_scores_gemma":[0.0002440501,0.000257723,0.000241125,0.0002988163,0.00009754342,0.0002776122,0.00004024316,0.0002283368,0.0003761583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002883022,"about_ca_system_score_gemma":0.00001794205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003396834,"about_ca_topic_score_gemma":0.0001443996,"domain_scores_codex":[0.9985788,0.00006405496,0.0002881206,0.0003929068,0.0003407376,0.0003354008],"domain_scores_gemma":[0.999241,0.0001285567,0.00003825431,0.0002858167,0.00002021419,0.0002861678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001033032,0.0002859605,0.08008757,0.001347318,0.0006195912,0.00002371997,0.002470192,0.02368614,0.665647,0.00003470297,0.04056596,0.1852215],"study_design_scores_gemma":[0.006737328,0.0001724003,0.4882812,0.000270491,0.00135932,0.000004057158,0.008057369,0.4503699,0.0188871,0.0004853782,0.0213221,0.004053405],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630536,0.001554023,0.1312397,0.0003476762,0.0003074463,0.0002443921,0.0001657723,0.0008178144,0.002269595],"genre_scores_gemma":[0.9912246,0.0005538696,0.006688721,0.0002101168,0.0002308103,0.00003316397,0.0009440666,0.00005850822,0.00005608443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6467599,"threshold_uncertainty_score":0.9999875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144194433503835,"score_gpt":0.2398563191893398,"score_spread":0.2284143748543014,"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."}}