{"id":"W2594770357","doi":"10.1016/j.neuroimage.2017.03.010","title":"Spinal cord grey matter segmentation challenge","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; UCLH Biomedical Research Centre; Fonds de recherche du Québec – Nature et technologies; Engineering and Physical Sciences Research Council; Medical Research Council; Natural Sciences and Engineering Research Council of Canada; National Defense Science and Engineering Graduate; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; National Institutes of Health; University College London; Multiple Sclerosis Society; Staatssekretariat für Bildung, Forschung und Innovation; National Multiple Sclerosis Society; Brain Research Trust; University College London Hospitals Biomedical Research Centre; Canadian Institutes of Health Research; National Institute of Neurological Disorders and Stroke; National Institute for Health and Care Research","keywords":"Grey matter; Segmentation; Computer science; White matter; Artificial intelligence; Spinal cord; Image segmentation; Pattern recognition (psychology); Magnetic resonance imaging; Computer vision; Medicine; Psychology; Radiology; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003673355,0.001121039,0.001620836,0.001706838,0.0014847,0.002501324,0.001944098,0.003071419,0.002925153],"category_scores_gemma":[0.01288815,0.00052107,0.001488429,0.001406693,0.00117014,0.001262525,0.002067207,0.001667011,0.002624814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234682,"about_ca_system_score_gemma":0.003323456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0118526,"about_ca_topic_score_gemma":0.01548328,"domain_scores_codex":[0.9967289,0.0005225835,0.0003519665,0.001079455,0.001120518,0.0001966885],"domain_scores_gemma":[0.9946544,0.002158872,0.00038071,0.0007929337,0.001733801,0.0002793298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002012148,0.0002942248,0.01660533,0.005562768,0.0009256772,0.003041385,0.001911369,0.02987439,0.07759331,0.009252625,0.1611461,0.6917806],"study_design_scores_gemma":[0.0005850205,0.00134264,0.1013074,0.002404511,0.001139445,0.02438733,0.003022924,0.2553136,0.1968517,0.05532621,0.3578163,0.0005029896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4606846,0.03074052,0.4070172,0.0200372,0.003940878,0.00220991,0.03170707,0.01693126,0.02673139],"genre_scores_gemma":[0.5745598,0.006226907,0.3355797,0.00686072,0.001466984,0.00122319,0.04970073,0.004549572,0.01983231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0118526,"threshold_uncertainty_score":0.0235672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06509133763397067,"score_gpt":0.3968662157551371,"score_spread":0.3317748781211664,"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."}}