{"id":"W4412532612","doi":"10.1162/imag.a.98","title":"EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data","year":2025,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital; Institut Universitaire de Gériatrie de Montréal; Polytechnique Montréal","funders":"National Center for Complementary and Integrative Health; National Institute of Neurological Disorders and Stroke; National Institute of Biomedical Imaging and Bioengineering; Fonds de Recherche du Québec - Santé; National Science Foundation; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Institute on Drug Abuse; Institut de Valorisation des Données; National Institutes of Health; Canada First Research Excellence Fund; Polytechnique Montréal; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada Foundation for Innovation; Center for Bio-Inspired Energy Science, Northwestern University; Craig H. Neilsen Foundation","keywords":"Segmentation; Echo (communications protocol); Center (category theory); Planar; Echo-planar imaging; Artificial intelligence; Computer vision; Spinal cord; Computer science; Medicine; Magnetic resonance imaging; Radiology; Computer graphics (images); Chemistry; Computer network","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":[0.001212402,0.002098577,0.001128402,0.001907797,0.0006183013,0.001644157,0.002928667,0.001947865,0.005098304],"category_scores_gemma":[0.004094734,0.00101732,0.002087645,0.001170573,0.0004639987,0.001104752,0.002056044,0.001710425,0.00370258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099952,"about_ca_system_score_gemma":0.002231969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01298755,"about_ca_topic_score_gemma":0.03554613,"domain_scores_codex":[0.9995004,0.00007645126,0.00003888308,0.0002372194,0.00009227262,0.0000548352],"domain_scores_gemma":[0.9994342,0.000190145,0.0000694005,0.0001385524,0.0001173517,0.00005028855],"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":[0.002282079,0.000653657,0.01199983,0.00280638,0.002526077,0.001277112,0.0005412386,0.1592665,0.059579,0.00459177,0.2568887,0.4975876],"study_design_scores_gemma":[0.0004730507,0.0005425972,0.01340554,0.000448057,0.0005380157,0.002289652,0.0001623035,0.8313009,0.06749905,0.01715714,0.06592264,0.0002611316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09557378,0.004267131,0.6623771,0.00151,0.0007207179,0.001238909,0.1041964,0.126601,0.003514943],"genre_scores_gemma":[0.2055661,0.002289337,0.5886667,0.001196102,0.0001943961,0.001620876,0.1833414,0.009972987,0.007152041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01298755,"threshold_uncertainty_score":0.02582389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1190755853448365,"score_gpt":0.4353025166983773,"score_spread":0.3162269313535407,"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."}}