{"id":"W4406239147","doi":"10.1101/2025.01.07.631402","title":"EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"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":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Polytechnique Montréal; Craig H. Neilsen Foundation; Canada First Research Excellence Fund; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institutes of Health; National Science Foundation","keywords":"Segmentation; Computer science; Artificial intelligence; Ghosting; Spinal cord; Computer vision; Functional magnetic resonance imaging; Ground truth; Preprocessor; Pattern recognition (psychology); Medicine; Neuroscience; Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274123,0.002137388,0.001100893,0.001940067,0.0006057113,0.001564805,0.002945354,0.001887516,0.005326116],"category_scores_gemma":[0.003871553,0.0009906141,0.001971669,0.001185825,0.0004678955,0.001088452,0.002060881,0.001656321,0.004154862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051067,"about_ca_system_score_gemma":0.002305925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01238436,"about_ca_topic_score_gemma":0.0360407,"domain_scores_codex":[0.9994888,0.00008110278,0.00003897099,0.0002404993,0.00009565716,0.0000549359],"domain_scores_gemma":[0.9994605,0.0001803717,0.00006755621,0.0001351955,0.0001067567,0.0000496174],"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":[0.002219099,0.0006286926,0.01200386,0.002718387,0.002151506,0.001222346,0.0005213738,0.1497529,0.04968592,0.005204416,0.3063389,0.4675527],"study_design_scores_gemma":[0.000500109,0.0005013146,0.01286472,0.0004695111,0.0004978129,0.002249058,0.0001521737,0.8068794,0.0685271,0.01971307,0.08738072,0.0002650084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07643466,0.003598132,0.6679719,0.001334254,0.0006332592,0.001148773,0.11298,0.1320737,0.003825289],"genre_scores_gemma":[0.1653841,0.002053002,0.5997539,0.001101141,0.000168884,0.001665297,0.2117087,0.01053862,0.007626341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01238436,"threshold_uncertainty_score":0.02462453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1930826694032688,"score_gpt":0.4375408830752436,"score_spread":0.2444582136719748,"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."}}