{"id":"W3043519368","doi":"10.1007/s00415-020-10062-8","title":"VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI","year":2020,"lang":"en","type":"article","venue":"Journal of Neurology","topic":"Vestibular and auditory disorders","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Graduate School of Systemic Neurosciences; Deutsche Stiftung Neurologie; Deutschen Schwindel- und Gleichgewichtszentrum; Friedrich-Baur-Stiftung; Bundesministerium für Bildung und Forschung","keywords":"Segmentation; Sørensen–Dice coefficient; Data set; Magnetic resonance imaging; Thresholding; Artificial intelligence; Computer science; Medicine; Nuclear medicine; Image segmentation; Radiology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002214931,0.00010005,0.0003189401,0.0001287763,0.00003142958,0.00002149169,0.000389028,0.00005025572,0.00002969703],"category_scores_gemma":[0.001401637,0.00008844644,0.00006776689,0.0002557895,0.00006857032,0.0002230353,0.00007632229,0.0001614694,0.000005020379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001003917,"about_ca_system_score_gemma":0.00009089561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000504378,"about_ca_topic_score_gemma":0.000004579109,"domain_scores_codex":[0.9987999,0.0001538964,0.000528894,0.0001725751,0.0001762997,0.0001684669],"domain_scores_gemma":[0.9988221,0.0003960188,0.000565137,0.00008849444,0.0000501689,0.00007811093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005439092,0.000338819,0.001972545,0.00009311398,0.000007877037,0.00006053817,0.001199412,0.03733023,0.9529978,0.0001157545,0.003189045,0.002150936],"study_design_scores_gemma":[0.01938986,0.007357443,0.005636235,0.0001050072,0.0001262271,0.0009652696,0.0003462807,0.7355699,0.2074445,0.0009206222,0.02170454,0.0004341213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8950264,0.00003779222,0.0885682,0.01543205,0.0004480839,0.0004010121,0.000008809751,0.0000113238,0.00006637201],"genre_scores_gemma":[0.9914324,0.0000173527,0.001601457,0.006756046,0.0001328849,0.00000569003,5.892595e-7,0.00001886674,0.00003476522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7455533,"threshold_uncertainty_score":0.3606741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04107394503787486,"score_gpt":0.2941527800166217,"score_spread":0.2530788349787468,"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."}}