{"id":"W4307722372","doi":"10.1016/j.neuroimage.2022.119703","title":"CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institute on Aging; NIH Blueprint for Neuroscience Research; Horizon 2020; Medical Research Council; McDonnell Center for Systems Neuroscience; Bundesministerium für Bildung und Forschung; Alzheimer’s Society; Fundo Regional para a Ciência e Tecnologia; ZonMw; HORIZON EUROPE Framework Programme; University of Southern California; Alzheimer's Society; U.S. National Library of Medicine; National Institute of Neurological Disorders and Stroke; Northern California Institute for Research and Education; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; EU Joint Programme – Neurodegenerative Disease Research; National Institutes of Health; National Ataxia Foundation; Deutsches Zentrum für Neurodegenerative Erkrankungen; GlaxoSmithKline","keywords":"Spinocerebellar ataxia; Human Connectome Project; Computer science; Artificial intelligence; Segmentation; Preprocessor; Deep learning; Mossy fiber (hippocampus); Cerebellum; Pattern recognition (psychology); Deep cerebellar nuclei; Normalization (sociology); Backbone network; Cerebellar cortex; Neuroscience; Ataxia; Biology; Functional connectivity","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":[0.0001271063,0.0001302151,0.000174028,0.00008823339,0.0004238778,0.00002126818,0.00007659316,0.00002012654,0.00008476927],"category_scores_gemma":[0.00006662032,0.0001402133,0.00005737119,0.0001995614,0.00004431333,0.00007771812,0.0001236555,0.0003196906,0.000004909195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004390414,"about_ca_system_score_gemma":0.00001994393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006814006,"about_ca_topic_score_gemma":0.000001867327,"domain_scores_codex":[0.9989737,0.00003432518,0.0002082875,0.0003953526,0.000164229,0.0002240562],"domain_scores_gemma":[0.9994369,0.00007660367,0.000103347,0.0002440811,0.00005495266,0.0000841543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005209402,0.0004268908,0.01939196,0.000226356,0.00001930918,0.00006427552,0.0003218141,0.001097928,0.8427184,0.001077286,0.009560191,0.1245746],"study_design_scores_gemma":[0.006851141,0.002110781,0.01817612,0.00004668075,0.0003245271,0.0007670069,0.0006950264,0.3171864,0.08377475,0.002230042,0.5670934,0.0007440685],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3939324,0.0005581008,0.5838512,0.009708459,0.0002263065,0.005364012,0.0001073846,0.001482409,0.004769723],"genre_scores_gemma":[0.9546206,0.0001973313,0.03599601,0.00221816,0.0001512844,0.001156414,0.0002187473,0.0001087768,0.005332665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7589437,"threshold_uncertainty_score":0.5717734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03527676917839827,"score_gpt":0.3144704341371228,"score_spread":0.2791936649587245,"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."}}