{"id":"W4385191001","doi":"10.1016/j.neuroimage.2023.120288","title":"FIESTA: Autoencoders for accurate fiber segmentation in tractography","year":2023,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"NIH Blueprint for Neuroscience Research; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institute on Aging; National Institutes of Health; Québec Consortium for Drug Discovery; Alliance de recherche numérique du Canada; Alzheimer's Disease Neuroimaging Initiative; Michael J. Fox Foundation for Parkinson's Research; McDonnell Center for Systems Neuroscience; U.S. Department of Defense","keywords":"Artificial intelligence; Bundle; Tractography; Computer science; Segmentation; Autoencoder; Human Connectome Project; Pattern recognition (psychology); Atlas (anatomy); Fiber bundle; Computer vision; Deep learning; Diffusion MRI; Geology; Magnetic resonance imaging","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":[0.001397747,0.001398593,0.0008921011,0.0008788681,0.0005540594,0.001020706,0.001576259,0.00163221,0.003576994],"category_scores_gemma":[0.003947937,0.000951193,0.001394846,0.0006831448,0.0008768167,0.001583175,0.001578736,0.002854048,0.001950906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048872,"about_ca_system_score_gemma":0.001644915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082546,"about_ca_topic_score_gemma":0.01546954,"domain_scores_codex":[0.9994895,0.0001026769,0.00002907437,0.0001695973,0.0001482676,0.00006085518],"domain_scores_gemma":[0.9989074,0.0005224848,0.0001221531,0.0002153963,0.000180792,0.00005174924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002283641,0.00009138723,0.001581972,0.0001677514,0.0002073657,0.0002053336,0.0002143856,0.5871289,0.02630918,0.0102832,0.008826957,0.3647552],"study_design_scores_gemma":[0.000007139623,0.00001455912,0.0002603622,0.00001052878,0.000007817307,0.00003476591,0.000006597942,0.9910361,0.003912227,0.003292392,0.001408176,0.000009362304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005550566,0.0001635218,0.9890888,0.0000862251,0.00002870391,0.00003136661,0.0001779784,0.004366697,0.0005060583],"genre_scores_gemma":[0.1460575,0.000354017,0.8464707,0.0002146526,0.00005317576,0.0002474515,0.00128859,0.001226113,0.004087795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01082546,"threshold_uncertainty_score":0.02152491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232877449310769,"score_gpt":0.4103552631157604,"score_spread":0.2870675181846835,"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."}}