{"id":"W3003448798","doi":"10.1016/j.bpsc.2020.01.004","title":"Fully Automated Habenula Segmentation Provides Robust and Reliable Volume Estimation Across Large Magnetic Resonance Imaging Datasets, Suggesting Intriguing Developmental Trajectories in Psychiatric Disease","year":2020,"lang":"en","type":"article","venue":"Biological Psychiatry Cognitive Neuroscience and Neuroimaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; McGill University; Douglas Mental Health University Institute; University Health Network","funders":"General Armaments Department, People's Liberation Army; Fundação de Amparo à Pesquisa do Estado de São Paulo; National Alliance for Research on Schizophrenia and Depression; Wellcome Trust; Fondation Brain Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Compute Canada; Health Canada; Brain and Behavior Research Foundation","keywords":"Schizophrenia (object-oriented programming); Segmentation; Bipolar disorder; Magnetic resonance imaging; Habenula; Reliability (semiconductor); Neuroimaging; Computer science; Artificial intelligence; Neuroscience; Psychology; Medicine; Psychiatry; Radiology; Cognition; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002324218,0.0002862941,0.0002640197,0.000106166,0.0005638184,0.0001604849,0.0001314113,0.00003805949,0.000004405685],"category_scores_gemma":[0.0009817289,0.0002606251,0.00003487423,0.0008461846,0.0003749349,0.0005144825,0.0002344933,0.000370363,0.000002651959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002697213,"about_ca_system_score_gemma":0.0001071846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001088315,"about_ca_topic_score_gemma":0.000002119913,"domain_scores_codex":[0.9976256,0.0000818881,0.0004305764,0.001087279,0.000209004,0.0005656406],"domain_scores_gemma":[0.9992731,0.0001017215,0.0001466995,0.0001226161,0.00006934358,0.0002865141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002091927,0.0001450828,0.9710019,0.0000999772,6.583819e-7,0.00007012693,0.0001436262,0.00002684737,0.00884315,0.00006849307,0.000292535,0.01909836],"study_design_scores_gemma":[0.001137379,0.0002311636,0.8344232,0.0001737678,0.00002448742,0.0001267946,0.000417853,0.1623048,0.0001400315,0.0001995237,0.0005431435,0.0002778611],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770374,0.002122292,0.01065314,0.007996318,0.0001358494,0.001069405,0.0003139765,0.0006419358,0.000029655],"genre_scores_gemma":[0.9709848,0.0003565755,0.02041339,0.007933185,0.0000623472,0.00007591308,0.0001427938,0.00002544899,0.000005571861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.162278,"threshold_uncertainty_score":0.9999846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05539616559211782,"score_gpt":0.341163746139767,"score_spread":0.2857675805476492,"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."}}