{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002962825,0.001029283,0.000715998,0.002001249,0.0008260309,0.001957561,0.00100675,0.00101595,0.002577119],"category_scores_gemma":[0.007935779,0.0008170046,0.0009754281,0.0009610664,0.0007158001,0.001215848,0.001915043,0.001015013,0.001273933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005264437,"about_ca_system_score_gemma":0.001418133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003835399,"about_ca_topic_score_gemma":0.01276118,"domain_scores_codex":[0.9987534,0.0003550745,0.0001277517,0.0004186588,0.0002536226,0.00009152637],"domain_scores_gemma":[0.9978033,0.0008062136,0.0003183565,0.0006275296,0.0003596875,0.00008494289],"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":[0.001001612,0.0002838649,0.04624518,0.001278942,0.001556902,0.0008696933,0.001510241,0.0474133,0.3599904,0.00764877,0.01759836,0.5146028],"study_design_scores_gemma":[0.0002960765,0.0007242433,0.2083259,0.0005585316,0.0007616444,0.004784525,0.0008522838,0.4181265,0.2685849,0.03945309,0.05700757,0.0005247769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2636855,0.002841099,0.7084398,0.0007270427,0.0002323172,0.0004619065,0.006699068,0.01278068,0.004132538],"genre_scores_gemma":[0.3524721,0.001074948,0.6269921,0.0003044245,0.0001488086,0.0006163716,0.01140874,0.004706911,0.002275538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003835399,"threshold_uncertainty_score":0.01566911,"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."}}