{"id":"W2199073307","doi":"10.1016/j.neuroimage.2015.12.009","title":"Patch-based augmentation of Expectation–Maximization for brain MRI tissue segmentation at arbitrary age after premature birth","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Hospital for Sick Children","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Maximization; Magnetic resonance imaging; Artificial intelligence; Brain development; Atlas (anatomy); Gestational age; Expectation–maximization algorithm; Pattern recognition (psychology); Machine learning; Psychology; Medicine; Neuroscience; Anatomy; Radiology; Mathematics; Biology; Statistics","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":[0.001463286,0.0005527489,0.001068859,0.0004810684,0.0003460978,0.0005657363,0.001665233,0.001485303,0.001551014],"category_scores_gemma":[0.003130564,0.0009071062,0.001085035,0.0005601996,0.0005367515,0.0007356044,0.001090569,0.001357204,0.0007779651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000408148,"about_ca_system_score_gemma":0.001192301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005273076,"about_ca_topic_score_gemma":0.006495518,"domain_scores_codex":[0.9996951,0.00009209797,0.0000182668,0.00009883742,0.00005128067,0.00004455929],"domain_scores_gemma":[0.9990188,0.0005837614,0.00007893773,0.0001121413,0.0001599416,0.00004634442],"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":[0.001098136,0.0001833603,0.003200963,0.000334244,0.0002068374,0.0003185254,0.0004246068,0.4444107,0.05323137,0.005204118,0.007017403,0.4843697],"study_design_scores_gemma":[0.00001047694,0.00003623123,0.0005656072,0.000008243642,0.00001793429,0.00009520965,0.00001201811,0.9907635,0.00588807,0.001743822,0.0008481166,0.00001090103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0330186,0.0004040282,0.963863,0.0001755533,0.00005145487,0.00006388016,0.0002048851,0.001800087,0.0004185193],"genre_scores_gemma":[0.3449872,0.0004535899,0.6489571,0.0001822527,0.0001048135,0.0002831179,0.001297403,0.0008329479,0.002901501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005273076,"threshold_uncertainty_score":0.01048476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324422170875268,"score_gpt":0.2915187404048105,"score_spread":0.2682745186960578,"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."}}