{"id":"W2166219471","doi":"10.1016/j.nicl.2014.08.008","title":"Statistical normalization techniques for magnetic resonance imaging","year":2014,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":413,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; Canadian Institutes of Health Research; National Institutes of Health; Servier; Eisai; National Institute of Neurological Disorders and Stroke; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Synarc; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Medpace; Bristol-Myers Squibb; Eli Lilly and Company; National Institute of Mental Health; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Foundation for the National Institutes of Health","keywords":"Normalization (sociology); Spatial normalization; Artificial intelligence; Histogram; Computer science; Magnetic resonance imaging; Pattern recognition (psychology); Functional magnetic resonance imaging; Histogram matching; Neuroimaging; Image processing; Computer vision; Medicine; Image (mathematics); Psychology; Radiology; Voxel; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03384082117144467,"score_gpt":0.3731487252613628,"score_spread":0.3393079040899182,"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."}}