Bayesian construction of geometrically based cortical thickness metrics.
作者： Michael I. Miller , Allan B. Massie , J.Tilak Ratnanather , Kelly N. Botteron , John G. Csernansky
关键词: Property (programming) 、 Cerebrospinal fluid 、 Artificial intelligence 、 Computer science 、 Neocortex 、 Segmentation 、 White matter 、 Computer vision 、 Pattern recognition 、 Cortical surface 、 Bayesian probability
摘要: Abstract This paper describes the construction of cortical metrics quantifying probabilistic occurrence gray matter, white and cerebrospinal fluid compartments in their correlation to geometry neocortex as measured 0.5–1.0 mm magnetic resonance imagery. These profiles represent density tissue types a function distance surface. are consistent when generated across multiple brains indicating fundamental property neocortex. Methods proposed for incorporating such into automated Bayes segmentation.
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