Estimating Uncertainty in Brain Region Delineations

This paper presents a method for estimating uncertainty in MRI-based brain region delineations provided by fully-automated segmentation methods. In large data sets, the uncertainty estimates could be used to detect fully-automated method failures, identify low-quality imaging data, or endow downstream statistical analyses with per-subject uncertainty in derived morphometric measures. Region segmentation is formulated in a statistical inference framework; the probability that a given region-delineating surface accounts for observed image data is quantified by a distribution that takes into account a prior model of plausible region shape and a model of how the region appears in images. Region segmentation consists of finding the maximum a posteriori (MAP) parameters of the delineating surface under this distribution, and segmentation uncertainty is quantified in terms of how sharply peaked the distribution is in the vicinity of the maximum. Uncertainty measures are estimated through Mark...

• Type: Conference
• Year: 2009
• Where: IPMI
• Authors: Karl R. Beutner, Gautam Prasad, Evan Fletcher, Charles DeCarli, Owen T. Carmichael
• Keywords: Brain Region Delineations, IPMI 2009, Medical Imaging, Segmentation Uncertainty, Uncertainty Measures