TY - GEN
T1 - Estimating uncertainty in brain region delineations
AU - Beutner, Karl R.
AU - Prasad, Gautam
AU - Fletcher, Evan
AU - DeCarli, Charles
AU - Carmichael, Owen T.
PY - 2009
Y1 - 2009
N2 - 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 Markov Chain Monte Carlo (MCMC) sampling of the distribution in the vicinity of the MAP estimate. Experiments on real and synthetic data show that the uncertainty measures automatically detect when the delineating surface of the entire brain is unclear due to poor image quality or artifact; the experiments cover multiple appearance models to demonstrate the generality of the method. The approach is also general enough to accommodate a wide range of shape models and brain regions.
AB - 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 Markov Chain Monte Carlo (MCMC) sampling of the distribution in the vicinity of the MAP estimate. Experiments on real and synthetic data show that the uncertainty measures automatically detect when the delineating surface of the entire brain is unclear due to poor image quality or artifact; the experiments cover multiple appearance models to demonstrate the generality of the method. The approach is also general enough to accommodate a wide range of shape models and brain regions.
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U2 - 10.1007/978-3-642-02498-6_40
DO - 10.1007/978-3-642-02498-6_40
M3 - Conference contribution
SN - 3642024971
SN - 9783642024979
VL - 5636 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 479
EP - 490
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 21st International Conference on Information Processing in Medical Imaging, IPMI 2009
Y2 - 5 July 2009 through 10 July 2009
ER -