Estimating uncertainty in brain region delineations

Karl R. Beutner, Gautam Prasad, Evan Fletcher, Charles DeCarli, Owen T. Carmichael

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages479-490
Number of pages12
Volume5636 LNCS
DOIs
StatePublished - 2009
Event21st International Conference on Information Processing in Medical Imaging, IPMI 2009 - Williamsburg, VA, United States
Duration: Jul 5 2009Jul 10 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5636 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on Information Processing in Medical Imaging, IPMI 2009
CountryUnited States
CityWilliamsburg, VA
Period7/5/097/10/09

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Beutner, K. R., Prasad, G., Fletcher, E., DeCarli, C., & Carmichael, O. T. (2009). Estimating uncertainty in brain region delineations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5636 LNCS, pp. 479-490). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5636 LNCS). https://doi.org/10.1007/978-3-642-02498-6_40