Incorporating parameter uncertainty in Bayesian segmentation models: application to hippocampal subfield volumetry. Academic Article uri icon

Overview

abstract

  • Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more accurately by using Monte Carlo sampling. We demonstrate our technique by sampling atlas warps in a recent method for hippocampal subfield segmentation, and show a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the method also yields informative "error bars" on the segmentation results for each of the individual sub-structures.

publication date

  • January 1, 2012

Research

keywords

  • Algorithms
  • Alzheimer Disease
  • Hippocampus
  • Image Interpretation, Computer-Assisted
  • Imaging, Three-Dimensional
  • Magnetic Resonance Imaging
  • Pattern Recognition, Automated

Identity

PubMed Central ID

  • PMC3623551

Scopus Document Identifier

  • 84872915656

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-33454-2_7

PubMed ID

  • 23286113

Additional Document Info

volume

  • 15

issue

  • Pt 3