Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

Published

Journal Article

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

Full Text

Duke Authors

Cited Authors

  • Ali, AA; Dale, AM; Badea, A; Johnson, GA

Published Date

  • August 15, 2005

Published In

Volume / Issue

  • 27 / 2

Start / End Page

  • 425 - 435

PubMed ID

  • 15908233

Pubmed Central ID

  • 15908233

International Standard Serial Number (ISSN)

  • 1053-8119

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2005.04.017

Language

  • eng

Conference Location

  • United States