Diffusion specific segmentation: Skull stripping with diffusion mri data alone

The Alzheimer’s Disease Neuroimaging Initiative

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

3 Scopus citations

Abstract

Most processing pipelines for diffusion MRI (dMRI) require an intracranial mask image to exclude voxels outside the skull, and some dMRI analyses also need a segmentation between the voxels that are primarily tissue or cerebrospinal fluid (CSF). dMRI is challenging for most segmentation methods because it usually has relatively severe image artifacts and coarse resolution. However, it does provide information about the physical properties of the material(s) in each voxel, which can be directly applied to segmentation. We describe the training of a random forest classifier to segment dMRI into intracranial, brain, and CSF masks, and compare its results to three other segmentation methods commonly used in dMRI processing. The effect of correcting smooth spatial intensity variations on dMRI segmentation is also tested.

Original languageEnglish (US)
Title of host publicationComputational Diffusion MRI - MICCAI Workshop, 2017
EditorsEnrico Kaden, Francesco Grussu, Lipeng Ning, Chantal M.W. Tax, Jelle Veraart
PublisherSpringer
Pages67-80
Number of pages14
ISBN (Print)9783319738383
DOIs
StatePublished - 2018
EventMICCAI Workshop on Computational Diffusion MRI, CDMRI 2017 - Quebec, Canada
Duration: Sep 10 2017Sep 10 2017

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

Conference

ConferenceMICCAI Workshop on Computational Diffusion MRI, CDMRI 2017
CountryCanada
CityQuebec
Period9/10/179/10/17

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

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  • Cite this

    The Alzheimer’s Disease Neuroimaging Initiative (2018). Diffusion specific segmentation: Skull stripping with diffusion mri data alone. In E. Kaden, F. Grussu, L. Ning, C. M. W. Tax, & J. Veraart (Eds.), Computational Diffusion MRI - MICCAI Workshop, 2017 (pp. 67-80). (Mathematics and Visualization). Springer. https://doi.org/10.1007/978-3-319-73839-0_5