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


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
Number of pages14
ISBN (Print)9783319738383
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


ConferenceMICCAI Workshop on Computational Diffusion MRI, CDMRI 2017

ASJC Scopus subject areas

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


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