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Automatic segmentation of rodent spinal cord diffusion MR images
Author(s) -
Tidwell Vanessa K.,
Kim Joong H.,
Song ShengKwei,
Nehorai Arye
Publication year - 2010
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.22416
Subject(s) - spinal cord , diffusion mri , segmentation , voxel , pattern recognition (psychology) , white matter , computer science , artificial intelligence , spinal cord injury , medicine , magnetic resonance imaging , nuclear medicine , radiology , psychiatry
MRI, is a key tool for noninvasive spinal cord lesion analysis; however, accurate, quantitative methods for this analysis are lacking. A new, multistep, multidimensional approach, utilizing the classification expectation maximization algorithm, is proposed for MRI segmentation of spinal cord tissues. Diffusion tensor imaging is used to generate multiple images of each spinal slice, with different diffusion direction weightings. The maximum likelihood tissue classifications are then jointly estimated to produce a binary classification image, corresponding to voxels containing either spinal cord or background. Edge detection is employed to find a nonparametric curve encapsulating the entire spinal cord. The algorithm is evaluated using data from in vivo diffusion tensor imaging of control and injured mouse spinal cords. The algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation in the presence of significant injury. The results of the method are shown to be at least on par with expert manual segmentation. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.