Automatic segmentation of rodent spinal cord diffusion MR images.
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.
Tidwell
Vanessa K
VK
Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA. vkt2@ese.wustl.edu
Kim
Joong H
JH
Song
Sheng-Kwei
SK
Nehorai
Arye
A
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013116:Spinal Cord / Q000033:anatomy & histology*