TOPOLOGY PRESERVING AUTOMATIC SEGMENTATION OF THE SPINAL CORD IN MAGNETIC RESONANCE IMAGES.
Magnetic resonance images of the spinal cord play an important role in studying neurological diseases, particularly multiple sclerosis, where spinal cord atrophy can provide a measure of disease progression and disability. Current practices involve segmenting the spinal cord manually, which can be an inconsistent and time-consuming process. We present an automatic segmentation method for the spinal cord using a combination of deformable atlas based registration and topology preserving classification. Using real MR data, our method is shown to be highly accurate when compared to segmentations by manual raters. In addition, our results always maintain the correct topology of the spinal cord, therefore providing segmentations more consistent with the known anatomy.
Image Analysis and Communications Laboratory, Dept. of ECE, Johns Hopkins University; Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke.
Min
M
Chen
Image Analysis and Communications Laboratory, Dept. of ECE, Johns Hopkins University.
Aaron
A
Carass
The Laboratory of Medical Image Computing, Dept. of Radiology, Johns Hopkins University.
Jennifer
J
Cuzzocreo
The Laboratory of Medical Image Computing, Dept. of Radiology, Johns Hopkins University.
Pierre-Louis
PL
Bazin
Dept. of Neurology, Johns Hopkins University; Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke.
Daniel S
DS
Reich
Image Analysis and Communications Laboratory, Dept. of ECE, Johns Hopkins University.
Jerry L
JL
Prince
Magnetic resonance imaging; Magnetization transfer images; Topology-preserving segmentation; digital homeomorphism; spinal cord segmentation