Segmentation of cortical MS lesions on MRI using automated laminar profile shape analysis.
Cortical multiple sclerosis lesions are difficult to detect in magnetic resonance images due to poor contrast with surrounding grey matter, spatial variation in healthy grey matter and partial volume effects. We propose using an observer-independent laminar profile-based parcellation method to detect cortical lesions. Following cortical surface extraction, profiles are extended from the white matter surface to the grey matter surface. The cortex is parcellated according to profile intensity and shape features using a k-means classifier. The method is applied to a high-resolution quantitative magnetic resonance data set from a fixed post mortem multiple sclerosis brain, and validated using histology.
Tardif
Christine L
CL
McConnell Brain Imaging Centre, Montreal Neurological Institute, Canada.
Collins
D Louis
DL
Eskildsen
Simon F
SF
Richardson
John B
JB
Pike
G Bruce
GB
D000368:Aged; D000465:Algorithms; D002540:Cerebral Cortex / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity