Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis.
Several methods exist and are frequently used to quantify grey matter (GM) atrophy in multiple sclerosis (MS). Fundamental to all available techniques is the accurate segmentation of GM in the brain, a difficult task confounded even further by the pathology present in the brains of MS patients. In this paper, we examine the segmentations of six different automated techniques and compare them to a manually defined reference standard. Results demonstrate that, although the algorithms perform similarly to manual segmentations of cortical GM, severe shortcomings are present in the segmentation of deep GM structures. This deficiency is particularly relevant given the current interest in the role of GM in MS and the numerous reports of atrophy in deep GM structures.
Derakhshan
Mishkin
M
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada. mishkin@bic.mni.mcgill.ca
Caramanos
Zografos
Z
Giacomini
Paul S
PS
Narayanan
Sridar
S
Maranzano
Josefina
J
Francis
Simon J
SJ
Arnold
Douglas L
DL
Collins
D Louis
DL
D000465:Algorithms; D001284:Atrophy / Q000473:pathology; D001921:Brain / Q000473:pathology*; 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*; D009474:Neurons / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity