Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.
We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
Fischl
Bruce
B
Massachusetts General Hospital, Nuclear Magnetic Resonance Center, Rm. 2328, Building 149, 13th Street, Charlestown, MA 02129, USA.
Salat
David H
DH
Busa
Evelina
E
Albert
Marilyn
M
Dieterich
Megan
M
Haselgrove
Christian
C
van der Kouwe
Andre
A
Killiany
Ron
R
Kennedy
David
D
Klaveness
Shuna
S
Montillo
Albert
A
Makris
Nikos
N
Rosen
Bruce
B
Dale
Anders M
AM
D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis / Q000473:pathology; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D001931:Brain Mapping; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results