Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location.
Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143-155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060-1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001] Neuroimage 13:856-876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer's, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies.
Fennema-Notestine
Christine
C
Laboratory of Cognitive Imaging, Department of Psychiatry, University of California, San Diego, and Veterans Affairs SAn Diego Healthcare System, San Diego, La Jolla, California 92093, USA.
Ozyurt
I Burak
IB
Clark
Camellia P
CP
Morris
Shaunna
S
Bischoff-Grethe
Amanda
A
Bondi
Mark W
MW
Jernigan
Terry L
TL
Fischl
Bruce
B
Segonne
Florent
F
Shattuck
David W
DW
Leahy
Richard M
RM
Rex
David E
DE
Toga
Arthur W
AW
Zou
Kelly H
KH
Brown
Gregory G
GG
D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001927:Brain Diseases / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D011859:Radiography; D012680:Sensitivity and Specificity; D012984:Software