Mbirn: Skull Stripping: Goals and Methods
- Goal
For multi-site neuroimaging studies, such as the Biomedical Informatics Research Network (BIRN), the selection of a robust, automated method for the isolation of brain from extracranial, or "non-brain," tissues is of great interest. Performance of automated methods to isolate brain from non-brain 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.
- Methods
The present study compared the performance of four methods, Brain Extraction Tool (BET, Smith 2002); 3dIntracranial (Ward 1999, in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. 2004, in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy 1997; Shattuck et al. 2001), to manually stripped images. The methods were applied to un-corrected 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 non-brain 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, multi-site neuroimaging studies.
